1. Introduction
The word “transport” can be considered to be a partition-able word (trans-port), where trans is a prefix that emanates from Latin, referring to movement or carrying from one place to another. The suffix, “port”, on the other hand, implies a place (city, town or land) with access to water, through which loads can be conveyed from one end to the other. Transportation systems could therefore be described as a means of moving items, goods, people, and objects from one point to another. Transport can also be described as a passage, conveying things from one location to another in a safe mode. The authors of [1] highlighted that a sustainable transport system must provide mobility and accessibility to all urban residents in a safe and environment friendly mode of transport.
Transportation systems are intricately interwoven with nearly every facet of human activity, from work and production to leisure and consumption, supporting a worldwide flow of goods, and linking societies. It is little wonder that the authors of [2] described it as the backbone of any economic, cultural, social and industrial development. As these large technical systems become pervasive and integral constituents of modern society, the manner in which human forces perceive, analyze, and attempt to shape the development of these systems becomes increasingly complex, and increasingly important [3].
Mobility in the olden age can be considered undoubtedly different from modern-day transportation, since transportation was achieved through the use of animals (horse-riding, donkey-riding, oxen-riding) and wheeled vehicles (wagons and carts). Today, transportation systems have witnessed significant evolution, which has been driven largely by changes and developments in digital technologies, which include artificial intelligence, big data, machine learning, blockchain, cloud computing, the IoT, fog computing, etc. The level of impact of these technologies in easing mobility cannot be over-emphasized. The revolution that started in the 18th century undoubtedly had huge impacts on the growth recorded in the transport sector and gave birth to a modern transportation structure that is today described as the Intelligent Transportation System (ITS).
Figure 1 provides an overview of the various Industrial Revolutions. The integration and deployment of these innovations led to a sustainable global economy. The cyber physical system (CPS) was key in the birth of the fourth revolution, in 2016, which was first defined after the World Economic Forum (WEF) [4]. The revolution saw the adoption of artificial intelligence, the internet of things (IoT), big data, machine learning, virtual and augmented reality, and blockchain, and emerging digital transformation approaches are contributing to the great transformative experience currently being witnessed in the evolution of the industrial space.
The use of 4IR technologies in the transportation sector is transforming the way in which transportation systems function, improving efficiency, safety, and sustainability. This is especially true regarding data collection and processing. Although the 4IR technologies have demonstrated great prowess in transforming the transportation industry and are aiding in the fulfilment of a smart transportation network, these technologies rely heavily on data. Modern transportation systems rely heavily on data, since they allow for real-time analysis, monitoring, and decision-making. The process of creating effective, safe, and sustainable mobility systems might be hampered by data collection techniques that are not optimal or efficient for the transportation industry. By revisiting and evaluating earlier studies in these fields and offering pertinent recommendations for the future, in this study, we seek to carefully dissect the data collecting and processing methods of these technologies in order to address these difficulties.
The remaining parts of the structure of this research study are as follows: The methodology is presented in Section 2; related works (general literature review and specific literature review) are explained in Section 3; the identified gaps are in Section 4; Section 5 is a discussion of the findings; and the conclusion and recommendations are presented in Section 6.
2. Methodology
To identify and evaluate existing related research works in this domain, in this study, we adopted systematic literature review method. As part of the steps for achieving the purpose of this study, an S3-Triangular procedure was used for the execution of the systematic review considered in the study. The first part of the procedure is the search key approach; the second is source selection; and the last is the selection of closely related papers.
2.1. Search Key/String Approach
Table 1 displays the number of articles retrieved under each group, as well as the search terms used to retrieve the articles used for this investigation. Two clusters were created out of the search results: the first focused on “4IR technologies”, and the second used the term “Data Collection and Data Processing” to find additional relevant papers. The search was limited to papers published between 2014 and 2024 in order to include research articles that were not older than 10 years, as there were not many publications on 4IR during that time frame, according to our search (104 papers were found to be referenceable during the chosen period). We additionally employed keywords in serial numbers 3 through 10 to enable a more thorough search that would provide access to more papers on this subject (a total of 61 were found to be citable).
2.2. Source/Database Selection
Essential studies that addressed the subject under investigation were accessed (relevant articles published between 2014 and 2024) from recognized publishing outlets and repositories. The selected database sources (IEEE Xplore, Elsevier Science Direct, ACM, Taylor and Francis, Springer, and Google Scholar) were based on the premise of accessibility/subscription, as well as their vast collections of peer-reviewed journals with high density/repository of Engineering-, Computing Science-, and Technology-related articles. The source selection, presented in Table 2, shows the number of papers retrieved from the various chosen publishing outfits using the search strings presented in Section 2.1 (Table 1).
2.3. Selection of Closely Related Articles (Inclusion and Exclusion Criteria)
Upon initiating this study, we located and downloaded 203 journal articles as part of our comprehensive review of the literature on “4IR Applications in the Transport Industry: Systematic Review of the State of the Art with respect to Data Collection and Processing Mechanisms”. Applying preset inclusion and exclusion criteria to guarantee relevance, quality, and applicability led to the deletion of 79 articles and the admission of 124 articles. Articles from peer-reviewed publications published in English between January 2014 and December 2023 met the inclusion criteria. These articles included empirical investigations, randomized controlled trials, systematic reviews, and meta-analyses.
Included in the list of exclusion criteria were articles that were published outside of the specified time range, in languages other than English, from non-peer-reviewed sources, and that contained opinion pieces, book reviews, letters to the editor, or editorials. Articles lacking full-text availability and failing to directly tackle the research problem were likewise excluded. However, following rounds of expert assessment/review by reviewers of this paper, the included papers were found to be inadequate. As a result, the authors accessed and downloaded seventy (70) additional articles with fresher, more sensible and logical keywords. The searched year range was also extended to cover articles published in 2024. By using the same filtering technique, 29 journal articles were eliminated and 41 were deemed worthy of inclusion. In total, 165 journals were used, making up 60.4%, while 108 journals were discarded, making up 39.6% (Figure 2). Table 3 and Figure 3 present further information on the number of articles used and not used in this study.
3. Related Works
3.1. General Literature Review
4IR has a vast number of advantages that demonstrate critical services’ benefits. The fourth Industrial Revolution has brought forth several benefits for the health sector, including better healthcare outcomes and more precise diagnoses, thanks to machine learning and artificial intelligence. The authors of [5] highlighted that Internet of Things, cloud and fog computing, and big data technologies are revolutionizing eHealth and its whole ecosystem, moving it towards Healthcare 4.0. In [6], the authors investigated the need for the transition of Industry 4.0 to Pharmacy 4.0. The authors identified and argued for the elimination of the barriers to the full implementation of Industry 4.0 for the pharmaceutical arm of the health sector in developing nations. Agriculture and food products are further essential services to which 4IR has greatly contributed. A further study [7] demonstrated the deployment of ML and IoT in the automatic prediction of crop water requirements. The authors of this article implemented a 4IR-enabled Smart Irrigation System that minimizes water usage within the agriculture sector to assist farmers in obtaining live data on soil moisture, weather API, the water capacities of different crops, and plugging sensors to predict when farmers can irrigate. The technologies of the fourth Industrial Revolution (4IR/Industry 4.0) have been catalysts for all fields of human endeavor, permeating the water, energy, and food (WEF) nexus. The application of 4IR to the WEF nexus will lead to economic sustainability, social sustainability, and environmental sustainability [8].
Apart from these and a few other essential services, transportation is a notable and essential service whose important features cannot be over-emphasized. A critical look at the transportation ecosystem shows that these technologies are enhancing safety and efficiency, streamlining operations, and enabling data-driven decision-making. Several systematic literature review research works have been carried out on the general influence and impact of 4IR technologies on the transportation sector. The authors of [9] developed a simulation model to evaluate various traffic scenarios involving truck platoons. The authors reviewed the issue of the safety of mixed traffic flows involving truck platoons, human-driven cars, and human-driven trucks, with a focus on the stochastic behavior of human drivers. The authors extended their work in [10] to assess the changing conditions of the transportation system due to political and technological shifts and discussed the issue of the sustainability of the system. The work reviewed in [11] highlighted the history of IRs and established the relationship between 4IR and the previous IRs. The paper presented the enhancement and improvement 4IR brought to the transportation sector in developing countries. Another study [12] reviewed the transformative impact of 4IR on transport systems in low-income, high-growth countries. The combination of IoT and RFID technology provided a more responsive and efficient public transport system that reduced congestion and waiting times [13]. The authors of [14] gave a comprehensive review on the impact of 4IR emerging technologies on freight transportation. In [15], the authors divulged and discussed five 4IR technologies that brought transformation to the transportation system. The authors of [16] emphasized the increasing importance of smart technologies (AI, ML, IoT etc.) in enhancing higher service levels and ensuring lower operating costs in logistics and transportation systems. The authors of [17] assessed the effects of 4IR technologies on the effective management of road transport assets, while [18] demonstrated the deployment of 4IR technologies in reducing traffic jams.
The work presented by the authors of [19] discussed the disruptive changes 4IR brought to the maritime sector, as well as its neglect of human elements in its transition, thereby advocating for the full adoption and implementation of Industry 5.0. The focus of [20] was the application of an Intelligent Transportation System in the context of 4IR. The authors examined path computation algorithms for determining optimal routes in Advanced Traveler Information Systems. The authors of [21] discussed the influence and applications of 4IR technologies in optimizing transportation systems in smart cities, with an emphasis on how they can minimize traffic problems and increase infrastructure usage. Similarly, the authors of [22] reviewed the roles and impact of AI and ML in solving real-time transportation challenges. The authors of [23] highlighted the transformative capabilities of 4IR in achieving smart and sustainable logistics. The systematic review paper examined how 4IR enhances economic efficiency and environmental performance. The authors of [24] provided an in-depth review on the 4IR technologies used in developing smart transportation systems, highlighting their capabilities and the communication protocols used. The work presented by the authors of [25] presented the Intelligent Transportation System as the solution to transportation challenges, through its leveraging of 4IR technologies like AI, ML, cloud computing, and the Internet of Vehicles to enhance efficiency, address and avert congestion, pollution and accidents in smart cities. Furthermore, the authors of [26] emphasized the exploration of an Intelligent Transportation System in tackling traffic challenges in urban India, involving the leveraging of the transformative effects of 4IR. The research work presented in [27,28,29] emphasizes how a big data analytics framework can be integrated to enhance Intelligent Transportation Systems’ design and application, making transportation systems safer, more efficient, and profitable in the era of 4IR, whereas [30] prioritizes data-driven models in enhancing transportation efficiency in the era of 4IR. The authors of [31,32] underscore the important roles of 4IR in advancing the field of Intelligent Transport Systems by leveraging natural language processing and modern sensor technologies to ensure further advancement in Intelligent Transportation Systems’ reliability, security, scalability, and sustainability, whereas the emphasis of the authors in [33,34] was on its roles in road safety. The authors of a further study [35] recognized and reviewed the transformative effects 4IR has brought to the data collection mechanism in the transportation sector. The authors of [36] addressed the transformative effects of 4IR on the workforce landscape.
Table 4 provides a summary of the impact of IR technologies on the Transport System, while Figure 4 and Figure 5 provide an overview of the trends in the evolutionary impacts and the relationship between the technological revolutions, respectively.
3.2. Specific Literature Review
The technologies of the fourth Industrial Revolution (4IR) are changing many industries, and the transportation industry is no exception. The confluence of technologies such as artificial intelligence (AI), the Internet of Things (IoT), big data, and cloud computing is revolutionizing the transportation sector at a rate never seen before (Table 5; Figure 6 is the corresponding chart). The merger of digital, biological, and physical technology has given rise to previously unheard-of degrees of automation, connectedness, and intelligence, defining this new era. The transportation ecosystem as a whole is becoming safer and more efficient thanks to these technologies, which are also enabling data-driven decision-making and the optimization of operations.
These research works highlight how important it is to comprehend and adjust to continuous changes in the transportation sector, especially in light of emerging technologies and shifting social demands. The papers emphasize how urgently the transportation system needs to change in order to become more efficient, sustainable, and future-proof. They emphasize how crucial it is to make strategic decisions and create all-encompassing frameworks in order to handle the opportunities and complex challenges that come with these changes.
The transportation industry is undergoing a transformation in its operations with the deployment of 4IR technologies, especially in data collecting and processing, improving sustainability, safety, and efficiency. Because of their heavy reliance on data, these technologies cannot function properly without efficient data collection and processing. Hence, the main objective of this review work is to assess this topic. The paragraphs that follow provide an overview of the writers whose writings have addressed 4IR technologies’ data collection and processing techniques in particular. Figure 7 illustrates the recentness of the publications used, Figure 8 shows the articles and corresponding 4IR technologies, and Table 6 provides a summary of the review of these relevant studies.
An Internet of Things (IoT)-based intelligent transport system prototype was created by the authors of [111]. It uses GPS, NFC, and environmental sensors to track vehicles, facilitate ticket sales, assess crowd and bus ambience, and display information. With its successful demonstration of real-time data display, precise bus tracking, crowd analysis, and environmental monitoring, the system raises the possibility of enhanced passenger experiences and more effective transportation. Subsequent investigations may tackle problems such as misplaced stations, misplaced cards, and air quality monitoring.
To enhance urban mobility, the authors of [112] created an IoT-based smart transportation system. The system included wireless sensors, RFID, and GPS for real-time data collecting and processing. This lowered CO2 emissions, controlled traffic congestion, provided parking recommendations, and improved transportation efficiency. The Ooty parking support and traffic monitoring system demonstrated efficacy, indicating potential enhancements in traffic safety and management. Future studies should concentrate on improving system scalability, security, and privacy, as well as efficiency.
Using 20 million GPS traces from Maryland, the authors of [113] reviewed the literature on the use of trajectory data in road transportation. They demonstrated both new and established uses of trajectory data by utilizing V-Analytics software for data exploration and applying a density-based grouping technique. However, problems with population bias, device identification, trip division, the sample rate, and spatial precision were brought to light. Subsequent investigations should tackle these data concerns and foster cooperation between the transportation and visual analytics domains.
An Internet of Things (IoT)-based Intelligent Transportation System (ITS) was created by the authors in [114] to offer real-time bus location and seating information. The Arduino Uno, GPS, touch sensors, and Internet of Things are used by the system to gather and present data on an Android app. The technology increases the dependability and effectiveness of public transportation while assisting users in making wiser travel selections. Subsequent studies should improve the precision of arrival times and provide bus position displays based on maps.
The authors of [115] corrected missing temporal and spatial data from Gangnam-gu (November 2018) using LSTM in order to predict traffic congestion. In comparison to previous models, the accuracy of the model was higher in suburban regions. This method can be used elsewhere and improves the accuracy of traffic congestion forecasts. Subsequent studies should enhance user efficiency, make more precise predictions in low-speed zones, and verify the system in alternative locations. In order to shorten passenger wait times, the authors of [116] created an Internet of Things-based real-time public transit monitoring system. The system uses GPS, an ESP32 Wi-Fi microcontroller, and the Blynk IoT platform to calculate arrival times and distances using the Haversine formula. By effectively providing real-time bus information, it raises the standard and ease of use of public transportation. Subsequent research endeavors may augment the system with functionalities such as passenger counts and ticket booking, and evaluate its efficacy across several urban areas. The authors of [12] explored the creation of a smart transportation system with big data analytics, Hadoop, and Spark to analyze real-time transportation data efficiently, enhancing road safety and traffic flow. The aim of the authors of [117] was to enhance smart city transportation by developing a sophisticated traffic control system using VANET and connected car technology, focusing on efficient data collection, traffic flow standardization, safety improvement, and congestion management.
Ref. [118] presented an enhanced intelligent transport system with IoT-based RSUs using Raspberry Pi for real-time data transmission to drivers, improving traffic safety and supporting smart cities with accurate information and vehicle detection. In [100], an IoT-based smart transportation security system architecture was examined to address geospatial, cyber-physical, and urban security concerns in Beijing, China. The authors incorporated public infrastructure data, geographic factors, metro convenience indices, national standards, and 5G base station coverage into their modeling and simulation of the system using GIS technology. The outcomes demonstrate how security challenges are handled well and highlight how crucial it is to integrate cyber physical security, 5G networks, and public infrastructure. Developing universal standards, improving IoT-geospatial integration, and testing in diverse urban environments should be the main goals of future research. The authors of [119] discussed the issues with smart transportation that developing nations face because of their quickening population growth. Using information from roadside units (RSUs) and Internet of Vehicles (IoV) technologies, the authors suggested an IoT architecture for congested intersections. Game theory and Nash equilibrium were used to determine the best route. In addition to highlighting the advantages of smart transportation for the environment, economy, public health, and quality of life, the study also draws attention to the problems with infrastructure, governance, education, and expenses. Future work should concentrate on sophisticated IoT frameworks, useful evaluations, and the combination of AI and machine learning while taking the effects on society and the economy into account.
The authors of [120] examined three parking garages and predicted the parking occupancy and traffic assignment for a mid-size institution. The authors sought to create an application that will guide drivers and provide insights for upcoming garages by utilizing forecasts derived from statistical and machine learning approaches. Utilizing Autoregressive LSTM, ANN, and LSTM models, the pressure sensor data obtained at garage entrances and exits were processed and saved in a MySQL database. An ensemble model was created by combining these forecasts. The peak demand was seen during class hours based on the data gathered at one-minute intervals over a two-week period. Because the ensemble model predicts parking availability with accuracy, less time is spent searching, which enhances traffic flow. Subsequent studies should employ deep learning methodologies, gather data in real-time, and juxtapose outcomes with those of bigger cities. The authors of [121] looked at how emerging technologies (ETs) are chosen for intelligent cars. They also identified important variables that affect ET choice and suggested a draft PTM framework. A qualitative examination of these variables emphasizes the necessity of developing corporate internal skills and more specific PTM standards.
In order to enhance incident management and response times, the authors of [122] suggest an Internet of Things (IoT)-based emergency vehicle service system within intelligent transportation networks. To reduce traffic delays and assist emergency vehicles in navigating intersections, the system incorporates a UAV-guided priority system. Through the use of real-time traffic data and incident records, the system optimizes traffic signal phases, resulting in 12% faster incident clearance times and 8% faster emergency vehicle response times. Subsequent studies should concentrate on improving the integration of UAVs, tackling problems related to battery life and communication latency, and broadening the operating scope of the system.
Through an analysis of technological novelty and breadth, the authors of [123] focused on the digital revolution in the automotive industry. They used topic modeling to discover 26 digital technology issues, classified as enhancing, spanning, transforming, and disrupting technologies, using 455 autonomous car technology patents from Swedish automakers. The findings reveal that these technologies can both exhibit gradual advancement and drastically alter the sector. The study highlights the benefits of combining multiple digital technology categories and urges more research into how these technologies affect different industries. In order to fuel the Industrial Revolution, the authors also advise investigating new technology classifications and how businesses may create them.
The authors of [124] explored the real-time implementation of machine-learning-based data collection protocols for intelligent transport systems using Dublin’s M50 as a case study. The authors proposed ML-TDG, a lightweight methodology utilizing Apache Spark for efficient data management, reduced network load, and optimized resource use. The results show that ML-TDG outperforms baseline methods with higher efficiency and lower resource consumption. Future research should focus on cost-effectiveness, integration with existing traffic systems, and further machine learning applications in intelligent transport systems.
In order to examine urban mobility patterns and enhance public transportation, the authors of [125] made use of smart card data from a public transit system. The study was intended to improve the understanding of travel patterns and related socioeconomic variables by generating temporal passenger profiles and clustering passengers and stations based on utilization. Rebuilding trip chains and estimating alighting locations from more than 5 million journey transactions were part of the data processing procedure. In [126], a Mobility-as-a-Service (MaaS) solution featuring modular autonomous vehicles (MAVs) is proposed to improve energy efficiency and reduce emissions in airport-related transportation. The study evaluates MAVs using virtual models based on shuttle bus characteristics and incorporates advanced scheduling and optimization techniques. The results indicate that MAVs can reduce energy consumption, improve passenger comfort and safety, and integrate well with existing transportation networks. Future work should focus on developing MAV virtual models, integrating them with IT systems for GPS tracking and ticketing, and deploying MAVs for further evaluation.
The system in [127] minimizes control efforts, maximizes safety, and imitates human behavior to improve autonomous driving at uncontrolled junctions. Using information from actual intersections, it makes use of motion planning, driving mode selection, and goal state prediction. Testing and a simulation confirmed its efficacy, and further study is advised to enhance prediction and incorporate V2X communication.
In order to enhance vehicle trajectory data from video cameras, the authors of [128] created a postprocessing pipeline that addresses noise, outliers, and fragmentation. The pipeline enhances trajectory length and precision by merging fragments using online data association and convex optimization for outlier removal. Up to 500,000 pieces can be processed in an hour, which helps with traffic analysis and management. Subsequent research should improve the parameters and include interactions with vehicles. In [129], taxi trajectory data are used to evaluate the efficacy of on-board sensor networks in smart cities. Strong network coverage and connectivity are found in the study, facilitating a range of applications for smart cities. Subsequent investigations must concentrate on network capacity and practical applications. Using AIS data, the authors of [130] assessed five machine learning and seven deep learning techniques for forecasting ship trajectories. The comparison offers new guidelines for trajectory prediction and insights for improving marine safety and autonomous navigation.
In [131], model-free measures such as the Procrustes distance are used to examine vehicle interactions through the development of unsupervised learning techniques. These techniques increase the stability and effectiveness of clustering in vehicle-to-vehicle data. Trajectory prediction and anomaly detection in traffic are two examples of applications; the handling of complicated interactions and noise is recommended for future research. The authors of [132] examined developments in mapping, sensing, and localization for autonomous vehicles. Better sensor systems, real-time mapping, and the incorporation of autonomous functions in consumer cars were all emphasized, along with the significance of safety evaluations and real-world testing.
The goal of the authors of [133] was to lower wait times at the maritime terminal in Messina–Tremestieri by incorporating automated and linked vehicles into daily operations. The authors discovered that the new framework efficiently controls vehicle flow and shortens wait times using simulations and data from smart systems, and they proposed enhancements like autonomous reservation systems and priority freight processing. The YOLOv3 algorithm is used in [134] for vehicle identification and classification in toll systems. Compared to earlier techniques, YOLOv3 exhibits great accuracy and better precision, increasing the effectiveness of toll collecting. Future studies ought to investigate broader applicability and multi-scale identification methods.
A method for tracking vehicle trajectories and fuel rates using 360-degree cameras and OBD-II scanners is presented in [135]. This approach offers highly accurate data for phantom traffic wave and fuel efficiency analysis, indicating areas for further research on real-time scenarios and the effects of autonomous vehicles. The adoption of connected and autonomous cars (CAVs) is reviewed in [136], emphasizing how they might enhance traffic flow and energy efficiency. Integration problems and infrastructural requirements are challenges. Future studies should concentrate on the effects of CAVs in diverse contexts, optimized traffic management, and dedicated lanes for CAVs.
Through the integration of autonomous vehicles (AVs) and a multi-agent reinforcement learning (MARL) methodology, the research presented in [137] is designed to improve urban transportation. Through the optimization of AV collaboration and the simulation of human-driven vehicle behavior, the methodology enhances environmental effects, traffic flow, and lane-changing judgments. The paper emphasizes the use of AI in creating sustainable urban transportation and makes recommendations for further studies on multi-agent systems and environments with varying scalability. According to the evaluation of multi-AGV scheduling approaches in [138], the Co-SLMAB approach outperforms the others, with MAB-based solutions outperforming CPLEX in terms of speed. The authors recommend more investigations on enhancing robustness and application across many sectors. The authors of [139] introduce a hybrid algorithm for optimizing multidepot vehicle routing with shared resources and recycling. The model, using 3D k-means clustering and SGA-PSO, effectively improves routing, resource use, and recycling efficiency. Future research should explore diverse settings, pricing strategies, and environmental impacts.
The Dec-CTDSP technique for routing optimization in congested networks of connected and autonomous vehicles (CAVs) is presented in [140]. In high-density CAV situations, the Dec-CTDSP technique performs better than conventional techniques like Dijkstra, and the authors recommend additional validations and enhancements. According to the authors of [141], DSR is generally the most successful routing system for data distribution in autonomous cars, particularly in grid and metropolitan environments, when AODV, DSR, and DSDV are compared. Subsequent investigations should examine protocol performance in other circumstances. By merging deep reinforcement learning (DRL) with model predictive control (MPC), the authors of [142] created a regional route guiding system. They concluded that the use of multi-agent techniques is more successful in easing traffic congestion and enhancing traffic efficiency, and they recommended further research on the integration of connected cars and hybrid systems. The authors of [143] examined the advantages and moral dilemmas of artificial intelligence (AI)-enabled urban routing for unmanned aerial vehicles (CAVs). They emphasized how AI may enhance sustainability and traffic management while tackling moral concerns like safety and justice. They demanded methods for striking a balance between ethical and sustainable issues.
“Intersense”, a method for identifying and classifying traffic regulators using GPS data gathered from the public, is described [144]. It makes use of XGBoost, obtaining 97.06% accuracy and exhibiting strong performance in many cities. The technology enhances digital maps for autonomous vehicles and demonstrates how machine learning works well for traffic control. Subsequent investigations should concentrate on incorporating supplementary data sources and on real-time execution.
Outperforming conventional techniques and providing insights for advanced driving systems, a multi-agent Transformer-based deep reinforcement learning model (MA-TDDPG) for simulating lane change behaviors in connected automobiles is presented in [145]. In order to improve prediction accuracy and make recommendations for future research in real-time co-driving systems, the authors of [146] introduced a dynamic graph convolution network model to anticipate driver intentions to change lanes based on head motions and traffic circumstances. According to the research in [147], roundabout geometry and vehicle precision were found to be critical factors that influence autonomous vehicle performance at roundabouts when evaluating how design features and safety regulations affect it. The integration of intelligent transportation systems and the optimization of roundabout designs should be the main areas of future research.
With an emphasis on electric vehicles (EVs) in South Tyrol, the authors of [148] employed SWOT and AHP analyses to assess issues affecting Italy’s road transport industry. The authors list the advantages, disadvantages, opportunities, and threats associated with EV technology and make recommendations for further studies on the financial implications, infrastructure for charging, and policy implications.
In order to improve road safety, the authors of [125] used sensor data and video footage analysis to identify accidents and minimize false alarms in order to improve road safety by combining IoT and deep learning. To improve the sustainability of urban transportation systems, the authors of [149] argued for improved traffic management to increase safety and sustainability by researching the effects of gridlock and chaos on accident rates. The goal of the authors of [150] was to use fuzzy algorithms and dynamic programming to create a decision model for autonomous vehicles that takes safety and social ethics into consideration. The authors of [151] examined how biometric data might improve the effectiveness, security, and caliber of service provided by public transit, while the authors of [152] built a framework for sustainable urban mobility utilizing IoT and ITS, with a focus on pollution management, traffic forecasting, and traffic reduction.
A blockchain-based system for multi-UAV task processing is proposed in [153], with the goal of improving situation awareness, decision-making, and task-scheduling effectiveness. The system makes use of a Pointer Network for optimal path planning and blockchain technology for safe, unchangeable task management. The efficiency and security of the system have increased, and future research will concentrate on testing practical applications and refining algorithms.
In order to improve urban mobility services, the authors of [154] present the Future Mobility Sensing Advisor (FMSA), a federated platform that blends IoT, big data, AI, and cloud, fog, and edge computing. Enhancing performance and sustainability, the FMSA facilitates multi-modal data management and standardized interfaces. The report suggests more investigations into new technologies, data integration, and platform capabilities. In order to address concerns over data privacy and integration, the research in [155] suggests Federated Learning for big data analytics in IoT-enabled Urban ITSs. With the use of a modified Federated Averaging technique, Federating Learning achieves superior scalability and great accuracy. Algorithm advancements and real-time adaptation should be the main topics of future research. In order to provide safe, distributed deep learning model services and improve security and efficiency in transportation systems, the authors of [156] integrated blockchain technology with IPFS. They recommended more investigations into developing the answer and looking into other technologies. In order to estimate passenger flow and increase operational efficiency, the authors of [157] built JP-DAP, an intelligent big data platform for metro rail systems. They achieved this by employing machine learning. The study draws attention to the necessity of sophisticated data analytics and the authors suggest incorporating new technologies and enhancing JP-DAP’s functionality.
The authors of [158] examined a number of train delay prediction methods, such as deep learning, machine learning, and statistical regression. They discovered that accuracy is increased by accounting for outside variables, like weather and maintenance. Noisy and missing data are challenges, and advanced and hybrid machine learning models are becoming more popular in order to improve prediction and real-time adaptability.
Through the use of non-model-based techniques and roadside 3D LiDAR data, the authors of [159] increased the accuracy of vehicle tracking. For accurate trajectory extraction, the method makes use of sophisticated clustering algorithms and spatiotemporal stacks. The accuracy of real-time tracking, scalability, and resistance to occlusions are highlighted by the results. The authors’ suggestions for future study include expanding sensor coverage, integrating with other technologies, and testing in real-world settings. The trends indicate an increase in the use of LiDAR and sensor fusion.
Using real-time data from stationary sensors, the authors of [160] created an analytical framework for identifying cyberattacks on transportation networks. They validated the framework using city-scale traffic simulations, optimized sensor location, and compared LSTM-autoencoder and Gaussian process models for anomaly detection. Both models exhibit good scalability and accuracy (F1 scores 84–96%), with the best location for the sensors improving resource distribution and detection. Future research should concentrate on data source integration, multi-city testing, and practical implementation.
Using the extended STIRPAT model and ridge regression, the authors of [161] estimated and projected CO2 emissions from Beijing’s transportation sector, emphasizing the effects of efficiency, affluence, population, and technology. They made recommendations for efficiency enhancements, population control, and technology advancement. Using group-based and network-wide models, the authors of [162] assessed the impact of speed harmonization on emissions and traffic flow and found decreases in fuel consumption and emissions. Future research should concentrate on more extensive applicability and computing efficiency. According to the authors of [163], carbon pricing and technical improvements successfully cut emissions. The authors examined the effects of energy use and technological innovations on GHG emissions in 38 OECD nations. They suggested promoting carbon price, renewable energy, technology, and sector-specific emission-reduction strategies.
Deep reinforcement learning (DRL) is a technique that is explored in [164] to increase the energy efficiency of plug-in hybrid electric vehicles (PHEVs). The authors find that DRL-based systems offer significant energy savings compared with conventional techniques. In comparing spaceplanes to maglev trains for Mars colonization, the authors in [165] pointed out that while maglev trains are energy-efficient but require expensive infrastructure, spaceplanes are initially more versatile and cost-effective. The two studies demonstrate current developments in technology for exploration and sustainability.
4. Identified Gaps
Numerous studies highlight the necessity of enhancing systems’ scalability, integrating them with current technology, and broadening their applicability to additional metropolitan contexts. Improvements in data precision are also essential, especially for boosting the quality of traffic forecasts, trajectory data, and bus arrival times. The literature consistently emphasizes the significance of tackling issues related to data security and privacy, together with guaranteeing the resilience of IoT systems, particularly in urban settings and public infrastructure. Furthermore, numerous studies emphasize how emerging technologies like blockchain, AI, machine learning, and deep learning must be used in order to improve the efficacy and precision of current systems.
5. Discussion of Findings
The majority of the research focuses on creating and deploying different intelligent transportation systems (ITS) that make use of machine learning, artificial intelligence, IoT, and other cutting-edge technologies. The objectives of these systems are to improve energy efficiency, road safety, public transportation, and traffic management.
The following are the major technologies/tools deployed in the review papers:
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i.. IoT-based transportation systems: research on IoT applications in real-time data collecting, traffic management, and public transportation systems is the focus of studies like [111,112,114,116] (in Table 7).
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ii.. Machine learning and AI in transportation: studies [124,128,135,164] (in Table 7) examine how machine learning and AI can be used to improve energy efficiency, vehicle behavior, and traffic prediction.
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iii.. Data security and privacy: the difficulties with integrating IoT with public infrastructure while maintaining data security and privacy are covered in articles like [100,155] (in Table 7)
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iv.. Sustainability and environmental impact: studies such as [126,161,162] (in Table 7) look at how transportation systems affect the environment and offer solutions to cut emissions and increase energy efficiency.
Successful system demonstrations, increased traffic control and safety, and improved data gathering and processing capabilities are among the common findings in these research works. Nevertheless, there are certain drawbacks, including the requirement for improved integration, scalability, security, and privacy of data.
Despite tremendous advancements in the field of intelligent transportation systems development, there are still issues with making these systems secure, scalable, and ecologically sustainable. The systems have shown the potential to increase transportation safety and efficiency when cutting-edge technologies like AI, IoT, and machine learning are integrated, but further study is required to improve these systems and close the gaps that have been found. The significance of a comprehensive strategy for the future of transportation networks is underscored by these findings. Policies that guarantee security, privacy, and environmental sustainability must be created in a way that facilitates the use of cutting-edge technologies. The integration of AI and IoT in transportation, as well as the gaps in scalability and accuracy, require ongoing research. Making sure these sophisticated technologies are safe and scalable requires urban planners to concentrate on developing the infrastructure that supports them. Further, in line with the goals of global sustainability, the transportation industry should keep innovating to lower emissions and boost energy efficiency.
6. Conclusions and Recommendation
In this study, we conducted a thorough analysis of the implications and applications of fourth Industrial Revolution (4IR) technologies in the transportation industry, with an emphasis on data processing and gathering methods. It is clear that the transportation sector has changed dramatically over the course of the several Industrial Revolutions, and 4IR technologies like AI, IoT, big data, and machine learning have been essential to this progress. Because these technologies allow for real-time data analysis, monitoring, and decision-making, they have improved the sustainability, safety, and efficiency of transportation networks.
However, this review also brought to light a number of difficulties. There are several obstacles, including the variety of data collection techniques and the absence of data processing standardization. These difficulties may prevent 4IR technologies from being implemented effectively, which would restrict their ability to completely revolutionize the transportation sector. In this systematic study, we identified gaps in the literature, especially with regard to the harmonization of data gathering techniques and the well-coordinated integration of diverse 4IR technologies.
A number of crucial recommendations need to be given top priority if the transportation industry is to fully benefit from fourth Industrial Revolution (4IR) technologies. First, in order to guarantee industry-wide interoperability and the efficient use of various technologies, standardizing data gathering standards is crucial. The difficulties presented by a variety of data types can also be addressed by implementing cutting-edge computational techniques like artificial intelligence (AI) and machine learning, which can lead to more effective data processing and useful insights. In order to align technological capabilities with industrial needs and promote integrated systems, cooperation amongst technology developers, transportation authorities, and policymakers is also essential. In addition to governments and regulatory organizations establishing clear regulations that support the safe, effective, and sustainable deployment of 4IR technologies, ongoing research and development are required to keep up with the difficulties involved, which are consistently changing.
Some relevant models to this study could not be presented or analyzed due to data and ethical challenges. However, it is envisaged that during the course of our upcoming research project, this obstacle will be removed.
Conceptualization, A.M.K., K.D. and L.D.; methodology, O.O.A.; software, O.O.A.; formal analysis, O.O.A.; investigation, O.O.A.; resources, O.O.A.; data curation, O.O.A.; writing—original draft preparation, O.O.A.; writing—review and editing, A.M.K., K.D., L.D. and O.O.A.; visualization, O.O.A.; supervision, A.M.K. and L.D. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Not applicable.
The authors appreciate the funding provided by The Transport and Education Training Authority (TETA) for the execution of this research project. The encouragement and enabling environment from Tshwane University of Technology (TUT) is appreciated herewith.
The authors declare no conflicts 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.
Enlarge this image.Figure 6. References/citations on the influences/impacts of 4IR technologies (authors).
Enlarge this image.Figure 7. Recentness of articles used on 4IR data collection and processing mechanisms (authors).
Search keywords/strings and number of articles retrieved.
| S/N | Search Keywords/Strings | Number of Papers Retrieved |
|---|---|---|
| 1. | 4IR technologies and the transportation system (4IR + technologies + transportation system) | 88 |
| Impact of 4IR technologies on transportation system | ||
| Application of 4IR technologies on transportation system | ||
| 4IR and the transportation system | ||
| 2. | Data collection and processing approaches in the application of 4IR technologies | 16 |
| 3. | Research work on transportation system and 4IR | 2 |
| 4. | Sustainable cities and urban transportation | 3 |
| 5. | Traffic, human driven-and electric cars, buses and trucks | 8 |
| 6. | Research articles on revolutions in the transport industry | 3 |
| 7. | 4IR and essential services | 4 |
| 8. | Sustainable transportation technologies | 8 |
| 9. | Smart and Intelligent Transportation Systems | 17 |
| 10. | Connected Transport and Autonomous Vehicles | 16 |
| Total | 165 | |
Publishing companies/search engines and the number of papers retrieved.
| S/N | Publishing Outfit/Search Engine | Number of Papers Retrieved |
|---|---|---|
| 1. | IEEE | 85 |
| 2. | Elsevier Science Direct | 36 |
| 3. | ACM | 1 |
| 4. | Taylor and Francis | 1 |
| 5. | Springer | 2 |
| 6. | Google Scholar | 40 |
| Total | 165 | |
Articles accessed.
| S/N | Articles Accessed | Number of Papers | % |
|---|---|---|---|
| 1. | Used | 165 | 60.4 (60) |
| 2. | Unused | 108 | 39.6 (40) |
| Total Accessed | 273 | 100 | |
Technologies that made a revolutionary impact on transportation systems (authors).
| S/N | Major Technology | 1st Revolution (1IR) | 2nd Revolution (2IR) | 3rd Revolution (3IR) | 4th Revolution (4IR) |
|---|---|---|---|---|---|
| 1. | Artificial Intelligence | n/a | n/a | n/a | ✓ |
| 2. | Big Data | n/a | n/a | n/a | ✓ |
| 3. | Machine Learning | n/a | n/a | n/a | ✓ |
| 4. | IoT | n/a | n/a | n/a | ✓ |
| 5. | Blockchain | n/a | n/a | n/a | ✓ |
| 6. | Smart Grid | n/a | n/a | ✓ | ✓ |
| 7. | Cloud Computing | n/a | n/a | ✓ | ✓ |
| 8. | Robotics | n/a | n/a | ✓ | ✓ |
| 9. | Virtual and Augmented Realities | n/a | n/a | n/a | ✓ |
| 10. | 3D Printing | n/a | n/a | n/a | ✓ |
| 11. | Drones | n/a | n/a | n/a | ✓ |
| 12. | Fog Computing | n/a | n/a | n/a | ✓ |
| 13. | Internet Technology | n/a | n/a | ✓ | ✓ |
| 14. | Communication Technology | n/a | ✓ | ✓ | ✓ |
| 15. | Autonomous System | n/a | n/a | n/a | ✓ |
| 16. | Quantum Computing | n/a | n/a | n/a | ✓ |
| 17. | Electrical Technology | n/a | ✓ | ✓ | ✓ |
| 18. | Energy/Power Technology | ✓ | ✓ | ✓ | ✓ |
Key: n/a: not applicable; ✓: available.
Influence and impact of 4IR technologies (authors).
| S/N | Types of 4IR Technologies | Influence and Impact | References | No of Ref. |
|---|---|---|---|---|
| 1. | IoT/Sensor | The interconnection of vehicles, infrastructure, and users through a network of sensors and smart devices. | [ | 9 |
| 2. | Autonomous System | Improvements in autonomous vehicle performance, safety, and efficiency. | [ | 7 |
| 3. | Big Data | Novel techniques and methodologies that pave the way for smarter, more efficient, and user-friendly transportation systems. | [ | 15 |
| 4. | Artificial Intelligence | Contributes to the development and implementation of intelligent systems and control algorithms, which have revolutionized urban transportation and led to the creation of smarter and safer transportation systems. | [ | 24 |
| 5. | Machine Learning/Deep Learning | Achieve predictive data analysis, optimization and decision support. Revolutionized urban transportation systems and contribute to the creation of smarter and more efficient cities. | [ | 29 |
| 6. | Computing Paradigms | Introduces innovative technologies that enhance the safety and sustainability of smart and intelligent transportation systems. | [ | 10 |
| 7. | Geographic Information System | Enhances precision, safety, security, and satisfaction. | [ | 7 |
| 8. | Energy/Power Technology | Enhances environmental protection by reducing carbon emission. | [ | 1 |
| 9. | Communication Technology | Enables connectivity through faster and more reliable networks. | [ | 1 |
| 10. | Electrical Technology | Improves energy efficiency, and enables industrial automation and the development of electric vehicles. | [ | 3 |
| 11. | Internet Technology | Revolutionizes connectivity, and gives access to information and online services and interactions. | [ | 1 |
Summary of reviewed specific studies.
| S/N | Article Ref No | Article Year | Aim | Approach | Article Contribution | Article Limitation |
|---|---|---|---|---|---|---|
| 1. | [ | 2014 | To develop a prototype ITS that tracks vehicles, enables payment tickets, and analyzes crowds and ambience inside buses. | Prototype model approach using sensor, monitoring, and display systems. | Successfully developed a system that tracks/detects vehicle location, commuter information, and the ambience. | Measures implemented to safeguard the CIA of data and information were not discussed. |
| 2. | [ | 2015 | The study’s main objective was to deploy IoT technologies to build ITS in improving an urban transportation system | The authors used wireless sensors to obtain real-time traffic information. | The authors successfully developed a real-time traffic controlling and monitoring system that reduced traffic congestion in the urban area. | The authors did not approach the issues of RFID’s data reading range and data security privacy. |
| 3. | [ | 2018 | The purpose of the study is to support transportation agencies in determining the usefulness of trajectory data for their particular requirements and decision-making procedures. | ML and GPS trajectory data using V-Analytic software for visual data exploration, analysis and modelling. | The study contributed to advancing the understanding and utilization of trajectory data in road transportation systems analysis. | Factors to take into account prior to acquiring trajectory data were not considered. |
| 4. | [ | 2020 | The authors’ goal was to create a smart information system that offers all pertinent, connected information on buses, with a focus on seating arrangements. | A framework based on IoT using a touch sensor, which detects occupied and empty seats. | The authors successfully implemented a system that provides real-time information about the exact location, arrival time, and seat availability of a bus. | Waiting times and traffic congestion were not taken into consideration. The data privacy of the passenger’s location information was also an issue. |
| 5. | [ | 2020 | The aim of the study was to predict traffic congestion. | Adopts LSTM-based traffic congestion prediction approach based on the correction of missing temporal and spatial values. | The model achieved higher prediction accuracy for suburban areas, and in comparison with other relevant models. | Urban areas and low-speed zones need to be predicted in order to validate the model. |
| 6. | [ | 2021 | The authors aimed at designing a system that reduces passengers’ waiting times. | The system was implemented based on IoT technology using GPS and a microcontroller. | The implemented system was able to compute real-time information about buses (e.g., current location, arrival time, speed, etc.). | The authors were unable to implement passenger count and e-ticketing. Data privacy was also an issue. |
| 7. | [ | 2019 | To offer a framework for using big data analytics and Internet of Things (IoT) technology to design a smart transportation system. | The four layers of the system that the authors designed were data processing, the application, the network, and data gathering and acquisition. Each of these was designed to handle and process data efficiently. In the data processing layer, the authors made effective use of Spark and Hadoop to manage real-time traffic data. | A model that integrates IoT, big data analytics, and named data networking for smart transportation systems was proposed. The proposed model offers solutions to challenges such as processing big data in real time and disseminating information to citizens efficiently. | Challenges relating to data privacy and security concerns were not discussed. |
| 8. | [ | 2021 | Ton develop a model for traffic monitoring and control was the study’s goal. | The STMS model was adopted. | The study achieved superior results in the modelling of traffic congestion. | Challenges involved in implementing the model on a large scale was not discussed (e.g., data integration). |
| 9. | [ | 2021 | To present an enhanced Intelligent Transport System with roadside unit (RSU) using IoT. | The authors used Raspberry Pi Board as the main component for real-time data/information collection, while ZigBee wireless technology was used for communication. | Implemented IoT-based roadside unit for ITS with the aid of OpenCV library. | A precise vehicle count for the overlapping of vehicles was not achieved. |
| 10. | [ | 2021 | To address several IoT challenges with relative to cyber physical security, etc. | Applied geospatial modelling approach. | The authors simulated a set of geospatial indicators that support the master planning of IoT networks in facilitating the running of a Smart Transportation Security System. | Availability and quality of data. |
| 11. | [ | 2022 | The study highlights the challenges and consequences of an existing transportation system in Peshawar, in Pakistan, in response to the rapid growth in population. | IoT-based framework for busy traffic junction. | The implemented framework was able to successfully reduce travelling times, fuel consumption, and environmental pollution. | The framework was limited in the number of actors used, which could have possible effects on the effectiveness of the system in a scenario involving highly congested traffic. |
| 12. | [ | 2022 | Investigation of traffic assignment based on parking prediction. | Ensemble machine learning models were deployed to predict parking spaces after data were collected from an accumulated copy of the parking availability posted on digital signs at garages’ entrances. | Successful applicability of ensemble machine learning models in the accurate and precise prediction of ITS. | Deployment of deep learning models for a more accurate and precise prediction of ITS. |
| 13. | [ | 2022 | The article emphasizes the importance of technology selection in corporate ET strategies. | PTM framework for emerging technology selection. | A structured approach to guide engineering managers in making strategic decisions about ET adoption. | Non-establishment of more detailed criteria for PTM factors and corporate internal capabilities. |
| 14. | [ | 2023 | To obtain better clearance times and lower response times for emergency vehicles. | Adoption of unmanned aerial vehicle (UAV)-guided priority-based incident management model. | The proposed system has the potential to significantly enhance emergency response capabilities within urban transportation systems while minimizing disruption to other road users. | Real-life implementation challenge, and lack of scalability to handle larger datasets. |
| 15. | [ | 2023 | The study was designed to identify digital technology topics that are transforming the automotive industry. | Use of integrating frameworks to illustrate the value of digital technologies. | The result of the study’s use of pyLDAvis library to visualize shows that digital technologies in the automotive industry have the incremental characteristics to achieve potential in transforming the industry. | The call for a combinatorial radical (hybrid) application for implementing automotive control systems, such as collision prevention assistance technology. |
| 16. | [ | 2023 | Proposal of a lightweight machine-learning-based data collection protocol called ML-TDG. | Lightweight ML-based data collection procedure. | Presents ML-TDG as an innovative solution to address challenges in data collection and communication in urban traffic environments. | A better machine learning framework is needed to improve time, storage, energy, and communication efficiency, with possible security features incorporated. |
| 17. | [ | 2022 | To create temporal passenger profiles and to examine travel patterns. | Generative-model-based approach. | Enhanced public transportation systems. | Security of smart card data. |
| 18. | [ | 2024 | To improve energy efficiency and lower pollutant emission. | MAV virtual model. | Minimizes energy consumption. | Need to certify MAV virtual models. |
| 19. | [ | 2021 | To anticipate other vehicles’ movement and adjust driving maneuvers. | Motion-planning framework. | Guarantees the safety and replicates the actions of real drivers at junctions. | Need for more prediction accuracy. |
| 20. | [ | 2024 | To design a postprocessing pipeline to solve problems. | Simulation. | High-resolution trajectory data. | Refinement of parameters. |
| 21. | [ | 2021 | To analyze the effectiveness of on-board sensor networks. | Model for collecting delay-tolerance data for smart sensors. | Strong network coverage and connectivity. | Robust network to connect diverse smart city applications. |
| 22. | [ | 2023 | To tackle increasingly complicated traffic situations. | DL + ML. | Advancement of autonomous navigation technology. | Prediction accuracy issue. |
| 23. | [ | 2022 | To develop a reliable unsupervised learning technique for examining temporal dynamic interactions between vehicles. | Deployment of metrics on Safety Pilot DB. | Clustering efficiency. | Geometric approach. |
| 24. | [ | 2023 | Discussion of the difficulties with and development of autonomous vehicles. | Localization and mapping techniques. | Emphasizes the implication of presumptive knowledge. | Need for more reliable sensor systems. |
| 25. | [ | 2023 | To reduce wait times at terminals. | Simulation. | Increased port productivity and overall performance. | Automation and connectivity in maritime terminals. |
| 26. | [ | 2022 | To achieve vehicle detection and classification for toll management system. | Prediction with YOLOv3 algorithm. | High degree of accuracy of the deployed DL approach (YOLOv3). | Investigation of multi-scale vehicle identification. |
| 27. | [ | 2019 | To investigate free flow traffic and phantom traffic waves. | A novel technique for tracking vehicle trajectories and fuel rates. | Understanding of the effects of phantom traffic waves on fuel. | Need to corroborate results with empirical data. |
| 28. | [ | 2022 | To analyze the prospects and impacts of and difficulties in adopting autonomous vehicles. | Data retrieved from previous studies. | Enhanced energy efficiency and traffic flow. | Infrastructure and integration issues. |
| 29. | [ | 2024 | To improve the efficiency and sustainability of urban transportation. | Multi-agent reinforcement learning (MARL). | Safe, effective and sustainable transportation system. | Multi-agent lane switching. |
| 30. | [ | 2024 | To examine the effectiveness and efficiency of multi-AGV scheduling issues in warehouse picking. | Simulation. | Improved scheduling. | Needs to enhance DMAB and MAB methodologies’ resilience. |
| 31. | [ | 2024 | To create a hybrid algorithm that combines 3D k-means clustering and self-adapting genetic algorithm–particle swarm optimization. | Mixed-integer programming model. | Optimized routing problem. | The use and validation of the model in diverse settings. |
| 32. | [ | 2022 | To improve mobility and efficiency in a crowded transportation network. | Dec-CTDSP routing algorithm. | Higher performance in CAV networks. | Validation in a real-world scenario. |
| 33. | [ | 2020 | To implement routing strategies in autonomous cars. | Simulation. | High performance result. | More investigation into protocols. |
| 34. | [ | 2024 | To offer a regional route guiding system. | MPC + DRL. | Dynamic route guidance and improvement in traffic management. | Integration of real-time predictive data. |
| 35. | [ | 2024 | To examine the sustainability and moral dilemmas posed by AI-enabled urban routing for CAVs. | Review of the literature. | Support sustainable urban mobility with reduced emissions, accidents, and traffic. | Moral issues. |
| 36. | [ | 2023 | To improve the quality of digital maps for self-driving cars. | GPS + XGBoost. | Better mapping, traffic control, and scalability. | Integration of more data sources and real-time implementation. |
| 37. | [ | 2024 | To analyze observatory-action memory and learn the sequential decision-making process during lane changes. | MA-TDDPG. | Realistic lane changes and improvements in driving techniques and safety. | Multi-agent and recurrent reinforcement learning methodologies. |
| 38. | [ | 2023 | To forecast driver lane change intention. | Simulation. | Achieved higher safety and prediction accuracy. | Application of DL model. |
| 39. | [ | 2023 | To examine how different design elements and safety standards affect the operation of autonomous cars at junction. | Performance measurements + simulation model. | Improved the effectiveness and safety of roundabouts for autonomous vehicles. | Investigation of the effects of extra parameters. |
| 40. | [ | 2024 | To evaluate the variables impacting Italy’s road transport industry. | Literature review. | Offers support and opportunities to EV technology in Italy. | EV charging infrastructure. |
| 41. | [ | 2024 | To improve the sustainability of urban transportation systems. | Data from previous research. | Improved sustainability is achieved, as well as reduced accident and traffic chaos. | Environmental entropy features. |
| 42. | [ | 2024 | To create a decision-planning model for autonomous vehicles. | Fuzzy algorithm + dynamic programming. | More sustainable and safe transportation achieved. | Moral decision-making model. |
| 43. | [ | 2024 | To evaluate how biometric data affect a public transportation system. | Expert-data collection approach. | Use of model is positively correlated. | Improvement in the implementation of biometric techniques. |
| 44. | [ | 2023 | To address traffic-related issues in smart cities. | Sustainable framework, uses ITS devices and AI sensors to capture data. | Improved smart city sustainability. | Consideration of cutting-edge technologies. |
| 45. | [ | 2023 | To improve situation awareness and in-the-moment decision-making in a sustainable transportation scenario. | Distributed task-processing network + Pointer Network structure. | Improved collaborative processing and efficiency. | Algorithm optimization. |
| 46. | [ | 2023 | To improve smart mobility services. | Future mobility sensing advisor. | Enhanced urban livability and sustainability. | Newer techniques for more intelligent urban mobility solutions. |
| 47. | [ | 2023 | To improve big data analytics architecture for IoT-enabled urban ITS. | Federated Learning. | Better scalability. | Real-time adaptability. |
| 48. | [ | 2023 | To achieve a sustainable model service in deep learning. | Blockchain + IPFS. | Balanced security + system efficiency. | Investigation of supplementary technologies. |
| 49. | [ | 2022 | To guarantee seamless operation and enable effective management. | Real-time and historical data. | Improved customer experience. | Investigate additional technologies. |
| 50. | [ | 2023 | To examine and predict the causes of train delays. | GTFS data + NSW’s open data center. | Increased prediction accuracy. | Hybrid or sophisticated machine learning models. |
| 51. | [ | 2023 | To improve the accuracy of vehicle tracking. | Data collection from sensor. | Scalability and good accuracy. | Real-world circumstances. |
| 52. | [ | 2023 | To employ real-time sensory data to identify cyber-attacks on transportation networks. | Gaussian process model + LSTM-autoencoder model. | Better predictive performance. | Investigation of intricate assault scenario. |
| 53. | [ | 2024 | To estimate and forecast emissions. | Extended STIRPAT Model. | Efficiency enhancement. | Examination of supplementary impacting elements. |
| 54. | [ | 2022 | To handle the speed advisory problem in a network. | Microscopic and macroscopic methods. | Speed harmonization and emission reduction. | Computational efficiency. |
| 55. | [ | 2023 | To examine data from OECD and IEA sources and check for correlation. | Correlation analysis. | Reduced emissions. | Wider range of mitigation techniques. |
| 56. | [ | 2019 | To increase the energy efficiency of plug-in hybrid electric vehicles. | DRL. | Increased energy efficiency. | Process optimization. |
| 57. | [ | 2024 | To access energy efficiency. | Comparative analysis. | Energy-efficient surface transportation. | Enhancement of Mars exploration tactics. |
Data collection and processing mechanisms used in the reviewed studies.
| S/N | Article Ref. No | 4IR Technology | Data Collection Method/Means/Type | Processing Mechanism |
|---|---|---|---|---|
| 1. | [ | IoT | NFC reader, GPS receiver, environmental sensors | Information Processing System (IPS) |
| 2. | [ | IoT | Wireless sensor, RFID, GPS | Mobile Agents & GPRS |
| 3. | [ | GPS | Trajectory data | V-Analytics Software |
| 4. | [ | IoT | Arduino Uno, GPS, sensor, IoT | IoT Module, Android App |
| 5. | [ | ML/DL | Traffic data | Long-Short Term memory (LSTM) Model |
| 6. | [ | IoT | GPS data | GPS, ESP32, Microcontroller + Wifi, Blynk IoT Platform |
| 7. | [ | IoT | Sensor data on parking lots and roadways | Hadoop and Spark |
| 8. | [ | IoT | Real-time sensor data | Data Analysis Search Engine (Simulation) |
| 9. | [ | IoT | Raspberry Pi and Zigbee serial connection | OpenCV Library |
| 10. | [ | IoT | Public infrastructure, geographic variables, metro convenience indices, national standards, 5G basic station coverage | Simulation |
| 11. | [ | IoT | Roadside unit (RSU) and IoV data | Game Theory and Nash Equilibrium |
| 12. | [ | ML/DL | Pressure sensor data | LSTM, ANN, MySQL |
| 13. | [ | Emerging technologies (ETs) | Data on ET selection criteria | Qualitative analysis |
| 14. | [ | IoT | Real-time traffic data + incident record | UAV-guided priority system |
| 15. | [ | Autonomous | Patent data on autonomous car technology | Topic modelling |
| 16. | [ | ML | ML-TDG | Apache Spark |
| 17. | [ | Autonomous | Journey transaction data collected through automated fare collection system | MaaS (Mobility-as-a-Service) |
| 18. | {126} | Autonomous | Data on population, traveler routes, bus stop and buses | Sophisticated scheduling and optimization techniques |
| 19. | [ | Autonomous | Data from uncontrolled crossings in Midan city | Intelligent Driver Model (IDM) and K-means clustering |
| 20. | [ | Autonomous | Simulated and real-world data | Optimization |
| 21. | [ | Smart grid | T-Drive dataset for taxi trajectories | Grid clustering algorithm, SQL, visualization |
| 22. | [ | ML/DL | Automatic identification system (AIS) data | 7 DL Models + 5 ML models |
| 23. | [ | ML | Safety pilot dataset | Clustering model |
| 24. | [ | Autonomous | Research paper data + automotive companies‘ data | Comparative analysis |
| 25. | [ | Autonomous | Data from smart edge cloud system | Automated vehicle reservation systems and advisory-based arrival system |
| 26. | [ | DL | Video photo dataset | YOLOv3 algorithm |
| 27. | [ | DL | Video data | Computer vision algorithm |
| 28. | [ | Autonomous | Traffic data | Qualitative analysis |
| 29. | [ | Autonomous | Data on vehicle placements, speed, traffic signals, and environmental factors | MARL (multi-agent reinforcement learning) algorithm |
| 30. | [ | Autonomous | Syntetic data | Simulation and comparative analysis |
| 31. | [ | Autonomous | Data from recycling center location, quantities of goods returned, recycling costs, and transportation resources | SGA-PSO (Self-adapting genetic algorithm–particle swam optimization) |
| 32. | [ | Autonomous | Data from journey time and dependability | Dec-CTDSP (Decentralized and Collaborative Time-Dependent Shortest Path) |
| 33. | [ | Autonomous | Data from traffic patterns and car counts | OMNET++ and SUMO (throughput and latency were evaluated) |
| 34. | [ | Autonomous | Real-time traffic data | MPC (model predictive control) and DRL (deep reinforcement learning) |
| 35. | [ | Artificial intelligence | Data from previous studies | Qualitative analysis |
| 36. | [ | Autonomous | GPS data | XGBoost |
| 37. | [ | Electrical technology | Large-scale real-world connected vehicle data | Multi-agent Transformer-based deep deterministic policy gradient (MA-TDDPG) model |
| 38. | [ | Autonomous | Simulated and design data | Simulation and predictive model |
| 39. | [ | Electrical technology | Data gathered from literature review, official announcements, and local laws | SWOT-AHP |
| 40. | [ | Electrical technology | Data from previous research, traffic incidents, road accidents, and real-time videos | Regression analysis |
| 41. | [ | Autonomous | Data from traffic safety, self-driving cars, and moral decision-making | Fuzzy algorithms + dynamic programming |
| 42. | [ | IoT | Data collected from domain experts | Expert analysis + statistical analysis and computations |
| 43. | [ | IoT | Automobile data from ITS devices and AI sensors | ML + cloud computing |
| 44. | [ | Blockchain | Smart contracts | Distributed task-processing network + Pointer Network structure |
| 45. | [ | IoT, big data, AI, clouds, fog, and edge computing | Fine-grained data from cloud and edge | FMSA (Future Mobility Sensing Advisor) |
| 46. | [ | IoT and Big data | Udacity self-driving car dataset | Federated Learning technique/Federated Averaging algorithm |
| 47. | [ | DL and blockchain | Smart contract | IPFS sub-networks |
| 48. | [ | Big data | Real-time and historical data (KMRL and AFC data) | ML technique |
| 49. | [ | ML | Public and open dataset (GTFS data and NSW’s Open Data Centre) | Machine model and statistical regression analysis |
| 50. | [ | IoT | 3D LiDAR data from sensor | Clustering analysis |
| 51. | [ | IoT | Real-time sensory data | Gaussian process model and LSTM-autoencoder model |
| 52. | [ | Energy technology | Data from multiple statistics year-books | Extended STIRPAT model, scenario analysis, and ridge regression |
| 53. | [ | Autonomous | Data on network speed | Microscopic and macroscopic models |
| 54. | [ | Energy technology | Data from OECD and IEA sources | Statistical and econometric techniques |
| 55. | [ | Electricity technology | Real-world data on driving circumstances, power demand, and battery state of change | Deep reinforcement learning (DRI) model |
| 56. | [ | Senergy technology | Simulated and real-world Mars exploration missions and theoretical models | Comparative analysis, using simulation and Monte Carlos |
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; Kurien, Anish M 1
; Djouani, Karim 2 ; Dieng, Lamine 3
1 F’SATI, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa;
2 F’SATI, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa;
3 F’SATI, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa;