Congestion Pricing Research Articles

Potential distributional impacts of road pricing: A case study

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.

Spatial pricing in ride-sourcing markets under a congestion charge

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.

An analysis of electricity congestion price patterns in North America

The present paper illustrates the use of principal component analysis (PCA) on the congestion component of local (i.e. zonal) electricity price data to detect the most salient congestion patterns in electricity transmission grids managed by either a Regional Transmission Organization (RTO) or an Independent System Operator (ISO). Outputs from the PCA along with some data visualization tools are shown to make the identification of such patterns seamless and straightforward. An empirical analysis is conducted for three North American power systems, namely NYISO, ISO New England and PJM. Finally, a simple time series model representing the evolution of PCA scores is proposed.

Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data

BACKGROUND AND AIM: To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and results in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. METHODS: We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. RESULTS:Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. CONCLUSIONS:Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies. KEYWORDS: Traffic-related, Infectious diseases, Environmental epidemiology, Policy and practice

Welfare Implications of Congestion Pricing: Evidence from SFpark

Problem definition: Congestion pricing offers an appealing solution to urban parking problems—charging varying rates across time and space as a function of congestion may shift demand and improve allocation of limited resources. It aims to increase the accessibility of highly desired public goods and to reduce traffic caused by drivers who search for available parking spaces. At the same time, complex policies make it harder for consumers to make search-based decisions. We investigate the effect of congestion pricing on consumer and social welfare. Academic/practical relevance: This paper contributes to the theory and practice of the management of scarce resources in the public sector, where welfare is of particular interest. Methodologically, we contribute to the literature on structural estimation of dynamic spatial search models. Methodology: Using data from the City of San Francisco, both before and after the implementation of a congestion-pricing parking program, SFpark, we estimate the welfare implications of the policy. We use a dynamic spatial search model to structurally estimate consumers’ search costs, distance disutilities, price sensitivities, and trip valuations. Results: We find that congestion pricing increases consumer and social welfare by more than 4% and reduces search traffic by more than 10% in congested regions compared with fixed pricing. However, congestion pricing may hurt welfare in uncongested regions, in which the focus should be on increasing utilization. Moreover, an unnecessarily complex congestion-pricing scheme makes it difficult for consumers to make search-based decisions. We find that a simpler pricing policy may yield higher welfare than a complex one. Lastly, compared with a policy that imposes limits on parking durations, congestion pricing increases social welfare by allocating the scarce resource to consumers who value it most. Managerial implications: The insights from SFpark offer important implications for local governments that consider alternatives for managing parking and congestion and for public-sector managers who evaluate the tradeoffs between approaches to manage public resources.

Joint routing and pricing control in congested mixed autonomy networks

Routing controllability of connected and autonomous vehicles (CAVs) has been shown to reduce the adverse effects of selfish routing on the network efficiency. However, the assumption that CAV owners would readily allow themselves to be controlled externally by a central agency for the good of the system is unrealistic. In this paper, we propose a joint routing and pricing control scheme that aims to incentivize CAVs to seek centrally controlled system-optimal (SO) routing by saving on tolls while user equilibrium (UE) seeking human-driven vehicles (HVs) are subject to a congestion charge. The problem is formulated as a bi-level optimization program where the upper level optimizes the dynamic toll rates using the network fundamental diagram (NFD) and the lower level is a mixed equilibrium simulation-based dynamic traffic assignment model (SBDTA) considering different combinations of SO-seeking CAVs. We apply a feedback-based controller to solve for the optimal spatially differentiated distance-based congestion charge from which SO-seeking CAVs are exempt; but UE-seeking HVs are subject to the charge for entering the city center. To capture the distinct microscopic behavior of CAVs in the mixed autonomy traffic, we also implement an adaptive link fundamental diagram (FD) within the SBDTA model. The proposed joint control scheme encourages CAV owners to seek SO routing resulting in less total system travel time. It also discourages UE-seeking HVs from congesting the city center. We demonstrate the performance of the proposed scheme in both a small network and a large-scale network of Melbourne, Australia.

Forecasting with a joint mode/time-of-day choice model based on combined RP and SC data

The factors influencing trip departure time are taking more importance in urban planning practice since congestion is increasingly being addressed by travel demand management (TDM) strategies. In this paper we formulate and estimate a joint travel mode-departure time model for commuting trips using combined revealed preference (RP) and stated choice (SC) data. The RP data considered nine alternative modes and up to 11 time periods, and the level-of-service data were obtained at an unusual level of precision using GPS measurements. The travel time, cost and cost divided by the wage rate coefficients were fairly similar in both the RP and SC environments, suggesting equal error variances for both datasets. The only parameters that differed between each type of data were those associated with the schedule delay early (SDE) and late (SDL) variables required by Small’s Scheduling Model. This may be due to the potentially different temporal perspectives between RP choices (longer term decisions) and SC decisions, arguably shorter term given the nature of the experiment and the context presented in it (implementation of a congestion charging policy and a flexible working-hours scheme). The models were used to forecast the impacts of a hypothetic congestion charging scheme in Santiago, showing that the schedule delay coefficients derived from the SC context produced a smoother and less-peaked temporal distribution of travel demand than the RP parameters.

Coordinated pricing of coupled urban Power-Traffic Networks: The value of information sharing

The growing demand for the fast charging of electric vehicles has created stronger interdependency between the operation of urban traffic networks and power distribution networks. This paper investigates the necessity and feasible mechanism of coordinated pricing in coupled urban power-traffic networks. The retail charging pricing of the power distribution network operator and congestion pricing of the urban traffic network operator will simultaneously affect the routing and charging decision choices for electric vehicles. Then, the response of multiclass vehicles—i.e., the aggregated traffic flow and charging load—will in return affect the operation performance—i.e., power loss and traffic congestion—of the coupled networks. However, due to operators’ differing pricing goals and limited access to user information, the actual response for vehicles might deviate from the respective expectation of the two operators under isolated pricing. With the growing penetration of electric vehicles, such deviation might become more evident, calling for a focus on pricing coordination. To investigate the necessity of coordinated pricing, the Stackelberg game framework is employed to model the pricing interactions between operators (leaders) and vehicles (followers). Accounting for privacy issues, a decentralized coordination framework of the two networks is proposed for information sharing. The numerical test results confirm the negative impacts of isolated pricing and demonstrate the effectiveness of coordinated pricing. Sensitivity analysis is conducted to examine the influence of critical parameters: e.g., charging electric vehicle penetration and total travel demand.

To pool or not to pool: Equilibrium, pricing and regulation

We study a transportation network company (TNC) that offers on-demand solo and pooling e-hail services in an aggregate mobility service market, while competing with transit for passengers. The market equilibrium is established based on a spatial driver–passenger matching model that determines the passenger wait time for both solo and pooling rides. We prove, under mild conditions, this system always has an equilibrium solution. Built on the market equilibrium, three variants of pricing problems are analyzed and compared, namely, (i) profit maximization, (ii) profit maximization subject to regulatory constraints, and (iii) social welfare maximization subject to a revenue-neutral constraint. A comprehensive case study is constructed using TNC data collected in the city of Chicago. We found pooling is desirable when demand is high but supply is scarce. However, its benefit diminishes quickly as the average en-route detour time (i.e., the difference between the average duration of solo and pooling trips) increases. Without regulations, a mixed strategy—providing both solo and pooling rides—not only achieves the highest profit and trip production in most scenarios, but also gains higher social welfare. The minimum wage policy can improve social welfare in the short term. However, in the long run, the TNC could react by limiting the size of the driver pool, and consequently, render the policy counterproductive, even pushing social welfare below the unregulated level. Moreover, by maintaining the supply and demand of ride-hail at an artificially high level, the minimum wage policy tends to exacerbate traffic congestion by depressing the use of collective modes (transit and pooling). A congestion tax policy that penalizes solo rides promotes pooling, but may harm social welfare. However, it promises to increase both social welfare and pooling ratio when jointly implemented with the minimum wage policy.

On Time-Dependent Trip Distance Distribution with For-Hire Vehicle Trips in Chicago

For transportation system analysis in a new space dimension with respect to individual trips’ remaining distances, vehicle trips demand has two main components: the departure time and the trip distance. In particular, the trip distance distribution (TDD) is a direct input to the bathtub model in the new space dimension, and is a very important variable to consider in many applications, such as the development of distance-based congestion pricing strategies or mileage tax. For a good understanding of the demand pattern, both the distribution of trip initiation and trip distance should be calibrated from real data. In this paper, it is assumed that the demand pattern can be described by the joint distribution of trip distance and departure time. In other words, TDD is assumed to be time-dependent, and a calibration and validation methodology of the joint probability is proposed, based on log-likelihood maximization and the Kolmogorov–Smirnov test. The calibration method is applied to empirical for-hire vehicle trips in Chicago, and it is concluded that TDD varies more within a day than across weekdays. The hypothesis that TDD follows a negative exponential, log-normal, or Gamma distribution is rejected. However, the best fit is systematically observed for the time-dependent log-normal probability density function. In the future, other trip distributions should be considered and also non-parametric probability density estimation should be explored for a better understanding of the demand pattern.

Incorporating Travel Time Reliability in Equitable Congestion Pricing Schemes for Heterogeneous Users and Bimodal Networks

Congestion pricing is proposed as an effective travel demand management strategy to circumvent the problem of congestion and generate revenue to finance developmental projects. There are several studies focusing on optimal pricing strategies to minimize the congestion level or maximize the revenue of the system. However, with regard to equity issues, benefiting only users with higher value of time is claimed to be the main factor that prevents implementation of such policies in practice. While many studies aimed to tackle the equity issues by certain welfare analyses, most of these studies fail to fully consider realistic features of users’ behavior and the uncertainty in link travel times. Given the variability of travel time in real-world networks and the impacts of pricing policies on path travel time distributions, it is important to consider the users’ reliability valuations, in addition to their travel time valuations. Thus, the goal in this study is to find an equitable pricing scheme that minimizes the total travel time of auto users in a general bimodal network considering heterogeneous users with different values of time and reliability. A particle swarm optimization algorithm is proposed to find self-funded and Pareto-improving optimal toll values. A reliability-based user equilibrium algorithm is embedded into this optimization algorithm to assign travelers to the equilibrated paths for different user classes given toll values. The proposed approach is successfully applied to a modified Sioux Falls network to explore impacts of subsidization, congestion level, and considering travel time reliability on the pricing strategy and its effectiveness.

Mitigating traffic congestion induced by Transportation Network Companies: a policy analysis

This paper analyzes and evaluates several policies aiming to mitigate the congestion effect a Transportation Network Company (TNC) brings to bear on an idealized city that contains a dense central core surrounded by a larger periphery. The TNC offers both solo and pooling e-hail services to the users of public transport. We develop a spatial market equilibrium model over two building blocks: an aggregate congestion model describing the traffic impact of TNC operations on all travelers in the city, including private motorists, and a matching model estimating the TNC's level of service based on the interactions between riders and TNC drivers. Based on the equilibrium model, we formulate and propose solution algorithms to the optimal pricing problem, in which the TNC seeks to optimize its profit or social welfare subject to the extra costs and/or constraints imposed by the congestion mitigation policies. Three congestion mitigation policies are implemented in this study: (i) a trip-based policy that charges a congestion fee on each solo trip starting or ending in the city center; (ii) a cordon-based policy that charges TNC vehicles entering the city center with zero or one passenger; and (iii) a cruising cap policy that requires the TNC to maintain the fleet utilization ratio in the city center above a threshold. Based on a case study of Chicago, we find TNC operations may have a significant congestion effect. Failing to anticipate this effect in the pricing problem leads to sub-optimal decisions that worsen traffic congestion and hurt the TNC's profitability. Of the three policies, the trip-based policy delivers the best performance. It reduces traffic congestion modestly, keeps the TNC's level of service almost intact, and improves overall social welfare substantially. The cruising cap policy benefits private motorists, thanks to the extra congestion relief it brings about. However, because other stakeholders together suffer a much greater loss, its net impact on social welfare is negative. Paradoxically, the policy could worsen the very traffic conditions in the city center that it is designed to improve.

Quantifying responses to changes in the jurisdiction of a congestion charge: A study of the London western extension.

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.

Near-Source Risk Functions for Particulate Matter Are Critical When Assessing the Health Benefits of Local Abatement Strategies.

When mortality or other health outcomes attributable to fine particulate matter (PM2.5) are estimated, the same exposure–response function (ERF) is usually assumed regardless of the source and composition of the particles, and independently of the spatial resolution applied in the exposure model. While several recent publications indicate that ERFs based on exposure models resolving within-city gradients are steeper per concentration unit (μgm−3), the ERF for PM2.5 recommended by the World Health Organization does not reflect this observation and is heavily influenced by studies based on between-city exposure estimates. We evaluated the potential health benefits of three air pollution abatement strategies: electrification of light vehicles, reduced use of studded tires, and introduction of congestion charges in Stockholm and Gothenburg, using different ERFs. We demonstrated that using a single ERF for PM2.5 likely results in an underestimation of the effect of local measures and may be misleading when evaluating abatement strategies. We also suggest applying ERFs that distinguish between near-source and regional contributions of exposure to PM2.5. If separate ERFs are applied for near-source and regional PM2.5, congestion charges as well as a reduction of studded tire use are estimated to be associated with a significant reduction in the mortality burden in both Gothenburg and Stockholm. In some scenarios the number of premature deaths is more than 10 times higher using separate ERFs in comparison to using a single ERF irrespective of sources as recommended by the WHO. For electrification, the net change in attributable deaths is small or within the uncertainty range depending on the choice of ERF.

A Continuous Transportation Network Design Problem with the Consideration of Road Congestion Charging

This paper proposes a biobjective continuous transportation network design problem concerning road congestion charging with the consideration of speed limit. The efficiency of the traffic network and the reduction of pollution in the network environment are improved by designing a reasonable road capacity enhancement and speed limit strategy. A biobjective bilevel programming model is developed to formulate the proposed network design problem. The first target of the upper problem is the optimization of road charging efficiency, and the other target is the total cost of vehicle emissions; these objectives are required to devise the optimal road capacity enhancement scheme, speed limiting schemes for different time periods, and the road pricing scheme. The lower-level problem involving travellers’ route choice behaviours uses stochastic user equilibrium (SUE) theory. Based on the nondominated sorting genetic algorithm, which is applied to solve the bilevel programming model, a numerical example is developed to illustrate the effectiveness of the proposed model and algorithm. The results show that the implementation of congestion charging measures on the congested road sections would help to alleviate road congestion in the transportation network, effectively save transportation infrastructure investment and limited urban land resources, increase fiscal revenue, and open up new sources of funds for urban infrastructure construction.

Optimizing the number of employment subcenters to decentralize a congested city

We present a core-periphery-like multicentric urban spatial equilibrium model to explore the endogenous relationships among traffic congestion, urban population, and job decentralization, and investigate the urban optimization problem with fiscal considerations by optimizing the number of employment subcenters. The solution properties of this model, which is associated with the urban spatial structure, are analytically explored. We then compare four urban fiscal regimes to answer how the city authority should utilize fiscal instruments, such as aggregate differential rent, congestion toll, and revenue redistribution to support the construction of employment subcenters. Our results demonstrate the centralization effect of congestion toll and the decentralization effect of revenue redistribution. Revenue redistribution induces an increasing population in the suburban area, as well as a larger number of employment subcenters. Furthermore, we find that achieving the city authority's fiscal balance may not necessarily enhance the residents' utility level when congestion pricing is not implemented, depending on the unit construction cost of employment subcenters. Nevertheless, it can always improve utility for the residents when the marginal-cost congestion pricing scheme is utilized to eliminate the externality on highways. In addition, the regimes with the city authority's fiscal balance may not necessarily lead to the maximal social welfare, depending on the parameter converting residents' utility level into equivalent monetary units. We also discuss the urban optimization problem by considering a utility improving constraint and the agglomeration from production in the core, and the presence of the optimal urban population.

Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data

IntroductionTo mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. MethodsWe used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. ResultsMobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. ConclusionUsing crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

Transportation capital and its effects on the U.S. economy: A general equilibrium approach

We analyze the effect of the US transportation system on economic activity by building a quantitative dynamic general equilibrium model with a taxpayer-funded transportation capital stock. We highlight stark differences between the positive welfare effects of additional infrastructure spending in the long run, and its potentially negative effects when we account for the large transition (time and delay) costs to build. We also quantify large differences between the effects of additional infrastructure spending and efficient transportation policies, such as congestion pricing and eliminating laws that artificially inflate input prices, concluding that taxpayer-funded transportation improvements that increase GDP significantly may produce smaller welfare gains than efficient policies that increase GDP modestly.

From the public sector to the gig economy

From the public sector to the gig economy

Delineating traffic congestion zones in cities: An effective approach based on GIS

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.