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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.

Ridesharing is one of the effective urban traffic supply and demand management policies to reduce car ownership and mitigate traffic congestion. The origin-destination (OD) based surge pricing strategy is widely adopted by ridesharing service operators in practice due to its fairness and effectiveness. In this study, we aim to investigate the ridesharing user equilibrium (RUE) problem for an urban transportation network under the OD-based surge pricing strategy. We first build a variational inequality (VI) model for the proposed RUE problem. In particular, we explicitly formulate the necessary ride-matching constraints for the participants of multiple ridesharing services and rigorously demonstrate the existence and uniqueness of the RUE solution under some mild conditions. A parallel self-adaptive projection method (PSPM) incorporating column generation is developed to find an RUE solution for the large-scale problems. Finally, numerical experiments are conducted to provide valuable insights and examine the effectiveness of the proposed solution method. The results quantitatively show that the ridesharing under the OD-based surge pricing strategy reduces not only the travel cost for travelers but also the deliberate detours. Traffic congestion is significantly mitigated by ridesharing. Moreover, the proposed solution method has satisfactory computational efficiency for solving the large-scale problems.

Travel demand management (TDM) is an important measure that will aid in the realization of efficient and sustainable transportation systems. However, in cities where the most serious traffic congestion occurs, implementation of a single TDM measure might not be enough to reduce congestion, because the congestion mechanism in this case is highly complex and involves different transportation modes interacting with each other. Implementation of multiple TDM measures has rarely been discussed in the literature. Therefore, in this study, we propose a simulation-based joint optimization framework composed of dedicated bus lanes and vehicular congestion pricing. The objective of the optimization process is to minimize the congestion cost based on an advanced macroscopic flow theory called the multimodal macroscopic fundamental diagram (mMFD), which can capture the macroscopic traffic dynamics of multimodal transportation systems. In the proposed framework, we develop mMFD-based congestion pricing scheme and incorporate traveler’s behavioral model (i.e. joint departure time and mode choices) with the microscopic traffic simulator. We consider the Tokyo central area as a case study. The simulation results indicate that space allocation of 4.7% for the dedicated bus lanes would be optimal for Tokyo’s network, while the optimal congestion pricing scheme indicates that charges of 900 JPY between 7:30 and 8:00 AM and 300 JPY between 8.00 and 8:30 AM should be levied.

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.

It is generally supposed that the most credible approach to mitigating urban road traffic congestion is by charging for road use. Congestion charging has been in operation in three major cities for some years. The most extensive data is available for London, where the introduction of charging led to an immediate decrease in both traffic and delays; however, congestion returned to previous levels over subsequent years. A similar initial decrease in traffic and delays was observed in Stockholm. In Singapore, where the cost of car ownership is substantial, charging has enabled acceptable traffic speeds to be maintained. Evidence from these cities provides little support for the general use of congestion charging to limit demand for car use in urban areas. Congestion is therefore difficult to mitigate, but is also self-limiting, given the time constraints on travel behaviour. While delays arising from congestion are not easy to alleviate, there is scope for reducing journey time uncertainty.

In US urban areas, traffic congestion remains a growing and costly problem. The Texas Transportation Institute estimated that in 2014 delays due to congestion caused an extra 7 billion extra hours in delay, leading to wasted fuel, increased environmental degradation, and high user costs. Previous studies have focused on the impacts to congestion via highway toll lanes and the income distributional effects of tolling. While tolling can be effective in reducing congestion levels, these tolls have been found to be at least as regressive as fuel taxes. Further, these studies do not investigate the impacts tolling has on rural households compared to urban households. This study presents a per-mile congestion tax as an alternative policy tool to congestion tolling. Using a Suits Index with the Oregon Households Activity Survey, we find a per-mile congestion charge to be progressive on income. This suggests that the administratively challenging toll revenue redistribution payments that would be necessary to make congestion tolling progressive would not be needed. At the same time, however, the Suits Index suggests that a per-mile congestion charge would be regressive with regards to rural households. Econometric models are estimated to further support and explain these outcomes. While rural households, in general, drive more during peak hours compared to urban households, we find that low-income rural households are less likely to drive during these periods than wealthier rural households.

This paper addresses the equilibrium traffic assignment problem involving battery electric vehicles (BEVs) with flow-dependent electricity consumption. Due to the limited driving range and the costly/time-consuming recharging process required by current BEVs, as well as the scarce availability of battery charging/swapping stations, BEV drivers usually experience fear that their batteries may run out of power en route. Therefore, when choosing routes, BEV drivers not only try to minimize their travel costs, but also have to consider the feasibility of their routes. Moreover, considering the potential impact of traffic congestion on the electricity consumption of BEVs, the feasibility of routes may be determined endogenously rather than exogenously. A set of user equilibrium (UE) conditions from the literature is first presented to describe the route choice behaviors of BEV drivers considering flow-dependent electricity consumption. The UE conditions are then formulated as a nonlinear complementarity model. The model is further formulated as a variational inequality (VI) model and is solved using an iterative solution procedure. Numerical examples are provided to demonstrate the proposed models and solution algorithms. Discussions of how to evaluate and improve the system performance with non-unique link flow distribution are offered. A robust congestion pricing model is formulated to obtain a pricing scheme that minimizes the system travel cost under the worst-case tolled flow distribution. Finally, a further extension of the mathematical formulation for the UE conditions is provided.

A variety of innovative value pricing schemes have been applied in the US to mitigate traffic congestion in a sustainable manner. This paper proposes a new concept, called Multilevel Premium Highways (MPH), to tackle traffic congestion with different options addressing travelers' diverse needs. The new concept is featured by: (i) a fully priced highway with hierarchical speeds designated on different lanes; (ii) a discriminated toll strategy; and (iii) a package of complementary services directly funded by the toll revenues. To examine the effectiveness of MPH, this paper firstly develops a qualitative comparison with the conventional value pricing (VP) schemes, and conducts a spreadsheet-based benefit-cost analysis under two scenarios with the realistic input data, and then performs a sensitivity analysis to examine the impacts of key factors (e.g., traffic demand and travelers’ willingness-to-pay). Discrete choice models are also applied to unveil the impacts of heterogeneous travelers on the performance of MPH. Results show that MPH appears to be economically and financially sustainable for large-scale applications, although the initial settings (e.g., the designated speeds and toll values) of highway lanes influence the relative performance between MPH and the conventional VP schemes.

Motivated by the imbalance between demand (i.e., passenger requests) and supply (i.e., available vehicles) in the ride-hailing market and severe traffic congestion faced by modern cities, this dissertation aims to improve the efficiency of the sharing economy by building an agent-based methodological framework for optimal decision-making of distributed agents (e.g., autonomous shared vehicles), including passenger-seeking and route choice. Furthermore, noticing that city planners can impact the behavior of agents via some operational measures such as congestion pricing and signal control, this dissertation investigates the overall bilevel problem that involves the decision-making process of both distributed agents (i.e., the lower level) and central city planners (i.e., the upper level). First of all, for the task of passenger-seeking, this dissertation proposes a model-based Markov decision process (MDP) approach to incorporate distinct features of e-hailing drivers. The modified MDP approach is found to outperform the baseline (i.e., the local hotspot strategy) in terms of both the rate of return and the utilization rate. Although the modified MDP approach is set up in the single-agent setting, we extend its applicability to multi-agent scenarios by a dynamic adjustment strategy of the order matching probability which is able to partially capture the competition among agents. Furthermore, noticing that the reward function is commonly assumed as some prior knowledge, this dissertation unveils the underlying reward function of the overall e-hailing driver population (i.e., 44,000 Didi drivers in Beijing) through an inverse reinforcement learning method, which paves the way for future research on discovering the underlying reward mechanism in a complex and dynamic ride-hailing market. To better incorporate the competition among agents, this dissertation develops a model-free mean-field multi-agent actor-critic algorithm for multi-driver passenger-seeking. A bilevel optimization model is then formulated with the upper level as a reward design mechanism and the lower level as a multi-agent system. We use the developed mean field multi-agent actor-critic algorithm to solve for the optimal passenger-seeking policies of distributed agents in the lower level and Bayesian optimization to solve for the optimal control of upper-level city planners. The bilevel optimization model is applied to a real-world large-scale multi-class taxi driver repositioning task with congestion pricing as the upper-level control. It is disclosed that the derived optimal toll charge can efficiently improve the objective of city planners. With agents knowingwhere to go (i.e., passenger-seeking), this dissertation then applies the bilevel optimization model to the research question of how to get there (i.e., route choice). Different from the task of passenger-seeking where the action space is always fixed-dimensional, the problem of variable action set emerges in the task of route choice. Therefore, a flow-dependent deep Q-learning algorithm is proposed to efficiently derive the optimal policies for multi-commodity multi-class agents. We demonstrate the effect of two countermeasures, namely tolling and signal control, on the behavior of travelers and show that the systematic objective of city planners can be optimized by a proper control.

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 study models a multi-modal network with ridesharing services. The developed model reproduces the scenario where travelers with their own cars may choose to be a solo-driver, a ridesharing driver, a ridesharing rider, or a public transit passenger while travelers without their own cars can only choose to be either a ridesharing rider or a public transit passenger. The developed model can capture the (clock) time-dependent choices of travelers and the evolution of traffic conditions, i.e., the within-day traffic dynamics. In particular, the within-day traffic dynamics in a city region is modeled through an aggregate traffic representation, i.e., the Macroscopic Fundamental Diagram (MFD). This paper further develops a doubly dynamical system that examines how the within-day time-dependent travelers’ choices and traffic conditions will evolve from day to day, i.e., the day-to-day dynamics. Based on the doubly dynamical framework, this paper proposes two different congestion pricing schemes that aim to reduce network congestion and improve traffic efficiency. One scheme is to price all vehicles including both solo-driving and ridesharing vehicles (for ridesharing trips, the price is shared by the driver and rider), while the other scheme prices the solo-driving vehicles only in order to encourage ridesharing. The pricing levels (under each scheme) can be determined either through an adaptive adjustment mechanism from period to period driven by observed traffic conditions, or through solving a bi-level optimization problem. Numerical studies are conducted to illustrate the models and effectiveness of the pricing schemes. The results indicate that the emerging ridesharing platform may not necessarily reduce traffic congestion, but the proposed congestion pricing schemes can effectively reduce congestion and improve system performance. While pricing solo-driving vehicles only may encourage ridesharing, it can be less effective in reducing the overall congestion when compared to pricing both solo-driving and ridesharing vehicles.

The case for some form of congestion tolling has long been made given the extent of traffic congestion in most urban transportation networks. However, there is little consensus on whether this tolling should be in the form of dollars (traditional congestion pricing schemes or price regulation) or in the form of tokens (credit based mobility schemes or quantity regulation). Although the comparison of price and quantity regulation has received significant attention in the economics community, the literature is relatively sparse in the context of transportation systems. The two systems differ not only in terms of transaction costs but also in their efficiency in dealing with demand and capacity shocks, frequent in road transport systems. This difference has been explored by economists in market contexts but the possible parallel in transport systems has largely been overlooked. This paper develops a methodology to compare the performance of price and quantity instruments under uncertainty using a simple transportation network consisting of parallel highway routes and a public transport alternative. Tolls (in dollars or tokens) are differentiated across roads and the permits or tokens can be traded across roads at fixed rates. The demand for each route is determined by a logit mixture model and the supply consists of static congestion. The comparison is based on the optimum social welfare which is computed for each instrument by solving a non-convex optimization problem involving stochastic user equilibrium constraints. Equity considerations are also examined. Numerical experiments across a wide range of demand/supply inputs lead us to the following insights. First, when the tolls in dollars and tokens can be fully adapted to demand and supply shocks (i.e. are day-to-day adaptive), both instruments are equivalent and attain the same optimum social welfare. Second, when the tolling system is not day-to-day adaptive and the supply of tokens is fixed, the quantity instrument performs better than the price instrument typically when the marginal external congestion function has a steeper slope than the demand function. Third, when the tolling system is non-adaptive, but the supply of tokens is day-to-day adaptive, the quantity instrument always outperforms the price instrument in terms of social welfare. Fourth, when the toll revenues and permits are distributed uniformly, this has a positive equity effect but the quantity instrument is more equitable than the price instrument. In view of these results, we argue the case for quantity control in the presence of uncertainty and strongly convex congestion costs.

This paper is part of an ongoing research that analyzes the feasibility of implementing “buyout” auctions for metering traffic inflow at special use lanes (such as high-occupancy/toll lanes). The term “buyout” refers to the fact that these games allow skipping the auction by paying a price. The ongoing research suggests that high consideration needs to be put on the road design before developing a game-theoretic model. It also suggests that from an operations point of view, if a “buyout lottery” system can be implemented successfully, then its conversion to a buyout auction system becomes straightforward. Therefore, this paper aims at: (1) testing a buyout lottery-based metering (LBM) system in a real case scenario, and (2) introducing a game-theoretic model that recommends to its users the strategy to adopt when deciding between paying the toll or playing the lottery. This paper introduces a microsimulation model of an LBM system that is applied to the city of Minneapolis. This model is then used to evaluate the system’s congestion and revenue effects, always assuming that drivers adopt what is referred as the “safest strategy”. In the evaluation, a set of most likely lottery scenarios is also obtained. Finally, a game-theoretic model is introduced for obtaining the best strategy. This strategy is compared with the safest strategy, in the context of the most likely lottery scenarios. The results from the microsimulation model suggest that the LBM system reduces congestion significantly and increases revenue. The best strategy obtained from the game-theoretic model presents challenges for its calculation. But in the context of the most likely scenarios, this paper suggests that drivers should adopt the safest strategy. The safest strategy may coincide with what are commonly known as “minimax regret” and “maximin” strategies.

Congestion pricing is one of the most widely contemplated methods to manage traffic congestion by charging fees for the use of roads, more where and when it is congested, and less where and when it is not. This study presents a bi-level distributed approach for optimal time-dependent congestion pricing in large networks. The bi-level procedure involves a theoretical model of dynamic congestion pricing and a distributed optimization algorithm. The bi-level toll optimization module is integrated into a testbed of hybrid departure time choice and dynamic traffic assignment simulation models for the Greater Toronto Area (GTA). The integrated system provides a unified (location- and time-specific) congestion pricing system that determines optimal tolling and evaluates its impact on road traffic congestion and travellers’ behavioural choices, including departure time and route choices. For the system’s large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel computing cluster. The system is applied to a simulation-based case study of tolling major highways in the GTA while capturing the network-wide regional effects of tolling. The travel demand and drivers’ attributes are extracted from regional household travel survey data that reflect travellers’ heterogeneity. The main results indicate that: (1) optimal variable pricing reflects congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays, (2) optimal tolls intended to manage traffic demand are significantly lower than those intended to maximize toll revenues, (3) tolled routes have different sensitivities to identical toll changes, (4) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (5) toll payers benefit from tolling even before toll revenues are spent, and (6) the optimal tolling policies determined offer a win–win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.

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.

This paper proposes a model of the ridesourcing market in presence of traffic congestion and with the provision of both solo and pooling services. Our analysis of the first-best solution shows that, under a highly congested scenario, the ridesourcing platform may enjoy non-negative profits. However, when congestion is low, the ridesourcing market must be subsidized due to low marginal costs of operation. This mirrors previous findings in the traditional taxi literature. We also demonstrate that a commission cap on the solo service combined with a congestion toll (however small) on ridesourcing vehicles can induce any desired, sustainable equilibrium under the assumption of homogeneous value of travel time and sufficient supply of homogeneous drivers. Furthermore, numerical experiments suggest that the most important problem that a regulator should address when faced with a monopoly may not be that of congestion but rather that of market power. Indeed, when congestion is high, similar to previous findings in the literature, decisions by the monopolist tend to mirror that of the regulator. This is because customers on the platform must also bear the congestion cost, which hurts the platform’s revenues. Additionally, numerical examples reveal that, even when accounting for heterogeneity in the value of travel time, the commission cap is able to achieve the second-best–whether combined with a toll or not. This confirms the effectiveness of the commission cap strategy illustrated in previous literature and provides decision makers with a simple, non-intrusive mechanism for regulating the market.

Identifying congested areas for implementing road pricing is a complicated task, especially as traffic speed and other census data may not be readily available before a detailed feasibility study is commissioned. In this study, we introduce a spatial approach to identify areas with road traffic congestion within cities. The results obtained are validated against the empirical traffic speed data in four Asian cities, i.e. Bangkok, Hong Kong, Kuala Lumpur, Singapore, from 2018 to 2020. Moreover, the findings are also compared with a more detailed conventional method of using job density to identify areas of high traffic congestion. The comparisons show that the spatial approach is both efficient and effective. Through applying the method to London, Milan and Stockholm, the study also demonstrates other potential empirical applications in reviewing the boundaries of existing congestion charging zones.

Reducing the air pollution from increases in traffic congestion in large cities and their surroundings is an important problem that requires changes in travel behavior. Road pricing is an effective tool for reducing air pollution, as reflected currently urban road pricing outcomes (Singapore, London, Stockholm and Milan). A survey was conducted based on establishing a hypothetical urban road pricing system in Madrid (a random sample size n = 1298). We developed a forecast air pollution model with time series analysis to evaluate the consequences of possible air pollution decreases in Madrid. Results reveal that the hypothetical road pricing for Madrid could have highly significant effects on decreasing air pollution outside of the city and in the inner city during the peak operating time periods of maximum congestion (morning peak hours from 7:00 to 10:00 and evening peak hours from 18:00 to 20:00). Furthermore, this system could have significant positive effects on a shift toward using public transport and non-motorized modes inside the hypothetical toll zone. This reveals that the system has a high capacity to motivate a decrease in air pollution and impose more sustainable behavior for public transport users.

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.

Congestion charging is being considered as a potential measure to address the issue of substantially increased traffic congestion and vehicle emissions in Beijing. This study assessed the impact of congestion charging on traffic and emissions in Beijing using macroscopic traffic simulation and vehicle emissions calculation. Multiple testing scenarios were developed with assumptions in different charging zone sizes, public transit service levels and charging methods. Our analysis results showed that congestion charging in Beijing may increase public transit use by approximately 13%, potentially reduce CO and HC emissions by 60–70%, and reduce NOx emissions by 35–45% within the charging zone. However, congestion charging may also result in increased travel activities and emissions outside of the charging zone and a slight increase in emissions for the entire urban area. The size of charging zone, charging method, and charging rate are key factors that directly influence the impact of congestion charging; improved public transit service needs to be considered as a complementary approach with congestion charging. This study is used by Beijing Transportation Environment and Energy Center (BTEC) as reference to support the development of Beijing’s congestion charging policy and regulation.

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...

Congestion pricing is one of the widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a framework for large-scale variable congestion pricing policy determination and evaluation. The proposed framework integrates departure time choice and route choice models within a regional dynamic traffic assignment (DTA) simulation environment. The framework addresses the impact of tolling on: (1) road traffic congestion (supply side), and (2) travelers’ choice dimensions including departure time and route choices (demand side). The framework is applied to a simulation-based case study of tolling a major freeway in Toronto while capturing the regional effects across the Greater Toronto Area (GTA). The models are developed and calibrated using regional household travel survey data that reflect the heterogeneity of travelers’ attributes. The DTA model is calibrated using actual traffic counts from the Ontario Ministry of Transportation and the City of Toronto. The case study examined two tolling scenarios: flat and variable tolling. The results indicate that: (1) more benefits are attained from variable pricing, that mirrors temporal congestion patterns, due to departure time rescheduling as opposed to predominantly re-routing only in the case of flat tolling, (2) widespread spatial and temporal re-distributions of traffic demand are observed across the regional network in response to tolling a significant, yet relatively short, expressway serving Downtown Toronto, and (3) flat tolling causes major and counterproductive rerouting patterns during peak hours, which was observed to block access to the tolled facility itself.

Airport demand management aims to mitigate air traffic congestion by limiting the imbalances between demand and capacity at busy airports through administrative measures (e.g., slot controls) or economic incentives (e.g., congestion pricing, slot auctions). This paper provides an integrated synthesis of the contributions of the fields of operations research/management science (OR/MS) and economics on the subject matter. From an operating standpoint, assessing the benefits of demand management requires estimates of airport capacity and models of airport on-time performance. From a managerial standpoint, the design of demand management mechanisms can be supported by decision-making models of flight scheduling. From an economic standpoint, the welfare impact of congestion pricing, slot controls and slot auctions depends on the market structure at the airport. This paper proposes an integrated framework that underscores the interdependencies between these operating, managerial and economic aspects to foster cross-disciplinary approaches toward more effective demand management policies at busy airports worldwide.

The second-best congestion pricing schemes including common optimum, one cordon, and multiple cordons schemes are compared with the first-best optimum pricing scheme. A cross-subsidy effect exists in these second-best pricing models. However, the scheme with more cordons will diminish the cross-subsidy and approach an efficient and equitable outcome. The relative efficiency of a cordon pricing scheme for the case of Taipei metropolis is very high. One single cordon yields excellent performance of 93% relative efficiency. There might be some factors causing the good results: the uncongested traffic condition, the linear unit distance cost in traffic flow forming a nonlinear cost function, and the trip demands with continuous space and the same destination (the central business district) in the network.

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.

Road pricing (RP) is currently under development as a way to mitigate the acute traffic congestion in Jakarta. The RP has been recognized as a powerful tool in delivering efficient road use. Despite of a well-established rationale that tackled congestion, RP policy faces lack of public support and is seen rather controversial due to charge required to enter charging zone. To encounter shortcomings of RP, we introduce a parking deposit system (PDS) as an alternative. The PDS is based on refunds to car users. Refunds are offered only on parking fees or as discounts on purchases within the charging zone. Thus, this paper explores the key influences and the extent to which RP and PDS schemes are acceptable to the public. Taking into account citizen’s consciousness based on a fictitious voting conducted in Jakarta, we estimated recursive simultaneous bivariate voting models to link perceived acceptability among RP and PDS bundles. We found that there is a complementary relationship between acceptance a...

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.

A model system was developed to investigate the feasibility of an integrated transportation demand management (TDM) programme in the Nanhai district of China by employing the multi-nomial logit and nested logit modelling methods. Using the model system, the impacts of the programme concerning residents' acceptance and behaviour adjustments in trip frequency, mode choice, departure time and route choice are predicted. The study results indicate that the programme could make a significant contribution to the improvement of traffic conditions in Nanhai. Furthermore, it is recommended that implementation of the integrated TDM programme in Nanhai be divided into two stages – stage 1 for the implementation of bus priority and motorcycle restriction policies and stage 2 for implementing a congestion pricing policy. The study results may facilitate the establishment of related TDM programmes to aid the mitigation of traffic congestion and the sustainable development of urban transportation systems.

Fast and Intertwined Regular (FAIR) highway had been proposed as a road pricing concept that distributes credits to the low-income commuters. The paper models the FAIR highway operations considering the heterogeneity of commuters and lane-by-lane variations. A bilevel programming framework is established to concurrently model the FAIR operator’s pricing strategies (at the upper level) and commuters’ mode and lane choices (at the lower level). Various operation policy scenarios are designed and tested to evaluate the FAIR highway performance on an experimental highway corridor. A series of sensitivity analyses are conducted with respect to the key conditional factors such as travel demand levels and lane-by-lane variations. Numerical results show that compared to the do-nothing case the FAIR scheme can significantly alleviate the traffic congestion and promote the use of high-occupancy vehicles. The FAIR highway, however, has two potential disadvantages: (i) the toll level is increased when subsidizing the low-income commuters and (ii) the divergence of lane utilization is aggravated on the FAIR highways compared to the do-nothing case. The study provides insights for the decision-makers and practitioners on the potential outcomes of a FAIR highway application.

This paper investigates airport determination of per-flight and per-passenger charges in a hub-spoke network. The hub airport is congestible and it levies a per-flight charge on its carriers and discriminatory per-passenger charges on the local and connecting passengers. Our main results are: (i) the socially optimal per-passenger charges should take the higher congestion contribution by connecting passengers into account, leading to a higher charge on a connecting passenger than on a local passenger; (ii) generally, the social optimum cannot be achieved when the hub only levies a per-flight charge on carriers; (iii) the optimal per-connecting passenger charge should be lower (higher, respectively) than the per-local passenger charge when the per-flight charge is large (small, respectively); and (iv) a profit-maximizing hub can impose lower per-connecting passenger charges as compared to per-local passenger charges, owing to its market power, and this possibility is further strengthened by economies of traffic density.

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.

Congestion pricing schemes have been implemented in cities worldwide as a means of addressing externalities associated with inefficient price signals in transport systems. Limited evidence exists however on the secondary impacts of these schemes, which may include both environmental and health benefits associated with a resultant reduction in motor vehicle usage. There is increasing recognition that transport behaviours may play a role as opportunistic population level targets to reduce physical inactivity. Yet limited evidence currently exists on the effectiveness of transport interventions, such as congestion pricing schemes, for improving physical activity levels. This study aims to examine the physical activity effects of congestion pricing, with the health benefits of physical activity well established. Congestion pricing schemes implemented internationally were considered as ‘natural experiments’ and evidence of modal shift from vehicle to active forms of transport or physical activity effect was reviewed. Twelve studies were included from a search of peer-reviewed and ‘grey’ literature, with overall evidence for a physical activity or modal shift effect considered weak. The quality of the available evidence was also considered to be low. This is not to say that congestion pricing schemes may not have important secondary physical activity related health benefits. Instead, this review highlights the paucity of evidence that has been collected from real-world implementation of congestion pricing schemes. Given the growing recognition of the importance of distal mediators and determinants of health and the need for an ‘all-of-government’ approach more and better quality evidence of effectiveness of transport interventions for a broad range of outcomes, including health, is required. Significant barriers to the collection of such evidence exist, with strategies for overcoming some of these barriers identified. Only with a better understanding of the full range of potential health impacts can transport policy be fully utilised as a tool for population health.

None of the airport-pricing studies have differentiated the congestion incurred in the terminals from the congestion incurred on the runways. This paper models and connects the two kinds of congestion in one joint model. This is done by adopting a deterministic bottleneck model for the terminal to describe passengers’ behavior, and a simpler static congestion model for the runway. We find that different from the results obtained in the literature, uniform airfare does not yield the first-best outcome when terminal congestion is explicitly taken into account. In particular, business passengers are at first-best charged a higher fare than leisure passengers if and only if their relative schedule-delay cost is higher. We further identify circumstances under which passengers are, given a uniform airport charge scheme, under- or over-charged with respect to the terminal charge. Furthermore, when concession surplus is added to the analysis, the airport may raise (rather than reduce) the airport charge in order to induce more business passengers who in turn will lengthen leisure passengers’ dwell time and hence increase their chance of purchasing concession goods. Finally, the impacts of terminal capacity expansion and time-varying terminal fine toll are discussed.

Real-time network control strategies such as congestion pricing have been used in a number of metropolitan areas around the world for traffic congestion mitigation. Recent advances in Global Navigation Satellite System (GNSS) technology have led to increasing interest in distance- or usage-based road pricing as an effective alternative to traditional facility-, cordon- and area-based pricing that typically rely on fixed infrastructure. In this paper, we propose the use of feature-variant clustering methods, OPTICS and HDBSCAN*, as a systematic approach for tolling zone definition to operationalize distance-based tolling schemes. Subsequently, we develop a framework for predictive distance-based toll optimization to evaluate network performance for the various tolling zone definitions derived from the aforementioned feature-variant clustering methods. In this framework, for a specific tolling zone definition, tolling function parameters are optimized using a simulation-based Dynamic Traffic Assignment (DTA) model operating within a rolling horizon scheme. Predictive optimization is integrated with the guidance information generation. Behavioral models capture drivers’ responses to the tolls in terms of trip cancellation and choices of mode, route and departure time. Experiments on the real-world Expressway and Major Arterials network of Singapore demonstrate improved effectiveness of distance-based toll optimization given tolling zone definitions derived from feature-variant clustering, compared to fixed cordon-based pricing, adaptive cordon-based pricing, as well as distance-based pricing with ad hoc tolling zone definitions. Further, the results indicate that the use of the marginal link cost tolls as a clustering feature produces the most robust tolling zone definitions and yields significant improvements in social welfare over ad hoc zone definitions and cordon-pricing. Finally, experiments on the Boston CBD network also demonstrate the effectiveness of distance-based toll optimization schemes on urban traffic networks.