Road Congestion Pricing Research Articles

Review on road congestion pricing: a long-term land use effect perspective

Road congestion pricing (RCP) is an important way of controlling traffic congestion from the perspective of traffic demand management. This paper introduces the concept and four main types of applications of RCP, summarises the development of RCP theory, reveals the first- and second-order effects of RCP on transportation systems and land use, and analyses the long-term impact of RCP on the spatial location structure of land use. Relevant research progress considering land use effects is systematically and comprehensively reviewed from three research perspectives: demand modelling (pre-RCP), monitoring and analysis (post-RCP) and modelling methods (integration models and other models). The results showed that RCP will affect residents’ travel behaviour in the short term and will lead to changes in spatial location decisions in the long run, thus creating new travel demands and travel modes, affecting the transportation system and urban spatial patterns again. Implementing cordon charges has an agglomeration effect on the population in city centres. Cordon charges also have a significant negative impact on businesses in the toll area and a dispersive effect on jobs. Combined with appropriate strategies for shared autonomous vehicles, RCP can simultaneously improve transportation efficiency and regional accessibility.

Joint optimal pricing strategy of shared autonomous vehicles and road congestion pricing: A regional accessibility perspective

Current studies on shared autonomous vehicles (SAVs) and road congestion pricing (RCP) mainly focus on their independent effect on mobility, and rarely consider the joint impact of the two policies on accessibility. In light of this incentive, this paper establishes a bi-level programming model with multi-objective functions and multi-decision variables to solve the joint optimal pricing problem of SAVs and RCP considering accessibility. Because of the complex relationship between the decision variables and the objective functions, the proposed bi-level programming model belongs to a black-box problem. A machine learning algorithm based on multi-objective Bayesian optimization was developed to solve the proposed model, by taking Jiangyin City as an empirical case study. The results show that the combined implementation of SAVs and RCP plays an essential role in enhancing the performance of land-use and transportation systems. However, this advantageous outcome relies on an appropriately appropriate pricing strategy. Failing to account for the influence of the pricing strategy on implementation efficacy, SAVs and RCP could result in a 22.2 % and 5.0 % degradation in regional accessibility and total flow time, respectively. In contrast, the joint optimal pricing strategy of SAVs and RCP can not only mitigate traffic congestion and improve transportation network efficiency but also optimize urban spatial distribution and promote compact urban development, ultimately achieving a 14.0 % improvement in regional accessibility and 18.5 % reduction in total flow time. Sensitivity analysis reveals that with the increase in SAV market penetration rate, regional accessibility sees a steady rise. Conversely, the total flow time initially escalates before experiencing a decline. The method proposed in this paper serves not only in determining the joint optimal pricing strategy of SAVs and RCP to enhance accessibility, but also in analyzing and optimizing the accessibility impact of other land-use and transportation policies.

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.

Peak-Hour Road Congestion Pricing: Experimental Evidence and Equilibrium Implications

Peak-Hour Road Congestion Pricing: Experimental Evidence and Equilibrium Implications

GPS data Mining at Signalized Intersections for Congestion Charging.

Nowadays more private car trips have caused worse congestion due to the Covid-19 pandemic in many cities. Congestion charging is one of the taxes that is levied on vehicle owners to reduce urban traffic congestion. One of the most important reasons congestion charging is not accepted by the public is the high cost. Monitoring the state of traffic congestion in real time requires a lot of expensive installations. The purpose of this paper is to make congestion charging more accurate and acceptable using artificial intelligent algorithm. Massive real-time Global Positioning System (GPS) data provides new data for road congestion charging. The queuing length at intersections is an important measurement for the degree of traffic congestion, and it is also the basis for road congestion pricing. GPS positioning cannot provide sufficient position accuracy for lane identification of vehicles. In this study, a comprehensive model consisting of a real-time lane identification model and a real-time queue length estimation model is developed based on the traffic shockwave theory using GPS data. The comprehensive model can identify the lane where the queuing vehicle is located and estimate the real-time queue length of the lane. The proposed models were evaluated using field-collected data in Guangzhou, China. The testing results show that the proposed comprehensive model can identify lanes and estimate queue lengths with satisfactory accuracy. The model proposed in this paper provides real-time data for road dynamic pricing in a cost-effective way, which can promote the implementation of congestion charging in cities.

Active learning for multi-objective optimal road congestion pricing considering negative land use effect

The road congestion pricing policy is implemented to alleviate traffic congestion and improve the efficiency of the transportation system during peak hours. However, the negative land use effect caused by this policy could not be ignored. How to design the optimal congestion toll that can not only ensure its positive effect on the transportation system but also reduce its negative effect on land use is an urgent problem to be solved. Given this, this paper proposed a multi-objective bi-level programming road congestion pricing model based on the integrated land use and transportation model to optimize the regional average accessibility, regional average land use diversity, and regional total flow time. Since the proposed problem is NP-hard, this paper innovatively proposed an active learning optimization algorithm based on multi-objective Bayesian optimization, which improves the computation efficiency of the bi-level programming model by automatically finding the next sampling point (candidate solution) according to the probability information. An empirical analysis of Jiangyin City demonstrated the effectiveness of the proposed approach in coordinating the relationship between land use and transportation and alleviating the negative land use effect caused by road congestion pricing. Moreover, the algorithm proposed in this paper can also be used to solve other transportation-related black box problems with high computation complexity.

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.

Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility

Most of the existing research on shared autonomous vehicles (SAVs) and road congestion pricing have studied the short-term impact on traffic flow. These types of studies focused on the influences on mobility and ignored the long-term impacts on regional job accessibility. Given this, from the perspective of land use and transportation integration, this study explored the long-term effects of SAVs and cordon-based congestion pricing on regional land use, transportation, and job accessibility. The contributions of this study have been summarized by the following three purposes. First, to the best of the authors’ knowledge, this study was the first attempt to identify the long-term impact of the combination of these two technologies on regional job accessibility. Second, compared to the previous research methodology, this study adopted the land use and transportation integrated model (TRANUS model) and scenario planning to ensure the comprehensiveness and validity of the results. Third, this study analyzed the spatial heterogeneity of the impact of the combination of the two technologies on regional job accessibility in different areas with different built-environment attributes. To realize this purpose, this study quantitatively classified traffic analysis zones (TAZs) using data mining technology, i.e., factor analysis and clustering analysis. Results showed that the introduction of SAVs will contribute to job and population development in the charging zone and reduce the negative effect of road congestion pricing. From the perspective of reducing the average travel time between TAZs, the best strategy will be to implement SAVs alone, followed by integrated use of SAVs and road congestion pricing, while the worst strategy will be to implement the cordon-based congestion pricing policy alone. By comparison, from the perspective of improving regional job accessibility, the effect of introducing SAVs was better than that of road congestion pricing, while the combination of these two technologies was not superior to implementing SAVs alone.

Preface

After successfully held for three consecutive year, 2020 4th International Conference on Traffic Engineering and Transportation System (ICTETS 2020) was successfully held during August 21-23, 2020 in Guilin, China. ICTETS 2020 is to bring together innovative academics and industrial experts in the field of traffic engineering and transportation system to a common forum. The primary goal of the conference is to promote research and developmental activities in traffic engineering and transportation system and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an enableing platform for people to share views and experiences in traffic engineering and transportation system and related areas.The conference featured four keynotes (40 minutes each, including Q&A) and oral presentations. The papers in the oral sessions were allotted 15 minutes each for presentation. There were a total of 110 participants, including keynote speakers, invited speaker, committee members, session chairs, authors, presenters and listeners. We are greatly honored to have invited Prof. Qizhou Hu from Nanjing University of Science and Technology, China and Prof. Dongfang Yang, from Guizhou University For Nationalities, China to serve as our General Conference Chair. The rest of the committee was composed from Turkey, Australia, India, Brazil, Malaysia, Iran and other countries.In the keynote presentations part, our beloved Conference Chair, Prof. Qizhou Hu was also invited to present his talk Thoughts on bicycle traffic in Europe. In the comprehensive analysis of the development trend of bicycle traffic in European countries, this paper analyzes the successful road of European bicycle development. In addition, we had A. Prof./Dr. Baohua Guo from Henan Polytechnic University, China to present his latest research on Rock mechanics in Road Engineering, Road safety and congestion control. The third keynote speaker. A. Prof. Shaopeng Zhong, Dalian University of Technology, China presented his latest research: Exploring the joint effect of shared autonomous vehicles and congestion pricing on regional job accessibility. On his speech, he presented shared autonomous vehicles (SAVs) and road congestion pricing studies. A. Prof./Dr. Law Teik Hua, from Road Safety Research Center, Engineering Faculty, Universiti Putra Malaysia, Malaysia, as the last keynote speaker, he presented an excellent talk: Development of safety performance index for intercity buses: An exploratory factor analysis approach. In the last part of the conference, all participants were invited to discuss and explore the academic issues after the presentations. The discussion was lasted for about 30-60 minutes. The conference provided a forum for discussing traffic engineering and transportation system and in particular for promoting the interchange of novel ideas and the presentations of the latest developments in this field. This conference will also provide an ideal environment to develop new collaborations and meet experts on the fundamentals, applications, and products of the mentioned fields.We are glad to share with you that we received lots of submissions from the conference and we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Traffic Engineering, Transportation system and supply chain and other related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Organizing Committee of ICTETS 2020List of Committee member are available in the pdf.

THE TRANSITION FROM PIGOU’S IDEAS ON ROAD PRICING TO THEIR APPLICATION

A century ago Arthur Pigou published The Economics of Welfare. Within this volume are a few lines relating to the pricing of traffic congestion. Over the years, these lines have attracted the attention and thoughts of some of the leading economists of their day. These have included Knight, Clapham, and Robertson in the 1920s; Kahn and Viner in the 1930s; Ellis and Fellner in the 1940s; and Buchanan, Vickrey, Solow, Friedman, Walters, Demsetz, Newbery, and Allais in the post-World War II period. Here, I look at the academic arguments from the 1920s concerning how roads should be priced and by whom. I then move to consider the continuation of the intellectual debates after World War II and their implications for policy design and implementation. Finally, I briefly look at the extent to which some of the existing road congestion pricing systems meet the requirements of the economic theory.

Traffic Congestion and Intelligent Transport System in a Fast-growing Nigeria City

The dismal performance of the traditional traffic congestion measures and motorist attitudes on Nigeria roads have not only made traffic flow unpredictable and worsening but also worrisome and defiling every measures put-in-place by the Government. To solve these problems, the study assessed traffic congestion and Intelligent Transport System (ITS) in Ibadan, Nigeria. In achieving this, the study examined among others, factors aiding traffic congestion; effectiveness of the existing traffic congestion measures; and the performance and disposition to the use of ITS in the study area. Systematic random sampling was used to administer 386 copies of a questionnaire on commercial motorists, while data collected were analyzed using both descriptive and inferential (Pearson Chi-Square and Fisher's Exact Tests) statistics. Findings revealed that faulty intersections (3.586) and excesses of traffic officers (3.558) were worsening factors aiding traffic congestion in Ibadan city. Meanwhile, electronic traffic signalization (3.558) and law enforcement and regulation (3.499) were rated the most effective existing traffic congestion measures. In other words, real-time traffic signal devices (3.609); automated travel advisory system (3.595) and road congestion pricing devices (3.581) were highly rated and favourably disposed to use ITS. However, the Chi-Square Tests shows that the use Intelligent Transport System is statistically influenced by users' level of education (Sig. value < 0.05). The study concludes that there is a total necessity and willingness to use ITS in the study area. Hence, the study recommends exigent and holistic approach in the provision and enforcement of ITS towards mitigating congestion and other suggestions for improved traffic flow in Nigerian cities including Ibadan.

Discrete-time dynamic road congestion pricing under stochastic user optimal principle

Road pricing is believed to be an effective instrument for alleviating roadway congestion. Most existing road pricing schemes are developed based on traditional static traffic equilibrium models, in which a fixed toll can be obtained to support the corresponding traffic flow pattern as an equilibrium. However, static models cannot consider the evolution process of traffic flow caused by the day-to-day fluctuations of road users’ route choices. Under practical traffic conditions, the multiple traffic equilibria may exist (e.g., due to the asymmetric travel cost function). Indeed, the fixed road pricing scheme derived from the equilibrium model cannot guarantee that the dynamic traffic system can converge to the desired equilibrium state from any initial traffic state. This study, assuming that travelers follow the stochastic user optimal principle, develops a day-to-day dynamic road pricing scheme that can drive the traffic dynamic system to converge to a given stochastic user equilibrium (SUE) even when the traffic system has multiple SUE states. The characteristic of this dynamic road pricing scheme is verified by rigorous proof and numerical tests in this study.

Pigouvian road congestion pricing under autonomous driving mode choice

Advanced autonomous vehicle technology is suggested to offer a unique solution to many of the current problems in road transport. This paper studies the impact of the transition in automated driving capabilities (driving mode choice) on road congestion pricing and vice versa, accounting for the interdependencies between traffic flow, the choice level of autonomous driving, effective road capacity and marginal travel cost. It is shown that (in a more or less distant future) when drivers can choose among various levels of car automation during a trip and when shortcomings associated with autonomous driving – e.g. adverse effects regarding privacy, software hacking, liability, safety, driving pleasure, manual control recovery – are non-negligible, their generalized user cost curve is the lower envelope of a swarm of traffic flow dependent individual cost curves whose slope is determined by the level of autonomous driving. This has several implications for our understanding of Pigouvian road congestion pricing. Most importantly, marginal social trip costs are then no longer convex and strictly increasing in traffic flow even when traffic is only ‘normally congested’, thereby introducing the possibility of multiple congestion pricing equilibria even in the absence of what is usually called ‘hypercongestion’. When inconveniences related to autonomous driving are sufficiently high, the imposition of congestion tolls may lead to a situation where drivers abandon autonomous technologies entirely and opt instead for fully manual driving. Our findings suggest that the emergence of advanced autonomous driving capabilities and the transition from manual to automated control on the road will probably make congestion pricing more, not less, complex for researchers and policymakers.

Optimal Road Congestion Pricing for Both Traffic Efficiency and Safety under Demand Uncertainty

AbstractThe impacts of road congestion charges on traffic safety are often overlooked in evaluating the benefits of congestion charging practices and searching for traffic safety strategies. This p...

Crossing the bridge: The effects of time-varying tolls on curbing congestion

This paper estimates the traffic volume and travel time effects of the road congestion pricing implemented on the San Francisco-Oakland Bay Bridge. I employ both difference-in-differences and regression discontinuity approaches to analyze previously unexploited data for the two years spanning the price change and obtain causal estimates of the hourly average treatment effects of the policy. I find evidence of peak spreading in traffic volume and decreases in travel time during peak hours. I also find suggestive evidence of substitution to a nearby bridge and decreases in travel time variability. In addition, I calculate own- and cross-price elasticities.

Research on Urban Road Congestion Pricing Strategy Considering Carbon Dioxide Emissions

Congestion pricing strategy has been recognized as an effective countermeasure in the practical field of urban traffic congestion mitigation. In this paper, a bi-level programming model considering carbon dioxide emission is proposed to mitigate traffic congestion and reduce carbon dioxide emissions. The objective function of the upper level model is to minimize the sum of travel costs and the carbon dioxide emissions costs. The lower level is a multi-modal transportation network equilibrium model. To solve the model, the method of successive averages (MSA) and the shuffled frog leaping algorithm (SFLA) are introduced. The proposed method and algorithm are tested through the numerical example. The results show that the proposed congestion pricing strategy can mitigate traffic congestion and reduce carbon emissions effectively.

Optimization for Multiclass Residential Location Models with Congestible Transportation Networks

There has been extensive research on the optimal design of road congestion pricing schemes based on traffic network equilibrium models. Since travel demands are closely related to the locations of workplace and residence, it is of both theoretical and practical importance to extend the optimization approach to treat location choices as well as travel route choices. This paper addresses the problem of optimal location of housing supply and transportation network pricing in an integrated location and transportation model with heterogeneous households that have different values of travel time. In the model, housing exhibits external economies and diseconomies of scale, depending on the population density in residential zones. It is shown that in such a model the marginal cost pricing principle applied to internalize these externalities in general results in local optima of social surplus. Optimization algorithms based on sensitivity analysis are proposed to find combinations of housing supply patterns and road tolls that optimize social surplus under various conditions. A numerical example is given to illustrate the proposed method. The potential policy implications of the optimization approach are examined and compared with that of the marginal cost pricing principle.

Stochastic Congestion Pricing among Multiple Regions: Competition and Cooperation

Previous studies of road congestion pricing problem assume that transportation networks are managed by a central administrative authority with an objective of improving the performance of the whole network. In practice, a transportation network may be comprised of multiple independent local regions with relative independent objectives. In this paper, we investigate the cooperative and competitive behaviors among multiple regions in congestion pricing considering stochastic conditions; especially demand uncertainty is taken into account in transportation modelling. The corresponding congestion pricing models are formulated as a bilevel programming problem. In the upper level, congestion pricing model either aims to maximize the regional social welfare in competitive schemes or attempts to maximize the total social welfare of multiple regions in cooperative schemes. In the lower level, travellers are assumed to follow a reliability-based stochastic user equilibrium principle considering risks of late arrival under uncertain conditions. Numerical examples are carried out to compare the effects of different pricing schemes and to analyze the impact of travel time reliability. It is found that cooperative pricing strategy performs better than competitive strategy in improving network performance, and the pricing effects of both schemes are quite sensitive to travel time reliability.

Model of Urban Road Congestion Pricing Based on Game Theory

This paper proposes a game-based model to conduct the issue of road congestion pricing. The ride comfort of travel modes, e.g., cars or buses, is introduced into the travel cost function of the traditional bottleneck model. Furthermore, based on different travel cost functions of various travel modes, the Nash equilibriums are achieved among the government and various travelers. The results can be employed to describe internality and externality of traffic system respectively. Finally, numerical examples are presented. The findings of our work indicate a relationship between the government’s goal and the charge rate and that the emergence of ride comfort obviously is a key determinant of travelers’ behavior.

Winning or losing from dynamic bottleneck congestion pricing?: The distributional effects of road pricing with heterogeneity in values of time and schedule delay

This paper analyses the efficiency and distributional impacts of congestion pricing in Vickrey's (1969) dynamic bottleneck model of congestion, allowing for continuous distributions of values of time and schedule delay. We find that congestion pricing can leave a majority of travellers better off even without returning the toll revenues to them. We also find that the consumer surplus losses or gains from tolling are not strictly monotonic in the value of time, because they also depend on the value of schedule delays. The greatest losses are not incurred by drivers with the lowest value of time, but by users with an intermediate value of schedule delays and the lowest value of time for that value of schedule delays. For second-best pricing with an untolled alternative, the pattern of distributional effects is quite similar to that for first-best pricing. In contrast with results from prior static models, users who are indifferent between the two alternative routes are not the ones who gain least from this type of second-best pricing. Our results suggest that, in assessing the distributional impacts of road congestion pricing, it is important to take into account both the distribution of the value of time and of the value of schedule delays, as well as the dynamics of departure time choice.