Driving Restriction Policy Research Articles

Research on the group effect of new energy vehicles from different policy perspectives

Abstract Policies play a pivotal role in fostering the growth of new energy vehicles. This paper delves into the benefits and risks associated with the development of new energy vehicles under varying policy landscapes. Firstly, the intricate relationship between the competition and growth of new energy vehicles and internal combustion engine vehicles is captured through the application of the extended Lotka-Volterra model. Secondly, a simulation flow diagram is constructed, taking into account the actual mediums of policy impact and drawing insights from diverse research methodologies. This flow diagram enables a comprehensive evaluation of environmental benefits, health risks, and traffic pressure. Finally, a critical evaluation of the actual impact of policies on new energy vehicles is conducted, along with an exploration of the root causes of potential policy paradoxes. The findings reveal that a standalone infrastructure support policy, research and development support policy, or carbon tax policy can, in certain periods, lead to an increase in carbon emissions and traffic pressure. Although the driving restriction policy does not exert negative impacts on the environment, health, and traffic at the national level, it can result in a surge in the total vehicle population. Therefore, it is imperative to adopt a combination of policies to mitigate the potential risks associated with any single policy.

The role of household energy consumption behavior in environmental policy outcomes —the case of driving restriction policy in zhengzhou

The evaluation of the environmental effects of the driving restriction policy often sparks widespread discussions in the literature due to its complex and controversial assessment methods. Fewer studies have delved into the exploration of household energy consumption behavior, the role played by the targeted subjects of policy implementation, and the policy's impact on climate change. This paper focuses on the case of Zhengzhou, a city in China, to thoroughly discuss the formulation process of the driving restriction policy. By utilizing publicly available statistical data and conducting surveys among 795 households, this research employs statistical methods such as regression discontinuity, difference-in-differences estimation, and improved causal stepwise regression to quantitatively estimate the environmental consequences of the driving restriction policy. It also examines the impact of changes in residents' consumption behavior on policy effects and climate change under this policy shock. The study aims at bridging the gap between environmental policy effects, individual behavior changes, and climate change research. Our findings indicate that the driving restriction policy is effective in the short term, significantly improving air quality. However, due to the stickiness of residents' household consumption behavior and the adaptive adjustments in long-term travel behavior, the pollution-reducing effects of the policy gradually weaken and even exacerbate air pollution, resulting in an additional increase of approximately 147,950 tons of carbon dioxide emissions after the policy had been implemented for nine months. Therefore, this paper further suggests that implementing environmental policies requires close coordination with adjustments in household energy consumption behavior.

고령운전자 운전제한에 대한 중장년층의 찬반 의견

This study aims to examine whether Korean middle-aged adults agreed with a driving restriction policy for older adults (i.e., limiting driving of drivers at a certain age or older), and how their endorsements were associated with individual characteristics (i.e., demographic, physical, psychological, and social), drawing on the biopsychosocial model on driving. We also examined how the associations varied by respondents’ age. This study analyzed a sample of 4,682 adults (aged 49-64) combined from the 2012 Korean Baby Boomer Panel Study and the 2012 Korean Forgotten Generation Study, using logistic regression models. The main results are as follows. First, only 33% of our respondents endorsed implementing a driving restriction for older adults. Second, results from logistic regression analyses indicated that respondents were more likely to support driving restrictions for older adults―when they were younger, did not live in a metropolitan city, and participated in more cultural and artistic activities. Third, we found significant interaction effects of age with education, residential areas, chronic diseases, subjective health, and social participation. These results highlight the importance of multi-dimensional (biopsychosocial) approaches to driving restriction policies for older adults.

Managing travelers’ mode choices in the era of shared mobility through traditional traffic regulation policies

The rapid expansion of ride-hailing service has imposed a huge amount of additional traffic on urban transportation systems. To reduce congestion, different regulatory policies have been announced to control the number of ride-hailing vehicles in metropolitan areas around the world. However, these measurements, shooting at ride-hailing vehicles only, deprive non-car owners of their private-car mobility rights (endowed by ride-hailing services), therefore are debatable in themselves. Instead of imposing specialized restrictions on ride-hailing vehicles, we propose to cure the deteriorating traffic conditions with classic traffic regulation policies implemented on all road space users. In this study, in a simple one corridor scenario with two different types of ride-hailing services, i.e., ride-sourcing and ride-sharing, we model travelers’ mode choices at user equilibrium under no policy intervention to reveal the negative impacts of ride-hailing on urban transportation efficiency, and compare the different system optimum states with and without ride-hailing to demonstrate the great potential of ride-hailing in mitigating congestion. Based on the model, we then examine the impacts of three traffic regulation policies, i.e., road space rationing, congestion pricing and tradable credit schemes, and investigate the existence of traffic regulation policies that can achieve system optimum in the presence of ride-hailing.

Public acceptance of congestion pricing policies in Beijing: The roles of neighborhood built environment and air pollution perception

While public acceptance is a major obstacle to policy implementation, the factors affecting the acceptance of anti-congestion policies such as congestion pricing remain unclear. Few studies investigate whether public acceptance varies across neighborhoods of variant built environment features and with perceived air pollution levels which people are exposed to. In this article we developed a generalized multilevel structural equation model (GMSEM) with latent class variables to investigate the direct effects and mediation effects among built environment, air pollution perception, satisfaction with existing driving restriction policies, and public acceptance of congestion pricing strategies. We draw on a questionnaire survey of about 1300 residents from 26 neighborhoods in Beijing, where driving restriction policies have been implemented and congestion pricing policies under consideration. This research reveals that residents living in neighborhoods with high population density and low diversity of land uses and those proximity to transit service and in the central urban areas tend to more strongly support the congestion pricing policy. Residents who perceive serious air pollution and who believe in the policy effectiveness are more supportive to the pricing policy; they may pay more attention to the level of air pollution caused by congestion caused by driving. These findings demonstrate the importance of recognizing neighborhood variations in policy acceptance and provide policy implications for incorporating appropriate land use planning and promotion strategies.

Panacea or placebo? Exploring the causal effects of nonlocal vehicle driving restriction policies on traffic congestion using a difference-in-differences approach

Panacea or placebo? Exploring the causal effects of nonlocal vehicle driving restriction policies on traffic congestion using a difference-in-differences approach

Externalities from restrictions: Examining the short-run effects of urban core-focused driving restriction policies on air quality

To combat urban challenges such as air pollution stemming from excessive vehicle use, policy makers have adopted various driving restriction policies (DRPs) worldwide. Although previous studies have extensively justified the effects of citywide DRPs on air quality, little attention has been paid to urban core-focused license-plate-based DRPs (UCLDRPs), which have gained increasing popularity among major cities. To bridge this gap, we carried out an empirical evaluation of the short-run effectiveness of Shanghai's UCLDRPs in improving air quality using difference-in-differences (DID) modeling approaches on high-frequency air quality data. The results show that UCLDRPs did not lead to any reduction of air pollutants including NO2, PM2.5, and PM10 during peak hours (the restricted time window) in the inner ring area (the restricted area), while contributing to a 14% increase in the concentration of CO. Moreover, the air quality of surrounding zones (the 5 km buffer area) got worse as the concentration of CO and PM10 raised by 16% and 8% respectively. Results also justified the temporal-spatial spillover effects in the dynamics: the concentrations of CO and PM10 got increased by 10%-20% in the 5 km buffer area within time periods right before or after the restricted time windows.

The effect of China's driving restrictions on air pollution: The role of a policy announcement without a stated expiration

Driving restrictions keep cars off the street largely on the basis of the last digit of the license plate number. This paper evaluates how different driving restriction policies affected the air quality of the 17 cities in Henan, one of China’s most populous provinces, from 2017 to 2019. I offer a novel way to categorize driving restrictions by making a distinction between cities that announced driving restrictions without a stated expiration and other cities that announced driving restrictions with a stated expiration. I provide some suggestive evidence on the exogeneity of timing, and using two-way fixed effects, I find significant heterogeneity in policy effectiveness. The policy reduced particulate matter concentration in cities that announced driving restrictions without a stated expiration, but had no effect in cities that announced driving restrictions with a stated expiration. To explain this difference, I build a model, which implies that driving restrictions announced without a stated expiration can induce people to pay a high fixed cost in exchange for a low transit time and not to choose non-compliance, two behavioral changes with the potential to further reduce air pollution. Thus, managing the duration of a policy and people’s expectations of its duration can matter crucially for the policy’s effectiveness.

Looking for the missing link: Driving restriction, car trips, and air pollution in Beijing

Driving restriction policy is a commonly used measure to control congestion and air pollution in many large cities across the globe. The growing international literature that empirically investigates the effect of this policy on air quality yields conflicting findings. Yet, existing studies tend to overlook individual travel reactions to the driving restriction policies, although the policies are designed to reduce travel directly. To fill this gap, this study uses individual-level travel survey data to trace out environmental consequences of driving restriction policy through its direct effect on individual driving behavior. The new analytical framework addresses the endogeneity problem and obtains consistent estimates of the coefficients of interest, by using exogenous variables and a two-stage estimation. The point estimates suggest that, in the short run, driving restriction policy does not affect the total concentrations of PM2.5 pollutants. This insignificant relationship between driving restriction policy and air quality suggests adaptive individual behavior in response to rigid policies and calls for innovative approaches to address driving-related air pollution. • The first study on environmental impact of driving policy via personal auto-trips • The change of air pollution level is used to capture marginal environmental effect. • Robustness tests are conducted to assess the sensitivity of the estimates. • Results show that driving restriction policy can’t reduce pollution in short term.

Social Benefits and Private Costs of Driving Restriction Policies: The Impact of Madrid Central on Congestion, Pollution, and Consumer Spending

Abstract Low Emission Zones are defined areas within a city where driving restrictions are introduced with the aim to reduce pollution, but they may also unintentionally distort consumer spending decisions. By increasing transportation costs to ban-affected areas, driving restrictions could discourage spending in stores of those areas. This paper empirically evaluates the effects of a driving restriction regulation in Madrid, Spain, known as Madrid Central. First, using a difference-in-differences identification strategy, we find an immediate decrease of 19% in pollution and of 16% in congestion with pollution dropping further once fines were levied. Second, we rely on credit card transaction data to show consumers affected by the regulation reduced their brick-and-mortar spending in the regulated area by 21%. Finally, because affected consumers partially substitute their consumption spending from brick-and-mortar to online shopping, we find suggestive evidence that e-commerce may smooth the impact of changes in transportation costs due to environmental regulations.

Effect of urban traffic-restriction policy on improving air quality based on system dynamics and a non-homogeneous discrete grey model.

With the acceleration of urbanization, traffic congestion and vehicle exhaust pollution are becoming increasingly serious problems. Focusing on the problem of urban pollution from vehicle exhaust, this study used system dynamics to establish an urban congestion mitigation and emission-reduction management model. Specifically, a nonlinear function that integrates system dynamics and a non-homogeneous discrete grey model (SD-NDGM) was used to construct an algorithm, which improved the accuracy of the model. Thereafter, the mid- and long-term effects of the restriction policy were explored. The main findings from dynamic model simulations were as follows: All types of restrictions alleviated traffic congestion to varying degrees, but “odd and even” restrictions had more obvious effects, with an average annual reduction rate of 43.53% in the number of motor vehicle trips. The driving-restriction policy had a time effect, significantly reducing the number of vehicle trips in the short term. However, it could have negative effects in the long term (e.g., agglomeration effect, emission-reduction paradox), and it does not fundamentally solve traffic and environmental problems. Thus, it could only be used as a phased policy, not a long-term measure. The purchase-restriction policy controlled excessive increases in the number of private cars, but it had little effect in terms of solving environmental problems. Compared with a single policy, the combination of public-transport development and driving-restriction policy not only reduced traffic congestion, air pollution, and air quality health indexes by 29.13%, 52.63%, and 54.63%, respectively, but also improved environmental carrying capacity by 294.26%. A combined approach can therefore be said to have certain benefits for society, health, and the environment.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s10098-022-02319-9.

Traffic restrictions during the 2008 Olympic Games reduced urban heat intensity and extent in Beijing

Satellite thermal remote sensing has been utilized to examine the urban heat dynamics in relation to the urban traffic restriction policy. During the 2008 Olympic Games in Beijing, the traffic volume was approximately cut off by half through the road space rationing. Based on daily MODIS satellite thermal observations on the surface temperature, statistical models were developed to analyze the contribution of traffic volume reduction to the urban heat intensity and spatial extent. Our analyses show that cutting off half of the traffic volume has led to a marked decrease in the mean surface temperature by 1.5–2.4 °C and shrinkage of the heat extent by 820 km2 in Beijing. This research suggests that the impact of urban traffic on heat intensity is considerably larger than previously thought, and the management of urban traffic and vehicle fossil fuel use should be included in the future urban heat mitigation plan.

Understanding the Heterogeneity in the Effect of Driving Restriction Policies on Air Quality: Evidence from Chinese Cities

Understanding the Heterogeneity in the Effect of Driving Restriction Policies on Air Quality: Evidence from Chinese Cities

Do driving restriction policies effectively alleviate smog pollution in China?

China has become one of the countries with the most severe smog pollution, especially in winter. Many local governments had to take various measures to combat the smog. The driving restriction is one of the policies being widely implemented throughout the country, but the actual effect has been controversial. Over 30 cities in China adopted the driving restriction policy in winter specifically in recent years. In this study, regression discontinuity (RD) method is applied to 16 cities to study whether the policy was effective or not and to find out the reason behind. It is found that (1) the effect has shown differences in different cities, in Cangzhou, Linfen and Shijiazhuang, the policy was effective on winter smog control, while it was ineffective in Luoyang, Tangshan, Xinyang and Zhoukou. (2) High proportion of secondary industry in GDP and inconvenient public transport system has inhibitory effect on the effectiveness of the driving policy. (3) Population density, GDP, the proportion of urban green area and driving restriction intensity have no significant impact on the effectiveness of the driving restriction policy. (4) In terms of meteorological factors, average temperature, rainfall, wind speed and air pressure have a negative impact on PM2.5, while humidity has a positive impact on PM2.5.

Investigating the impacts of driving restriction on NO2 concentration by integrating citywide scale cellular data and traffic simulation

Urban traffic is one of the main sources of NO2, and driving restriction (DR) has been widely used to alleviate air pollution in China, which means that private vehicles are not allowed to enter or pass a specific area according to their last digit of license plate numbers. This study investigates the impacts of DR on NO2 concentration at the traffic analysis zone (TAZ) level, which is particularly defined for traffic-related spatial data analysis. Measured NO2 data, citywide scale cellular data, and traffic-related data were collected for analysis. Inverse distance weighting (IDW) model was used to obtain NO2 at each TAZ. The traffic model of Nanjing was built in TranStar, a mesoscopic traffic simulation platform. Based on TAZ level NO2, traffic demand, and traffic status data, a geographically weighted regression (GWR) model was developed. DR with different proportions and spatial scales were simulated in TranStar and evaluated based on the predictions of the GWR model. Results suggest that morning rush hour is the only time in the day when urban traffic is the main cause of NO2's rise. Larger traffic volume and severer congestion with lower speed lead to the higher rise of NO2. The main factors influencing the rise of NO2 could be different at distinct locations. DR strategies can decrease the rise of NO2 significantly for most TAZs. Restriction proportion has significantly higher impacts than spatial scale, and the effects of scale enlarge with the rise of proportion. A side effect of DR is that the rise of NO2 would be higher in certain regions due to travelers' mode shift and detour, which is a key point for policymakers to weigh the pros and cons.

Effectiveness, Spillovers, and Well-Being Effects of Driving Restriction Policies

We study the effectiveness, spillovers, and well-being effects of low emission zones in Germany, an emission-intensity-based driving restriction rapidly growing in popularity. Using regression discontinuity and group-time difference-in-differences designs, we show that previous estimates of the policy’s impact on traffic-related air pollution significantly underestimate its effectiveness. We provide evidence of beneficial and harmful policy spillovers to neighboring areas, and increases in ozone due to changes in the chemical balance with precursor contaminants. Policy effects are heterogeneous by season, with greater decreases in traffic pollutants during winter and increases in ozone during spring and summer. Using individual-level data from the German Socio-Economic Panel, we further find that the policy decreases subjective well-being despite clear evidence of health benefits. The decline in well-being is especially pronounced in the first year after policy implementation and is transitory.

Managing Travelers’ Mode Choices in the Era of Shared Mobility Through Traditional Traffic Regulation Policies

The rapid expansion of ride-hailing service has imposed a huge amount of additional traffic on urban transportation systems. To reduce congestion, different regulatory policies have been announced to control the number of ride-hailing vehicles in metropolitan areas around the world. However, these measurements, shooting at ride-hailing vehicles only, deprive non-car owners of their private-car mobility rights (endowed by ride-hailing services), therefore are debatable in themselves. Instead of imposing specialized restrictions on ride-hailing vehicles, we propose to cure the deteriorating traffic conditions with classic traffic regulation policies implemented on all road space users. In this study, based on an aggregate model with two different types of ride-hailing services, i.e., ride-sourcing and ride-sharing, we model travelers' mode choices at user equilibrium under no policy intervention to reveal the negative impacts of ride-hailing on urban transportation efficiency, and compare the different system optimum states with and without ride-hailing to demonstrate the great potential of ride-hailing in mitigating congestion. Based on the model, the impacts of three traffic regulation policies, i.e., road space rationing, congestion pricing and tradable credit schemes, and the existence of traffic regulation policies that can achieve system optimum in the presence of ride-hailing are examined.

Do driving restrictions improve air quality: Take Beijing-Tianjin for example?

To manage air quality, Tianjin introduced driving restriction (DR) in 2014 and new driving restriction (NDR) in 2018. Ever since February 22th, 2018, private cars that belong to Beijing have been treated equally as local cars of Tianjin. However, existing studies rarely compare the effects of driving restrictions in different cities. In this paper, using the regression discontinuity design (RDD) model and urban air quality monitoring data from Beijing and Tianjin during the period 2014 to 2018, we explore whether driving restriction policies can improve air quality in Beijing and Tianjin. The main results are as follows. (1) Our results of regression discontinuity design revealed that the DR had no obvious improvement in the air quality in Tianjin. Furthermore, the effect of the DR decreases along with its implementation. (2) The implementation of the DR in Tianjin had slight improvement in air quality in Beijing, which mainly reduced emissions from cars. In other words, the DR brought about weak externality. (3) We found that the effect of NDR on the air pollution was limited in both Tianjin and Beijing. To sum up, we compared the NDR with DR and found out that the DR was more effective in the short run, while the NDR could work well in the long term.

Benefits and Risks of the Driving Restriction Policy: A Case Study of Xi’an, China

With the deterioration of air pollution and traffic congestion especially in urban areas, the policy restricting cars operating on the road is deemed as an effective strategy to mitigate the negative impacts. After implementing the driving restriction policy (DRP), some benefits were yielded albeit a few problems appeared. It is desirable to assess the benefits and risks of the DRP and thus propose measures to maintain the benefits. A revealed preference (RP) survey was conducted in the field and via the internet. There are 585 valid samples collected, which were classified by socioeconomic factors, and the corresponding reactions were analyzed and compared. The correlation analysis was used to identify significant and independent demographic/characteristic variables. The results show that most travelers perceive benefits from reducing car ownership and have positive views on DRP. However, others would negatively react to the DRP, which is mainly manifested by buying another car, driving during non-restricted hours and traveling against regulations. Furthermore, socio-demographic characteristics show a relatively high correlation with travel mode choice. Automobile is favorable to middle-aged travelers with upper-middle income. Family characteristics (i.e. household structure, pick up children and cars ownership) are also important inducing travelers to use private cars. These findings will be helpful for formulating the DRP to yield greater benefits after implementation.

Impact of Car Restrictive Policies: A Case Study of Srinagar City in J&K State India

Urbanization and economic development in developing countries has led to an exponential increase in the ownership and usage of private vehicles in the past decades. Such growths have made city roads choked with congestion. Srinagar city in India is not different wherein the volume to capacity (v/c) ratio of 22 arterial roads was found to be more than 1.0 and associated level of service (LOS) on these links varied between E and F. In addition to this, the city has inadequate public transport and on-street parking is predominant. This case study aims at easing out the traffic situation in Srinagar city by analyzing the impact of car restrictive policy options like Odd-Even road-space rationing, revamping public transportation including inland waterways and prohibiting on-street parking.Household personal interview survey is conducted in 390 households among various Traffic Analysis Zones across the city. The information on existing travel mode used and perceived usage of private vehicles under proposed car restrictive policies are collected. The results indicate that the commuters cover an average distance of 9.89 km with an average speed of 15.40 km/h. The average delay estimated for this distance came out to be 16.03 minutes. 77% of cars and 62% of 2-wheelers comply with Bharat Stage III (Euro 3) emission standards which when compared with the car and 2-wheeler registration in the city reveal that CO and HC+NOx emissions are 8.58 and 4.03 tonnes per day, respectively. When addressed, 57% of the respondents favoured the implementation of Odd-Even policy and 75% of the respondents stated that they will not buy another vehicle if the policy is implemented. Around 64% of the respondents with origin or destination along the river and lake side showed interest in using inland waterways if implemented. All respondents complied with the prohibition of on-street parking provided there are adequate parking facilities available.