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Toll-based congestion pricing (CP) policies are increasingly implemented globally for alleviating road traffic congestion. Several interconnected factors affecting or induced by CP implementation include air quality/emissions, travel time, and road user safety. We sought to examine and characterize research output and patterns across several domains (e.g., health, policy acceptability) surrounding toll-based CP policies, in order to identify where research has focused and where gaps exist. We conducted a structured review and identified 2333 relevant publications, using semi-supervised and machine learning strategies combined with manual review. Annual publication counts peaked in 2015 (n = 122). Themes identified from title and abstract terms included policy implementation characteristics, advanced transportation modeling methods and approaches, and public perception and acceptability. Authorship networks indicated a lack of interdisciplinary research. Country analyses identified the US, China, and the UK as the most frequently represented countries, and underrepresentation from low-income countries. Findings indicate that research focused on specific road user types (e.g., pedestrians) and safety impacts, and equity considerations were relatively sparse compared to other topics (e.g., policy economics, public perception). Additional research on these critical topics is necessary to ensure that such policies are designed to promote positive and equitable effects on road user health and safety.

Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos, Congestion is still a big challenge for urban mobility while vehicle sharing, eCommerce and autonomous vehicles will likely increase the unit veh-km of each vehicle and the density of vehicles moving on the streets. Urban vehicle congestion pricing schemes have been taken as effective solutions to this problem. This paper first reviews the research and application cases of urban congestion pricing through recent years, although with the well-developed theoretical basis and successful practices in Singapore, London, Stockholm, Milan, etc., public acceptance and equity concerns are still the main issues for such policies’ implementation. To circumvent this shortcomings of congestion pricing, a scheme of tradable mobility credits is proposed as an alternative. As travellers are distributed mobility credits within a specific urban area, which are allowed to be traded, those with low vehicleusing demands can sell their credits to those with more demands. Therefore with this scheme, people have the incentive to reduce the using of vehicles. This paper reviews the studies on this new urban mobility management strategy and compared it with ordinary congestion pricing schemes. Finally, we conclude the gap and possible directions for future work in this area., The first author is funded by the Joint Scholarship of China Scholarship Council and Universitat Politècnica de Catalunya.

Using data on number of car commuting trips, routes and speeds, we estimate the initial generalised cost changes, traffic reductions and likely distributional impacts of a £2 congestion charge on inbound car commuting traffic in the city of Cardiff, UK. We find that the initial potential reductions in the number of commuting trips by car finishing in Cardiff are likely to be 3.7% and 11.1%, for elasticities of number of trips with respect to generalised costs of -1 and -3, respectively. We also find that higher percentage reductions of up to 14.6% to 43.7% could be achieved by also charging trips with origin and destination in Cardiff. Not charging commuting trips by car that start and finish in Cardiff, which represent over half of the car commuting traffic with destination in Cardiff, will result in the share of charge revenues contributed by car commuters coming to Cardiff from other local authorities being roughly twice as high as the share of car commuting traffic they generate in Cardiff. We also find that the charge would be regressive but the number and percentage of commuting trips by car from different local authorities are quite different, and so the number and percentage of households from these local authorities that would be negatively affected, would also be different. The regressive impacts from the charge, however, have the potential of being reverted as long as the public transport improvements planned by Cardiff Council are in place before the congestion charge is implemented. Ideally, these should be combined with a reallocation of road space to public transport, pedestrians, and cyclists. Although this is a case study, there are valuable lessons for other cities considering the introduction of a congestion charge.

Author(s): Itani, Ibrahim, MS; Cassidy, Michael J., PhD; Daganzo, Carlos F., PhD | Abstract: An agent-based, multichannel simulation of a downtown area reveals the impacts of both redistributing traffic demand with time-dependent congestion pricing, and supplying extra capacity by banning left turns. The downtown street network was idealized, and loosely resembles central Los Angeles. On the demand-side, prices were set based on time-ofday and distance traveled. On the supply side, left-turn maneuvers were prohibited at all intersections on the network. Although both traffic management measures reduced travel costs when used alone, the left-turn ban was much less effective than pricing. When combined with pricing under congested conditions, however, the left-turn ban’s effectiveness increased considerably—it more than doubled in some cases. Furthermore, the two measures combined reduced travel costs in synergistic fashion. In some cases, this synergistic effect was responsible for 30% of the cost reduction. This strong synergy suggests that turning bans should be considered as an added option when contemplating congestion pricing.

Public support for the implementation of congestion relief policies is critical for the policies’ technical and political success. To identify the personal, social, and city-level factors associated with higher acceptance towards such policies, this study uses a 2016 survey of 8178 residents from 11 cities across 10 Latin American countries collected by the Development Bank of Latin America (Corporacion Andina de Fomento or CAF). We examined support for two demand-side approaches to managing the traffic congestion externality: congestion pricing – a market-based approach, and driving restrictions or bans – a command-and-control approach. Logit regression models show that personal mobility such as owning or using a private vehicle during a respondent’s main commute trip are associated with decreased support, while higher congestion delay in one’s commute and having a young child recently diagnosed with respiratory problems increases support for either congestion relief policy. In addition, residents of cities with higher levels of median annual particulate matter and with prior experience with traffic bans expressed higher support for either policy. Residents of cities with higher income inequality supported only driving restrictions; while those of cities with higher GDP per capita had lower support only for congestion pricing. To improve the public acceptance of congestion relief policies in Latin America, policy makers could: (1) explicitly seek to mitigate the costs it brings on individuals by investing in substitutes like public transportation; (2) promote the personal and social environmental and health benefits; (3) consider beginning with temporary, pilot programs; and in the case of driving restrictions, (4) take into account city-specific conditions related to income inequality that may influence public support for the policies.

Congestion pricing is classified as a transportation demand management measure adopted to reduce impacts modern cities are suffering in terms of traffic congestion, road accidents and air and noise pollution. Such a measure allows linking road transport externalities directly to travelers producing them. The definition of proper tolling schemes enables this measure to act on demand short-term choices, forcing travellers to shift towards low impact road itineries (route diversion) and sustainable transport systems (modal diversion). This paper presents key findings in the design of pricing policies to a specific real size and complex case (city of Rome, Italy) addressing overall transport performances (on the multimodal network) and impacts (affecting the entire community), in a demand elastic context. Main contribution derives from equity matters dealt with in the impact assessment of pricing policies, so filling a gap not extensively studied in this field. Specifically, different tolling schemes have been defined from a quantitative (toll level) and spatial (city zones where pricing is implemented) viewpoint in order to guarantee equity aspects in the application of the measure: affected road users are limited to those travelling in city zones where the mass public transport network (metro and rail) is available; besides, the toll amount is related to the level of accessibility to public transport (whether only in origin/destination of the trip or in both). Results, evaluated adopting a simulation-based approach, are consistent with those obtained in other real world cases, and highlight that the adoption of a proper pricing policy in the city of Rome guarantees a demand diversion towards sustainable transport modes up to 25% for those zones directly involved by the pricing implementation; promising benefits have been observed even in a wider area, entire Province of Rome, not directly affected by the measure, in terms of reduction both of road users (up to 6%) and congestion costs (about 2%).

This article models a novel driving-day-based tradable credit scheme (DD-TCS) to alleviate urban traffic congestion. In this model, car-using allowances (in terms of the number of days in a month, termed as “credit”) are freely and uniformly allocated to all travellers, who are also allowed to trade them in a market according to his/her travel needs (e.g. driving more or fewer days than the free endowment). As opposed to most studies on TCS, this paper explicitly considers the transaction cost (e.g. information cost of finding potential traders) in the trading market. To assess the feasibility of DD-TCS, we compare it against the license plate rationing (LPR) scheme, which has been practically implemented in many cities such as Beijing and Chengdu in China. Taking the performance of LPR as a benchmark, we quantify the threshold values of the transaction cost in DD-TCS when the two schemes yield equivalent performance (in terms of the total generalized cost). In numerical studies, we also compare the DD-TCS and LPR with the no-action case and the congestion pricing case (representing the theoretical optimum). Results show that both DD-TCS and LPR outperform the no-action case under certain conditions. With small transaction cost, DD-TCS may achieve a lower system cost that can be very close to the ideal optimum. In addition, parameter analysis shows that DD-TCS performs better than LPR in a wide range of transaction cost, where the threshold values appear to account for a considerable portion of the auto travel time. This implies that DD-TCS will be more appealing than LPR in practice because a transaction cost lower than the extremely large threshold values can be easily achieved for the trading market, e.g. via a mobile platform and modern communication techniques.

Transportation demand management (TDM) covers strategies for reducing traffic congestion within the affected urban areas. Congestion pricing includes a branch of TDM strategies; among them, the entry-based cordon pricing, i.e., applying charge on entry, is the most popular because of practicality and social acceptance. Many researchers have investigated different second-best approaches for evaluations of cordon pricing plans, mostly by applying static traffic assignment methods. In this paper, a joint entry- and distance-based scheme is proposed to circumvent the deficiencies intrinsic to each. The optimal joint design is considered as the solution to an optimization problem, in which an equilibrium dynamic traffic assignment model is used to take account of flow variations and represent congestion effects more realistically. The problem is solved for the network of Sioux Falls by using an enumeration algorithm, and the solution is compared with those obtained for distinct entry- and distance-based schemes. Based on the results, the joint tolling has the best performance in reducing the total travel time of the travelers and in alleviating the congestion level inside the cordoned area, while generating a higher level of revenue from tolls. Furthermore, the numerical experiments show the unreliability of the results by static against dynamic modeling approach.

This paper studies the optimal spatial pricing for a ride-sourcing platform subject to a congestion charge. The platform determines the ride prices over the transportation network to maximize its profit, while the regulatory agency imposes the congestion charge to reduce traffic congestion in the urban core. A network economic equilibrium model is proposed to capture the intimate interactions among passenger demand, driver supply, passenger and driver waiting times, platform pricing, vehicle repositioning and flow balance over the transportation network. The overall optimal pricing problem is cast as a non-convex program. An algorithm is proposed to approximately compute its optimal solution, and a tight upper bound is established to evaluate its performance loss with respect to the globally optimal solution. Using the proposed model, we compare the impacts of three forms of congestion charge: (a) a one-directional cordon charge on ride-sourcing vehicles that enter the congestion area; (b) a bi-directional cordon charge on ride-sourcing vehicles that enter or exit the congestion area; (c) a trip-based congestion charge on all ride-sourcing trips. We show that the one-directional congestion charge not only reduces the ride-sourcing traffic in the congestion area, but also reduces the travel cost outside the congestion zone and benefits passengers in these underserved areas. We establish that in all congestion charge schemes the largest share of the tax burden is carried by the platforms, as opposed to passengers and drivers. We further show that compared to other congestion charges, the one-directional cordon charge is more effective in congestion mitigation: to achieve the same congestion-mitigation target, it imposes a smaller cost on passengers, drivers, and the platform. On the other hand, compared with the other charges, the trip-based congestion charge is more effective in revenue-raising: to raise the same tax revenue, it leads to a smaller loss to passengers, drivers, and the platform. We validate these results through realistic numerical studies for San Francisco.

As traffic congestion gets worse year by year in metropolitan areas, cities search for solutions to improve their traffic performance and reduce their environmental impacts. This paper focuses on parking pricing and congestion pricing and their short-term effects not only on traffic congestion but also on the potential revenue for a city. We develop an easy to implement multimodal macroscopic traffic and parking search model for a central area based on aggregated data at the network level. Our methodology allows us to analyze how introducing parking pricing inside a network, or a congestion toll combined with a park and ride (P+R) scheme can affect the drivers’ decision between entering the network by car (private vehicle) or using P+R instead. This decision directly influences the number of drivers using P+R, and this impacts, in turn, the traffic performance. Based on such analysis, a city can get valuable insights to evaluate whether congestion pricing is a necessity or if the traffic improvements resulting from implementing parking pricing strategies are sufficient when combined with P+R facilities. A search algorithm is used to find the best trade-off between the parking fees and the congestion toll. Any additional revenue collected through these schemes can then be used to improve public transport or the P+R facilities themselves. With minor data collection efforts and little computational costs compared to most existing parking and congestion pricing models, we illustrate our proposed framework in a case study of an area with a high parking demand for public parking spaces within the city of Zurich, Switzerland. Results show that parking pricing combined with P+R is indeed a viable option compared to congestion pricing for improving traffic performance, even if parking pricing schemes do not target all the drivers.

This paper quantifies behavioural responses to changes in the jurisdiction of a congestion charge, with a successive focus on (i) an extension and (ii) a reduction in the size of the charging zone. We exploit the unanticipated nature of both the implementation and removal of London’s Western Expansion Zone (WEZ) as quasi-natural experiments to test whether individual responses to policies are asymmetric. We use the UK Department of Transport Annual Average Daily Flow (AADF) data, which records traffic flows for seven transport modes (including cars, buses, bicycles, heavy and light goods vehicles). Using a difference-in-differences approach, we find that the introduction of the WEZ led to a 4.9% decline in road traffic flows in the new congestion charge area. These results are robust to different model specifications. HGVs traffic did not significantly change post-WEZ, which indicates that their road demand is price inelastic. The removal of the WEZ led to no significant variations in traffic. This result indicates asymmetry in behaviour with persistent changes in post-intervention traffic demand levels.

Congestion pricing is seen as an effective policy to address traffic congestion. In such policies where money, people and authorities are involved, the success generally hinges upon two factors: equity (being fair) and acceptability (to both people and authorities). The primary concern is the equity, for which “tradable credit scheme (TCS)” has been introduced and extensively studied in the literature. Nevertheless, due to the complexity of the trading schemes, the TCS has yet to find any foot in the real world. To this end, a novel idea of rewarding has substituted the trading component to be known as toll-and-subsidy scheme (TSS). The idea is to charge the drivers on some roads (toll) while rewarding them to use other alternative—and perhaps underutilized—roads (subsidy). The research of the TSS is in its infancy stage. The problem to be tackled in this study is as follows: Given a set of roads constituting a cordon line around the central business district (CBD) or across a screen line, how much toll or subsidy should be assigned to each road? The problem is first transformed into a capacitated traffic assignment problem. We employ a solution method based on augmenting the travel time of roads up to the level at which the traffic volumes do not exceed some target rates. A real dataset from the city of Winnipeg, Canada, is used as a pilot study. We then discuss policy-related applications of the TSS. It is proved in the literature that one can obtain optimal TSSs for various objectives and considerations. To this end, the non-negativity of the toll values is relaxed which results in a valid toll set. Nevertheless, the computational time is found to be of highest significance. Our method differs in the fact that the traffic volumes are bounded from the above and it is quite affordable. The main contribution is first to highlight the concept of subsidy along with traditional thought of merely toll. Second is to interpret the Lagrangian values of the capacity constraints as the values of the toll/subsidy.

Numerous cities around the world are considering the implementation of road pricing to ease urban traffic congestion, following on from the success in cities such as London and Singapore. However, policy-makers are also all too aware of the generally negative public opinion toward such measures. This study makes use of data collected in four cities (two in Sweden, one in Finland, and one in France) using a very consistent survey probing for citizens’ attitudes toward pricing. We find very strong similarities across the four cities in terms of a number of underlying attitudinal constructs that help explain people’s answers in a hypothetical referendum on congestion pricing. The similarities across cities indicate that the increase in the opinion toward congestion pricing once they are introduced is not primarily an effect of changes in underlying attitudes, changes in how the underlying attitudes influence the support for congestion pricing, or differences in anticipated versus experienced or perce...

This paper investigates optimal congestion pricing strategies using a real-world oriented agent-based simulation framework which allows for complex user behavior. The applied simulation approach accounts for iteratively learning transport users, stochastic demand, and only approximates the user equilibrium, which may be considered as closer to real-world than a model where transport users behave completely rational, have a perfect knowledge about all travel alternatives, and travel behavior strictly follows the user equilibrium. Two congestion pricing rules are developed and investigated. The first one directly builds on the Pigouvian taxation principle and computes marginal external congestion costs based on the queuing dynamics at the bottleneck links; resulting toll payments differ from agent to agent depending on the position in the queue (QCP approach). The second one uses control-theoretical elements to adjust toll levels depending on the congestion level in order to reduce or eliminate traffic congestion; resulting toll payments are the same for all travelers per time bin and road segment (LP approach). The pricing rules are applied to Vickrey's bottleneck model and the case study of the Greater Berlin area. The simulation experiments reveal that with and without mode and departure time choice, the rather simple LP rule results in a higher system welfare compared to the more complex QCP approach. The LP rule appears to better take into account the system's dynamics and the agents' learning behavior. The results also reveal that pricing significantly reduces traffic congestion, however, there is still a remaining delay, even with departure time choice. Overall, this paper points out further need for research and contributes to the exploration of optimization heuristics for real-world oriented simulation approaches.

We design a coordination mechanism for truck drivers that uses pricing-and-routing schemes that can help alleviate traffic congestion in a general transportation network. We consider the user heterogeneity in Value-Of-Time (VOT) by adopting a multi-class model with stochastic Origin-Destination (OD) demands for the truck drivers. The main characteristic of the mechanism is that the coordinator asks the truck drivers to declare their desired OD pair and pick their individual VOT from a set of $N$ available options, and guarantees that the resulting pricing-and-routing scheme is Pareto-improving, i.e. every truck driver will be better-off compared to the User Equilibrium (UE) and that every truck driver will have an incentive to truthfully declare his/her VOT, while leading to a revenue-neutral (budget balanced) on average mechanism. This approach enables us to design personalized (VOT-based) pricing-and-routing schemes. We show that the Optimum Pricing Scheme (OPS) can be calculated by solving a nonconvex optimization problem. To improve computational efficiency, we propose an Approximately Optimum Pricing Scheme (AOPS) and prove that it satisfies the aforementioned properties. Both pricing-and-routing schemes are compared to the Congestion Pricing with Uniform Revenue Refunding (CPURR) scheme through extensive simulation experiments where it is shown that OPS and AOPS achieve a much lower expected total travel time and expected total monetary cost for the users compared to the CPURR scheme, without negatively affecting the rest of the network. These results demonstrate the efficiency of personalized (VOT-based) pricing-and-routing schemes., Comment: Accepted as a Journal article in Transportation Research Part C: Emerging Technologies

A literature review reveals that economists have had limited success in promoting economically efficient transportation and environmental externality policies. Evidence shows that policy makers are more open to using taxes and cap-and-trade systems to combat climate change than levying tolls to manage traffic congestion. Although carbon taxes are too low, and caps on tradable permits too high, to induce significant Greenhouse Gas (GHG) emissions reductions, governments at all levels are starting to implement these instruments, and climate change is now on the international political agenda as the Paris Agreement demonstrates. By contrast, congestion charging is rare. One reason may be that the science of climate change has become virtually impossible to ignore. Another is that GHG concentrations are cumulative, and the consequences of climate change are global, irreversible and potentially catastrophic. Traffic congestion is localized and transient, and more of an inconvenience than a threat to life. Responsibility for transportation policy is also often divided across multiple levels of government. These differences may explain why the use of economic instruments has been more widespread in dealing with climate change than with congestion.

For a predetermined set of an upper bound of link flows, this paper proposes a price-based congestion control scheme for achieving such a restraint target of traffic flow that evolves from day to day. On each day, drivers have to pay a toll selected from a feasible set. The tolls on each day are determined by the link flows and toll charges on the previous day and the predetermined upper bound of link flows. Several properties of the dynamical system model with the control scheme are analyzed, including the invariance of its evolutionary trajectories; the equivalence between its stationary state and user equilibrium under toll charge; the uniqueness, existence, and boundedness of its stationary state; and the convergence of its evolutionary trajectories. A special case of the model and implementation of the control scheme for several alternative targets are also given. Finally, application of the model to a traffic network is demonstrated with a numerical example. The study is helpful for better understanding the mechanism of congestion control under day-to-day traffic flow dynamics.

In this paper, we investigate an area-based pricing scheme for congested multimodal urban networks with the consideration of user heterogeneity. We propose a time-dependent pricing scheme where the tolls are iteratively adjusted through a Proportional-Integral type feedback controller, based on the level of vehicular traffic congestion and traveler's behavioral adaptation to the cost of pricing. The level of congestion is described at the network level by a Macroscopic Fundamental Diagram, which has been recently applied to develop network-level traffic management strategies. Within this dynamic congestion pricing scheme, we differentiate two groups of users with respect to their value-of-time (which related to income levels). We then integrate incentives, such as improving public transport services or return part of the toll to some users, to motivate mode shift and increase the efficiency of pricing and to attain equitable savings for all users. A case study of a medium size network is carried out using an agent-based simulator. The developed pricing scheme demonstrates high efficiency in congestion reduction. Comparing to pricing schemes that utilize similar control mechanisms in literature which do not treat the adaptivity of users, the proposed pricing scheme shows higher flexibility in toll adjustment and a smooth behavioral stabilization in long-term operation. Significant differences in behavioral responses are found between the two user groups, highlighting the importance of equity treatment in the design of congestion pricing schemes. By integrating incentive programs for public transport using the collected toll revenue, more efficient pricing strategies can be developed where savings in travel time outweigh the cost of pricing, achieving substantial welfare gain. (C) 2015 Elsevier Ltd. All rights reserved.

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.

The costs of congestion can be measured using three approaches: the total costs, the marginal costs and the ‘excess burden’. Understanding variation in these measures with particular policies is important for planning and resource management. Assessing the cost distribution (e.g. according to priority routes or urban segments) is key to assessing the delivery of both transport objectives and wider social objectives. The aim of this research is to illustrate how the costs of congestion vary with policy-related demand changes around the city of Milan. The case study used is the “Cerchia dei Bastioni” (called for administrative purposes Area C). This is an old urban area within the inner centre of City of Milan network, with a ‘real life’ charging policy that is applied to private vehicles. A large number of scenarios with differing demand levels and elasticities by vehicle classes were explored and equilibrium assignment used to assign demand to the network. Alternative measures for congestion costs were calculated along with other link parameters. Further data collection, including a parallel field survey of changes in PT speed, was also undertaken. The results indicate a high degree of correlation between changes in the different measures of congestion and changes in vehicle speed (at different levels of demand). Changes in the total cost of congestion are, however, more marked than changes in the excess burden of congestion. Sub-optimal conditions appear to exist in certain parts of the network which (it is conjectured) arise as a consequence of the configuration of the network i.e. the presence of one way streets and vehicle restrictions. Identifying a more optimal network is left for further research, as is identifying the precise conditions for which vehicle speeds can be used as a proxy for changes in congestion.

A driver's daily commute involves making three types of decisions: choice of departure time, route choice and mode choice. The objective of this paper was to investigate potential impacts of implementing variable congestion charging on driver's daily commute choice. Additionally, this study focused on the departure time and route choice process and investigated Random Parameter Nested Logit model (RPNL) for the combined departure time and route choice. Software R was used for stated choice experiment design, while software BIOGEME was used for model estimation based on the collected data. Compared with the basic Multinomial Logit model (MNL), the estimation results indicated that Random Parameter Nested Logit model (RPNL) improved the model performance, due to considering inter-alternative correlations as well as commuters’ heterogeneity. The combined choice models were utilized to present commuters’ trade-offs among commute time, monetary cost and schedule delay. The results showed that under the situation without flexible work patterns, the disutility of late arrival and early departure were high, which resulted in less attractiveness for commuters to switch their departure time and further weaken the performance of implementing congestion charging. Therefore, in order to enhance the performance of a congestion charging policy for relieving traffic congestion in a community, elastic work patterns as well as other complimentary measures should be introduced in parallel.

© 2015 Elsevier Ltd. The bottleneck model of congestion with endogenous scheduling has become a standard tool of transportation economics. It provides surprising insights about the time pattern of congestion, optimal pricing, and many distinct inefficiencies of unpriced equilibria including wrong departure order with heterogeneous preferences, wrong allocation of users across links of a network, and wrong order in which parking spaces are occupied. It illuminates the roles of travel-time reliability, traffic information, and extreme congestion ("hypercongestion"). It has been developed for use in practical network planning. Future use will probably emphasize greater realism, leading to more practical applications.

In this paper we investigate a bottleneck model in which the capacity of the bottleneck is assumed stochastic and follows a uniform distribution. The commuters' departure time choice is assumed to follow the user equilibrium principle according to mean trip cost. The analytical solution of the proposed model is derived. Both the analytical and numerical results show that the capacity variability would indeed change the commuters' travel behavior by increasing the mean trip cost and lengthening the peak period. We then design congestion pricing schemes within the framework of the new stochastic bottleneck model, for both a time-varying toll and a single-step coarse toll, and prove that the proposed piecewise time-varying toll can effectively cut down, and even eliminate, the queues behind the bottleneck. We also find that the single-step coarse toll could either advance or postpone the earliest departure time. Furthermore, the numerical results show that the proposed pricing schemes can indeed improve the efficiency of the stochastic bottleneck through decreasing the system’s total travel cost.

Autonomous vehicles (AV) create new opportunities to traffic planners and policy-makers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator’s profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator’s side, the routing- and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users’ side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both, the SAV and CC mode in order to improve a city’s transport system.

Recent empirical work has suggested that there is an important distinction between short-run versus long-run scheduling behaviour of commuters, reflected in differences in values of time and schedule delays, as well as in preferred arrival moments, for the short-run versus the long-run problem. Peer et al. (2015) for example find that the average value of time when consumers form their routines in the long-run problem may exceed by a factor 6 the short-run value that governs departure time choice given these routines. For values of schedule delay, in contrast, the short-run value exceeds the long-run value, by a factor 2. And, when forming routines, consumers in fact choose a most preferred arrival time that may deviate from the value they would choose in absence of congestion because a change in routines may mean that shorter delays will be encountered. This paper investigates whether this distinction between short-run and long-run scheduling decisions affect optimal pricing of a congestible facility. Using a stochastic dynamic model of flow congestion for describing short-run equilibria and integrating it with a dynamic model of routine formation, it is found that consistent application of short-run first-best optimal congestion pricing does not optimally decentralize the optimal formation of routines in the long-run problem. A separate instrument, next to road pricing, is therefore needed to optimize routine formation.

This study describes the result of six transportation demand management (TDM) implementations in three major traffic concentration periods of 2009-2010 with an expressway pre-registration and incentive scheme for electronic toll collection (ETC) vehicles in order to mitigate traffic congestion of the Tomei Expressway in Japan. The new TDM scheme offers an incentive to a limited number of pre-registered target travelers selected by drawings, from the viewpoint of reducing revenue loss, after confirming the eligibility of their travel as specified by the new TDM scheme with the ETC data. This scheme is considered more useful than the early applied TDM scheme with toll discount. The operator can control the total cost in the new TDM scheme, avoiding the large revenue loss incurred by the operator because of the toll discount in the early TDM scheme. The new scheme also has the advantage of easily conducting web surveys of the registrants' behavioural changes with regard to the TDM scheme. The characteristics of travelers' behavioural changes and the implementation effects of the TDM scheme are described.

The cordon-based Electronic Road Pricing (ERP) system in Singapore adopts the average travel speed as an index for evaluating the traffic congestion within a cordon area, and the maintenance of the average travel speed within a satisfactory range is taken as the objective of the toll adjustment. To formulate this practical speed-based toll design problem, this paper proposes a mathematical programming with equilibrium constraint (MPEC) model with the objective of maintaining the traffic condition in the cordon area. In the model, the network users’ route choice behavior is assumed to follow probit-based stochastic user equilibrium with elastic demand, asymmetric link travel time functions and continuous value-of-time. A distributed revised genetic algorithm is designed for solving the MPEC model. Finally, a network example based on the ERP system is adopted to numerically validate the proposed models and algorithms, and further indicates that the computation speed can be improved greatly by using a distributed computing system.

The morning commute problem for a single bottleneck is extended to model mode choice in an urban area with time-dependent demand. This extension recognizes that street space is shared by cars and public transit. It is assumed that transit is operated independently of traffic conditions, and that when it is operated it consumes a fixed amount of space. As a first step, a single fixed-capacity bottleneck that can serve both cars and transit is studied. Commuters choose which mode to use and when to travel in order to minimize the generalized cost of their own trip. The transit agency chooses the headway and when to operate. Transit operations reduce the bottleneck’s capacity for cars by a fixed amount. The following results are shown for this type of bottleneck: 1. If the transit agency charges a fixed fare and operates at a given headway, and only when there is demand, then there is a unique user equilibrium. 2. If the transit agency chooses its headway and time of operation for the common good, then there is a unique system optimum. 3. Time-dependent prices exist to achieve system optimum. Finally, it is also shown that results 2 and 3 apply to urban networks.

Standard textbook analyses of road pricing tend to assume that users are homogenous, that there is no travel time risk, and to have a view of congestion as static. The simple analysis also ignores that real pricing schemes are only rough approximations to ideal systems and that the general economic context may also have implications for optimal pricing. This paper reviews these issues and discusses how taking them into account may affect estimates of optimal tolls.

Pricing is considered an effective management policy to reduce traffic congestion in transportation networks. In this paper we combine a macroscopic model of traffic congestion in urban networks with an agent-based simulator to study congestion pricing schemes. The macroscopic model, which has been tested with real data in previous studies, represents an accurate and robust approach to model the dynamics of congestion. The agent-based simulator can reproduce the complexity of travel behavior in terms of travelers’ choices and heterogeneity. This integrated approach is superior to traditional pricing schemes. On one hand, traffic simulators (including car-following, lane-changing and route choice models) consider travel behavior, i.e. departure time choice, inelastic to the level of congestion. On the other hand, most congestion pricing models utilize supply models insensitive to demand fluctuations and non-stationary conditions. This is not consistent with the physics of traffic and the dynamics of congestion. Furthermore, works that integrate the above features in pricing models are assuming deterministic and homogeneous population characteristics. In this paper, we first demonstrate by case studies in Zurich urban road network, that the output of a agent-based simulator is consistent with the physics of traffic flow dynamics, as defined by a Macroscopic Fundamental Diagram (MFD). We then develop and apply a dynamic cordon-based congestion pricing scheme, in which tolls are controlled by an MFD. And we investigate the effectiveness of the proposed pricing scheme. Results show that by applying such a congestion pricing, (i) the savings of travel time at both aggregated and disaggregated level outweigh the costs of tolling, (ii) the congestion inside the cordon area is eased while no extra congestion is generated in the neighbor area outside the cordon, (iii) tolling has stronger impact on leisure-related activities than on work-related activities, as fewer agents who perform work-related activities changed their time plans. Future work can apply the same methodology to other network-based pricing schemes, such as area-based or distance-traveled-based pricing. Equity issues can be investigated more carefully, if provided with data such as income of agents. Value-of-time-dependent pricing schemes then can also be determined.

The congestion charging schemes in London and Singapore are compared and assessed in the light of guidelines set out in the Smeed Report, published by the UK Ministry of Transport in 1964, and their performance in reducing congestion and raising net revenue. The aim is to draw lessons for other towns and cities considering the introduction of congestion charging. One important result from Electronic Road Pricing in Singapore is that a per entry charge is more effective at reducing congestion than a perday charge. It is concluded that although Electronic Road Pricing in Singapore is more in line with the desirable properties outlined in the Smeed Report, both schemes are part of a wider package of transport policies, and that is probably the most important reason for their success. The main lesson for other towns and cities around the world considering the possibility of introducing congestion charging is that any such scheme ought to be accompanied by complementary measures that will provide motorists with a valid alternative to the car.

This article proposes a new road toll–design model for congested road networks with uncertain demand that can be used to create a sustainable urban transportation system. For policy assessment and strategic planning purposes, the proposed model extends traditional congestion pricing models to simultaneously consider congestion and environmental externalities due to vehicular use. Based on analyses of physical and environmental capacity constraints, the boundary conditions under which a road user on a link should pay either a congestion toll or an extra environmental tax are identified. The sustainable toll design model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of the future travel demand aims to minimize a risk-averse objective by determining the optimal toll. The second stage after the uncertain travel demand has been determined is a scenario-based route choice equilibrium formulation with physical and environmental capacity constrai...

The Delhi Metro (DM) is a mass rapid transit system serving the National Capital Region of India. It is also the world’s first rail project to earn carbon credits under the Clean Development Mechanism of the United Nations for reductions in CO2 emissions. Did the DM also lead to localized reduction in three transportation source pollutants? Looking at the period 2004–2006, one of the larger rail extensions of the DM led to a 34 percent reduction in localized CO at a major traffic intersection in the city. Results for NO2 are also suggestive of a decline, while those for PM25 are inconclusive due to missing data. These impacts of pollutant reductions are for the short run. A complete accounting of all long run costs and benefits should be done before building capital intensive metro rail projects.

Over the last century, the urban spatial structure of cities has transformed dramatically from the traditional monocentric configuration to varying forms of decentralized organization. This paper reviews the theory and empirical evidence to understand the urban morphology of jobs and land use within a city. This survey highlights four broad insights: (i) The evolution of monocentric to polycentric centers has been accompanied by structural changes in the city. (ii) The internal geography of a city is an outcome of the trade-off between the pull from agglomeration economies and the push from congestion. (iii) The presence of externalities implies that the equilibrium spatial organization achieved by profit-maximizing firms may not necessarily be optimal. This justifies the role of public policy in addressing the associated market failures. (iv) The productive edge and competitiveness of a city can be enhanced by introducing policies that increase the overall connectivity to take advantage of economic opportunities across the metropolitan area. The survey also puts together a wide range of policy instruments that are useful in closing the gap between equilibrium urban spatial structure and the optimal outcome.

Pricing of roadways opens doors for infrastructure financing, and congestion pricing seeks to address inefficiencies in roadway operations. This paper emphasizes the revenue-generation opportunities and welfare impacts of flat-tolling schemes, standard congestion pricing, and credit-based congestion pricing policies. While most roadway investment decisions focus on travel time savings for existing trips, this work turns to logsum differences (which quantify changes in consumer surplus) for nested logit specifications across two traveler types, two destinations, three modes and three times of day, in order to arrive at welfare- and revenue-maximizing solutions. This behavioral specification is quite flexible, and facilitates benefit-cost calculations (as well as equity analysis), as demonstrated in this paper. The various cases examined suggest significant opportunities for financing new roadway investment while addressing congestion and equity issues, with net gains for both traveler types. Application results illustrate how, even after roadway construction and maintenance costs are covered, receipts may remain to distribute to eligible travelers so that typical travelers can be made better off than if a new, non-tolled road had been constructed. Moreover, tolling both routes (new and old) results in substantially shorter payback periods (5 versus 20 years) and higher welfare outcomes (in the case of welfare-maximizing tolls with credit distributions to all travelers). The tools and techniques highlighted here illustrate practical methods for identifying welfare-enhancing and cost-recovering investment opportunities, while recognizing multiple user classes and appropriate demand elasticity across times of day, destinations, modes and routes.

This paper considers a congested bottleneck. A fast lane reserves a more than proportional share of capacity to a designated group of travelers. Travelers are otherwise identical and other travelers can use the reserved capacity when it would otherwise be idle. The paper shows that such a fast lane is always Pareto improving under Nash equilibrium in arrival times at the bottleneck and inelastic demand. It can replicate the arrival schedule and queueing outcomes of a toll that optimally charges a constant toll during part of the demand peak. Within some bounds, the fast lane scheme is still welfare improving when demand is elastic.

We consider a public and congested airport served by airlines that may have market power, and two types of travelers with different relative values of time. We find that in the absence of passenger-type-based price discrimination by airlines, it can be useful to increase the airport charge so as to protect passengers with a great relative time value from excessive congestion caused by passengers with a low relative time value. As a result, the socially efficient airport charge can be substantially higher than what we learned from the recent literature on congestion pricing with non-atomistic airlines.

This study investigates Pareto-improving congestion pricing and revenue refunding schemes in general transportation networks, which make every road user better off as compared with the situation without congestion pricing. We consider user heterogeneity in value of time (VOT) by adopting a multiclass user model with fixed origin–destination (OD) demands. We first prove that an OD and class-based Pareto-improving refunding scheme exists if and only if the total system monetary travel disutility is reduced. In view of the practical difficulty in identifying individual user’s VOT, we further investigate class-anonymous refunding schemes that give the same amount of refund to all user classes traveling between the same OD pair regardless of their VOTs. We establish a sufficient condition for the existence of such OD-specific but class-anonymous Pareto-improving refunding schemes, which needs information only on the average toll paid and average travel time for trips between each OD pair.

In this paper, we develop models for adjusting or setting fares on a transit system to encourage passengers to choose travel strategies that lead to the least travel delay for the entire system. In our problem setting, these fares vary with time of day. Similar to the one used to reduce congestion on vehicular traffic networks, our goal is to adjust or set fares so that a user equilibrium solution under the new fares yields the least delay or is system optimal. On the other hand, pricing frameworks for traffic networks such as marginal cost pricing do not readily apply because the travel delay in transit systems involves factors different from those in vehicular traffic and cannot be expressed in closed functional forms. The models herein are schedule based and account for loading priorities and individual vehicle capacities explicitly. Differences among the proposed models are illustrated with a small network.

The origins of a road user charging concept for central London and the development of the central London congestion charging scheme are outlined. The paper then explains the objectives and the main features of Transport for London's programme of impacts monitoring of the scheme. It summarises the main results that were obtained. Drawing on the extensive data from the monitoring programme, the paper then compares the patterns of congestion and traffic flows that were observed in the original scheme and in its western extension. This shows how congestion was initially eased as traffic levels were reduced and how it subsequently increased as the effective capacity of the road network was reduced or reallocated. The paper concludes with some reflections on the achievements of the charging scheme.

The bid-rent network equilibrium model represents the relationship between transport and the location of activities. A systematic application of the model to a real network is examined in this paper. The objective of this case study is to demonstrate the ability of the model to address the two-way relationship. A base run and two policy runs are simulated. The base run shows a numerical test conducted using survey data. The policy runs involve the introduction of a congestion charge and the release of land for housing development. Class-specific spatial behavior is obtained. This behavior is explained using network performance indices of transport impedance and locational attractiveness. Some conclusions and suggestions are also presented.

The Stockholm congestion charging trial in 2006 resulted in large effects on traffic volumes and congestion. During the trial, public opinion gradually changed from a large majority opposed to the charges to a small majority in favour of them, and a referendum resulted in the charges being reintroduced in 2007. This paper summarises effects on traffic, travel times, travel patterns etc., and discusses what lessons can be learnt from the trial and the development after the reintroduction of the charges.

By introducing the concept of speed elasticity of flow and density, this paper provides a generic characterization for a class of well-behaved equilibrium speed-flow curves that are differentiable and backward bending and possess monotone speed-density curves. We start with a comprehensive review on classical models, including Greenberg, Greenshields, and Underwood. A major limitation of these models is that the specification is uniquely determined by three road-specific parameters: the free-flow speed, the capacity flow, and the corresponding speed at the capacity flow, which makes the empirical application difficult due to lack of flexibility. Through a careful review of Del Castillo and Benitez [Del Castillo, J. M., F. G. Benitez. 1995. On the functional form of the speed-density relationship—I: General theory, II: Empirical investigation. Transportation Res. Part B 29 373–406], we have provided a new characterization for the Newell-Frankin model and made a connection between the family of rational generating functions and the Greenshields-type models. Based on three primitive regularity conditions, we derive a generic characterization for well-behaved macroscopic equilibrium speed-flow curves that integrate all classical models. Using the generic characterization, we show that the equilibrium speed-flow curves derived from the car-following models can be classified as a two-parameter extension of the Greenshields-type model. We have also made an attempt in proposing a plausible rectification to the speed-flow models from the Highway Capacity Manual 2000 so that they possess the backward-bending property. A formal linkage between the generic characterization of speed-flow curves and the theory of congestion pricing is also established.

Under a credit-based congestion pricing policy, net revenues are distributed uniformly among qualifying travelers, to partially offset toll payments. This work predicts the traffic impacts, air-quality changes, welfare effects, and system implementation costs of such a policy, as applied to the Dallas-Fort Worth (DFW) region of Texas. Joint destination-mode choice models were estimated and applied. The status quo and two marginal cost pricing (MCP) scenarios were simulated for the short and long terms, with full feedback of trip costs and times. Monetarized logsum differences suggest that marginal cost pricing of congested freeways in this urban region, followed by travel credit distribution to all workers, is welfare improving for the great majority of such travelers. Moreover, high levels of recurring congestion ( V / C ratios exceeding 1.5) are predicted to practically disappear.

In determining the marginal cost of congestion, economists have traditionally relied upon directly measuring traffic congestion on network links, disregarding any “network effects,” since the latter are difficult to estimate. While for simple networks the comparison of the network-based congestion costs with the link-based ones can be done within a theoretical framework, it is important to know whether such network effects in real large-scale networks are quantitatively significant. In this paper we use a strategic transportation planning model (START) to compare marginal congestion costs computed link-by-link with measures taking into account network effects. We find that while in aggregate network effects are not significant, congestion measured on a single link is a poor predictor of total congestion costs imposed by travel on that link. Also, we analyze the congestion proliferation effect on the network to see how congestion is distributed within an urban area.

The introduction of the congestion charge in central London on the 17th of February, 2003, led to a reduction in congestion. One factor that has not been fully analysed is the impact of the congestion charge on traffic casualties in London. Less car travel within the charging zone may result in fewer traffic collisions, however, as the number of pedestrians, cyclists, and motorcyclists increased after the introduction of the congestion charge, the number of traffic casualties associated with these groups may also have increased. Reductions in congestion can also lead to faster speeds. Therefore, there could be increases in injury severity for those crashes that do occur. An intervention analysis was conducted to investigate the effect of the congestion charge on traffic casualties for motorists, pedestrians, cyclists, and motorcyclists, both within the charging zone and in areas of London outside the zone. This was done for killed and serious injuries (known as KSI in British terminology) and for slight injuries to examine whether there were any shifts in severity outcomes. Our results suggest no statistically significant effect for total casualties in London, but within the charging zone there has been a statistically significant drop in motorist casualties, and possibly an increase in cyclist casualties. There is an associated effect of an increase in casualties of motorcyclists and cyclists in some areas outside the charging zone, suggesting that changes in the design of the congestion charge may be needed to achieve reductions in casualties.