Road Weather Monitoring System Shows High Cost-Effectiveness in Mitigating Malfunction Losses

Abstract

Understanding the environmental impacts of road networks and the success of policy initiatives is crucial to a country’s socioeconomic development. In this study, we propose a comprehensive approach to quantitatively assessing whether a given response is effective in mitigating the impacts of environmental shocks on roads. Our approach includes factor analysis, direct and indirect loss quantification, and cost-benefit analysis. Using nationwide data on road malfunctions and weather service performance in China, we found that the macro-level indirect economic losses from road malfunctions were more than the direct losses in multiples ranging from 11 to 21, and that information provided by the weather service could reduce losses, with benefits exceeding costs by a ratio of 51. The results of our study provide a quantitative tool as well as evidence of the effectiveness of sustainability investment, which should provide guidance for future disaster mitigation, infrastructure system resilience, and sustainability-building policy-making.

1. Introduction

An efficient road transportation system is crucial for economic growth and social development, and thus road safety has attracted considerable attention given its relevance to society in terms of the human, economic, and property costs resulting from road malfunctions [1]. Environmental factors affecting transportation, including meteorological and geological hazards, often lead to road malfunctions such as congestion and road closures, threatening the orderly development of society and causing significant economic losses through damaged infrastructure and increased transportation costs. Understanding the comprehensive impact of road malfunctions and the performance of existing policies is crucial to the planning and management of the transportation system.

It is demonstrated that the ongoing climate change will increase its impact on transportation infrastructure [2], and the long-term risk of climate change could be strongly influenced by policy choices [3]. Integrated assessments implemented by institutions and research groups may lead to inconsistent estimates because of different assumptions and scenarios across models. Existing research includes the application of a self-consistent comprehensive modeling framework to assess climate impacts [4], the influence evaluation of road damage on connected reliability from the perspective of traffic function loss [5], the post-disaster road network analysis and emergency planning [6], etc. Our study focuses on the overall impact of multiple hazards on a national scale, as well as how road disaster mitigation and sustainability-building can be quantified and addressed at the policy level.

In 2012, China’s Ministry of Transport established a Highway Monitoring and Response Center (HMRC). Given that weather conditions play a key role in the occurrence of emergencies [7], an accurate weather forecasting and early warning system is an effective means of mitigating road transport emergencies. Thus, the HMRC commenced a collaboration with meteorological agencies in an effort to provide an enhanced road traffic weather service. The Highway Information Management System of the Ministry of Transport was developed in 2009, and the Highway Traffic Interruption Information Reporting System (HTIIRS) was formally introduced in 2011 [8]. Since then, the HTIIRS has been continually upgraded. The assessment of such systems relies on quantitative analysis, and previous studies have found that major improvements in road safety can be accomplished by implementing cost-effective road safety measures [9].

The novelty of this study is that we use actual road malfunction data primarily derived from the HTIIRS in relation to road monitoring and emergency responses. We also propose a framework to comprehensively assess the effects of response measures, such as weather early warning. This includes factor analysis, the quantification of direct and indirect losses and cost-benefit analysis (see Figure 1). We evaluated the economic impact of road malfunctions as a result of meteorological and geological events using the computable general equilibrium (CGE) model to organically integrate supply, demand, and trade factors. The optimization ability of the CGE model offers advantages over other economic models. In particular, indirect benefits are also considered via a cost-benefit analysis of the weather service system. As a country prone to various natural hazards, China can provide useful lessons regarding sustainability investment for other countries and regions.

2. Materials and Methods

2.1. Materials

The main road networks we considered were primary roads extracted from the OpenStreetMap platform [10]. A total of 81,945 road malfunctions from 2014 to 2019 were identified using the HTIIRS, and the information included the province, road name, hazard type, length of road affected by the malfunction, time of discovery, and recovery time.

Figure 2 shows the various causes of road malfunctions over the period 2014–2019. The greatest share of road malfunctions was caused by haze (~55%), followed by snowfall and accumulated snow (~18%), rainfall and flooding (~11%), and freezing conditions (~7.5%). Haze can hinder a driver’s judgment and observation and increase transportation costs by reducing vehicle speed. Precipitation (including snowfall and rainfall) can hamper visibility and make roads slippery, and can also cause subsequent events such as landslides, thereby reducing road safety and transport efficiency. Several examples of road malfunctions caused by bad weather are shown in Table 1.

2.2. Loss Assessment Methods

The main function of road networks is to transport people and goods in a timely manner. If a malfunction occurs as a result of bad weather or a natural disaster, there will be a negative impact on road transport facilities and capacity. Direct losses $L_{direct}$ can be evaluated as follows:

(1)$L_{direct}=C_{unit}·M·r_{damage}$

where $C_{unit}$ represents the average cost per mile of domestic roads, $M$ represents the length of malfunctioning road, and $r_{damage}$ represents the degree of damage based on survey statistics.

However, the indirect losses caused by road malfunctions are often greater than the direct losses. To evaluate the impact of road malfunctions on economic production, we used the CGE model [11] to comprehensively analyze the indirect economic losses. The interactions among various sectors in the economy can be quantitatively analyzed by including various agents such as producers, governments, and consumers to capture commodity flows and market factors in relation to production, consumption, income, importing, and exporting (see Figure 3). The specific areas are as follows.

The nesting production functions are used here to describe the input structure of production sectors. At the first level of the input structure, intermediate inputs (including road transportation) and primary factors are composited with constant elasticity of the substitution (CES) function, while primary factors comprise labor and capital inputs with Cobb–Douglas function at the second level. The consumption of household and government is also based on Cobb–Douglas preferences. Following most CGE models, the general equilibrium requires the clearance of all commodity and factor markets, zero profit of producing sectors, and the balance between saving and investment.

The introduction of environmental shock is represented by the intermediate input increment of transportation caused by the post-disaster efficiency reduction. The original function of the production of intermediate input by each department is given by:

(2)$Q_{int}\left(C,PD\right)=ut\left(C,PD\right)·U\left(PD\right)$

where $Q_{int}\left(C,PD\right)$ represents the intermediate demand for commodity $C$ by production department $PD$, $ut\left(C,PD\right)$ represents the ratio of intermediate demand to total intermediate input, and $U\left(PD\right)$ represents the level of composite intermediate input. After introducing the road transport malfunction impact coefficient $D{I}_{road}$ and the freight demand elasticity coefficient $E_{fd}$, the intermediate input function for each department is given by:

(3)$Q_{int}{\left(C,PD\right)}^{\prime }=\left(1+D{I}_{road}·E_{fd}\right)·ut\left(C,PD\right)·U\left(PD\right)$

as for the determination of the coefficient value,

(4)$D{I}_{road}=L_{delay}/C_{road}$

(5)$C_{road}=C_l·r_{trans}·r_R$

(6)$L_{delay}=V·TV·DT$

where $C_{road}$ represents road transportation logistics costs, $C_l$ represents total logistics costs, $r_{trans}$ represents transportation logistics costs as a share of total logistics costs, $r_R$ represents the proportion of freight transported on trunk roads, $L_{delay}$ represents the loss as a result of road freight delays, $V$ represents the value that each truck can generate per unit of time, $TV$ represents the volume of truck traffic per unit of time, and $DT$ refers to the length of the delay. Freight demand elasticity $E_{fd}$ with respect to economic activity (as measured by gross domestic product (GDP)) can be estimated by decoupling road transport growth from economic development [12]. Because there was insufficient data to analyze the change in elasticity estimates over time, the empirical value from around 2010 [13], which was referenced in the 2014 Sustainable Development Goals, was used.

2.3. Cost-Benefit Analysis

The weather service system was introduced in 2013 and has been continually upgraded since then. Regarding the cost-benefit analysis, the cost of the weather service $C_{weather}$ and the benefits $B_{weather}$ are given by:

(7)$C_{weather}=F_{service}+F_m$

(8)$B_{weather}=L{R}_{infra}+L{R}_{toll}$

where $F_{service}$ represents the weather service fee from the meteorological department, and $F_m$ represents the additional costs of materials such as woven bags, snow melting agents, and anti-skid materials. $L{R}_{infra}$ and $L{R}_{toll}$ refer to the loss reduction of road infrastructure and toll revenue, respectively.

3. Results

3.1. Statistical Analyses of Road Malfunctions

3.1.1. Types of Induced Hazards

Natural hazards affect the functioning of roads to varying degrees, and thus we conducted statistical analyses of the total delay time and length of road affected by the malfunction as a result of various hazards. Table 2 shows the top 10 hazards by delay time and distance impacted in descending order. It can be seen that haze, severe precipitation, and induced geological disasters pose the greatest threats to road availability and safety. These hazards reduce road serviceability, leading to traffic jams and increasing the probability of incidents. Since the maximum capacity of the road network changes dynamically in response to a hazard or natural disaster [14], it is necessary to increase coordinated scientific governance to mitigate the adverse impact of multiple hazards on traffic safety and economic efficiency.

The number and length of road malfunctions caused by meteorological and geological hazards and disasters have shown an increasing trend overall, while the average duration of each malfunction has shown a downward trend. Of the top five hazards in terms of the total delay time, ground collapse was responsible for the longest average duration, followed by landslide. The average duration of road malfunctions caused by ground collapse, rainfall, and snowfall was significantly shorter in 2019 than in 2014. The average duration of haze-related road malfunctions remained stable, while that of road malfunctions caused by landslides increased by 58 hours from 2014 to 2019 (see Figure 4), which was probably the result of more frequent extreme precipitation events. This implies that the road management department has gradually accumulated experience and developed appropriate emergency control measures for various types of hazards and disasters, which has had a positive impact. However, emergency responses to landslide-induced road malfunctions needs to be reviewed with the aim of further shortening the duration of malfunctions, and thus reducing the associated economic losses.

3.1.2. Spatial Distribution of Road Malfunctions

Figure 5 shows the spatial distribution of road malfunctions as a result of meteorological and geological hazards and disasters. From 2014 to 2019, about one-third of the primary roads in China’s provinces experienced some degree of malfunction.

To eliminate the influence of the different lengths of roadway in various regions, the number of malfunctions per unit of distance was used to estimate the possibility of malfunction in a given region. The darker the color, the greater the probability that a malfunction might occur. Sichuan, Henan, and Shanxi Provinces were the top three provinces in terms of road malfunctions. The probabilities of malfunctions were similar in the eastern and western regions, while that in the central region was twice as high. High-risk areas generally display the following characteristics: they are located at the junction of plates with an active crust and a fractured rock mass; they are located at the places with undulating terrain and unstable geological conditions; and they experience a monsoon climate with concentrated precipitation and severe erosion, and thus are prone to rockfalls and landslides. Meanwhile, some areas (e.g., Jiangsu) report a higher number of road malfunctions as a result of their advanced highway management system, which involves a more detailed and timelier incident reporting system.

3.2. Economic Impact Assessment

The term “road traffic economic loss” refers to the loss of various vested or expected economic benefits from road transport by organizations, households, and individuals following a malfunction, and includes both direct and indirect losses. In this study, we focused on the adverse effects of road malfunctions caused by meteorological and geological hazards and disasters in terms of both of these types of losses.

3.2.1. Direct Loss Assessment

Direct economic losses are generally divided into four categories: road traffic infrastructure losses, vehicle losses, the economic value of lost time, and losses from traffic accidents. Due to limited data availability, in this study, we only considered the first category. Road malfunctions caused by various types of hazards or disasters are manifested in different ways. Haze, accumulated water or snow, and freezing conditions mainly affect visibility or the safety of the road surface, while landslides, earthquakes, typhoons, and floods can result in damage to roads and other transportation infrastructure. Here, we took physical damage to represent direct economic losses and estimated it by multiplying the unit cost of the road by the length of damaged road.

The government’s 2019 annual report indicated that the cost of damaged roadbeds, road surfaces, bridges, tunnels, culverts, and protective engineering was approximately CNY 38.73 billion. Because the cost of roads depends on numerous factors including the technical grade of the road, the regional environment, and the local development level, we used 0.04 billion CNY/km as the average unit cost of domestic roads. Based on an average level of damage to road facilities of 7%, the total direct economic loss $L_{direct}$ from 2014 to 2019 was about CNY 170.2 billion, which is an average annual loss of CNY 28.4 billion, or about 0.03% of GDP. Ground collapse, mudslides, and typhoons were the top three causes of direct economic losses. The above method provides a quick snapshot, which could provide decision-making support for government agencies prior to allocating disaster relief funds and help them to develop a post-disaster reconstruction plan for the road transportation system.

3.2.2. Indirect Loss Assessment

Meteorological and geological hazards and disasters generally weaken the capacity of the transportation system and cause traffic delays and interruptions, which not only increases people’s travel costs, but also reduces the efficiency of freight transportation and interrupts the production processes in various industries. We used the impact of road malfunctions on the road freight industry to evaluate the indirect impact on related industries. From 2014 to 2019, average annual total logistics costs $C_l$ were CNY 12190.83 billion [15], and transportation costs $r_{trans}$ comprised about 55% of the total [16], of which road freight $r_R$ accounted for 39% [17]. Thus, average annual road logistics costs $C_{road}$ were about CNY 2614.93 billion. Given that each truck can generate a value of 25 CNY/hr [18] and combining the delay time $DT$ in the malfunction records and the average hourly traffic volume $TV$ from the Statistical Bulletin [19], the average total annual loss from $L_{delay}$ was about CNY 8.91 billion.

A previous study [13] found that the elasticity coefficient $E_{fd}$ with respect to economic activity, which reflects the correlation between road freight demand and GDP, was 1.54. Based on the social accounting matrix in 2015, three scenarios were developed wherein Chinese road transportation was affected by mild $DT$ less than 400,000 h per year), moderate ($DT$ between 400,000 and 600,000 h per year) and severe ($DT$ greater than 600,000 h per year) disasters. The corresponding $D{I}_{road}·E_{fd}$ in these scenarios was 0.0035, 0.0055, and 0.0065, respectively.

When meteorological and geological hazards and disasters occur, the efficiency of road transportation decreases, and the intermediate inputs required for the production of various commodities increase. Further, the disruption of the road transportation system will have a broad impact on production activities in other sectors of the economy. Figure 6 shows that production activity (PA), commodity investment activity (CIA), imports (IV), and exports (EV) are affected to varying degrees by road malfunctions. The impacts on CIA in the secondary industry sector and IV in the primary and tertiary industry sectors are particularly significant, reflecting the high level of dependence of various industry sectors on road transportation.

At the macro level, indicators such as GDP, residents’ income, government revenue, household consumption, and total government consumption reflected non-linear changes as the disaster impact coefficient of road transportation increased, especially in relation to household consumption (see Figure 7). This is because on the one hand, an increase in intermediate production costs leads to an increase in domestic commodity prices, while road malfunctions lead to a decline in imports and a rise in the price of imported goods, while on the other hand, road malfunctions also lead to a loss of human resources and income, and thus household consumption suffers a double blow. Under the scenario wherein disasters had effects to varying degrees on road transportation, indirect economic losses were more than the direct losses in multiples ranging from 11 to 21. In reality, the implementation of compensatory measures and the resilience of the economic system might result in a lower overall impact. However, this kind of industry chain effect requires more attention and a higher level of preventive investment. Policies related to rapid emergency response and road network risk control should be developed and strengthened to enhance the reliability of the economic system.

3.3. Weather Service Cost-Benefit Analysis

Management of key road infrastructure requires reliable cost-benefit analyses to support effective decision-making [20]. The damage caused by meteorological and geological hazards and disasters can be summarized as follows: the destruction of road infrastructure in the form of events such as bridge collapses and land subsidence caused by disasters such as floods and earthquakes, and impaired transport functioning, such as traffic delays caused by haze and freezing conditions. The benefits that a weather service system provides to a transport agency fall into two main categories: reduced loss of road facilities through preventative measures, and reduced emergency preparation time and reduced loss of toll income through the shift in response time from post-disaster to pre-disaster.

An industry survey conducted by the Ministry of Transportation found that timely weather warnings can reduce the loss of road infrastructure by about 50% and the average road closure time by an average of six hours. Considering the abovementioned estimated annual direct losses of CNY 28.4 billion, the first-part benefit $L{R}_{infra}$ was about CNY 14.2 billion. Based on the National Toll Road Statistics Bulletin [21], the average hourly revenue from toll roads between 2014 and 2019 was 330 CNY/km. Combined with the average annual malfunction distance of 794,620 km, the annual reduction in lost toll revenue $L{R}_{toll}$ could be about CNY 1.64 billion, or 0.32% of annual toll revenue. Thus, the total benefit from $B_{weather}$ was approximately CNY 15.84 billion.

In terms of costs, these are mainly divided into the initial input costs (including observation equipment, emergency rescue equipment, and training and salary costs) and later operational costs (e.g., equipment and communications maintenance). For the transportation department, the first part $F_{service}$ is the cost of the weather service provided by the meteorological department, which was approximately CNY 0.01 billion per year. The second part $F_m$ was the additional cost of storage materials such as woven bags, snow-melting agents, and anti-skid material [22], which was approximately CNY 5.4 billion. There were additional costs associated with unnecessary consumption as a result of inaccurate weather warnings. However, the low probability of this situation and the resident nature of emergency equipment and personnel meant that this did not result in excessive additional costs. Therefore, the total costs in relation to weather service fees and emergency equipment $C_{weather}$ incurred by the maintenance unit were CNY 5.41 billion.

The above estimates show that the introduction of an early warning service increased the benefit-cost ratio to 3, which means that every CNY 1 spent on the road weather service system results in savings of CNY 3. This is consistent with the findings of previous studies [23,24], which found that road weather information systems produced benefit-cost ratios of between 1.1 and 45.4. It is worth noting that this only covers the direct benefits. If the indirect benefits are included based on the ratio of indirect losses to direct losses, the benefit-cost ratio increases to around 51.

Using the tolls from the malfunctioning section of road during the reduced closure time to represent the reduction in toll losses, it can be seen that the reduction in toll losses as a result of access to the weather warning service has accounted for an increasing proportion of annual toll revenue [21] in recent years (see Figure 8), indicating that such investments have significant potential to further increase the sustainability of the transportation industry by making full use of weather information.

4. Discussion

While various international organizations are calling for investment in sustainable infrastructure [25], we need to consider the most appropriate type of investment: construction investment or service investment.

The Ministry of Transport has announced that China will further strengthen its transportation network and continue to increase its investment in fixed assets [26]. However, the efficiency of the existing infrastructure and the focus of future investments remain to be discussed. Here, we evaluate the relative effectiveness of China’s road construction program using data envelopment analysis based on cost inputs and benefit outputs, which avoids subjectivity by not applying predetermined weights [27]. For each year from 2014 to 2019, road maintenance costs (RMC), construction investment (CI), and freight energy consumption (FEC) were used as cost indicators, while road density (RD) and road service level (RSL) [28] were used as benefit indicators to evaluate the efficiency of China’s road construction.

It can be seen from Table 3 that in four of the six years examined, road construction was effective (comprehensive efficiency was equal to 1), meaning that lower costs resulted in greater benefits. In 2017 and 2018, returns to scale were diminishing, illustrating the reduction in efficiency caused by excessive scale. In those years, construction investment declined slightly, as did road service levels (see Table 4). Investment started to increase again in 2019. To some extent, this indicates that construction investment is approaching saturation point, and any additional investment in construction may not deliver proportional benefits. According to the significance of pre-disaster retrofitting for cost saving [29], it is necessary to consider how to increase sustainability through effective service investment, such as improving the coverage of the monitoring system, the permeability of the pavement system and the durability of the drainage system [30], etc.

5. Conclusions

In this study, we undertook a comprehensive assessment of responses to environmental impacts based on China’s weather service system, a typical type of service investment. Our empirical findings indicated that this service system is of significant benefit to both the transportation system and the overall economy, especially given the increasing incidence of extreme weather events in the context of climate change. Road safety and availability affects the production activities of various industry sectors, and road malfunctions lead to nonlinear losses at the macroeconomic level. Indirect losses from road malfunctions were more than the direct losses in multiples ranging from 11 to 21. However, it was found that the existing weather service system was effective in reducing losses and had a benefit–cost ratio of 51.

The weather service system, which is one means of improving the sustainability of China’s road transport system but is still under development [31] and lacks metrics [32], has been shown to provide significant benefits at relatively low cost. Given this economic potential, other ex-ante measures, such as risk projection and disaster preparation, might be worth more consideration in future planning and management of transportation systems, which is in accordance with the call from the United Nations to incentivize increased ex-ante disaster risk management [33].

Thus, consistent early warning systems and precautionary measures should be given higher priority and attract increased investment in response to increasing environmental impacts in the context of climate change. Moreover, risk managers and decision-makers need to consider the indirect costs of these impacts. This will enable the development of optimal strategies for sustainable natural disaster mitigation and resilience-building.

Author Contributions

Conceptualization, S.Y., J.W., F.Y. and X.Y.; methodology, J.W. and S.Y.; data curation, F.Y. and X.Y.; visualization, J.W.; writing—original draft preparation, J.W.; writing—review and editing, S.Y. and F.Y.; supervision, S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Science and Technology of China, grant number 2019QZKK0906; and by the National Key Research and Development Program of China, grant number 2018YFC1508903.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Restrictions apply to the availability of these data. Data was obtained from the Highway Monitoring and Response Center, Ministry of Transport of China.

Acknowledgments

Many thanks to the editors and reviewers for their valuable suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Framework for the comprehensive assessment of the weather service system. Solid arrows indicate direct associations, while hollow arrows indicate indirect associations.

Figure 2. Causes of road malfunctions (2014–2019).

Figure 3. CGE model structure.

Figure 4. Average duration of road malfunctions as a result of various hazards or disasters (2014–2019).

Figure 5. Spatial distribution of road malfunctions.

Figure 6. Impact of road malfunctions on the various industry sectors.

Figure 7. Changes in macroeconomic indicators affected by road malfunctions.

Figure 8. Annual reduction in road toll losses as a result of access to weather warnings (2014–2019).

Table 1

Examples of road malfunctions (2014–2019).

Province	Road Name	Hazard Type	Malfunction Mileage (km)	Discovery Time	Recovery Time
Jiangsu	Changshen Highway	Snowfall (accumulated snow)	50.28	2014-02-05 21:40	2014-02-06 06:54
Sichuan	Jingkun Highway	Haze	260.24	2015-01-16 05:40	2015-01-16 12:31
Henan	Lianhuo Highway	Freeze	60.49	2016-01-11 21:17	2016-01-12 11:10
Shanxi	Rongwu Highway	Rainfall (accumulated water)	93.45	2017-04-08 13:21	2017-04-10 9:18
Shandong	Jinghu Highway	Haze	169.94	2018-12-26 19:18	2018-12-27 20:49
Sichuan	Hunie Highway	Landslide	150.42	2019-06-30 08:24	2019-07-07 12:00

Table 2

Top 10 hazards by delay time and distance impacted (in descending order).

By Total Delay Time	By Malfunction Mileage
Landslide	Haze
Haze	Snowfall (accumulated snow)
Rainfall (accumulated water)	Freeze
Ground collapse, subsidence or cracking	Rainfall (accumulated water)
Snowfall (accumulated snow)	Drifting snow
Collapse	Others
Freeze	Typhoon
High temperature	Earthquake
Earthquake	High temperature
Others	Gale (cross wind)

Table 3

National road construction efficiency (2014–2019).

Year	Comprehensive Efficiency	Technical Efficiency	Scale Efficiency	Return to Scale
2014	1	1	1	Fixed
2015	1	1	1	Fixed
2016	1	1	1	Fixed
2017	0.998	1	0.998	Decreasing
2018	0.913	1	0.913	Decreasing
2019	1	1	1	Fixed

Table 4

Redundancy analysis of inputs and outputs (2014–2019).

Year	Input Redundancy Rate			Output Deficiency Rate
	RMC	CI	FEC	RD	RSL
2014	0	0	0	0	0
2015	0	0	0	0	0
2016	0	0	0	0	0
2017	0	0.118	0	0	0.149
2018	0	0.025	0	0	0.147
2019	0	0	0	0	0

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