Influence of Environmental Factors on Pedestrian Summer Vitality in Urban Pedestrian Streets in Cold Regions Guided by Thermal Comfort: A Case Study of Sanlitun—Beijing, China

Abstract

In the context of increasingly extreme high temperatures during the summer in cold regions, the spatial vitality of urban blocks is significantly affected. In order to enhance the summer vitality of sustainable urban neighborhoods, this study explores the mechanisms of street elements and vitality under the guidance of thermal comfort. Taking an urban pedestrian street in Sanlitun, Beijing, during a summer day as the research subject, this study systematically analyzed the impact of environmental factors such as street orientation, aspect ratio, three-dimensional greenery of trees, and tree spacing on the thermal comfort of street space. By quantitatively analyzing the relationship between thermal comfort and pedestrian vitality in different street spaces, this study reveals the mechanisms by which environmental factors enhance the summer usability and spatial vitality of pedestrian streets. The research results indicate that street vitality shows a gradually increasing trend throughout the day, particularly between 12:00 and 16:00, when thermal comfort significantly affects people’s vitality. Furthermore, univariate analysis reveals that the street aspect ratio is the primary factor influencing the spatial vitality of the southern section of the Sanlitun Taikoo Li pedestrian street, with a characteristic average of 8.28 over a week. Finally, the results of the multi-factor analysis show that, when enhancing street vitality, priority should be given to the interactions between street aspect ratio, tree greenery, temperature, and physiological equivalent temperature, where the correlation coefficient between street aspect ratio and vitality is 0.70, and that between temperature and physiological equivalent temperature is 0.63, both showing positive correlations. Research shows that, in street planning, a reasonable building aspect ratio (1:2 to 1:4) and scientific green design are key. By carefully controlling the planting density and greenery configuration of trees, optimal street space effects can be achieved. This study proposes sustainable urban design strategies by optimizing street space design and improving environmental thermal comfort, aiming to enhance the summer vitality of urban blocks in cold regions and advocate for a sustainable development model that combines ecological and social benefits.

1. Introduction

Against the backdrop of global climate change, the frequency and intensity of natural disasters such as tropical storms and extreme temperatures are on the rise [1,2]. The increase in heatwave intensity and its negative impact on outdoor thermal environments cannot be overlooked [3,4]. Furthermore, the pressure from rapid urbanization has rapidly increased, accelerating the deterioration of urban thermal environments [5]. Overpopulation, dense buildings, lack of green spaces, and traffic congestion lead to rising urban temperatures, directly affecting urban livability and sustainability due to the urban heat island effect, resulting in declining environmental quality, increased thermal discomfort, and exacerbated heat-related public health issues [6,7]. Consequently, there is increasing focus on how to address the deterioration of urban thermal environments and adopt sustainable measures to shield citizens from the effects of heatwaves.

Heatwaves pose a serious threat to human health. Prolonged exposure to high temperatures can damage multiple organ systems and disrupt metabolic functions, leading to heat-related illnesses, dehydration, and electrolyte imbalances [7]. Evidence suggests that, if the average temperature in China rises by 1.5 °C to 2 °C, more than 27,900 people may die from extreme heat each year [8,9]. At the same time, climate fluctuations are becoming more frequent and intense, causing temperatures in some regions to rise to unusually high levels in summer, while winter may see unusually cold conditions, seriously impacting people’s quality of life and sustainable development.

Current research finds that the interface properties of street spaces and their relationship with microclimates are particularly important for the design of daily life value in urban streets in cold regions [10]. The “Thermal Design Code for Civil Buildings” (GB 50176-2016) [11] divides China into five thermal design climate zones: severely cold regions, cold regions, summer hot and winter cold regions, summer hot and winter warm regions, and mild regions. However, current architectural design in cold regions focuses on winter insulation and windproofing, with relatively little attention given to the outdoor thermal environment in summer [12]. This has resulted in a lack of effective shading facilities in outdoor public spaces such as urban pedestrian streets, resulting in poor thermal comfort, which negatively impacts pedestrian experiences, thereby suppressing the summer usability and vibrancy of urban pedestrian streets.

In recent years, scholars have conducted in-depth studies on the factors influencing individual thermal perception in urban public spaces, finding that physical factors, especially those related to microclimatic parameters [13], significantly affect human thermal sensation. In different thermal environments, pedestrian vitality on urban streets shows significant differences [14,15]. However, existing research mainly focuses on the impact of environmental and climatic factors on thermal comfort, with few studies examining the relationship between human thermal comfort indices and street vitality. Additionally, traditional studies often rely on subjective surveys, which have certain subjectivity and limitations. With the development of technology, it is now possible to detect pedestrian vitality through more objective methods and quantify the impact of single or multiple factors on vitality, opening up new directions for research on the factors affecting vitality under thermal comfort guidance.

In order to guarantee the representativeness of the chosen area and timeframe, this research performed a comprehensive analysis of relevant literature and meteorological data. First, Beijing serves as a typical example of a cold city. According to its 2019 report on domestic and international visitor numbers, the number of tourists during non-holiday periods from June to August was notably higher than in the winter months of December to February [16]. Therefore, summer is the peak season for activities. Additionally, changes in the thermal environment during summer significantly affect pedestrian mobility, whereas winter is influenced by both thermal and wind conditions [17]. Thus, concentrating on summer allows for a clearer understanding of the direct impact of climate on pedestrian activities, thereby offering more specific recommendations for enhancing the design and planning of street environments.

Because of the focus on preserving the texture of pedestrian street spaces, current traditional designs and renovations of pedestrian commercial streets typically emphasize traffic flow and urban aesthetics, often neglecting the thermal comfort of outdoor public spaces, which can impact visitor numbers; indeed, J Lin noted through morphological spatial pattern analysis that the spatial pattern of built-up areas has a significant impact on surface urban heat islands [18]. CL Errea analyzed the urban heat island effect by studying surface remote sensing satellite maps of Victoria Gastecz from 1985 to 2021 [19]. Therefore, it is necessary to enhance the outdoor comfort of pedestrian commercial streets to attract more visitors and prevent urban pedestrian commercial streets from losing vitality. Currently, urban development in China has entered a phase of stock renewal, with a focus on the “micro-renovation” of neighborhoods; pedestrian commercial streets characterized by high density, high foot traffic, and high demands for outdoor comfort should be prioritized.

This study focuses on an urban pedestrian street in the southern area of Taikoo Li, Sanlitun, Beijing, selecting five days in August as the sample period, including weekdays and weekends. In this study, nine influencing factor variables were comprehensively considered, with the introduction of the calculated mediating variable, physiological equivalent temperature. Utilizing the XGBoost decision analysis method, the study derived correlation gradients for single and multiple factors and calculated their respective weight values. Finally, the study explored in-depth the relationship between pedestrian vitality and these factors under the guidance of thermal comfort and proposes corresponding updating strategies.

2. Literature

2.1. Measurement of Vitality of Urban Pedestrian Streets

In measuring the vitality of urban pedestrian streets, the microclimate produced by the thermal environment has a particularly significant impact on smaller public spaces [20]. However, collecting spatiotemporal information of pedestrians that can represent vitality from a microspatial perspective remains a challenge for effective assessment [21]. The main issue lies in how to efficiently obtain spatiotemporal data that reflect vitality [22].

Previous studies typically employed low sample size survey methods, such as field measurements and questionnaires [23]. These methods have low accuracy when processing pedestrian location information and are time-consuming, costly, and complex, making large-scale implementation challenging [24]. With advancements in technology, researchers have begun to utilize video data to extract pedestrian movement trajectories and analyze their spatiotemporal distribution of stops [25]. Additionally, some studies have employed mobile signaling data [26] and social check-in data [27] to identify pedestrian flow and quantify the usage of urban spaces. Among these, a more mature method is capturing pedestrian trajectories using video data and constructing a vitality quantification model based on quantitative indicators through multiple linear regression analysis [28,29]. However, in light of the impact of the thermal environment’s microclimate on urban vitality, there is an urgent need to develop more efficient and convenient monitoring methods to enhance the accuracy and practicality of data collection, thereby facilitating a deeper understanding and assessment of urban spatial vitality.

2.2. Street Spatial Elements and Vitality

Currently, researchers have conducted preliminary explorations of the impact of urban space vitality from the perspective of thermal environment, focusing primarily on public activity areas such as parks, squares, and residential districts in hot cities. Some studies indicate that the thermal environment of urban public spaces significantly affects users’ psychological, physiological, and behavioral responses [30]. For instance, Zhang Y’s research analyzed heat stress mitigation measures and found a clear correlation between people’s subjective behavioral preferences and thermal comfort zones [30,31]. Fahed J explored the relationship between outdoor activity patterns in pedestrian spaces and environmental thermal comfort, noting that creating a comfortable thermal environment can effectively attract people to engage in outdoor exercise [32].

However, current research mainly focuses on single influencing factors [33], while studies on the thermal environment and spatial cognitive environment are relatively limited and primarily focus on hot regions in the south [34]. Research on human behavioral patterns in urban public spaces in cold regions is relatively scarce. Therefore, it is particularly urgent to explore the mechanisms influencing urban street space vitality in cold regions under the guidance of thermal comfort, which is crucial for spatial design strategies of urban public spaces in cold regions.

2.3. Research Questions

In this context, this study aims to explore the differences in pedestrian vitality in urban pedestrian streets in cold regions under different thermal environmental conditions through a comprehensive analysis of pedestrian vitality data, street environmental elements, and climatic physical factors, and to propose design strategies to enhance spatial vitality. The specific research content includes the following three aspects:

• (I). Are there differences in pedestrian vitality in urban streets of cold regions under varying thermal conditions? What are the manifestations of these differences?

• (II). What are the best factors or combinations of factors for improving spatial vitality in urban streets of cold regions guided by thermal comfort?

• (III). What are the design strategies to improve spatial vitality in urban streets of cold regions guided by thermal comfort?

The main innovation of this study lies in analyzing the differences in vitality and thermal environment of cold urban streets in summer from a micro perspective. At the same time, the study identifies the combined factors influencing street vitality while ensuring thermal comfort and proposes design strategies. This analysis is significant for enhancing the summer spatial vitality of urban streets in cold regions and improving the microclimate of these streets, while also providing new perspectives for understanding changes in street vitality under different environmental conditions. This will help to enhance the vitality of street spaces and promote the sustainable development of cities.

3. Methods

3.1. Research Framework

This study aims to explore how spatial elements of urban pedestrian streets, guided by thermal comfort, affect pedestrian vitality behaviors and to propose design optimization strategies based on vitality promotion. The research content and steps are as follows (Figure 1):

• (I). Formulate targeted questions and select appropriate research locations and times.

• (II). Before field measurements, filter relevant internet review data to select representative streets, choosing six street space samples with typical environmental characteristics in the South Sanlitun pedestrian street area of Beijing, China.

• (III). Select different time periods to monitor vitality and climate data for these six samples within the same time frame, recording pedestrian behavior occurrences, climate conditions, and the spatial element configurations of the samples.

• (IV). Process the collected data; the independent variables in this study include time, street orientation, aspect ratio, tree crown size, tree crown spacing, temperature, humidity, and wind speed. By calculating temperature, humidity, and wind speed, derive the mediating variable physiological equivalent temperature (PET) and construct decision tree models of various factors affecting the dependent variable of pedestrian vitality.

• (V). Data analysis involves extracting thermal environment and vitality data from the streets based on specific analytical requirements and assessing their differences.

• (VI). Analyze using the XGBoost model, calculating the gradients of single and multiple factors to determine the correlations and impacts between these variables, identifying the best factors or combinations to enhance street vitality.

• (VII). Suggest design strategies aimed at boosting the spatial vitality of urban streets in cold areas.

3.2. Study Area

Beijing is located in the North China Plain in northern China, with geographical coordinates between 115°20′ to 117°30′ E and 39°28′ to 41°05′ N, situated in the central part of the Haihe River Basin. The region is influenced by monsoons and has a warm, temperate, semi-humid and semi-arid climate [31]. The total annual solar radiation in Beijing ranges from 112 to 136 kcal/m², and the annual sunshine hours vary between 2000 and 2800 h [31]. Summer is the rainy season, during which the sunshine hours decrease, with an average of about 230 sunshine hours per month.

Regarding Beijing’s geographical features, it is surrounded by mountains in the west, north, and northeast, while the southeast borders plains, resulting in distinct climate characteristics in the region. Specifically, the summer is hot and rainy (Figure 2), while winter is cold and dry, with spring and autumn being relatively short [33]. According to meteorological data from Beijing for 2023 to 2024 (CMA, 2023), the average annual temperature in Beijing for 2023 was 12.7 °C, which is 0.9 °C higher than the normal level, with extreme maximum temperatures generally between 35 °C and 40 °C and six occurrences of sustained high-temperature weather (with daily maximum temperatures ≥ 35 °C) [34]. The number of high-temperature days recorded at the Beijing Observatory was 34 days, which is 2.4 times more than the average; these high-temperature days mainly occurred in August (15 days), with the highest average monthly temperature in August (28 °C) and the lowest average monthly temperature in January (−5 °C). The annual average relative humidity generally ranges from 50% to 60% [35].

This study focuses on pedestrian streets in the South District of Taikoo Li in Sanlitun, Beijing, China. The surrounding area is one of the most vibrant entertainment, leisure, and business districts in Beijing (Figure 3). The district has a square layout, measuring 220 m in length and 170 m in width, with a total area of 28,000 square meters and annual foot traffic exceeding 20 million visitors [35]. The district consists of 19 relatively independent buildings, with a central square and three vertical and five horizontal streets. The spatial morphology of the streets is diverse. This study conducts a preliminary investigation of selected streets in Sanlitun, considering spatial elements, climatic temperatures, and pedestrian flow. Streets were selected based on similar length, width, and aspect ratios. Ultimately, six typical streets were selected for the study (Figure 4): the northern section of the main street (Street 1), the central section of the main street (Street 2), the northwestern section of the secondary street (Street 3), the southwestern section of the secondary street (Street 4), the eastern section of the secondary street (Street 5), and the southern section of the main street (Street 6).

3.3. Physical Measurement

3.3.1. Street Spatial Elements

This study employs the method developed by Tong Niu to capture pedestrian trajectories from video data [29] and uses WiFi detection technology to collect pedestrian flow data on the streets. The WiFi probe module scans and captures wireless signals emitted by nearby mobile devices, recording each device’s MAC address, real-time location, movement trajectory, and speed [36]. Since the WiFi probe is a device based on the RSSI triangulation algorithm, its optimal collection radius is within 30 m, to ensure that a complete dataset of pedestrian data on the street is collected; this is necessary to ensure that the entire area is covered by the WiFi probe signals. Technical detection is conducted for 5 min during each time interval, while simultaneously selecting the northern section of the main street (Station 1) and the southern section (Station 6) for video recording. Bidirectional cross-sectional pedestrian flow is recorded to validate the accuracy of the technology, and after statistical processing of the pedestrian flow and trajectories on the street, experimental analysis is conducted (Figure 5).

• (I). Sunshine time (ST)

The sunshine time reflects the varying times of sunlight detection throughout the day. In the pedestrian street space, sunshine time directly affects the intensity and quality of light and indirectly influences the number of pedestrians during this time period. The sunlight period starts from 12 o’clock, with each hour representing a time interval, recording the range of the measurement period.

• (II). Street orientation (SO)

The street orientation refers to the direction of the pedestrian street in relation to the position of the sun. This directly affects the extent to which the street space receives sunlight, and different orientations lead to varying impacts of natural winds on the street. The street orientation is divided based on the angle between the main axis and the cardinal directions, with each 45° representing a direction. For example, if the angle between the street’s main axis and the north–south direction is within a ±22.5° range, it is defined as being oriented north–south. In the Figure 5, we use arrows to indicate the orientation.

• (III). Street aspect ratio (AR)

The street aspect ratio refers to the proportional relationship between the average width of the street space and the average height of the buildings on both sides. This ratio directly affects the contact area through which sunlight can enter the street space and indirectly influences the flow of natural winds within the street.

• (IV). Tree greenness (Tg)

The tree greenness volume refers to the average three-dimensional volume of tree crowns within the street space, which helps to lower the temperature of the space and reduce heat accumulation. Generally speaking, a higher tree greenness volume results in a larger area of shade provided.

• (V). Tree spacing (Ts)

The tree spacing refers to the average distance between each tree within the street space. Appropriate spacing can promote air circulation, while excessively large spacing may lead to insufficient shade coverage, resulting in certain areas becoming overly hot.

3.3.2. Climatic Physical Elements

When assessing human thermal comfort, studies often use indicators such as WGBT, PMV, PET, and UTCI [37]. Among these, PET and UTCI are the most commonly used and the most accurate indicators. PET stands for Physiologically Equivalent Temperature, which takes into account external environmental factors (such as air temperature, relative humidity, and wind speed), as well as internal human factors (such as clothing resistance and metabolic rate) [38]. In contrast, the PMV questionnaire survey faces certain difficulties in data collection [39]. Additionally, the calculation of UTCI is complex and does not adequately consider clothing resistance, which may lead to certain errors.

This research was carried out in the summer (from August 20 to 24 August 2023) in Beijing. The measurement period was from 10:00 AM to 6:00 PM, coinciding with the solar path and the peak usage times of public spaces in the city. All equipment recorded data at 30 min intervals. Assuming an average human body height of 1.1 m and following ISO 7730 [40] testing requirements, the sensor probes were positioned at a height of 1.1 m above the ground. Fixed-point walking records were taken at the center points of different streets to obtain meteorological parameters, including air temperature (Ta), relative humidity (Rh), wind speed (Va), and global temperature (Tg). In summary, PET was chosen as the thermal index evaluation metric to investigate spatial thermal benchmarks in this study.

• (I). Temperature (Ta)

Air temperature refers to the temperature level of the air in the pedestrian street environment. An appropriate range of air temperature can enhance pedestrians’ vitality and dwell time, encouraging them to engage more in street activities. This study selects the air temperature at the endpoints and the center of the measured street area and calculates the average to represent the air temperature level of the region.

• (II). Relative humidity (Rh)

Relative humidity refers to the ratio of the actual water vapor content in the air to the maximum amount of water vapor that the air can hold at a given temperature. In high-temperature environments, excessive humidity makes heat dissipation difficult, increases discomfort, and inhibits outdoor activities. This study measures the relative humidity at the endpoints and the center of the surveyed street area, then computes the average to represent the region’s relative humidity.

• (III). Wind speed (Va)

Wind velocity refers to the speed of air movement in the street space. In high-temperature environments, an appropriate wind speed can enhance heat dissipation, reduce the perceived temperature, and improve thermal comfort. In this study, the wind speed at the endpoints and center of the measured street area is recorded, and the average is calculated to represent the overall wind speed in the region.

• (IV). Physiologically equivalent temperature (PET)

Physiologically equivalent temperature (PET) is calculated by considering multiple environmental factors such as air temperature, relative humidity, and wind speed. When PET is too high, it can cause fatigue and discomfort in pedestrians, reducing their dwell time and willingness to engage in activities on the pedestrian street.

According to ISO 9920 standards [41], clothing data are converted into Clo values. The average summer clothing insulation value for this area is determined to be 0.5 Clo. The thermal comfort software program RayMan 1.3 was used to analyze experimental and survey data, calculating the PET value for each street.

The formula for calculating spatial thermal benchmarks based on quantitative indicators is as follows [42]: first, calculate the radiant temperature (1); second, calculate the humidity factor (2) and (3); finally, calculate the physiologically equivalent temperature (PET) value (4):

(1)$T_r=T_a+0.5\times (T_g-T_a)$

(2)$e_{\delta }=6.11\times 10\left({\displaystyle \frac{7.5\times T_a}{237.3+T_a}}\right)$

(3)$e={\displaystyle \frac{RH}{100}}\times e_{\delta }$

(4)$PET=T_a+(0.33\times e-0.70\times V_a-4.00)$

In the calculation of physiologically equivalent temperature (PET), air temperature (Ta) refers to the temperature of the air, relative humidity (Rh) is the ratio of the amount of water vapor in the air to the maximum water vapor capacity at that temperature, wind speed (Va) is the velocity of air movement, and global temperature (Tg) refers to the weighted average of solar radiation or ambient temperature.

3.3.3. Pedestrian Vitality Index

This research employs the spatial vitality quantification model based on quantitative indicators, as proposed by Tong Niu, to evaluate the vitality levels of streets [28]. This method provides an effective alternative for enhancing low-flow surveys in micro-scale public spaces. By introducing a vitality expert scoring system, this method ensures the reliability of various quantitative indicators. Ultimately, a vitality quantification model was constructed by analyzing the correlation between quantitative indicators and overall vitality. The primary focus of this model is to effectively assess the vitality of public spaces by quantifying pedestrian behavior, and it also reveals the impact of different behaviors on space usage.

In addition, this model has notable features and advantages. First, it comprises four quantitative indicators: population, duration of stay, trajectory diversity, and trajectory complexity. These indicators cover critical factors including pedestrian volume, activity time, spatial utilization, and interaction degree, enabling a more holistic evaluation of street vitality. Second, the model’s quantitative indicators mainly reflect the attractiveness level of the space, eliminating the need to explicitly define behavior types. This approach aligns with this study’s direct application of WiFi detection technology to objectively capture relevant metrics, thus allowing for more precise evaluation of street vitality. Finally, a significant distinction of urban pedestrian streets from other public spaces is that their primary users are young people. Due to the high demand for shopping, dining, and entertainment among young people, pedestrian streets often require support for mobile payment functions, which makes WiFi detection technology feasible in this environment. Thus, the spatial vitality quantification model based on quantitative indicators proposed by Tong Niu is both scientifically sound and comprehensive for evaluating street vitality, and is especially applicable for measuring urban pedestrian street vitality under the guidance of thermal comfort [28].

In the assessment of pedestrian vitality data, considering that both behavioral observation and video recording methods cannot concurrently gather extensive and precise trajectory data, and due to the high collection costs and the coarse granularity of trajectory measurement results, this research utilizes WiFi detection technology to gather pedestrian trajectory data. This research utilizes WiFi probe modules to scan and capture wireless signals from nearby mobile devices, documenting the MAC address, real-time position, movement trajectory, and speed of each mobile device. Since WiFi probes operate on an RSSI triangulation algorithm and have an optimal capture radius of up to 30 m, it is crucial to ensure comprehensive coverage of the area by the WiFi probe signals to gather a complete dataset of pedestrian data on the street. A technical detection period of 5 min is carried out for each time segment, while also selecting the northern segment of the main street (Street 1) and the southern segment (Street 6) for video measurement. This captures bidirectional pedestrian flow to validate the technique’s accuracy, and subsequent statistical processing of the pedestrian flow and trajectory data is performed for experimental analysis.

The calculation Formula (5) for the spatial vitality quantification model based on quantitative indicators is as follows:

(5)$Spatial Vigour=0.582 num+0.254 dur+0.307 TD+0.159 TC$

where num is the number of people in the street space, dur is the pedestrian’s dwell time, TD is the diversity of pedestrian trajectories, and TC is the complexity of pedestrian trajectories.

• (I). Number of Pedestrians (num)

The number of pedestrians is an important indicator for assessing the attractiveness of pedestrian street spaces. This study obtains the number of pedestrians in the space by calculating the number of trajectories and deduplicating the data using unique device identifiers (e.g., MAC addresses) to reduce tracking errors. Simultaneously, considering the detection range errors, this study only uses filtered trajectories, excluding pedestrians who briefly enter the space’s boundaries or walk along the edges, to ensure the accuracy of the count.

• (II). Stay Duration (dur)

This paper measures dwell time by counting the number of trajectory points recorded at specified intervals. WiFi detection devices can log trajectory points every 5 s, with dwell time reflecting the average length of time pedestrians remain in the space. Analyzing the duration of connections between the WiFi detection devices and pedestrians indicates the ongoing vibrancy of pedestrian activity in the street space.

• (III). Trajectory Diversity (TD)

Trajectory diversity reflects the structural differences in pedestrian pathways. By collecting single trajectory vector information from the starting point to the endpoint, the similarity of path structures can be calculated. Similar differences in trajectory structures may indicate the presence of pedestrian groups or similar clustering behaviors. The higher the vibrancy and the more varied the activities, the greater the trajectory diversity.

• (IV). Trajectory Complexity (TC)

Trajectory complexity reflects the movement characteristics of pedestrians between different starting and ending points. Although the path structures may be similar, the movement characteristics of pedestrians differ due to various movement purposes. The complexity of trajectories is reflected in the changes in the angles and the vectors between them. By connecting adjacent turning points, the vector differences between them can be calculated. Generally, the clearer the movement purpose, the simpler the shape of the trajectory, and the smaller the vector differences between turning points.

3.4. Data Analysis and Validation

3.4.1. Accuracy Verification of Climate Factors

This research necessitates the accuracy verification of climatic factors, with the monitoring site positioned 15 m away from a small-scale climate monitoring station on urban streets in Beijing, focusing on a high-temperature period in mid-August. Four measurement locations were chosen for climate monitoring at a height of 1.5 m above the ground using a handheld meteorological device. The measurement parameters and their corresponding errors and resolutions are as follows: air temperature error ± 0.5 °C (resolution 0.1 °C); relative humidity error ± 3% Rh (resolution 0.1 Rh); and wind speed error ± 5% m/s (resolution 0.1 m/s). The monitoring was carried out under clear and gentle wind conditions (wind speed ≤ 2 m/s) between 12:00 and 18:00, with data recorded every minute. The compiled monitoring values were subjected to linear fitting with the simulated data, revealing that the monitoring values at the four locations exhibited minimal discrepancies with the simulated data, indicating that the measured thermal environmental climate data is highly accurate and meets the experimental criteria.

3.4.2. Pedestrian Vitality Accuracy Verification

To ensure the accuracy of measured spatial vitality data, the study conducts a reliability test on the vitality data. First, a 5 min video recording is conducted simultaneously at the same collection point for each time period, capturing the bidirectional pedestrian flow activity at the measurement point. Next, the research team extracts stable and smooth segments from the measurement point video and annotates each frame to determine the pedestrians’ trajectories. Finally, due to the discrepancy in the shape of pedestrian trajectories detected by WiFi and the angles captured in the video, the study needs to convert the annotated pedestrian points from the video into a top-down view trajectory based on reference objects. The number of pedestrians in the video, their dwell times, and trajectory coordinates are compared with the detection data to determine consistency.

The WiFi detection coverage includes open areas of the street, and the trajectory points will be recorded, while the recorded points in indoor areas will be deleted. By comparing the video data with the WiFi detection data, the study calculates the root mean square error (RMSE) and the coefficient of determination ($R^2$). The RMSE is 1.348, and the $R^2$ is 0.84. The MSE value and $R^2$ are statistical indicators of model fit; a lower RMSE indicates a better model fit. Conversely, a value closer to 1 indicates a better model fit. Therefore, the verification results indicate that the method of comparing video recordings with WiFi vitality data can ensure the accuracy of measured spatial vitality data.

3.4.3. XGBoost Model Validation

XGBoost is an ensemble machine learning algorithm based on decision trees, operating within a gradient boosting framework. It is known for improving the precision of the loss function, providing better data fitting, supporting various objective functions and evaluation metrics, and allowing for flexible customization of the model’s optimization goals. Additionally, this algorithm supports distributed computing and parallel learning, enabling it to handle large-scale datasets. This is suitable for the experimental environment in this study, which involves a large number of street sample data and complex influencing factors. Due to space limitations, this paper references Han Yue’s method when constructing the objective function for XGBoost. To more clearly demonstrate the model’s effectiveness [43], this paper presents the validation results between the illustrative data and the predicted data, so that readers can better understand the model’s performance and applicability.

In this research, we employed the XGBoost model for accuracy validation of the data, evaluating the performance of the training and testing sets. For the training set, the model achieved a mean squared error (MSE) of 3.89, suggesting that the prediction error was minimal. The root mean squared error (RMSE) was 1.97, further emphasizing the closeness between the predicted values and the actual values. Additionally, the mean absolute error (MAE) was 1.10, demonstrating the model’s stability and reliability in overall predictions.

On the test set, the model also exhibited good performance, with a mean squared error (MSE) of 4.44, a root mean squared error (RMSE) of 2.11, and a mean absolute error (MAE) of 1.19. Although these metrics are slightly higher than those of the training set, they remain within an acceptable range, indicating the model’s good generalization capability.

Furthermore, we calculated the scores, with the training set scoring 0.9142 and the test set scoring 0.8748, indicating that the model can effectively explain the variability of the target variable. In summary, it can be concluded that the XGBoost model demonstrated high predictive accuracy and robustness in this study, making it suitable for further experimental analysis.

3.5. Ethics

During this research, we collected a total of 21,312 valid data points regarding pedestrian activity. This dataset comprises both the number of pedestrians and the trajectories of each individual pedestrian. We applied anonymization to the pedestrian trajectory information, retaining only the active time, visit frequency, distance, signal strength, and duration of stay for each data point, and visualized the data without involving any personal user information.

To guarantee that the data collection process aligns with research ethics, we consulted relevant studies and established corresponding collection methods. Throughout the data collection phase, we took the following steps to comply with ethical norms: first, we consulted with community management departments to secure permission for sample collection during the data gathering, ensuring adherence to legal requirements; second, we entered into an agreement with the community being surveyed, explicitly stating that the data collected would undergo anonymization and would only be used for research purposes, not shared with third parties; third, we informed the pedestrians being recorded on-site, letting them know that their movements were being documented and clarifying that these data would solely be used for crowd behavior research, not directed at any individual. We additionally pledged to anonymize personal data to safeguard pedestrian privacy.

4. Results

According to the analysis of the aforementioned research results, the pedestrian vitality of each street is influenced by factors beyond environmental climate indicators, which may include street morphology and data collection time. Although most studies indicate that street environmental factors significantly impact pedestrian vitality, this study focuses on identifying the factors affecting urban streets in cold regions under thermal comfort conditions, which is crucial for improving the design quality of public spaces in cold regions.

Therefore, this research takes into account two categories of elements, namely street space and climatic physics, along with nine influencing variables. First, the street space and climatic physical factors are analyzed as independent variables, with the introduction of the calculated mediating variable known as physiologically equivalent temperature (PET). Next, the XGBoost decision analysis method is utilized to compute the gradients for both single-factor and multi-factor analyses. Lastly, the study examines the relationship between pedestrian vitality and these factors. This method aids in a better understanding of the correlation trends and strengths among these factors, allowing for a comprehensive analysis of the potential influences on thermal perception.

4.1. Changes in Temporal and Spatial Vitality

There are significant differences in the vitality distribution of different streets at different times. Overall, the vitality of the streets gradually increases throughout the day, starting from 12:00 PM and peaking at 8:00 PM. Particularly at 8:00 PM, the vitality value of the streets increases by 1.1 to 1.4 times compared to the noon and afternoon periods. Over the course of a week, street vitality on weekends is generally higher than on weekdays. On average, the vitality on weekends is 1.3 times that of weekdays. Notably, on Saturdays, street vitality reaches its peak. This indicates that more people choose to visit urban pedestrian streets on Saturdays.

The research also revealed that differences in street hierarchy result in varying vitality distributions across different spatial regions. Main streets typically exhibit higher vitality levels compared to secondary streets. Particularly during weekends, the vitality of main streets is markedly greater than on weekdays. Additionally, the growth rate of street vitality is progressively increasing. In addition, the changes in PET values across all streets exhibit a trend of rising initially before declining. The peak usually takes place between 3:00 PM and 4:00 PM. Notably, the mean PET of main streets is 3.2 °C lower than that of secondary streets.

There is a correlation between the thermal comfort of the streets and the environmental elements associated with them (Figure 6). By incorporating physiologically equivalent temperature (PET) as a mediating variable, as the three-dimensional greening volume (Tg) of street trees increases, both shading area and transpiration improve, leading to a gradual rise in the PET experienced by pedestrians. Moreover, changes in the orientation of SO (from north–south to east–west) further expand shaded areas, which in turn raises the PET for pedestrians even more. However, increasing the spacing between trees (Ts) decreases the shading effect per unit area, thereby diminishing the shading effect and leading to a decrease in pedestrians’ PET.

4.2. Working Days and Weekends

4.2.1. Single Factor Analysis

This research examines how various factors influence pedestrian vitality under thermal comfort guidance during workdays and weekends in the south area of Sanlitun Taikooli. As shown in Figure 7, the influence of each factor is ranked from highest to lowest based on their eigenvalues, revealing that all factors significantly impact pedestrian vitality. Sunlight duration, AR, and SO have particularly significant effects on pedestrian vitality, with the mean characteristic value of sunlight duration being 14.40273, the impact of Rh being the smallest, and other physical indicators showing positive effects.

It is important to note that the sequence of influence changes between weekdays and weekends. During weekdays, the average characteristic value of SO is 4.39957, which is higher than Tg’s 3.38089, while Ts’s average value is only 0.86315. In contrast, on weekends, the average characteristic value of Tg increases to 4.76928, higher than Ts’s 2.70928, and Ts’s impact exceeds wind speed, reaching 1.5378. Within the six street spaces, the influence levels of other physical indicators differ slightly, yet the overall trend remains consistent.

From the overall SHAP summary diagram (Figure 7), there are a greater number of red points on weekdays that have a significant impact on vitality predictions. The greater the positive SHAP values indicating beneficial effects, the further they are from the central zero line, resulting in a more dispersed SHAP point distribution, suggesting a larger influence on vitality output and stronger significance. In comparison to weekends, the red and blue scatter points for sunlight duration and AR are more focused around the central zero line during weekdays, leading to a decrease in their characteristic vitality values.

Between weekdays and weekends, the influences of street spatial factors, environmental climate factors, and physiological equivalent temperature on pedestrian vitality differ to varying degrees. Environmental climate factors exert less influence than street spatial factors; however, the mean impact characteristics of environmental climate factors increase on weekends, while the mean influence characteristics decline.

This could be due to a larger number of pedestrians on weekends, leading to a significant increase in street activity levels and usage. Pedestrians on weekdays primarily consist of commuters and short stays, resulting in lower sensitivity to climate factors. In comparison to weekdays, during weekends, as foot traffic increases, the influence of street spatial layout and design on pedestrian vitality becomes more intricate. Under these circumstances, even minor climate variations, such as a light breeze or moderate temperature rise, can have a positive effect on pedestrian vitality.

4.2.2. Multi-Factor Analysis

The vitality analysis of the southern district of Sanlitun Taikoo Li, oriented towards thermal comfort, takes into account the interactions among different factors to provide a more comprehensive explanation of vitality formation and distribution. The results of the multi-factor analysis show consistent correlations among individual factors between weekdays and weekends.

The correlation heatmap employs Pearson correlation coefficients for analysis; a coefficient nearing 1 signifies a stronger correlation between two elements, while a coefficient close to 0 indicates more independence among elements (Figure 8). The results indicate significant positive correlations between SO and Tg, AR and Ts, Tg and Ts, and Ta and PET during both weekdays and weekends, with correlation coefficients all above 0.6, suggesting these relationships are notable. Additionally, positive correlations were found between SO and Va, AR and Tg, AR and Rh, Tg and Va, Tg and Ta, Ts and Ta, Ts and Rh, Ts and PET, and Rh and PET, with correlation coefficients all exceeding 0.3. There is a significant negative correlation between SO and Rh, with a correlation coefficient of −0.66. Additionally, PET shows a negative correlation with SO, Tg, and wind speed, although the correlation coefficients are relatively low, ranging from −0.1 to −0.3.

It is noteworthy that the correlation between wind speed and street spatial factors is significantly higher on weekdays than on weekends. Specifically, the correlation coefficients between wind speed and AR, Tg, and Ts increased significantly, with an average increase of approximately 8%. Validation through SHAP interaction effect plots shows that the relationship between wind speed and street spatial factors is more dispersed on weekdays compared to weekends. Furthermore, during both weekdays and non-weekdays, the SHAP scatter points of AR, Tg, and Ta exhibit significant dispersion, indicating strong interactions between these three factors and others.

4.3. Different Time Periods Within Normal Days

4.3.1. Single Factor Analysis

This study investigates how various influencing factors related to thermal comfort affect pedestrian vitality at different times of the day, particularly during the peak pedestrian period on Saturdays. The time frame for analysis is from 12:00 PM to 8:00 PM, segmented into two-hour periods. Based on Figure 9, the influence of various factors on pedestrian vitality is sorted in descending order according to their characteristic values. The findings suggest that all influencing factors have a significant effect on pedestrian vitality, and the degree of their influence varies significantly as time progresses.

First, the physiological equivalent temperature (PET) significantly affects pedestrian vitality from 12:00 PM to 4:00 PM. From 12:00 PM to 2:00 PM, the characteristic mean of PET has the highest influence, peaking at 9.34354; In contrast, between 2:00 PM and 4:00 PM, its characteristic mean drops to 4.7928, which is below that of AR and Ta. During the period from 4:00 PM to 8:00 PM, the influence of PET sharply declines to its lowest level, recorded at just 0.96322. This changing trend suggests that the impact of PET on pedestrian vitality significantly weakens as time progresses.

Furthermore, SO and AR continue to exert a strong influence on pedestrian vitality throughout the observed period. The data indicates that during the period from 12:00 PM to 8:00 PM, the characteristic mean of SO rises from 1.55825 to 9.87666. This implies that an increase in SO values correlates with a continuous rise in pedestrian vitality. Within the SO framework, regions with values of 1–2 are designated as main streets, whereas those with values of 3–6 are considered secondary streets. This suggests that, from 12:00 PM to 8:00 PM, the increase in pedestrian vitality is more pronounced in secondary streets.

Finally, from the overall SHAP summary graph analysis, the influence factors on vitality prediction show significant variations between noon and evening. During the period from 12:00 to 16:00, the red and blue scatter points of PET, Ta, and AR are further away from the central line zero point, while the scatter points of the other influencing factors are more concentrated around the central line zero point, indicating that these three features have a greater impact on vitality output. From 16:00 to 20:00, the red and blue scatter points of all influencing factors gradually disperse away from the central line zero point, indicating an increasing trend in their impact on vitality output.

This may be related to the adaptive changes of pedestrians during hot periods. Over time, the impact of climatic factors on pedestrian vitality becomes more significant between 12:00 and 16:00. After 16:00, as the sun’s height decreases, the impact of climate factors gradually weakens, potentially indicating that pedestrians’ adaptability to hot environments is increasing, and they may adjust their behavioral strategies in response to environmental changes to adapt to new climatic conditions, which in turn affects their vitality performance.

4.3.2. Multi-Factor Analysis

In the context of thermal comfort, the analysis of vitality in the southern district of Sanlitun Taikooli at different time periods reveals that the correlations among individual factors exhibit variability throughout the day.

The findings show that, during the period from 12:00 to 14:00, Tg is strongly positively correlated with Va, and Rh shows a strong positive correlation with PET, with correlation coefficients above 0.9. Moreover, AR, Ts, Ta, and PET exhibit positive correlations with other factors, with the correlation coefficients for SO with Tg, SO with Va, AR with Ts, and Ta with Rh all exceeding 0.7, suggesting these relationships are quite significant (Figure 10).

Between 14:00 and 16:00, the correlation between Va and the street spatial elements is consistent with the earlier time period from 12:00 to 14:00. In contrast, the correlation coefficients among street environmental factors and other variables decreased, while the correlation coefficients for climatic physical factors have generally increased. Specifically, the correlation coefficients of SO and AR with other factors decrease by about 5% on average; the correlation coefficients of Rh with Ta and PET increase by approximately 20% on average.

During the period from 16:00 to 18:00, the relationships of climatic physical factors with other elements decline rapidly, with the correlation between Ta and PET shifting from a positive 0.77 to a negative −0.67. Furthermore, the correlation between SO and Ta transitions from −0.442 (negative) to 0.81 (positive), reflecting a change in orientation from the main street to the secondary street, with a gradual rise in environmental temperature.

During the period between 18:00 and 20:00, AR, Ts, Ta, and Rh exhibited positive correlations with other factors, with the correlation between Rh and PET increasing from −0.15 (negative) to 0.95 (positive). Furthermore, SO is positively correlated with Tg, SO with Va, AR with Ts, and Ta with PET, with all their correlation coefficients exceeding 0.7.

Therefore, the formation and distribution of vitality in the southern area of Sanlitun Taikoo Li are influenced by multiple factors, and the correlation of these factors varies across different time periods. The verification through SHAP interaction effect diagrams shows that the red and blue scatter plots of various elements are inconsistently distributed across different time periods. From 12:00 to 16:00, the red and blue scatter plots of climatic physical factors are more dispersed, indicating a more significant impact of climatic physical factors on vitality. From 16:00 to 20:00, the red and blue scatter plots of street spatial factors are more dispersed, highlighting a more significant contribution of street spatial factors to vitality.

5. Discussion

5.1. Changes in Spatiotemporal Vitality of Streets

In terms of the impact of temporal changes on vitality, thermal comfort plays a guiding role between 12:00 and 16:00. During this time period, the influence of climatic physical factors on vitality is more significant. Climatic factors such as temperature, humidity, and wind speed directly affect people’s comfort and activity levels. High temperatures and humidity may cause fatigue and discomfort, thereby reducing people’s vitality levels. The reason for the applicable range being from 12:00 to 16:00 may relate to the evening hours, as the sun’s altitude is lower, the radiation intensity is diminished, and the shading effects of buildings and street trees are not very pronounced; therefore, the influence on the thermal comfort of the street environment is limited. However, in the midday period, direct sunlight is at its peak, with higher temperature and humidity levels prevalent, leading to a reduction in the comfort of outdoor activities, hindering individuals’ vitality, and the significance of shading gradually becomes evident, thus becoming a crucial factor influencing vitality. Therefore, enhancing thermal comfort for people can raise the vitality levels of street environments.

In terms of spatial changes in vitality, street hierarchy is an important factor. The vitality enhancement rate of secondary streets is lower than that of primary streets. The vitality level of primary streets gradually rises throughout the day, starting at 12:00 and reaching its peak at 20:00. At 20:00, the vitality level of primary streets is 1.4 times higher than at 12:00 and 1.4 times higher than at 16:00. Combining the influence order of individual factors on vitality discussed below, AR is the primary driving factor of vitality in commercial streets. Therefore, primary streets should reasonably set width-to-height ratios, while secondary streets should prioritize increasing Tg to attract foot traffic.

5.2. Street Thermal Comfort Changes

The spatial elements of the street not only influence pedestrians’ behavioral choices but also directly affect the PET (physiological equivalent temperature) model. An analysis of typical commercial streets in cold regions reveals significant design issues during summer, indicating considerable room for improvement. Numerous studies indicate that increasing or decreasing the aspect ratio of streets affects thermal comfort. Research by Zhang et al. conducted in hot and humid regions of China, similar to studies in hot semi-arid climates, shows that high-aspect-ratio street canyons provide better thermal comfort for pedestrians in summer [44]. In this study, the average wind speed across six street spaces remains between 0.5 and 1.2 m/s, with a maximum wind speed reaching 2.2 m/s. Cao’s research found that the street shading patterns change significantly with variations in the W/H ratio; the PET difference in summer between streets with a W/H ratio of 1:2 and those with a ratio of 1:4 can reach up to 4 °C [45]. Additionally, as the W/H ratio increases, the same street can accommodate more people, offering broader visibility and a better pedestrian experience [46]. However, excessively high aspect ratios can increase pedestrians’ feelings of insecurity, reduce their desire to shop, and accelerate their walking speed.

When pedestrians walk on the street, the direction of the street has little effect on their visual perception. However, different street orientations significantly affect the thermal environment and thermal comfort patterns of the streets. The PET temperature of east–west streets is 29.4% lower than that of north–south streets, which may be due to significant differences in the duration and intensity of heat stress experienced by streets of different orientations [47]. Therefore, street orientation should not be overlooked in the design of thermal environments for streets.

The three-dimensional green volumes of the six street spaces is 40 m³, 150 m³, and 1070 m³, respectively. The average temperature increases are 2.2 °C, 1.8 °C, and 1.3 °C, respectively. As the greenery volume increases, the temperature rise decreases. This clearly demonstrates that greenery volume plays a crucial role in reducing temperatures in the microclimate of street spaces [48,49]. When the three-dimensional greenery volume of street trees is too low, it fails to provide thermal comfort improvements. As the three-dimensional greenery volume of street trees increases, its impact on thermal comfort gradually diminishes. Chen found that, to maintain a street PET below 30 °C, a three-dimensional greenery volume of 317.35 m² of trees is necessary. This aligns with the findings of this study [50]. The spacing of trees on the street has a direct impact on pedestrians’ thermal comfort. A closely spaced and dense arrangement of trees can provide more lasting shade, thereby reducing the heat sensation caused by direct sunlight on pedestrians. This extends the time pedestrians spend walking in shaded areas. Furthermore, a dense arrangement of trees can somewhat slow the temperature rise of the street surface, which helps control the formation of urban heat islands. Further research indicates that, while street orientation can significantly improve the thermal environment, the effects of changes in street aspect ratio on PET vary. As the aspect ratio increases, the PET shows a decreasing trend. When the street aspect ratio is 1, even with high tree spacing and greenery volume, it is still difficult to maintain the street physiological equivalent temperature below 30 °C.

5.3. Factors Affecting Thermal Comfort Changes

5.3.1. Single Factor

This research performs a thorough analysis of how various factors influence changes in pedestrian vitality on weekdays versus weekends and at different times of the day. The analysis of individual factors indicates that higher values for most characteristics are significantly associated with increased pedestrian vitality (Figure 11). In particular, the street aspect ratio (8.28), tree density (3.39), and street orientation (3.27) are recognized as the three primary factors driving the increase in summer vitality of the pedestrian area in the southern part of Taikoo Li, Sanlitun, Beijing. This indicates that improvements in street design can greatly boost the vibrancy of pedestrian areas (Figure 11)

This finding aligns with the results of Achour-Younsi’s research. A higher street width-to-height ratio can enhance the openness of the space [51], making pedestrians feel more comfortable and safe and reducing feelings of crowding, thereby encouraging longer stays and interactions. The high ranking of tree canopy cover indicates that increasing vegetation can provide shade, improve thermal comfort, and enhance air quality, thereby increasing pedestrian dwell time and willingness to engage in activities. This result is consistent with Ren Z’s finding that differences in tree cover have a dominant impact on urban thermal environment and comfort [52]. Additionally, an appropriate street orientation optimizes sunlight and ventilation conditions, maximizing the use of natural resources.

However, the average street width-to-height ratio (8.28) is twice that of tree canopy cover (3.39) and street orientation (3.27). This disparity reflects the differing strengths of these three factors in influencing pedestrian vitality, supporting the research context of enhancing the livability and sustainability of urban public spaces presented in this paper [53]. Particularly in colder regions, pedestrian vitality in urban streets shows significant differences under varying thermal conditions [39]. Therefore, it is necessary to comprehensively discuss multiple factors influencing pedestrian vitality while considering thermal comfort, in order to formulate more effective design strategies.

The street width-to-height ratio is directly related to the openness of space and visual comfort, reducing the obstruction of pedestrians’ sightlines by buildings, enhancing spatial transparency, and making pedestrians feel safer and more relaxed, encouraging them to stay in the area. In contrast, while tree canopy cover and street orientation can improve the pedestrian environment, their role leans more towards providing additional comfort [54]. The shade from trees and the optimization of street orientation enhance the microclimate and landscape [55]; tree cover primarily focuses on improving comfort, while street orientation affects the guidance of sunlight [56]. Therefore, this significant difference emphasizes that prioritizing the street width-to-height ratio in urban street renewal is a key strategy for enhancing pedestrian vitality, while tree cover and orientation should serve as supplementary measures to enhance sustainability and adapt to thermal conditions in colder regions.

Although PET has a significant impact on spatial vitality, its role is not always dominant; it was only during the period from 12:00 to 16:00 that PET was proven to be the primary explanatory variable affecting spatial vitality, as thermal comfort during this time significantly influenced pedestrians’ willingness to engage in activities and their duration of stay. Therefore, when examining thermal comfort as a prerequisite for enhancing spatial vitality, it is necessary to analyze in depth the relationship between street spatial vitality and its influencing factors during the 12:00 to 16:00 period.

Overall, the street width-to-height ratio, tree canopy cover, and street orientation are key factors in enhancing pedestrian vitality, with the street width-to-height ratio having the most significant impact on effectively boosting street spatial vitality. Thermal comfort significantly influences pedestrian activity willingness during the specific time period studied (12:00–16:00); therefore, urban public space design should prioritize optimizing the street width-to-height ratio while increasing greenery and appropriately orienting streets to create a more livable and sustainable walking environment. Additionally, it is necessary to comprehensively discuss the relationships among multiple factors influencing pedestrian vitality under the premise of thermal comfort to formulate more effective design strategies.

5.3.2. Multi-Factor Analysis

The multi-factor analysis of this study consistently indicates that street spatial elements and climatic physical factors show a consistent correlation during weekdays and weekends. However, despite the overall consistent trend of influence, the intensity of impact of each influencing factor on spatial vitality varies. Furthermore, at different times of the same day, the interactions among influencing factors show significant differences in enhancing spatial vitality. The interaction effects among street spatial elements and climatic physical factors regarding vitality enhancement exhibit greater complexity.

This research focuses on the relationship between multiple factors affecting pedestrian vitality under the premise of thermal comfort (Figure 12). Specifically, during the period from 12:00 to 16:00, the physiologically equivalent temperature (PET) is the main explanatory variable, with a mean value of 9.34354. At this time, PET shows strong interactions with relative humidity (0.95), temperature (0.75), tree canopy cover (0.54), and street aspect ratio (0.31). This indicates that relative humidity has a strong influence on PET; higher humidity increases the moisture content in the air, which affects the body’s evaporative cooling capacity and thus raises the perceived temperature [57]. Temperature is another key factor influencing PET. High temperatures directly lead to an increase in the perceived temperature, affecting pedestrians’ comfort and vitality. An increase in tree canopy cover can effectively enhance shading, reduce surface temperatures, and improve the microclimate, thereby enhancing thermal comfort for pedestrians in high-temperature conditions [58]. The street aspect ratio influences the light and ventilation conditions in urban structures, subsequently affecting PET. A higher street aspect ratio can enhance air circulation, facilitate heat dissipation, reduce heat radiation accumulation, and improve thermal comfort for pedestrians [59].

In analyzing relative humidity and temperature, the interaction strength of street aspect ratio and tree canopy cover is consistent, with both around 0.4. This indicates that the interaction between relative humidity and temperature is jointly influenced by the street aspect ratio and tree canopy cover (Figure 12). This means that, under conditions of high temperature and humidity, these two factors can work together through different mechanisms to influence pedestrian thermal comfort. However, this consistency does not directly explain which combination of factors has the greatest impact. To clarify this, further discussion is needed on the relationship between street aspect ratio and tree canopy cover.

For tree canopy cover, the interaction strength with street aspect ratio and tree spacing is consistent, both exceeding 0.65. Street aspect ratio and tree spacing jointly affect the coverage effect and growth environment of trees. Li Z also proposed that, as the depth of the canyon increases, the wind speed in the street environment increases, which is conducive to the growth of large-crown trees, while smaller trees are suitable for leeward streets [60]. A higher street aspect ratio can provide better ventilation and lighting conditions, promoting tree growth, while appropriate tree spacing ensures the full spread of tree crowns, maximizing shading effects. However, the interaction strength between street aspect ratio and tree canopy cover is greater than that between street aspect ratio and tree spacing, mainly because aspect ratio significantly affects the growth environment of trees and microclimate regulation. A higher aspect ratio can enhance the shading effect and ventilation of trees, significantly lowering surface temperatures and reducing heat radiation. Tree spacing mainly affects the shading duration for pedestrians. Although appropriate spacing can enhance the shade received by pedestrians walking on the street, its overall impact on thermal regulation is relatively limited. Tree spacing mainly determines the direct shading area provided to pedestrians, while the aspect ratio influences the distribution of light, airflow, and the reflection and absorption of heat radiation through the overall structure [61]. Therefore, the urban heat island effect indicates that the design of street structure, such as aspect ratio, can significantly affect heat accumulation and dissipation.

For the street aspect ratio, the interaction intensity with the tree canopy cover is significantly higher than that with tree spacing. The interaction strength of the former is 0.69, while the interaction strength of the latter is 0.37. As tree canopy cover increases and street aspect ratio rises, a synergistic effect is formed. This indicates that, under conditions of thermal comfort, increases in street aspect ratio or tree canopy cover may stimulate more activities, enhancing vitality. This may be because street aspect ratio determines the enclosure and openness of space, while tree canopy cover improves the microclimate through shading and evapotranspiration, reducing temperature and heat stress. When the street aspect ratio increases, the enhancement of tree canopy cover can effectively alleviate the heat load, attracting more pedestrians to participate in activities.

This study investigates the impact of street spatial elements and climatic physical factors on pedestrian vitality under the premise of thermal comfort, finding a consistent correlation between them on weekdays and weekends, despite differences in the intensity of influence. Particularly between 12:00 and 16:00, the physiologically equivalent temperature (PET) serves as the main explanatory variable, demonstrating a strong interaction with relative humidity, temperature, tree canopy cover, and street aspect ratio. The research indicates that high humidity and high temperatures reduce pedestrian comfort, while increasing tree canopy cover and optimizing street aspect ratios can significantly enhance thermal comfort. Moreover, the synergistic effect of tree canopy cover and street aspect ratio is particularly pronounced in enhancing pedestrian vitality, as a higher aspect ratio can influence the distribution of light, wind flow, and the reflection and absorption of thermal radiation, thereby improving the microclimate and creating favorable conditions for tree growth, thus enhancing street vitality.

Therefore, under the guidance of thermal comfort, adjusting the interaction between street aspect ratio and tree canopy cover within street spatial elements can improve the interaction between temperature and relative humidity in climatic physical factors, thereby enhancing pedestrian vitality in cold urban street spaces under thermal comfort conditions.

5.4. Design Strategy

To enhance the thermal comfort and vitality of commercial streets, optimizing the aspect ratio design and the greenery layout can significantly improve pedestrian experience.

Firstly, the aspect ratio of streets is a crucial factor affecting the microclimate. Research shows that the ratio of building height to street width should be maintained between 1:2 and 1:4 to achieve ideal thermal comfort. When the aspect ratio is below 1:4, it can reduce wind speed to some extent, but often leads to increased temperatures, thereby raising pedestrian discomfort. Conversely, when the aspect ratio exceeds 1:2, it can lower temperatures, but if wind speeds increase significantly, it can cause discomfort, especially in areas where people need to stay. Therefore, during the design process, it is essential to find a reasonable aspect ratio to balance between enhancing building shading effects and improving ventilation. An appropriate aspect ratio can effectively reduce direct sunlight and lower surface temperatures, thereby enhancing pedestrian thermal comfort.

Secondly, the rational arrangement of street greenery also has a significant impact on thermal comfort. Planting a sufficient number of trees on both sides of the road, with an average greenery volume of over 300 cubic meters, can create an effective green barrier. These trees can lower surrounding temperatures through transpiration, typically reducing local temperatures by 1.5 to 2 °C, while providing natural shade. This shading can enhance pedestrians’ thermal comfort, making them more willing to linger and relax on the street. Additionally, the establishment of low green belts and shrubs not only helps to further lower surface temperatures but also enhances the street’s aesthetics, creating a more pleasant environment.

Specifically, in contrast to Lee H.’s design strategy, which selects trees under a fixed tree density configuration, this study explores a broader range of green space layouts with varied positions and arrangements [62]. The improvements in aspect ratio and green space layout in this study are primarily focused on enhancing thermal comfort and fostering a more human-centered street environment. These changes not only improve pedestrian comfort, but also significantly enhance the urban microclimate, particularly during the hot summer months, thus boosting the vitality and livability of the streets. Therefore, in the planning and design of street spaces, priority should be given to a reasonable aspect ratio design (maintaining between 1:2 and 1:4) and a scientific green layout, maximizing green area and increasing the diversity of plant configurations. For example, planting tall trees (such as those reaching heights of 8 to 15 m) can effectively slow down the rise in temperature, and creating more shaded areas can enhance thermal comfort on the street. Ultimately, this can enhance the overall vitality of urban streets in cold regions.

5.5. Impacts and Limitations

Although this study provides insights for the design and optimization of urban street spaces in Beijing, there are still certain limitations.

First, the study primarily focuses on urban street spaces in cold climate regions. Although these spaces are frequently used in urban public spaces, their small area and limited exposure to wind factors mean that other types of public and non-public spaces in cities, which have more complex morphological characteristics, warrant further investigation.

Secondly, the study focuses on observations and analyses within a short time frame, but long-term influencing factors are equally important. Therefore, there is a need to extend the time span to further investigate the long-term mechanisms affecting human outdoor thermal perception, thereby avoiding the incidental impacts of outdoor thermal environmental factors.

Finally, this study selects representative urban commercial streets as the research subject. Although commercial streets have high pedestrian traffic, facilitating observation and data collection, the pedestrian flow on residential streets differs from that of commercial streets, with higher space usage frequency, which merits further research.

Therefore, we advocate for the development of analytical approaches and frameworks focused on thermal comfort in urban street spaces within cold regions in future studies to enhance the vibrancy of urban public spaces in these areas.

6. Conclusions

This research conducted meteorological and pedestrian vitality measurements on six streets in the pedestrian area of the southern district of Sanlitun, Beijing, a cold region in China, to investigate the impact of thermal comfort on enhancing street vitality. The research systematically examines the effects of street environmental factors (such as sunlight duration, street orientation, aspect ratio, tree coverage, and tree spacing), climatic physical factors (including wind speed, temperature, and humidity), and physiological equivalent temperature on pedestrian vitality in street spaces, resulting in the following conclusions:

• (I). The vitality of the streets exhibits a gradual upward trend throughout the day, particularly during the time frame from 12:00 to 16:00, where thermal comfort significantly influences people’s vitality. The high temperatures and humidity during this time reduce individuals’ comfort and activity capabilities, thus inhibiting vitality levels. Therefore, improving thermal comfort in the street environment during the period from 12:00 to 16:00 is crucial for enhancing the vitality of street spaces.

• (II). The street aspect ratio is a primary design factor influencing the vitality of the pedestrian streets in the southern district of Sanlitun, Beijing, and its impact exceeds that of other design factors. When focusing on a single factor, priority is given to the street aspect ratio; in urban street spaces located in cold regions, the influence of street spatial elements is greater than that of climatic physical factors, and a higher street aspect ratio is positively correlated with increased vitality.

• (III). The correlation between street spatial elements and climatic physical elements is consistent on weekdays and weekends, but their influencing factors on vitality show significant differences at different time periods. During the period from 12:00 to 16:00, adjusting the interaction between the street aspect ratio and tree canopy in street spatial elements can improve the interaction between temperature and relative humidity in climatic physical elements, thereby enhancing pedestrian vitality in cold urban street spaces under conditions of thermal comfort.

• (IV). In street planning, a reasonable building aspect ratio (1:2 to 1:4) and scientific green design are key. By carefully controlling the planting density and greenery configuration of trees, optimal street space effects can be achieved. Planting trees that are 8 to 15 m tall can effectively reduce surface temperatures and increase shaded areas, but also significantly improves the thermal comfort of streets, thereby enhancing the vitality of urban streets in cold regions during summer.

Author Contributions

Methodology, W.Z.; Software, W.Z.; Investigation, X.L.; Writing—review & editing, W.Z., J.Z., X.L., F.G. and P.Z. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures

Figure 1. Research framework.

Figure 2. Study area and measurement sites.

Figure 3. Analysis of behavioral activities and main flow lines at each measurement site.

Figure 4. Temperature and relative humidity change chart of Sanlitun streets.

Figure 5. Schematic diagram of spatial elements in Sanlitun streets.

Figure 6. PET and vitality quantitative distribution map of Sanlitun streets.

Figure 7. Single factor SHAP chart for streets on weekdays and weekends.

Figure 8. Multi-factor SHAP diagram of streets on weekdays and weekends.

Figure 9. Single-factor SHAP chart of streets at different time periods within a day.

Figure 10. Multi-factor SHAP chart of streets at different time periods within a day.

Figure 11. Comprehensive analysis of street single-factor SHAP diagram.

Figure 12. Comprehensive analysis of street multi-factor SHAP diagram.

References

1. Taleghani, M. Outdoor thermal comfort by different heat mitigation strategies-A review. Renew. Sustain. Energy Rev.; 2018; 81, pp. 2011-2018. [DOI: https://dx.doi.org/10.1016/j.rser.2017.06.010]

2. Kim, Y.; Kim, H.; Shin, S.D.; Hong, Y. Different influence of outdoor temperature on traumatic and nontraumatic injuries. J. Trauma Acute Care Surg.; 2012; 73, pp. 944-949. [DOI: https://dx.doi.org/10.1097/TA.0b013e318256de02] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23034530]

3. Brocherie, F.; Girard, O.; Millet, G.P. Emerging environmental and weather challenges in outdoor sports. Climate; 2015; 3, pp. 492-521. [DOI: https://dx.doi.org/10.3390/cli3030492]

4. Guergour, H.; Cheraitia, M.; Dechaicha, A.; Alkama, D. Optimization of urban morphology to enhance outdoor thermal comfort: A microclimate analysis. J. Bulg. Geogr. Soc.; 2024; 51, pp. 107-130. [DOI: https://dx.doi.org/10.3897/jbgs.e128961]

5. Liu, H.; Huang, B.; Gao, S.; Wang, J.; Yang, C.; Li, R. Impacts of the evolving urban development on intra-urban surface thermal environment: Evidence from 323 Chinese cities. Sci. Total Environ.; 2021; 771, 144810. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.144810] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33545479]

6. Lee, I.M.; Shiroma, E.J.; Lobelo, F.; Puska, P.; Blair, S.N.; Katzmarzyk, P.T. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. Lancet; 2012; 380, pp. 219-229. [DOI: https://dx.doi.org/10.1016/S0140-6736(12)61031-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22818936]

7. Fahed, J.; Kinab, E.; Ginestet, S.; Adolphe, L. Impact of urban heat island mitigation measures on microclimate and pedestrian comfort in a dense urban district of Lebanon. Sustain. Cities Soc.; 2020; 61, 102375. [DOI: https://dx.doi.org/10.1016/j.scs.2020.102375]

8. Li, Y.; Hong, B.; Wang, Y.; Bai, H.; Chen, H. Assessing heat stress relief measures to enhance outdoor thermal comfort: A field study in China’s cold region. Sustain. Cities Soc.; 2022; 80, 103813. [DOI: https://dx.doi.org/10.1016/j.scs.2022.103813]

9. Kumar, A.; Singh, D.P. Heat stroke-related deaths in India: An analysis of natural causes of deaths, associated with the regional heatwave. J. Therm. Biol.; 2021; 95, 102792. [DOI: https://dx.doi.org/10.1016/j.jtherbio.2020.102792]

10. Henderson, J.V.; Turner, M.A. Urbanization in the developing world: Too early or too slow?. J. Econ. Perspect.; 2020; 34, pp. 150-173. [DOI: https://dx.doi.org/10.1257/jep.34.3.150]

11. GB 50176-2016 Thermal Design Code for Civil Buildings; China Architecture & Building Press: Beijing, China, 2016.

12. Sarrat, C.; Lemonsu, A.; Masson, V.; Guédalia, D. Impact of urban heat island on regional atmospheric pollution. Atmos. Environ.; 2006; 40, pp. 1743-1758. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2005.11.037]

13. Wang, K.; Li, Y.; Luo, Z.; Yin, S.; Chan, P.W. Harmonic analysis of 130-year hourly air temperature in Hong Kong: Detecting urban warming from the perspective of annual and daily cycles. Clim. Dyn.; 2018; 51, pp. 613-625. [DOI: https://dx.doi.org/10.1007/s00382-017-3944-y]

14. Huang, K.-T.; Yang, S.-R.; Matzarakis, A.; Lin, T.-P. Identifying outdoor thermal risk areas and evaluation of future thermal comfort concerning shading orientation in a traditional settlement. Sci. Total Environ.; 2018; 626, pp. 567-580. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2018.01.031] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29353795]

15. Chen, H.; Zhao, L.; Cheng, L.; Zhang, Y.; Wang, H.; Gu, K.; Bao, J.; Yang, J.; Liu, Z.; Huang, J. et al. Projections of heatwave-attributable mortality under climate change and future population scenarios in China. Lancet Reg. Health–West. Pac.; 2022; 28, 100582. [DOI: https://dx.doi.org/10.1016/j.lanwpc.2022.100582]

16. An, L.; Hong, B.; Cui, X.; Geng, Y.; Ma, X. Outdoor thermal comfort during winter in China’s cold regions: A comparative study. Sci. Total Environ.; 2021; 768, 144464. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.144464]

17. Liu, Y.; Xu, Y.; Weng, F.; Zhang, F.; Shu, W. Impacts of urban spatial layout and scale on local climate: A case study in Beijing. Sustain. Cities Soc.; 2021; 68, 102767. [DOI: https://dx.doi.org/10.1016/j.scs.2021.102767]

18. Lin, J.; Wei, K.; Guan, Z. Exploring the connection between morphological characteristic of built-up areas and surface heat islands based on MSPA. Urban Clim.; 2024; 53, 101764. [DOI: https://dx.doi.org/10.1016/j.uclim.2023.101764]

19. Errea, C.L.; de Almeida, C.R.; Gonçalves, A.; Teodoro, A.C. Remote sensing analysis of the surface urban heat island effect in vitoria-gasteiz, 1985 to 2021. Remote Sens.; 2023; 15, 3110. [DOI: https://dx.doi.org/10.3390/rs15123110]

20. Guo, F.; Guo, R.; Zhang, H.; Dong, J.; Zhao, J. A canopy shading-based approach to heat exposure risk mitigation in small squares. Urban Clim.; 2023; 49, 101495. [DOI: https://dx.doi.org/10.1016/j.uclim.2023.101495]

21. Mu, B.; Liu, C.; Mu, T.; Xu, X.; Tian, G.; Zhang, Y.; Kim, G. Spatiotemporal fluctuations in urban park spatial vitality determined by on-site observation and behavior mapping: A case study of three parks in Zhengzhou City, China. Urban For. Urban Green.; 2021; 64, 127246. [DOI: https://dx.doi.org/10.1016/j.ufug.2021.127246]

22. Liu, S.; Lai, S.-Q.; Liu, C.; Jiang, L. What influenced the vitality of the waterfront open space? A case study of Huangpu River in Shanghai, China. Cities; 2021; 114, 103197. [DOI: https://dx.doi.org/10.1016/j.cities.2021.103197]

23. Kim, Y.L. Seoul’s Wi-Fi hotspots: Wi-Fi access points as an indicator of urban vitality. Comput. Environ. Urban Syst.; 2018; 72, pp. 13-24. [DOI: https://dx.doi.org/10.1016/j.compenvurbsys.2018.06.004]

24. Song, L.I.; Siqi, L.A. Measurement of Urban Public Space Vitality Based on Big Data. Landsc. Archit.; 2019; 26, pp. 24-28.

25. Zheng, J.; He, J.; Tang, H. The vitality of public space and the effects of environmental factors in Chinese suburban rural communities based on tourists and residents. Int. J. Environ. Res. Public Health; 2022; 20, 263. [DOI: https://dx.doi.org/10.3390/ijerph20010263] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36612605]

26. Niu, T.; Qing, L.; Han, L.; Long, Y.; Hou, J.; Li, L.; Tang, W.; Teng, Q. Small public space vitality analysis and evaluation based on human trajectory modeling using video data. Build. Environ.; 2022; 225, 109563. [DOI: https://dx.doi.org/10.1016/j.buildenv.2022.109563]

27. Weilnhammer, V.; Schmid, J.; Mittermeier, I.; Schreiber, F.; Jiang, L.; Pastuhovic, V.; Herr, C.; Heinze, S. Extreme weather events in europe and their health consequences–A systematic review. Int. J. Hyg. Environ. Health; 2021; 233, 113688. [DOI: https://dx.doi.org/10.1016/j.ijheh.2021.113688]

28. Zhang, Y.; Zheng, Z.; Zhang, S.; Fang, Z.; Lin, Z. Exploring thermal comfort and pleasure in outdoor shaded spaces: Inspiration for improving thermal index models. Build. Environ.; 2024; 265, 111933. [DOI: https://dx.doi.org/10.1016/j.buildenv.2024.111933]

29. Ceccarelli, G.; Messa, F.; Gorrini, A.; Presicce, D.; Choubassi, R. Deep learning video analytics for the assessment of street experiments: The case of bologna. J. Urban Mobil.; 2023; 4, 100067. [DOI: https://dx.doi.org/10.1016/j.urbmob.2023.100067]

30. Makvandi, M.; Zhou, X.; Li, C.; Deng, Q. A Field Investigation on Adaptive Thermal Comfort in an Urban Environment Considering Individuals’ Psychological and Physiological Behaviors in a Cold-Winter of Wuhan. Sustainability; 2021; 13, 678. [DOI: https://dx.doi.org/10.3390/su13020678]

31. Zheng, Z.; Luo, F.; Li, N.; Gao, H.; Yang, Y. Impact of Local Climate Zones on the Urban Heat and Dry Islands in Beijing: Spatial Heterogeneity and Relative Contributions. J. Meteorol. Res.; 2024; 38, pp. 126-137. [DOI: https://dx.doi.org/10.1007/s13351-024-3081-6]

32. Liu, R.; Jiao, L.; Liu, Y.; Wang, Y. Multi-scale spatial analysis of satellite-retrieved surface evapotranspiration in Beijing, a rapidly urbanizing region under continental monsoon climate. Environ. Sci. Pollut. Res.; 2023; 30, pp. 20402-20414. [DOI: https://dx.doi.org/10.1007/s11356-022-23580-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36258113]

33. Cui, F.; Hamdi, R.; Kuang, W.; Yang, T.; He, H.; Termonia, P.; De Maeyer, P. Interactions between the summer urban heat islands and heat waves in Beijing during 2000–2018. Atmos. Res.; 2023; 291, 106813. [DOI: https://dx.doi.org/10.1016/j.atmosres.2023.106813]

34. Huang, J.; Shen, S.; Zhao, M.; Cheng, C. Assessment of Summer Regional Outdoor Heat Stress and Regional Comfort in the Beijing-Tianjin-Hebei Agglomeration Over the Last 40 Years. GeoHealth; 2023; 7, e2022GH000725. [DOI: https://dx.doi.org/10.1029/2022GH000725] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36594002]

35. Ding, W.; Liu, S. Impact assessment of air pollutants and greenhouse gases on urban heat wave events in the Beijing–Tianjin–Hebei region. Environ. Geochem. Health; 2023; 45, pp. 7693-7709. [DOI: https://dx.doi.org/10.1007/s10653-023-01677-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37418165]

36. Pintor, L. Advancements in Wi-Fi-Based Passenger Counting and Crowd Monitoring: Techniques and Applications. Ph.D. Thesis; Università degli Studi di Cagliari: Cagliari, Italy, 2024.

37. Zhang, H.; Wang, Y.; Guo, F.; Zhao, J.; Dong, J.; Zhu, P. Factors influencing outdoor thermal comfort in a coastal park during the transition seasons in cold regions of China. Urban Clim.; 2024; 55, 101856. [DOI: https://dx.doi.org/10.1016/j.uclim.2024.101856]

38. Guo, R.; Guo, F.; Dong, J.; Wang, Z.; Zheng, R.; Zhang, H. Finer-scale urban health risk assessment based on the interaction perspective of thermal radiation, human, activity, and space. Front. Archit. Res.; 2024; 13, pp. 682-697. [DOI: https://dx.doi.org/10.1016/j.foar.2024.02.002]

39. Bai, M.; Hu, R.; Lian, H.; Zhou, W. Correlation Study of Commercial Street Morphology and Pedestrian Activity in Cold Region Summers under Thermal Comfort Guidance: A Case Study of Sanlitun, Beijing. Buildings; 2024; 14, 1751. [DOI: https://dx.doi.org/10.3390/buildings14061751]

40. ISO 7730:2005 Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort Using Human Response to Temperature; International Organization for Standardization (ISO): Geneva, Switzerland, 2005.

41. ISO 9920:2019 Ergonomics of the Thermal Environment—Estimation of Thermal Insulation and Water Vapor Resistance of a Clothing Ensemble; International Organization for Standardization (ISO): Geneva, Switzerland, 2019.

42. Arsad, F.S.; Hod, R.; Ahmad, N.; Baharom, M.; Ja’afar, M.H. Assessment of indoor thermal comfort temperature and related behavioural adaptations: A systematic review. Environ. Sci. Pollut. Res.; 2023; 30, pp. 73137-73149. [DOI: https://dx.doi.org/10.1007/s11356-023-27089-9]

43. Yue, H. Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning. Accid. Anal. Prev.; 2024; 205, 107693. [DOI: https://dx.doi.org/10.1016/j.aap.2024.107693]

44. Zhang, Y.; Du, X.; Shi, Y. Effects of street canyon design on pedestrian thermal comfort in the hot-humid area of China. Int. J. Biometeorol.; 2017; 61, pp. 1421-1432. [DOI: https://dx.doi.org/10.1007/s00484-017-1320-6]

45. Nasrollahi, N.; Namazi, Y.; Taleghani, M. The effect of urban shading and canyon geometry on outdoor thermal comfort in hot climates: A case study of Ahvaz, Iran. Sustain. Cities Soc.; 2021; 65, 102638. [DOI: https://dx.doi.org/10.1016/j.scs.2020.102638]

46. Zhang, J.; Zhou, W.; Lian, H.; Hu, R. Research on Optimization Strategy of Commercial Street Spatial Vitality Based on Pedestrian Trajectories. Buildings; 2024; 14, 1240. [DOI: https://dx.doi.org/10.3390/buildings14051240]

47. Perini, K.; Magliocco, A. Effects of vegetation, urban density, building height, and atmospheric conditions on local temperatures and thermal comfort. Urban For. Urban Green.; 2014; 13, pp. 495-506. [DOI: https://dx.doi.org/10.1016/j.ufug.2014.03.003]

48. Cao, A.; Qiong, L.; Qinglin, M. Effects of orientation of urban roads on the local thermal environment in Guangzhou City. Proc. Eng.; 2015; 121, pp. 2075-2082. [DOI: https://dx.doi.org/10.1016/j.proeng.2015.09.209]

49. Watanabe, S.; Nagano, K.; Ishii, J.; Horikoshi, T. Evaluation of outdoor thermal comfort in sunlight, building shade, and pergola shade during summer in a humid subtropical region. Build. Environ.; 2014; 82, pp. 556-565. [DOI: https://dx.doi.org/10.1016/j.buildenv.2014.10.002]

50. Correa, E.; Ruiz, M.A.; Canton, A.; Lesino, G. Thermal comfort in forested urban canyons of low building density. An assessment for the city of Mendoza, Argentina. Build. Environ.; 2012; 58, pp. 219-230. [DOI: https://dx.doi.org/10.1016/j.buildenv.2012.06.007]

51. Achour-Younsi, S.; Fakher, K. Outdoor thermal comfort: Impact of the geometry of an urban street canyon in a Mediterranean subtropical climate–Case study Tunis, Tunisia. Proc. Soc. Behav. Sci.; 2016; 216, pp. 689-700. [DOI: https://dx.doi.org/10.1016/j.sbspro.2015.12.062]

52. Ren, Z.; Zhao, H.; Fu, Y.; Xiao, L.; Dong, Y. Effects of urban street trees on human thermal comfort and physiological indices: A case study in Changchun city, China. J. For. Res.; 2022; 33, pp. 911-922. [DOI: https://dx.doi.org/10.1007/s11676-021-01361-5]

53. Fu, M.; Ban, K.; Jin, L.; Wu, D. How Urban Street Spatial Composition Affects Land Surface Temperature in Areas with Different Population Densities: A Case Study of Zhengzhou, China. Sustainability; 2024; 16, 9938. [DOI: https://dx.doi.org/10.3390/su16229938]

54. Lam, C.K.C.; Gallant, A.J.E.; Tapper, N.J. Does irrigation cooling effect intensify during heatwaves? A case study in the Melbourne botanic gardens. Urban For. Urban Green.; 2020; 55, 126815. [DOI: https://dx.doi.org/10.1016/j.ufug.2020.126815]

55. Langenheim, N.; White, M.; Tapper, N.; Livesley, S.J.; Ramirez-Lovering, D. Right tree, right place, right time: A visual-functional design approach to select and place trees for optimal shade benefit to commuting pedestrians. Sustain. Cities Soc.; 2020; 52, 101816. [DOI: https://dx.doi.org/10.1016/j.scs.2019.101816]

56. Zheng, X.; Chen, L.; Yang, J. Simulation framework for early design guidance of urban streets to improve outdoor thermal comfort and building energy efficiency in summer. Build. Environ.; 2023; 228, 109815. [DOI: https://dx.doi.org/10.1016/j.buildenv.2022.109815]

57. Huang, X.; Song, J.; Wang, C.; Chan, P.W. Realistic representation of city street-level human thermal stress via a new urban climate-human coupling system. Renew. Sustain. Energy Rev.; 2022; 169, 112919. [DOI: https://dx.doi.org/10.1016/j.rser.2022.112919]

58. Cheng, K.Y.; Lau, K.; Shek, Y.T.; Liu, Z.; Ng, E. Evaluation on the performance of tree view factor in a high-density subtropical city: A case study in Hong Kong. Build. Environ.; 2023; 239, 110431. [DOI: https://dx.doi.org/10.1016/j.buildenv.2023.110431]

59. Sun, C.; Lian, W.; Liu, L.; Dong, Q.; Han, Y. The impact of street geometry on outdoor thermal comfort within three different urban forms in severe cold region of China. Build. Environ.; 2022; 222, 109342. [DOI: https://dx.doi.org/10.1016/j.buildenv.2022.109342]

60. Li, Z.; Zhang, H.; Juan, Y.-H.; Lee, Y.-T.; Wen, C.-Y.; Yang, A.-S. Effects of urban tree planting on thermal comfort and air quality in the street canyon in a subtropical climate. Sustain. Cities Soc.; 2023; 91, 104334. [DOI: https://dx.doi.org/10.1016/j.scs.2022.104334]

61. Chen, T.; Yang, H.; Chen, G.; Lam, C.K.C.; Hang, J.; Wang, X.; Liu, Y.; Ling, H. Integrated impacts of tree planting and aspect ratios on thermal environment in street canyons by scaled outdoor experiments. Sci. Total Environ.; 2021; 764, 142920. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.142920]

62. Lee, H.; Mayer, H.; Kuttler, W. Impact of the spacing between tree crowns on the mitigation of daytime heat stress for pedestrians inside EW urban street canyons under Central European conditions. Urban For. Urban Green.; 2020; 48, 126558. [DOI: https://dx.doi.org/10.1016/j.ufug.2019.126558]