Congestion Pricing Policy Research Articles

Beyond traffic jam alleviation: evaluating the health and health equity impacts of New York City’s congestion pricing plan

New York City (NYC) is slated to be the first jurisdiction in the USA to implement a cordon-based congestion tax, which will be levied on vehicles entering its Central Business...

Implications of Singapore's Congestion Pricing Policy for China's Transportation Governance

With the advent of the post-epidemic era, traffic congestion in urbanized areas around the world is showing a serious trend. China, as the world's largest developing country, faces serious traffic problems during urbanization. In contrast, Singapore, as one of the Asian countries, has successfully implemented a series of traffic management policies, including congestion pricing. The main theme of this paper is to study the inspiration of Singapore's congestion pricing policy on China's transportation governance. This paper mainly analyzes the reasons for the success of Singapore's congestion pricing policy. At the same time, according to the current situation of traffic congestion in China and related research results, it summarizes the three aspects of congestion pricing in China to implement the proposal. First, congestion pricing policies should be implemented in parallel with other means of transportation management. Secondly, the congestion charging policy should be flexibly adjusted in the light of urban conditions. Finally, the congestion charging policy should focus on publicity and recognition by the public. Through this study, it is hoped that it can provide the right direction guidance and reference suggestions when the Chinese government considers the implementation of congestion pricing and similar policies in the future, and it is hoped to come up with more comprehensive and universal reference suggestions if further studies are conducted.

A cross-country comparative analysis of congestion pricing systems: Lessons for decarbonizing transportation

Alongside national and sub-national climate action, local governments play a particularly important role in implementing climate change mitigation policy. While substantial efforts are being taken to reduce greenhouse gas emissions in some communities, additional efforts are needed to encourage behavioural change in driving demand and mode of transportation. Congestion pricing is a key policy which can motivate this change. Using a comparative analysis of congestion pricing systems worldwide, this study examines government policy documents and academic literature to identify policy design features that are most desirable for achieving GHG and air quality emissions targets. Specifically, congestion pricing policies are evaluated against the attributes of implementation context, design characteristics, effectiveness, and political acceptability. In total, 16 congestion pricing systems across 11 cities in OECD member countries are assessed. Findings suggest that area-based congestion pricing systems can provide local governments with a relatively cost-effective tool to implement consistent reductions in greenhouse gas emissions in areas with a pre-existing air quality concern. Congestion pricing policies can achieve broad acceptability with thoughtful design and implementation focusing on equity and user engagement.

Case Study Evaluation of a New Approach to Price Metropolitan Highways for Congestion Relief, Sustainability and Equity

This paper addresses the practically important challenge of devising efficient and politically feasible congestion pricing policies in the context of metropolitan highways. Congestion on metropolitan highways continues to grow while governments struggle to fund alternative modes of travel. The purpose of this paper is to explore the viability of a new approach to address highway congestion while also accommodating the mobility needs of those who don’t drive. It involves creating congestion-priced lanes on limited access highways without adding new lanes. The lanes would be taken from general use and reserved for high-occupancy vehicles (HOV), transit vehicles and toll-payers, with cash rewards paid to HOV and transit users to attract solo drivers to shared travel. Variable tolls charged to lower-occupancy vehicles on the dedicated lanes would limit traffic demand on the lanes, keep traffic flowing, and fund the cash rewards. Rewards would be high enough to attract a sufficient number of drivers to ride as passengers instead so that congestion would be eliminated on the toll lanes and reduced on the remaining toll-free lanes through mode shifts. The policy-level analysis using a real-world case study of a radial highway segment with directional peaking suggests that this congestion pricing/cash rewards strategy could generate surplus revenues and provide financial support for bus rapid transit operating on the congestion-free lanes.

Public acceptance of congestion pricing policies in Beijing: The roles of neighborhood built environment and air pollution perception

While public acceptance is a major obstacle to policy implementation, the factors affecting the acceptance of anti-congestion policies such as congestion pricing remain unclear. Few studies investigate whether public acceptance varies across neighborhoods of variant built environment features and with perceived air pollution levels which people are exposed to. In this article we developed a generalized multilevel structural equation model (GMSEM) with latent class variables to investigate the direct effects and mediation effects among built environment, air pollution perception, satisfaction with existing driving restriction policies, and public acceptance of congestion pricing strategies. We draw on a questionnaire survey of about 1300 residents from 26 neighborhoods in Beijing, where driving restriction policies have been implemented and congestion pricing policies under consideration. This research reveals that residents living in neighborhoods with high population density and low diversity of land uses and those proximity to transit service and in the central urban areas tend to more strongly support the congestion pricing policy. Residents who perceive serious air pollution and who believe in the policy effectiveness are more supportive to the pricing policy; they may pay more attention to the level of air pollution caused by congestion caused by driving. These findings demonstrate the importance of recognizing neighborhood variations in policy acceptance and provide policy implications for incorporating appropriate land use planning and promotion strategies.

Impacts of Connected and Automated Vehicles on Travel Demand and Emissions in California

The emerging connected and automated vehicle (CAV) technology is likely to bring significant changes to the transportation world. This study helps understand how the anticipated emergence of CAVs will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. We design a set of future system configurations under the California Statewide Travel Demand Model framework to simulate scenarios for the deployment of passenger CAVs in California by 2050. These scenarios consider the ownership and operational type (e.g., private and shared) of CAVs, as well as additional policies such as pricing and the use of zero-emission vehicles (ZEVs) to curb potential impacts. The scenarios are: 0. Baseline (no automation); 1. Private CAV; 2. Private CAV + Pricing; 3. Private CAV + ZEV; 4. Shared CAV; 5. Shared CAV + Pricing; 6. Shared CAV + ZEV. Impacts of the introduction of CAVs across the transportation system are quantitatively estimated. The results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled (VMT) and emissions will likely increase, because of the relative convenience of CAVs. The results also show that VMT could be substantially affected by a modification in auto travel costs. This means that the implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help reduce tailpipe pollutant emissions in future scenarios.

Urban congestion pricing based on relative comfort and its impact on carbon emissions

Congestion pricing policies are implemented with the goal of alleviating urban traffic congestion during morning and evening rush hours and thus improving urban efficiency. Therefore, designing an optimum congestion pricing scheme that ensures an efficient urban traffic system while reducing carbon emissions from urban traffic is an urgent issue to be solved. To this end, an urban congestion pricing model that accounts for relative comfort is established in this study to determine congestion price, which are subsequently optimized by calculating the corresponding carbon emissions from urban traffic. After a one-hour simulation analysis of the road network in some areas of Xi'an, it was found that when the congestion pricing is 7 yuan, the overall road network speed and carbon emissions are optimal, and the average road network speed increases from 29.64 km/h to 43.31 km /h, the carbon emission is reduced by 852.4 kg. This paper can provide references for formulating policies to alleviate traffic congestion and reduce carbon emissions for countries.

Mobility management – Hybrid model of congestion pricing

The sudden development of urbanization in cities consequently affects the degree of motorization of the population. Due to the imbalance between traffic demands and traffic supply, cities, and especially their central city zones (CZ), face the problem of traffic congestion. Management of traffic demands by applying the congestion pricing policy (CP) can significantly contribute to reducing traffic congestion and its negative impacts. However, after insufficient public and political support for the CP policy during implementation, as well as the application of the MC policy, which is based only on research in hypothetical situations, a hybrid management model was proposed. Bearing in mind the above, the objective of this study is to analyze the factors that influence the acceptability of the hybrid model, which represents combination of the previously mentioned policies (CP+MC). The obtained results indicate that the hybrid model can represents an adequate solution that would reduce the movement of passenger cars in CZ and increase the acceptance of such a policy among the public.

Cordon screen:A cordon-based congestion pricingpolicy evaluation methodfor U.S. cities

ABSTRACT Global trends toward urbanization will exacerbate traffic congestion, delays in economic productivity, and air pollution issues for growing cities. Traffic congestion pricing is one method available to help ameliorate these concerns. New York City is on the verge of implementing a cordon-based traffic congestion pricing policy around its central business district. For budget-constrained municipalities, evaluating implementation of such policy could be costly. This article proposes a sketch-planning methodology, called Cordon Screen, for major U.S. cities to evaluate the net income, traffic mitigation, and avoided pollution emissions from cordon-based traffic congestion pricing. This method relies on national datasets and limited user-specific data inputs, along with a range of user-selectable assumptions informed by academic literature to deliver order-of-magnitude results. The numerous limitations of this method are acceptable for preliminary policy evaluation to determine if greater financial investment to obtain more accurate results is justified. The Denver metropolitan area is used to demonstrate Cordon Screen capabilities, with mid-range assumption results suggesting the policy is most effective at generating net income and increasing vehicle speeds on major interstates. For Denver, the policy is comparably less effective at reducing air pollution and increasing speeds on minor roadways. Validation against early implementation results from the London cordon are acceptable. However, users should discount revenue generation projections. Choice of cordon area may be the most difficult obstacle when using the Cordon Screen. With refinement, Cordon Screen could serve as a low-cost, open-source planning evaluation tool for growing and congested U.S. cities. Implications: As global urbanization trends continue, impacted local governments will be looking to explore policies to mitigate traffic congestion and reduce environmental emissions. Internationally, cordon-based traffic congestion pricing has been implemented in London, Singapore, and several other large cities. In America, New York City is implementing cordon-based congestion pricing around its central business district to reduce traffic and environmental emissions. Financial resource constraints, exacerbated by the COVID-19 pandemic, may limit the ability for local governments to invest in studying new policy options. The Cordon Screen method detailed in the manuscript presents a low-cost, open-source approach to assessing the potential benefits of cordon-based traffic congestion policy. The method utilizes national datasets to minimize user-specific data requirements and allows users to toggle between a range of values to test sensitivities to key assumptions. For example, emissions reductions are highly sensitive to how drivers respond to tolling. In this example, sensitivity testing enables users to understand how policy design can impact air quality goals. The Cordon Screen approach presented provides a strong platform for future stakeholder deliberation, refinement, and implementation.

Trends and Insights from Transportation Congestion Pricing Policy Research: A Bibliometric Analysis.

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.

Simulating congestion pricing policy impacts on pedestrian safety using a system dynamics approach

Research on congestion pricing policy (CPP) impacts has generally focused on the economic and congestion-related benefits of CPPs. Few studies have examined safety effects and the interrelated factors that produce safety outcomes for vulnerable road users. We built a novel system dynamics simulation model to explore the potential mechanisms producing pedestrian injuries over time and the impacts of a CPP (and related interventions) on this trend. We found that pedestrian injury trends varied based on important decisions related to how the CPP is designed, including investments in potential safety-related supports for pedestrians. Infrastructure improvements and speed management interventions could help cities achieve both congestion-relieving goals while also improving safety. Additionally, certain CPP configurations (e.g., additional charges on for-hire vehicles) could further reduce daily vehicle trips and congestion but might lead to unintended negative safety consequences of greater pedestrian injuries. This is the first model to provide a holistic and endogenous look at how interconnected processes affecting congestion and CPP impacts also affect vulnerable road user safety. The use of system dynamics models can facilitate a holistic inspection of potential intended and unintended effects across a range of outcomes, prior to policy implementation.

A Comparison Analysis on Car User Decision When Faced with Different Congestion Pricing Policy (Case Study: Jakarta Metropolitan Area)

Abstract This research aims to analyze the car user’s decision in their mode choice when faced with two different congestion pricing, namely Road Pricing (RP) and Parking Pricing (P2) policy scheme. The comparison analysis is conducted through the developed binomial logit model both for RP and P2 policy schemes. Data is collected by utilizing a stated preference survey, where the car user is asked their preference towards the mode choice on a given set of price and timesaving combination for both policies. Then the utility function for each policy is established by conducting some statistical procedures to have a feasible and reasonable model. Having set the utility functions, the validity test is carried out to the developed model using a different set of data from the survey. Further analysis is done to select the model that satisfies the statistical feasibility and the validity threshold number. Comparison to both different choice models yields different decisions represented in the form of shifting mode percentages. The research results show that the RP policy scheme tends to shift the car user higher than the P2 policy scheme at the same total additional charging fare.

Trends and Insights from Transportation Congestion Pricing Policy Research: A Bibliometric Analysis

Trends and Insights from Transportation Congestion Pricing Policy Research: A Bibliometric Analysis

Congestion Pricing Policies and Safety Implications: a Scoping Review.

Congestion pricing policies (CPPs) are a common strategy for addressing urban traffic congestion. Research has explored several impacts of these policies (e.g., air quality, equity, congestion relief). The purpose of this review was to synthesize findings from publications examining CPP impacts on road user safety outcomes. We conducted a systematic search of relevant literature in four large research databases (Transport Research International Documentation, Web of Science, PubMed, and Scopus), searching from database inception through January 2021. We identified 18 eligible publications. Safety-related outcomes included overall crashes and injury crashes with stratification by injury severity and road user type (e.g., bicyclist, pedestrian). A majority of the publications examined zone-based CPPs (n = 13) and used observed data involving real policies (n = 10), as compared to a predicted or simulated analysis. Decreases in overall crashes and injuries for some road users were observed (e.g., car occupants). While some studies estimated short-term increases in injuries and crashes for bicyclists and motorcyclists (likely due to shifts from personal vehicle use to other transportation modes and increased exposure), most analyses focused on longer-term impacts and generally found a reversal and eventual decrease in injuries and crashes after a few years. The relative scarcity of safety outcomes in published literature, along with the wide breadth of CPP types, implementation contexts, and outcomes measured, demonstrates that more research on safety outcomes is needed. Cities and regions planning to implement CPPs should consider potential mode shifts and safety supports for all road users (e.g., bicycle and pedestrian infrastructure).Supplementary InformationThe online version contains supplementary material available at 10.1007/s11524-021-00578-3.

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

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.

Peak-Load Pricing in Portland: Theory, Application, and Recommendations for Central Business District Pricing Policy

In order to make good microeconomic policy in the public sector, non-rivalrous goods and joint costs need to be addressed. One field where non-rivalrous goods and joint costs are important to consider is in the economics of peak-load pricing and transportation policy. This concept is often applied to electrical utilities, but not often applied as a way to relieve the burden of downtown traffic congestion during peak traffic times or to increase city revenues. This paper discusses the theory of peak-load pricing as it applies to “central business districts” (CBDs). It demonstrates successful and unsuccessful examples of peak-flow pricing in CBDs in other cities, including how the private costs of driving interact with potentially implementing a policy, and how politics play a role in implementing peak-load pricing policies. Finally, it discusses current transportation policies in Portland, Oregon, and recommends the city implement a congestion pricing policy.

Healthier Streets: How Superblocks and Congestion Pricing Can Help Reduce Air Pollution in Washington, DC

Cities face big challenges, including environmental threats, livability concerns, lack of new infrastructure, and increasing social inequity, among others. These challenges call for new and innovative ways to address them. Air pollution, a common problem in many cities, causes between 100,000 and 340,000 Americans to die prematurely every year (Fann et al. 2012; Caiazzo et al. 2013; Bowe et al. 2019; and Goodkind et al. 2019) and, out of those premature deaths, 53,000 are due to car pollution (Caiazzo et al. 2013). The Washington DC Metro Area is one of the top 20 cities in the United States with the highest smog pollution, highlighting the need for new regulations to reduce air pollution (American Lung Association 2020). Federal policies aimed at lowering the maximum concentration of air pollutants allowed by the EPA could improve public health, but cities can also reduce air pollution levels through policymaking. DC currently has two main transportation and sustainability plans to improve public transportation, infrastructure, and combat climate change. Cities similar to DC, such as Barcelona and London, have applied superblocks, restricted car access city areas, and congestion pricing policies respectively, aimed at reducing air pollution and improving quality of life. Fortunately, DC has many of the characteristics needed to successfully implement superblocks in areas of the city where residential housing and businesses share space, and congestion pricing in highly congested areas. I recommend the District Department of Transportation to establish pilot programs for each of these policies to reduce air pollution levels in the city.

Active learning for multi-objective optimal road congestion pricing considering negative land use effect

The road congestion pricing policy is implemented to alleviate traffic congestion and improve the efficiency of the transportation system during peak hours. However, the negative land use effect caused by this policy could not be ignored. How to design the optimal congestion toll that can not only ensure its positive effect on the transportation system but also reduce its negative effect on land use is an urgent problem to be solved. Given this, this paper proposed a multi-objective bi-level programming road congestion pricing model based on the integrated land use and transportation model to optimize the regional average accessibility, regional average land use diversity, and regional total flow time. Since the proposed problem is NP-hard, this paper innovatively proposed an active learning optimization algorithm based on multi-objective Bayesian optimization, which improves the computation efficiency of the bi-level programming model by automatically finding the next sampling point (candidate solution) according to the probability information. An empirical analysis of Jiangyin City demonstrated the effectiveness of the proposed approach in coordinating the relationship between land use and transportation and alleviating the negative land use effect caused by road congestion pricing. Moreover, the algorithm proposed in this paper can also be used to solve other transportation-related black box problems with high computation complexity.

Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

The integration of the Internet of Vehicles (IoV) in future smart cities could help solve many traffic-related challenges, such as reducing traffic congestion and traffic accidents. Various congestion pricing and electric vehicle charging policies have been introduced in recent years. Nonetheless, the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system. In this paper, we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment. The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations. We propose a token management system that serves as a virtual currency, where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees. The proposed system is designed for Vehicular Ad-hoc Networks (VANETs) in the context of a smart city environment without the need to set up any expensive toll collection stations. Through large-scale traffic simulation in different smart city scenarios, it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.