Congestion Pricing Research Articles

How does trip frequency distort time value to impact congestion charging schemes?

Abstract A notable feature of a city or region with close economic and social connections with its neighbours is its highly mixed local and external traffic, and in some cases the external traffic volume is almost as high as that of local traffic. Whilst local traffic volume may be largely made up of the same regular local commuters making frequent trips, external traffic from outside of the city (region) may not be the same people making regular trips to/from the city. However, from a large pool of people making infrequent trips to/from the city, the existence of external traffic is proven by data from the licence plate recognition system of road vehicles in Changde, China. The function of value of time correlated with the income/wage rate and trip frequency is exploited and verified statistically. The time value distorted by trip frequency is defined as perceived time value (PTV), which also influences the way travellers perceive any travel impedance such as congestion delay and toll charges. This paper analyses the price of anarchy (POA) when explicitly considering the travel frequency of the trip-makers and their PTV, and compares with previous analysis without considering travel frequency. We show that when travel frequency is considered, the optimal toll of congested road pricing schemes which converts road traffic flow from user equilibrium into system optimization is much lower than that without considering travel frequency. The cost of licence plate auction cannot be treated as a congestion toll, which is only a threshold of vehicle ownership. That travellers choose routes by PTV rather than TV (time value) is proven by an example of Heishipu Bridge in Changsha, Hunan Province, China.

Urban congestion pricing based on relative comfort and its impact on carbon emissions

Congestion pricing policies are implemented with the goal of alleviating urban traffic congestion during morning and evening rush hours and thus improving urban efficiency. Therefore, designing an optimum congestion pricing scheme that ensures an efficient urban traffic system while reducing carbon emissions from urban traffic is an urgent issue to be solved. To this end, an urban congestion pricing model that accounts for relative comfort is established in this study to determine congestion price, which are subsequently optimized by calculating the corresponding carbon emissions from urban traffic. After a one-hour simulation analysis of the road network in some areas of Xi'an, it was found that when the congestion pricing is 7 yuan, the overall road network speed and carbon emissions are optimal, and the average road network speed increases from 29.64 km/h to 43.31 km /h, the carbon emission is reduced by 852.4 kg. This paper can provide references for formulating policies to alleviate traffic congestion and reduce carbon emissions for countries.

Tolls vs tradable permits for managing travel on a bimodal congested network with variable capacities and demands

Congestion pricing has long been considered an efficient tool for tackling road traffic congestion, but tolls are generally unpopular. Interest is growing in tradable permits as an alternative. Tolls and tradable permits are interchangeable if travel conditions are unchanging, but not if conditions vary and tolls and permit quantities are inflexible and cannot be adapted to current conditions. We compare the allocative efficiency of tolls and tradable permits on a bimodal network under uncertainty. Road links are congestion prone and public transit may be crowded. Road traffic entering a cordon area around the downtown is controlled using either a toll or a tradable permit. Two groups of travelers can drive or take transit. Group 1 travels downtown, and if it drives it must either pay the toll or use a permit. Group 2 travels to a suburb, and can avoid the cordon by taking a bypass. All demand and cost parameters of the model can vary, either systematically or irregularly. Each parameter combination constitutes a state. Travelers learn the state in advance, and adapt their mode and route choices accordingly. A planner minimizes expected total travel costs by either setting the level of the toll or choosing the quota of permits to distribute. Two cases are considered. In the first, the toll and quota are flexible and can be adjusted to daily travel conditions. In the second, the instruments are inflexible and must be set at the same level regardless of the state. If travelers have identical preferences, the optimal flexible toll is invariant to the numbers of travelers in each group and the capacity of the link entering the cordon. The toll is robust in the sense that inflexibility causes no welfare loss if these parameters vary. By contrast, the quota is not robust.We derive a general rule for ranking the efficiency of a fixed toll and fixed quota. We then explore a numerical example. In most instances, the fixed toll outperforms the fixed quota by a significant margin although the quota can do better in some states. We also compare the welfare-distributional effects of tolls and permits, and find that suburban travelers fare better than downtown travelers from both forms of regulation.

Optimal kilometre tax for electric vehicles

We approximate the spatial and temporal distribution of the Pigouvian kilometre tax for road traffic in the most urbanized part of Sweden, with four million inhabitants and a similar “degree of urbanization” to the Netherlands and the UK, in a future scenario where most vehicles are electric. We apply the national transport model and include all links and four time-of-day periods. We find that roughly half of the vehicle kilometres travelled in Mälardalen has a marginal external cost (congestion and other external costs included) below 0.04 €/km which is below the fuel tax in 2019). The mean marginal external cost is higher, at 0.09 €/km. Our focus is not the exact numbers but the magnitudes and the vast variation across links in a country-like region: 90 percent of the revenue is collected on 10 percent of the road network. Hence, a nation-wide kilometre tax, implying high enforcement cost, is likely not the best option. Instead, the marginal external cost could probably be internalized fairly accurate by a congestion tax in the big cities in combination with for instance an ownership tax. We find that the Pigouvian tax would cover the public costs for our target road system. We relate our findings to the mainstem fiscal tax literature.

Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory

Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory

Simulation of urban transport carbon dioxide emission reduction environment economic policy in China: An integrated approach using agent-based modelling and system dynamics

Reducing greenhouse gas emissions has become a hot topic in the 21st century. As the main source of urban carbon dioxide (CO2) emissions, reducing transportation CO2 emission has also attracted great attention worldwide. To reduce traffic CO2 emissions, we propose a complex model that combines multiagent-based models and system dynamics models. From the perspective of the job-housing balance, we explore the interaction among urban socioeconomic development, low-carbon transportation, environmental economic policies (prices, charging policies, etc.), and other factors, simulate the urban traffic CO2 emissions system and policies, investigate the impact of different strategies on traffic CO2 emissions reduction and the economic advantages to the environment, and then select an optimal policy scheme. The analysis results indicate that if we want to achieve the goals of Beijing's future development planning through comprehensive industrial structure adjustment and policy adjustment, then Beijing should accelerate the implementation of the policy of collecting traffic congestion charges. The pricing of traffic congestion charges should be 40 CNY, the parking fees should be increased to 13 CNY, the starting fares of buses and subways should be increased to 2.6 CNY and 4 CNY, respectively, and taxi starting prices should be raised to 17 CNY. At the same time, through the simulation results of the coupling model, the CO2 emissions reduction effect of each scenario in 2050 is better than that of business as usual scenario (BAU). Compared with BAU, C8 scheme has the largest emission reduction, with emission reduction of 19.5975 million tons in 2050, and CO2 emissions reduction intensity of more than 50%. As a result, given a comprehensive scenario, C8 is the optimal long-term development route. Our findings serve as a foundation for key Beijing administrative departments to enhance the jobs-housing link, as well as a guide for the development and execution of environmental and economic policies to reduce traffic CO2 emissions.

How Can AI Help in Reducing Traffic in India?

The paper explores the potential of AI-based solutions to improve traffic management in India. It examines different AI-based solutions such as intelligent traffic management, predictive traffic modelling, public transportation optimization, smart parking, autonomous vehicles, carpooling, congestion charging, and traffic monitoring, and how they can help reduce traffic congestion and improve traffic flow. The research also examines the current state of AI implementation in traffic management in India, including the challenges and barriers to implementation, such as lack of standardization, lack of funding, and lack of coordination between different agencies. Current challenges and barriers to the implementation of AI-based solutions in traffic management in India include lack of standardization, lack of funding, and lack of coordination between different agencies.

Reducing Urban Traffic Congestion via Charging Price

Traffic jams are one of the major transportation problems. The United States spends USD billions to mitigate the problem, and not always with good outcomes. This problem increases and has effects on sustainable transport, such as life quality, pollution, perishables, and costs. Large cities reduce traffic jams through congestion charges. This paper aims to reduce urban traffic congestion by estimating the charge through a multivariable model. It studies the main jammed areas in Santiago, Chile. The data came from published surveys. The model evaluation included Fisher multiple regression (F) and the determination coefficient (R2). These validations showed that the model is statistically significant. They also showed that the parameter estimation was good. Finally, this model contributes to improving the Sustainable Development Goals, such as SDG 3, SDG 11, and SDG 13, which may be successfully applied to Santiago City, as well as to any city worldwide.

Bayesian optimization for congestion pricing problems: A general framework and its instability

In this study, we proposed a generic Bayesian optimization (BO) framework to solve congestion pricing problems. In the BO framework, the Gaussian process (GP) serves as a surrogate model to approximate the highly nonlinear and expensive-to-evaluate objective functions. This study reveals that GP exhibits an instability phenomenon, which inherently limits the accuracy of BO. We investigate the sources and influences of instability from the perspective of error analysis, and then propose an improved GP (IGP) model to address the instability issue. The associated improvements are twofold: matrix inversion and matrix multiplication. A tailored preconditioner is developed to reduce the matrix inversion errors. To address multiplication errors, a tailored dot product algorithm in conjunction with a GP reformulation scheme is proposed. To validate the proposed models and methods, a link-based second-best congestion pricing problem is considered as an example. The results indicate that, in comparison to benchmark approaches (the sensitivity analysis method and genetic algorithm), the proposed BO framework shows higher computational efficiency and solution accuracy. With modifications on GP, the instability phenomenon is substantially mitigated in several instances, hence enhancing the accuracy of the BO framework.

Joint Impacts of Pricing Strategies and Persuasive Information on Habitual Automobile Commuters’ Travel Mode Shift Responses

Persuasive information developed by smartphone applications is a potential tool that can be utilized in order to increase the effectiveness of the impact of pricing strategies on triggering sustainable travel mode choice behavior. In order to address the joint impacts of pricing strategies and persuasive information on habitual automobile commuters’ travel mode shift responses, a stated-preference survey was conducted in Beijing’s inner district, from which over 1000 responses were collected. Four separate multivariable multilevel logistic regression models were estimated for more and less habitual automobile commuters when subjected to congestion pricing and reward strategies. The model estimation results showed that the influence of persuasive information was more effective in promoting travel mode shifts among more habitual automobile commuters with regard to reward strategies compared to congestion pricing. The results also showed that the impact of sociodemographic characteristics, commuter travel characteristics, the amount of congestion pricing or monetary award, and types of persuasive information on travel mode shift decisions under these strategies were deemed to be significantly different between more and less habitual automobile commuters. These findings suggest that more effective reward strategies can be explored by providing personalized and differentiated travel feedback information (e.g., pollution emission information and physical activity information), particularly for less habitual automobile commuters. This study also provides some degree of insight regarding the question as to how to design future congestion pricing, i.e., with respect to formulating differentiated charge rates according to the travel characteristics of habitual automobile commuters, as well as in developing complementary persuasive information that focuses on addressing public acceptability and fairness rather than travel feedback information.

Effects of congestion charging and subsidy policy on vehicle flow and revenue with user heterogeneity.

Traffic congestion is a major issue in urban traffic networks. Both congestion charging and subsidy policy can solve traffic congestion to some extent, but which one is better? Based on this, this paper constructs a typical transit network consisting of three travel tools in four common travel modes. Travelers' values of time affect their choice of transportation in the congestion network, thus a stochastic user equilibrium model is established by considering travelers' heterogenous values of time to evaluate the effects of different combinations of congestion charging and subsidy policies on vehicle flow and revenue. Numerical results indicate that the effectiveness of congestion charging and subsidy policy in alleviating traffic congestion depends on the object of charging or subsidizing. Congestion charging for private cars can reduce traffic flow and alleviate traffic congestion, but charging for ridesharing cars does not reduce traffic flow and may even cause traffic congestion. Subsidizing public buses does not reduce traffic flow, but it can ease congestion by coordinating traffic flow on both edges of the dual-modal transport. The combination of no subsidy for public buses and charging for both private cars and ridesharing cars can obtain the greatest revenue, but it does not alleviate traffic congestion. Although the combination of charging for private cars and subsidizing public buses does not bring the most benefits, it can reduce traffic flow, and its revenue is also considerable. This study can provide quantitative decision support for the government to ease traffic congestion and improve government revenue.

What is the potential of hybrid cordon pricing policy for reducing and shifting car usage in cities?

As cities need to urgently transition out of unsustainable mobility systems, following the Avoid-Shift-Improve framework, changes in travel behavior have a top priority. Although behavior change in is a complex phenomenon, previous research informs us about the importance of urban road-usage policy measures. One such policy measure is often referred to as congestion pricing (CP). Some cities have successfully implemented CP, showing its effectiveness potential in behavior change, but wide-spread implementation lags due to challenges in societal, organizational, and political acceptance and implementability. Emerging literature on mobility credits (MC), mostly based on experiments, has brought forward the questions of acceptance, showing different possibilities for credit allocation, accumulation and exchange. In addition to behavior change, cities in transition economies might also consider monetary policies as potential solutions to financing issues. This research aims to analyze benefits and challenges of implementing a hybrid scheme that is a combination of CP and MC, developed with an expectation for policy coherence and complementarity. To determine the effects of the proposed hybrid scheme, a comparative analysis of user attitudes on congestion charging was conducted. The research was conducted using an online questionnaire, and the target group consisted of the residents of Belgrade (Serbia). Since these strategies are not implemented in Belgrade, the stated choice approach was used. The survey contained questions about the socio-demographic characteristics of the users, trip characteristics, users’ acceptability of CP and the hybrid scheme, as well as the questions on transport mode usage for commuting trips and for trips with other purposes. Based on statistical and policy analysis, it was determined that the hybrid scheme represents a suitable compromise solution which would reduce passenger car trips in the city center and while increase public acceptance for such normative policies.

Possible Congestion-Mitigating Carrot-and-Stick Transport Policies for Jerusalem

We employ calibrated and validated MATSim multi-modal model of the Jerusalem Metropolitan Area traffic for assessing the effectiveness of Demand-Responsive-Transport (DRT) with ridesharing as a possible game-changer of the existing equilibrium between public transport and private cars. We investigate the combined effect of congestion charges, parking prices, and shared DRT services on the modal split and demonstrate that the DRT is effective when introduced together with congestion charges or parking prices. The policy effectiveness depends on the choice of the deterrence mechanism – given the overall payment for entering the controlled area, the effect of congestion charges depends on the size of the area. At the same time, parking prices are equally effective for an area of any size and, thus, seem advantageous.

Airport Congestion Pricing and Capacity: Cost Recovery With Per-Flight or Per-Passenger Charges

Airport Congestion Pricing and Capacity: Cost Recovery With Per-Flight or Per-Passenger Charges

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

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

Equity Impacts of the London Congestion Charging Scheme: An Empirical Evaluation Using Synthetic Control Methods

Congestion pricing has been long-believed to effectively regulate traffic in urban city centers. Practical implementations of such policies have been hindered by concerns that they would disproportionately and adversely affect low-income groups. This paper analyzes the impacts of two price increases to the congestion charge on different income groups in Central London, making use of the synthetic control method to leverage empirical data from the UK National Travel Survey. We estimate that the highest income earners contributed to more of the revenue than the lowest income earners, making the scheme progressive in the scale of its equity impact. Although high-income travelers appeared to drop more charge-eligible trips as the price increased, their total trips to Central London did not decrease, suggesting that they were able to substitute with non-chargeable modes of travel. Low-income travelers saw large declines across both chargeable and non-chargeable modes, revealing a much lower rate of substitution. The low-income group responded more to the 2011 price increase than the 2014 one, demonstrating the diminishing ability of subsequent price increases to regulate demand.

Pricing schemes of simultaneous optimization of congestion and air pollution: a case study in Iran.

The problem of traffic congestion and the environmental issues related to air pollution are among the essential problems of urban management that metropolitan cities are trying to mitigate. Given that the contribution of motor vehicles to air pollution is significant, both goals are achieved by managing urban transport. Among the various methods of travel demand management, congestion pricing is a very efficient measure. This study tried to simultaneously increase the efficiency of the transportation network and reduce the environmental effects by using a bi-level model for the multi-modal network. For this purpose, the upper-level model minimizes the objective function, i.e., pollution emission costs and overall commuting costs. The lower level also has a transportation network model that provides the condition of user equilibrium. The genetic and Frank-Wolfe algorithms have been used to solve the bi-level programming model. Two pricing schemes, cordon-based and link-based, are used to investigate and assist policymakers. The proposed algorithm is also applied to a real-world road network in Isfahan, Iran. The results of the proposed models for different pricing strategies were compared. According to the results, both pricing schemes mitigate traffic congestion and pollution, although the reduction in pollution outside the cordon is less than inside. Demand has also shifted from the private car mode to public transportation by an average of 15%. However, link-based pricing provides better performance than cordon-based pricing. This study indicated that a higher total collected toll in link-based pricing is accompanied by a sharper reduction in congestion and pollution mitigation, which can be spent on alternative facilities and infrastructure by the municipality, such as the development of public transportation and parking.

Analysis of social acceptability in the implementation of a congestion pricing area in Senegal

Urban road pricing is presented as an effective instrument in the context of public transportation policies to improve sustainability in large cities and their surroundings. Among the main problems of its implementation is the lack of social acceptance. The present article, through a population survey, aims to analyze the social acceptability in the implementation of an urban toll in the city of Dakar to identify the effectiveness of this measure on mobility patterns and the advantages and disadvantages declared by citizens as indicators of the degree of social acceptability. The results of the research reveal a set of significant implications in the field of transportation policies that serve as the basis for strategic decision making and to ensure an adequate level of social acceptability of the proposed toll system.

Quasi revenue-neutral congestion pricing in cities: Crediting drivers to avoid city centers

This paper introduces a predictive congestion pricing method in cities wherein the tolls alter from region to region. We consider a large urban network is partitioned into multiple regions each with a well-defined Macroscopic Fundamental Diagram (MFD) where multiple routes exist between each origin and destination regions. The proposed cordon pricing method is designed to (i) minimize vehicles’ total time spent in the network and (ii) aim for a revenue-neutral tolling. A controller based on model predictive control (MPC) approach is proposed to determine the (possibly negative) optimal time- and region-varying tolls. The MPC controller comprises a regional MFD-based traffic model with no need of destination information and a long-short term memory neural network (LSTM-NN) to obtain an accurate estimation of inter-region transfer flows. Results of numerical experiments indicate the effectiveness of the proposed congestion pricing method to achieve the two objectives simultaneously, compared with No toll and reactive feedback controllers.

Providing curb availability information to delivery drivers reduces cruising for parking

Delivery vehicle drivers are experiencing increasing challenges in finding available curb space to park in urban areas, which increases instances of cruising for parking and parking in unauthorized spaces. Policies traditionally used to reduce cruising for parking for passenger vehicles, such as parking fees and congestion pricing, are not effective at changing delivery drivers’ travel and parking behaviors. Intelligent parking systems that use real-time curb availability information to better route and park vehicles can reduce cruising for parking, but they have never been tested for delivery vehicle drivers. The current study tested whether providing real-time curb availability information to delivery drivers reduces the travel time and distance spent cruising for parking. A curb parking information system deployed in a study area in Seattle, Wash., displayed real-time curb availabilities on a mobile app called OpenPark. A controlled experiment assigned drivers’ deliveries in the study area with and without access to OpenPark. The data collected showed that when curb availability information was provided to drivers, their cruising for parking time significantly decreased by 27.9 percent, and their cruising distance decreased by 12.4 percent. These results demonstrate the potential for implementing intelligent parking systems to improve the efficiency of urban logistics systems.