Congestion Pricing Research Articles

A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City

Evaluation of the demand for emerging transportation technologies and policies can vary by time of day due to spillbacks on roadways, rescheduling of travelers’ activity patterns, and shifting to other modes that affect the level of congestion. These effects are not well-captured with static travel demand models. We calibrate and validate the first open-source multi-agent simulation model for New York City, called MATSim-NYC, to support agencies in evaluating policies such as congestion pricing. The simulation-based virtual test bed is loaded with an 8M + synthetic 2016 population calibrated in a prior study. The road network is calibrated to INRIX speed data and average annual daily traffic for a screenline along the East River crossings, resulting in average speed differences of 7.2% on freeways and 17.1% on arterials, leading to average difference of +1.8% from the East River screenline. Validation against transit stations shows an 8% difference from observed counts and median difference of 29% for select road link counts. The model is used to evaluate a congestion pricing plan proposed by the Regional Plan Association and suggests a much higher (127K) car trip reduction compared to their report (59K). The pricing policy would impact the population segment making trips within Manhattan differently from the population segment of trips outside Manhattan: benefits from congestion reduction benefit the former by about 110%+ more than the latter. The multiagent simulation can show that 37.3% of the Manhattan segment would be negatively impacted by the pricing compared to 39.9% of the non-Manhattan segment, which has implications for redistribution of congestion pricing revenues. The citywide travel consumer surplus decreases when the congestion pricing goes up from $9.18 to $14 both ways even as it increases for the Charging-related population segment. This implies that increasing pricing from $9.18 to $14 benefits Manhattanites at the expense of the rest of the city.

China’s millennial car travelers’ mode shift responses under congestion pricing and reward policies: A case study in Beijing

This paper investigates the potential impacts of congestion pricing and reward policies on migrant and resident millennial car travelers’ morning commute mode shift responses in China. A stated-preference survey developed for this study was conducted in 2017 among millennial car travelers living or working in Beijing’s inner district, from which approximately 2000 responses were collected. Separate random parameters bivariate ordered probit models were estimated for migrant and resident millennial car travelers to capture the differences between their mode shift responses, unobserved heterogeneity, and correlation between their mode shift responses to congestion pricing and reward policies. Sociodemographic characteristics, travel behavior and needs, residential location accessibility to and by transit, attitudes towards travel and congestion pricing and reward policies were found to affect millennial car travelers’ mode shift responses. In addition, the impacts of the contributing factors on the mode shift responses were very different between the congestion pricing and reward policies and between the migrant and resident millennial car travelers. The findings from the model estimation and descriptive statistics suggest that the implementation of pricing and reward policies, along with other unique regional (e.g., rapid increase in residential property price) and institutional (e.g., household registration system) characteristics, may potentially bring new challenges to migrant car travelers that may add to their economic burden and reduce their quality of life, particularly under congestion pricing policies. This study also provided insights for the design of future congestion pricing and reward policies and complementary measures that could address the travel needs of all travelers.

Mobility management using mobility credit models

Traffic congestion is one of the biggest global problems worldwide, for which appropriate solution has not been found yet. Bearing that in mind, the paper presents a new approach for solving traffic congestion through "Mobility credits" model. This model is an alternative to congestion pricing - a concept that successfully solves the problem of traffic congestion, but which is not socially and politically acceptable due to the additional costs of users. The "mobility credits" model has not been applied in practice yet, but it is the subject of numerous analyzes, in order to determine the efficiency of this model in solving the problem of congestion. The paper gives an overview of previous research regarding the "Mobility credits" model.

Planning for Congestion Pricing Policies in the Middle East: Public Acceptability and Revenue Distribution

ABSTRACT This study considered two types of congestion pricing, high-occupancy toll (HOT) lanes and cordon pricing, and the factors influencing their public acceptability if they are to be implemented in Abu Dhabi city in the United Arab Emirates (UAE). A revealed preference (RP) and stated preference (SP) surveys were conducted in the city to collect data. The public acceptability was analyzed based on respondents’ willingness to pay (WTP) a toll or cordon fee to circumvent congestion. Furthermore, the distribution of the generated revenue was analyzed as an additional indicator of public acceptability. The results indicated that trip conditions (travel speed, travel distance, and trip urgency); respondents’ age, income, employment status, and car ownership; and toll fees are significant factors in determining public acceptability of HOT lanes, while respondents’ average monthly income, age, employment status, car ownership, and saving in travel time are significant factors of determining public acceptability of cordon pricing.

Managing Travelers’ Mode Choices in the Era of Shared Mobility Through Traditional Traffic Regulation Policies

The rapid expansion of ride-hailing service has imposed a huge amount of additional traffic on urban transportation systems. To reduce congestion, different regulatory policies have been announced to control the number of ride-hailing vehicles in metropolitan areas around the world. However, these measurements, shooting at ride-hailing vehicles only, deprive non-car owners of their private-car mobility rights (endowed by ride-hailing services), therefore are debatable in themselves. Instead of imposing specialized restrictions on ride-hailing vehicles, we propose to cure the deteriorating traffic conditions with classic traffic regulation policies implemented on all road space users. In this study, based on an aggregate model with two different types of ride-hailing services, i.e., ride-sourcing and ride-sharing, we model travelers' mode choices at user equilibrium under no policy intervention to reveal the negative impacts of ride-hailing on urban transportation efficiency, and compare the different system optimum states with and without ride-hailing to demonstrate the great potential of ride-hailing in mitigating congestion. Based on the model, the impacts of three traffic regulation policies, i.e., road space rationing, congestion pricing and tradable credit schemes, and the existence of traffic regulation policies that can achieve system optimum in the presence of ride-hailing are examined.

A discrete-time second-best dynamic road pricing scheme considering the existence of multiple equilibria

When multiple equilibria exist, the desired traffic equilibrium, may not be reached through a day-to-day dynamic adjustment process. Han et al. [“Discrete-Time Day-to-Day Dynamic Congestion Pricing Scheme Considering Multiple Equilibria.” Transportation Research Part B: Methodological 104: 1–16.] proposed a day-to-day dynamic pricing scheme charged on all links to ensure the desired traffic equilibrium can be achieved even when multiple equilibria exist. However, due to many practical constraints, the second-best pricing scheme has more practical interests. This study first discussed the issues of existing congestion pricing schemes for second-best case. Then, this study developed a second-best dynamic road pricing scheme that is implemented on a dynamic subset of the road links in the network. Our rigorous theoretical proof and numerical tests prove that the dynamic pricing scheme can drive the traffic state evolution towards the desired second-best traffic user equilibrium state from any initial traffic state.

How will self-driving vehicles affect U.S. megaregion traffic? The case of the Texas Triangle

This paper anticipates the impacts of self-driving or “autonomous” vehicles (AVs), shared AVs, and Atrucks on travel across the Texas Triangle megaregion using year 2040 land use (and network) forecasts. A statewide travel demand model forecasts changes in trip generation, mode and destination choices, and thus vehicle-miles traveled (VMT), congestion, and travel patterns across the megaregion. Results suggest travelers' shifting to more distant destinations, with average person-trip distance rising from 14 to 16 miles. Within-region airline passenger travel is predicted to fall by 82%, as travelers shift to self-driving ground transport options. Without travel demand management (like credit-based congestion pricing and mandated tight headways between AVs), congestion issues will grow, due to an average 47% VMT increase, especially in the region's major cities (Houston, Dallas, San Antonio, and Austin). Automobile travel is anticipated to rise across all distance categories, with increases most evident between suburban and urban zones. Almost 9.6% of link flows will exceed capacity, relative to 4.6% of segments in the no-AV case (for year 2040). Four of the 15 freight industries are predicted to experience an increase of more than 100 million ton-miles per day, due to the introduction of Atrucks, with rising truck trades largely between Houston and other major cities.

The Cost of Traffic: Evidence from the London Congestion Charge

This paper exploits the implementation of the London Congestion Charge (LCC) to provide credible estimates on the willingness to pay to avoid traffic using the housing market. This policy curtails road traffic by imposing a fee on anyone driving into the charge zone. My results show that the LCC is associated with significant improvement in traffic conditions and housing values. Instrumenting local traffic conditions with the enforcement of the LCC, and limiting the analysis to properties proximate to the charge boundary, I find that the elasticity of housing values with respect to traffic is -0.30. These findings suggest that the consequential improvement in traffic conditions due to the LCC has generated substantial windfall of more than £3.8 billion for homeowners in the zone.

Spatial/temporal variability in transportation emissions and air quality in NYC cordon pricing

Understanding the temporal and spatial variation of air quality (AQ) impact due to congestion pricing is important since the health and economic benefits of air quality improvements depend on the distribution of traffic-related air pollution. Aiming to improve our knowledge of the AQ impacts from congestion pricing, this study integrates a disaggregate agent-based travel demand model with a hyper-local air quality model to examine emissions, air quality, and exposure. Studying congestion pricing schemes in NYC, we find that daily single-occupancy-vehicle trips to the charging area decreases by 14.5% and 24.3% under the low and high charging schemes, respectively. Correspondingly, the PM2.5 concentration decreases by 5–25% in the Central Manhattan areas in the low-toll scenario, and by more than 10% across almost all of New York City areas in the high-toll scenario. Our results indicate non-linear relations between the adaptation of travel behavior and the resulting air quality/exposure impacts.

Traffic and Welfare Impacts of Credit-Based Congestion Pricing Applications: An Austin Case Study

This study seeks smart credit-based congestion pricing (CBCP) solutions for maximally improving travelers’ welfare by varying toll levels and locations across the Austin, Texas network. Scenarios evaluated include selecting links with maximum delays by variably tolling bridges and by recognizing congestion externalities across all links. Travel demand models deliver inputs for normalized logsum differences to quantify and compare consumer surplus changes across traveler types, around the region. Results suggest limited tolling locations under four broad times of day can do more harm than good, unless travelers shift out of the PM and AM peak periods or revenues are returned to travelers as credits. When using CBCP across all congested links at congested times of day (with 10% of revenues retained to cover system administrative costs), an average net benefit of $1.61 per licensed driver per weekday is estimated, with almost all travelers benefiting. For example, 95% of the traffic analysis zones’ lowest value of travel time (VOTT) group (VOTT1 = $5/hour) are expected to benefit from the CBCP policy. Tolling at twice the difference between marginal social cost and average travel cost (on each subset of congested links) appears to benefit more people, although tolling high on various links adds to congestion elsewhere. For example: tolling Austin’s highest-delay-producing or “top 500” links is estimated to benefit 98.5% of the zones’ highest VOTT (VOTT5 = $45/hour) travelers, while raising vehicle-miles traveled by just 0.8% (as a result of more circuitous, congestion- and toll-avoiding travel).

Congestion pricing optimization for shared transport

This project includes the analysis of transportation systems by mathematically modeling the collective decision making of drivers via Nash games. This paper considers two types of traffic pricing models which capture the implication of pricing on shared transport. Both models follow a game theoretic framework and capture the perspective of private shared transport companies that aim to increase their profit while improving social welfare or users’ time cost. The first model was established for users who vary their routing choices but not their transportation modes when the demand levels are fixed. The second model is more appropriate for companies with users who optimize both their routing and choice of transportation based on the pricing policy of the system where demands are elastic. Two pricing policies can be determined by convex formulations of optimal system traffic assignment problems on simple networks with different origins and destinations. The models also show how pricing affects road users’ decisions under different parameter settings. The numerical results confirmed formulation feasibility and gave insight on how the company can have an impact on both their revenue and time cost by modifying their pricing scheme.

Determining Factor Influencing Public Acceptance in Congestion Pricing

Congestion pricing is a powerful tool for traffic demand management, which is used to divert traffic by imposing economic obstruction to the traveler. Whereas, Implementation of Congestion pricing is exaggerated issue. There are several barriers arise before the implementation of congestion pricing which influences Public attitude toward pricing policy. The Major problem associated with Congestion pricing is public acceptance, which is a crucial factor for the successful implementation of pricing policy. This paper highlight eight such factors that dominate public acceptability and different strategies adopted to improve public support. Constrain related to user and limitation of congestion pricing are discussed. This Study may help transport planners and policymakers to enhance public expectance and to examine attributes related to congestion pricing. Keywords: Congestion pricing, Public Attitude, Transport system.

On the Existence of System-Optimum–Inducing Tolling Functions for Transportation Networks

A category of congestion pricing that charges travelers based on their trips’ some particular measurable attribute has been proposed in the literature to avoid the difficulty of pricing individual links on dense urban networks. To dig into such pricing’s theoretical performance, given any readily measurable attribute, this paper aims to propose a general mathematical framework for determining the existence of the tolling functions that can induce the system optimum of a transportation network. Specifically, we first formulate tolled network equilibrium as variational inequality. Then, in the elastic demand case, we prove that determining the existence of the system-optimum–inducing tolling functions is equivalent to solving a linear programming problem. In the fixed demand case, we provide a mixed integer linear programming formulation and show that the existence is confirmed if and only if its optimal objective function value equals zero. To enhance computational efficiency, we also derive valid inequalities for the formulation. Furthermore, we identify a special case in which determining the existence in the fixed demand case can be reduced to solving a linear programming problem similarly to the one in the elastic demand case. Finally, numerical examples, using distance- and emission-based tolls, are presented to derive important insights and demonstrate the proposed approaches’ potential of solving large-scale problems.

Affected area and residual period of London Congestion Charging scheme on road safety

The London congestion charging (LCC) scheme was first introduced in 2003. It did not only help alleviate traffic congestion and reduce vehicle emissions, but also had favorable safety effect. Western Extension of LCC was applied in 2007, but then removed in 2011. It was suggested that adjacent areas of the congestion charging zone could also be benefited. As well, the benefits would not disappear immediately after the removal of congestion charging scheme, which is known as residual effect. This paper attempts to examine the affected area and residual period of the safety benefits by the LCC scheme using the traffic and crash data from 352 Middle Super Output Areas (MSOAs) of London, with which the original LCC scheme was imposed in 24 MSOAs (‘treatment’ units for Analysis I), the Western Extension scheme was imposed in 27 MSOAs (‘treatment’ units for Analysis II), and no congestion charging was implemented at all in 301 MSOAs (‘control’ units for both Analysis I and II). Factors including traffic flow, land use, built environment and population demographics are considered. To eliminate the bias by the selection of treatment and control groups, Propensity Score matching (PSM) method is applied. Results indicate that favorable effect on safety is prevalent in the 1.5 km buffer area of LCC zone. On the other hand, for the residual effect, considerable crash reduction could be found in the first year after the removal of Western Extension of LCC. However, no evidence could be established for significant crash reduction in the second and third years after the removal. Findings should be indicative to the transport management policy that could improve the road safety in the Greater London in the long run.

Who gains and who loses from congestion pricing in a monocentric city with a bottleneck?

This study develops a model in which heterogeneous commuters choose their residential locations and departure times from home in a monocentric city with a bottleneck. We show that commuters sort themselves both temporally and spatially according to their value of time and flexibility at the equilibria with and without optimal congestion pricing. These two equilibria exhibit fundamentally different properties, indicating that congestion pricing alters the urban spatial structure. We then consider two cases wherein richer commuters are either more flexible or less flexible and demonstrate that (a) congestion pricing makes cities denser and more compact in the former, whereas it causes cities to become less dense and to expand spatially in the latter; (b) in both cases, pricing helps rich commuters but hurts poor commuters. We further reveal that expanding capacity financed by the revenue from congestion pricing could be regressive in a city where richer commuters are less flexible.

Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility

Most of the existing research on shared autonomous vehicles (SAVs) and road congestion pricing have studied the short-term impact on traffic flow. These types of studies focused on the influences on mobility and ignored the long-term impacts on regional job accessibility. Given this, from the perspective of land use and transportation integration, this study explored the long-term effects of SAVs and cordon-based congestion pricing on regional land use, transportation, and job accessibility. The contributions of this study have been summarized by the following three purposes. First, to the best of the authors’ knowledge, this study was the first attempt to identify the long-term impact of the combination of these two technologies on regional job accessibility. Second, compared to the previous research methodology, this study adopted the land use and transportation integrated model (TRANUS model) and scenario planning to ensure the comprehensiveness and validity of the results. Third, this study analyzed the spatial heterogeneity of the impact of the combination of the two technologies on regional job accessibility in different areas with different built-environment attributes. To realize this purpose, this study quantitatively classified traffic analysis zones (TAZs) using data mining technology, i.e., factor analysis and clustering analysis. Results showed that the introduction of SAVs will contribute to job and population development in the charging zone and reduce the negative effect of road congestion pricing. From the perspective of reducing the average travel time between TAZs, the best strategy will be to implement SAVs alone, followed by integrated use of SAVs and road congestion pricing, while the worst strategy will be to implement the cordon-based congestion pricing policy alone. By comparison, from the perspective of improving regional job accessibility, the effect of introducing SAVs was better than that of road congestion pricing, while the combination of these two technologies was not superior to implementing SAVs alone.

Congestion pricing and capacity for internationally interlinked airports

This study investigates congestion pricing and capacity investment for an international network linking two countries’ airports and airlines. Under a Bertrand-Nash supposition for airports and Cournot competition for airlines in a duopoly, we characterize the locally optimal pricing rules for uniform and discriminatory charges levied by each country's airport. We clarify that the locally optimal pricing rules include a significant component resulting from the incentive to exploit the foreign airline, which is not included in the optimal pricing rules set by a social/global welfare-maximizing authority. We evaluate the pricing rules of the two airport charges for a round trip (i.e., the sum of each airport's per-departing-passenger charge). We clarify that the incentive to exploit the foreign airline is, theoretically, the only source of deviation from global welfare maximization (which may reach the first-best outcome) regardless of the uniform or discriminatory charges’ pricing regimes. Furthermore, we show that each airport's runway capacity investment could be globally efficient under globally optimal pricing rules, whereas each airport tends to overinvest under locally optimal pricing rules.

Why are smart cities proving to be so hard to deliver?

Why are smart cities proving to be so hard to deliver?

Effect of London cycle hire scheme on bicycle safety

This study evaluates the effect of London Cycle Hire scheme (LCH) on bicycle crashes, based on the data from 333 Lower Layer Super Output Areas (LSOAs) in the period 2011–2012. The Propensity Score Matching method (PSM) is applied to evaluate the effects of policy interventions (‘treatment’) on bicycle safety, with which the effects of confounding factors on the treatment effects are accounted, using the systematically established untreated groups. Covariates including land use, traffic and population characteristics are considered when selecting the untreated group for each treated unit. Results of PSM indicated that numbers of overall and slight injury bicycle crashes increased by 37.7% and 31.8% when LCH was introduced. Additionally, the interaction by another transport management policy – London Congestion Charging scheme (LCC) – on the effects of LCH on bicycle crash was estimated. Numbers of overall and slight injury bicycle crash further increased by 59.1% and 57.8% because of the implementation of LCC. For the killed or seriously injured (KSI) bicycle crashes, increases were observed in both cases (i.e. 81% for LCH and 66% for LCC), despite that they are not statistically significant. Results are indicative to the design and planning of bicycle infrastructure that could enhance the overall bicycle safety in London.

Negative Spillovers of New Technologies: The Unequal Burden of Congestion Created by Autonomous Vehicles

Autonomous vehicle (AV) technologies lower the private cost of driving, leading to induced demand for road use at peak times. Because adoption is likely to skew towards higher-income commuters, we show that the congestion externalities exacerbated by adoption are likely to be experienced disproportionately by lower-income drivers. Unless proactive policies like congestion pricing or improved access to public transit or ride-share are simultaneously implemented, autonomous vehicles are likely to make lower-income commuters actively worse off in congested cities where many higher-income commuters currently take public transit.