Congestion Pricing Research Articles

Mode shift impacts of optimal time-dependent congestion pricing in large networks: A simulation-based case study in the greater toronto area

This paper presents a case study on commuter’s departure time, route and mode choice responses to optimized tolling in the Toronto, Ontario, Canada. The study integrates a toll optimization component into an integrated framework of econometric departure time choice, dynamic traffic assignment model, and random utility maximization (RUM) based mode choice model. An iterative optimization algorithm is used to generate optimal tolling structure which explicitly considers heterogeneous user preferences through econometric departure time choice modelling component. The mode choice component is exogenously retrofitted in an integrated econometric departure time choice modelling component and dynamic operational traffic assignment model to capture individual mode choice behaviour in response to variable congestion pricing. The integrated model results show that individuals are more likely to switch driving routes and departure times than commuting modes when making tradeoffs between schedule delay cost, travel time cost, and toll cost. It is found that the optimal toll scenario improves the network-wide travel time. The modelling framework presented in this study can test a wide range of policy scenarios including the time-variable tolling of congested highways, and high occupancy toll lanes. While the integrated framework presented here is tested at a large scale on the Greater Toronto Area (GTA), we believe the approach can be applied to large-scale networks globally where the objective is seeking the best network-wide spatial and temporal traffic distribution.

A Trial-and-Error Congestion Pricing Method for Day-to-Day Dynamic Network Flows considering Travelers’ Heterogeneous Inertia Patterns

This study proposes a trial-and-error congestion pricing method to achieve system optimum under day-to-day flow dynamics with unknown demand. Travelers are assumed to adjust their route choice day by day so that the resultant traffic flow under a trial of tolls evolves from one day to another. We rigorously demonstrate that if psychological inertia is considered in travelers’ day-to-day route choice behavior, the convergence of the proposed trial-and-error congestion pricing method can be guaranteed without requiring the observed network flows to be in user equilibrium. Furthermore, the proposed method also allows tolls to be updated at irregular time intervals, which greatly relaxes the implementation requirements of existing congestion pricing schemes in the literature. This study is very significant from a practical point of view because it provides a flexibility approach that greatly reduces the implementation time of the traditional trial-and-error congestion pricing method. Numerical experiments are conducted to validate our theoretical findings.

Smart Cities, Dumb Infrastructure: Policy-Induced Competition in Vehicle-to-Infrastructure Systems

The coinciding development of 5G wireless technology, autonomous vehicles, and smart city applications is creating commercial demand for vehicle-to-infrastructure (V2I) systems. In theory, roadside V2I systems and sensors could provide real-time services, such as supplementary mapping information to autonomous vehicles, traffic detection, and congestion pricing. Although federal funding of V2I systems has ramped up in recent years, federal regulators have largely retreated from their top-down design of V2I services and devices. These trends — undirected government funding and private construction of V2I applications and devices — leave state and local transportation officials with powerful discretion over the construction and design of V2I systems. We describe and anticipate the tradeoffs that lawmakers, regulators, and state authorities will face as they budget for and fund V2I roadside networks. We find that public-private partnerships likely will have greater prominence in the construction of roadside V2I networks. Finally, we apply the “policy-induced competition model” to inform when public intervention into V2I funding and design is effective. We propose regulator adoption of an open access model for long-lasting roadside assets. This “dumb infrastructure” model promotes competition and innovation in V2I while minimizing use of regulator resources and public funding of networks.

Evaluating the impact of new congestion charging scheme using smartphone-based data: a spatial change detection study

Traffic congestion in urban areas is a challenging issue in transportation planning. Policy options have been proposed to evaluate the impacts of interventional action through change detection or before–after studies. In this research, low-cost traffic image data collected by smartphone-based application have been employed and the impact of new congestion charging scheme (CCS) upon congestion within congestion charging zone (CCZ) as well as the entire network in Tehran, the capital of Iran has been investigated. Applying statistical tests indicated the significance of change in congestion within CCZ by applying the new CCS. Differential Moran’s I as spatial autocorrelation index specified the spatial patterns of congestion between the critical time of changing the scheme on weekdays (17:00–19:00) and weekend (6:00–13:00) after implementing the new CCS. The approach in this paper can be used with a low-cost appropriate instrument to monitor the probable change in traffic congestion by introducing any new scheme or sudden change.

Congestion Pricing, Air Pollution and Children's Health

This study examines the effects of implementing a congestion tax in central Stockholm on both ambient air pollution and the population health of local children. We demonstrate that the tax reduced ambient air pollution by 5 to 10 percent, and this reduction in air pollution was associated with a significant decrease in the rate of acute asthma attacks among young children. The change in health was more gradual than the change in pollution suggesting that it may take time for the full health effects of changes in pollution to be felt. Given the sluggish adjustment of health to pollution changes, short-run estimates of the pollution reduction programs may understate the long-run health benefits.

Congestion Pricing, Air Pollution, and Children’s Health

This study examines the effects of a congestion tax in central Stockholm on ambient air pollution and the health of local children. We demonstrate that the tax reduced ambient air pollution by 5–15 percent and the rate of acute asthma attacks among young children. We do not see corresponding changes in accidents or hospitalizations for nonrespiratory conditions. As the change in health was more gradual than the change in pollution, it may take time for the full health effects of changes in pollution to materialize if the mechanism is pollution. Hence, short-run estimates of pollution reduction programs may understate long-run health benefits.

IS CONGESTION PRICING AN URBAN MOBILITY SOLUTION TO BRAZILIAN CITIES?

The congestion pricing is a measure of travel demand management to discourage car use with low public and political acceptability. In the Brazilian context, the Urban Mobility Policy considers this measure to mitigate the congestion. In this paper, we present an exploratory analysis to identify the characteristics considered important by Brazilian transport experts to the implementation of the congestion pricing. The residents assessed these characteristics to identify the preference and acceptability of this scheme in Belo Horizonte (Brazil). We obtained the data from a Web-based survey from experts and residents. With the results, we simulated scenarios reducing the number of private cars in order to evaluate the real benefits of congestion price in a real network. According to Brazilian transport experts, the congestion pricing can be an efficient and well-accepted demand management measure, if the profit subsidises the public transportation systems (bus and rail) and the infrastructure to non-motorized transportation, with active public participation in the decision-making. Considering the residents’ results, we identified favourable public acceptability considering the benefits of public transport investment from the revenue obtained out of the congestion charges. However, availability for payment has a high rejection rate, although implementation strategies are perceived as advantages. The simulation indicates the reduction of travel time (seconds/km), delay time (seconds/km), queue length (vehicle) and density time (vehicle/km) indicating congestion reduction and, consequently, improving the urban mobility. The contribution of this paper is one methodology considering the Brazilian context to evaluate the congestion-pricing scheme from experts and residents point of view. Also, the results could support the discussion about the implementation of the congestion charging in Brazilian cities.

IS CONGESTION PRICING AN URBAN MOBILITY SOLUTION TO BRAZILIAN CITIES?

The congestion pricing is a measure of travel demand management to discourage car use with low public and political acceptability. In the Brazilian context, the Urban Mobility Policy considers this measure to mitigate the congestion. In this paper, we present an exploratory analysis to identify the characteristics considered important by Brazilian transport experts to the implementation of the congestion pricing. The residents assessed these characteristics to identify the preference and acceptability of this scheme in Belo Horizonte (Brazil). We obtained the data from a Web-based survey from experts and residents. With the results, we simulated scenarios reducing the number of private cars in order to evaluate the real benefits of congestion price in a real network. According to Brazilian transport experts, the congestion pricing can be an efficient and well-accepted demand management measure, if the profit subsidises the public transportation systems (bus and rail) and the infrastructure to non-motorized transportation, with active public participation in the decision-making. Considering the residents’ results, we identified favourable public acceptability considering the benefits of public transport investment from the revenue obtained out of the congestion charges. However, availability for payment has a high rejection rate, although implementation strategies are perceived as advantages. The simulation indicates the reduction of travel time (seconds/km), delay time (seconds/km), queue length (vehicle) and density time (vehicle/km) indicating congestion reduction and, consequently, improving the urban mobility. The contribution of this paper is one methodology considering the Brazilian context to evaluate the congestion-pricing scheme from experts and residents point of view. Also, the results could support the discussion about the implementation of the congestion charging in Brazilian cities.

Pricing the Core

The possibility of a congestion charge for The Big Apple has been debated for nearly 90 years. Now, finally, it looks like it’s going to happen. Paul Willis takes a look at what form New York City’s road pricing system might take when it’s introduced, what it is meant to achieve, andwhy it has taken so long to be agreed upon

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.

Demand, Supply, and Performance of Street-Hail Taxi

Travel decisions are fundamental to understanding human mobility, urban economy, and sustainability, but measuring it is challenging and controversial. Previous studies of taxis are limited to taxi stands or hail markets at aggregate spatial units. Here we estimate the dynamic demand and supply of taxis in New York City (NYC) at street segment level, using in-vehicle Global Positioning System (GPS) data which preserve individual privacy. To this end, we model taxi demand and supply as non-stationary Poisson random fields on the road network, and pickups result from income-maximizing drivers searching for impatient passengers. With 868 million trip records of all 13,237 licensed taxis in NYC in 2009 - 2013, we show that while taxi demand are almost the same in 2011 and 2012, it declined about 2% in spring 2013, possibly caused by transportation network companies (TNCs) and fare raise. Contrary to common impression, street-hail taxis out-perform TNCs such as Uber in high-demand locations, suggesting a taxi/TNC regulation change to reduce congestion and pollution. We show that our demand estimates are stable at different supply levels and across years, a property not observed in existing matching functions. We also validate that taxi pickups can be modeled as Poisson processes. Our method is thus simple, feasible, and reliable in estimating street-hail taxi activities at a high spatial resolution; it helps quantify the ongoing discussion on congestion charges to taxis and TNCs.

Regulating TNCs: Should Uber and Lyft set their own rules?

We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) A minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings can push the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. Contrary to standard competitive labor market theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for almost all model parameters, and it occurs because the wage floor curbs TNC labor market power. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. The congestion tax has the expected impact: fares increase, wages and platform revenue decrease. We also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles.

A Study on Urban Road Congestion Charges

A Study on Urban Road Congestion Charges

Being fair or efficient? A fairness-driven modeling extension to the strategic airport slot scheduling problem

An increasing thrust of research has recently explored scheduling methods and models with a view to allocating efficiently scarce airport slots among competing airlines, a problem also known as strategic airport slot scheduling. Existing models have mainly placed the research focus on scheduling efficiency objectives, while it was only recently that fairness considerations were introduced in relevant literature. Borrowing principles from congestion pricing, our paper capitalizes on the conjecture that the intensity of congestion and delay phenomena are strongly affected not only by the demand volumes but also the temporal distribution and peaking characteristics of demand. Hence, we propose a fairness-informed extension of the strategic airport slot scheduling model aiming to ensure that each airline absorbs its “fair share” of congestion in the form of additional schedule displacement exerted onto other competing users. The results of the proposed model are demonstrated and validated in two IATA schedule coordinated airports in Portugal and Greece. The newly proposed fairness weights penalize mostly the “big contributors to congestion”, assigning them with higher displacement weights on the basis of “the contributor pays” principle. Furthermore, we assess the “cost of fairness” by quantifying the impacts of fairness-driven scheduling on efficiency and various Level of Service indicators. The main findings of our research suggest that the adoption of the proposed fairness-driven scheduling approach may come at reasonable “cost” particularly at low or moderate fairness levels as compared to the previous “fairness indifferent” case.

The role of transport supply in the acceptability of pollution charge extension. The case of Milan

Milan is one of the few cities in Europe where a congestion charge is in place. It was experimentally introduced at the beginning of 2008 as a pollution charge to enter the central area of Milan. During 2011, to confirm it and also to extend its scope, a city-wide referendum took place. Referendum results largely supported the extension.While we know well the economic rationale and the potential effects of such policies, the issue of acceptability is still quite unexplored and usually studied with limited interview campaigns, focus groups or theoretical assumptions. This referendum is an interesting occasion to study the real motivations of the acceptability of road pricing experiences in a consolidated context.This paper studies the determinants of the highly positive vote in favour of the extension of the former pollution charge into a congestion charge scheme. The scale of analysis is the aggregate result of the referendum at the neighbourhood level. The variables studied are the ideological orientation, some socio-economic variables, and the distance from the city centre. In contrast to the majority of existing literature, we also include in the analysis the effect of transport supply and previous mobility patterns, in order to verify if they were crucial to the voters’ acceptance of a road charge.

Determining factors affecting congestion pricing acceptability

Being aware of the factors affecting users' willingness to accept congestion pricing and pay for central zone usage is of key importance for the successful implementation of the congestion pricing system. Thus, the aim of this paper is to analyze users' attitudes, determine influential variables and define users' preferences toward different congestion pricing policies in the central zone of Belgrade. This study included a comprehensive analysis of factors which can affect the congestion pricing acceptability such as socio-demographic and economic characteristics, patterns of the respondents' movements in relation to the central zone, the respondents’ perception of traffic problems in the central zone, their familiarity with congestion pricing and preferences toward different congestion pricing policies, as well as their preferences toward revenue allocation. Binary logistic regression and structural equation modelling analysis were used to develop appropriate models. The obtained results provide guidelines for decision-makers to consider when defining congestion pricing policy. By following these suggestions, public acceptability would be increased, and the successful implementation of the congestion pricing system would be ensured.

Designing a hazardous materials transportation network by a bi-level programming based on toll policies

Hazardous materials (HAZMAT) are common in industry and agriculture, playing important roles in the development of society and economy. Meanwhile, in actual operation processes, because of the long distances between the producers and supply regions of HAZMAT, a mass of HAZMAT are always being transported, which bring risk within the corresponding areas. This study aims to design a feasible and practical HAZMAT transportation network, where the risk leading to incidents is mitigated. Adopting the congestion pricing principles for reference, link tolls are taken as the decision variables, and a bi-level programming optimization model with a variation inequality is established to interpret the problem. Moreover, a double-temperature simulated annealing algorithm inserted with a diagonalization algorithm is proposed to solve the model, and necessary computational experiments are executed. The calculated results and sensitivity analyses show that the newly proposed method is effective, which is able to be rational decision reference for relevant departments.

Estimation of consistent Logit and Probit models using best, worst and best–worst choices

The paper considers random utility models that use a single common vector of random utilities for the computation of best, worst and best–worst choice probabilities, i.e. consistent models. Choice probabilities are derived for two distributions of the random terms: i.i.d. extreme value, i.e. Logit, and multivariate normal, i.e. Probit. We prove strict log-concavity of the likelihood, with respect to the coefficients of the systematic utilities, for best, worst and best–worst choice probabilities in Logit, and for best and worst choice probabilities in Probit, under a mild necessary and sufficient condition of absence of perfect multicollinearity in the matrix of alternative and individual characteristics. This condition parallels that in ordinary least squares linear regression models. The hypothesis of equality of the utility coefficients of best choice models and of worst choice models is tested with data on mode choice, collected for the assessment of user responses to urban congestion charging policies. The numerical results show, in both Logit and Probit, statistically significant differences between utility coefficients of best and worst models. The estimations based on worst choice data exhibit coefficient attenuation and higher mean values of travel time savings with larger standard errors.

Fiscal principles for self-organizing cities

In ‘ Self-organizing’ cities, decisions are based on the unhampered, peaceful, and honest choices of individuals, and governance, aside from penalizing coercive harm, is based on voluntary agreements. Self-organization has become an increasingly important topic in planning theory, as such processes enable urban systems to more effectively adapt to various stimuli and contextual needs over time. Self-organization may lead to emergent spatial configurations that are more in tune with individuals’ values and preferences than the prevailing top-down approaches. The purpose of this article is to analyse how current tax systems impede emergent spatial configurations and, additionally, to explore what kind of fiscal rules and instruments are more supportive of creative (i.e. dynamically productive) processes of self-organization. The main finding is that the use of behavioural rules (such as contractual covenants and easements) and principles of taxation that do not distort the decentralized creation of value, such as user fees, congestion charges, and repayment of rental value received such as land value taxation, are superior to currently dominant approaches.

Optimizing Traffic System Performance with Environmental Constraints: Tolls and/or Additional Delays

Environmental concerns are increasingly important targets in urban traffic management. The way traffic spreads over routes in a network can affect substantially the environments well as the level of congestion. This paper explores the selection and location of control measures that influence route choice aimed at optimizing traffic system performance subject to environmental constraints (OSP-EC problems). We address two groups of link-based control instruments commonly considered in urban traffic management: tolls and additional delays. Tolls penalize drivers monetarily (e.g. congestion charging) while additional delays represent those measures directly increasing travel time (e.g. speed limits) on the controlled links. We first identify the parallels and differences of how tolls and delays reallocate traffic flows over routes and links in a network, how they affect total system cost and total emission, and finally how they produce different solutions for the OSP-EC problem. When only tolls can be used, one intuitive solution to the OSP-EC problem is a set of tolls that superimposes environmental shadow prices for actively constrained links onto the marginal system cost pricing that is to be charged on all links. When only delays are considered, the selection of the best locations to implement control variables is more complex. We show that the monotonicity of emission factors with speed plays a crucial role for the selection of the type of control variables. If both tolls and delays are available, tolls work better than additional delays in most cases. The theoretical results can contribute to the development of efficient algorithms for solving the OSP-EC problem, and help authorities to solve this policy problem.