Congestion Pricing Research Articles

Contextual Bayesian optimization of congestion pricing with day-to-day dynamics

Congestion pricing is a common approach to alleviate urban traffic congestion. The design of second-best congestion pricing schemes is typically formulated as non-linear programming and bi-level optimization problems, where the lower-level problem involves either a static or dynamic network equilibrium model. The complexity of these bi-level toll optimization problems increases considerably when incorporating day-to-day dynamic models of travel behavior and dynamic models of network congestion. These models are often operationalized using simulation, and consequently, the toll design problem is a computationally challenging simulation-based optimization problem where the evaluation of a single candidate pricing scheme involves simulating the day-to-day model until convergence. In order to circumvent this issue, we propose a contextual Bayesian optimization (BO) framework, where the BO scheme is embedded within the day-to-day dynamic model by using temporal contextual information. The framework implicitly incorporates the relationship between the objective function across days and uses past days’ observations (function evaluations) as weak priors when constructing the Gaussian process underlying the BO algorithm for the current day’s toll optimization problem, resulting in gains in computational efficiency.The contextual BO approach is applied to the design of distance-based pricing schemes for the morning commute problem. We demonstrate numerically that the scheme converges to the system optimum, and moreover, utilizes a significantly smaller number of simulation evaluations (ten-fold reduction) than the standard approach wherein each function evaluation involves simulating the day-to-day model until convergence. From a policy perspective, we find that the distance-based schemes yield significant welfare gains relative to area-based schemes and show that the design of the distance-based tariff scheme can significantly affect distributional impacts. A suitably designed two-part tariff structure can partially offset the relatively large welfare losses of travelers with longer commute distances while maintaining overall welfare. The proposed contextual BO scheme is also extended to incorporate context specific demand and supply information, which can be of value to policy-makers when evaluating optimal toll design schemes under a wide range of scenarios in a computational tractable manner.

Analysis of Traffic Volume and Travel-Time Relationship Using Continuous One-Hour Values on Urban Expressway

To make more efficient use of the expanded freeway and urban expressway networks, various measures such as bottleneck management and wide-area congestion pricing based on traffic data obtained from traffic detectors, including traffic volume and travel time, have been considered. Generally, the congestion status of the data varies from day to day. This study proposes a method for analyzing a graph of traffic volume and travel time to visually and intuitively grasp the change in the daily traffic situation using continuous one-hour values. These values are continuously generated hourly values obtained by shifting data every minute. Twenty-four hours 1 minute data for 128 days on 32 segments with detectors in the Nagoya Expressway Network in Japan were used to draw a continuous one-hour value graph. A number of graphs showed loops of continuous one-hour values with congestion and a smooth variation characteristic of values over time. These graphs provide an accurate estimate of the daily maximum one-hour traffic volumes and facilitate a sequential understanding of the congestion pattern changes on successive route segments. Hourly travel-time prediction models were constructed to macroscopically examine congestion measures over a range of several hours. These models were fabricated with high accuracy using multiple regression analysis based on the characteristics of continuous one-hour values. Exploratory predictive analysis of hourly travel-time models has allowed us to study and discuss various congestion factors in road structures and traffic flows, and it has been found to be easy to grasp the phenomenon and ensure accuracy and operability.

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.

Comparative Evaluation of Road Pricing Schemes: A Simulation Approach (Australian Perspective)

Road network pricing and congestion charging continue to be debated as efficient instruments to address traffic congestion and emissions. For cities where the schemes have not been implemented yet, the impacts of these schemes are typically evaluated using transport simulation models to understand the impacts and design effective solutions before the schemes are deployed. This paper considers a simulation approach for the city of Melbourne in Australia to investigate the potential impacts of road network pricing on reducing private vehicle travel, road congestion, and vehicle emissions. The study uses a dynamic traffic simulation model developed for Melbourne using the AIMSUN modeling tool, which was extended for modeling road user pricing and congestion charging, including considerations and formulations of distance-based, delay-based, joint-distance-and-delay-based, and cordon-based schemes under low-cost, medium-cost, and high-cost regimes. The study’s contributions also include an extension of the modeling framework to include public transport options to allow for providing travelers with the option of choosing an alternative mode of transport if they do not wish to pay. A mesoscopic stochastic route choice modeling approach was adopted to examine the impact of road pricing inside a nominated charging zone within the network. The results showed it would be possible to achieve a reduction of 11% in vehicle count, a 20% reduction in travel time, a 13% reduction in emissions, and a 3% increase in travel speed within the proposed pricing zone under a high-cost pricing scenario. The results also showed a significant reduction in emissions resulting from shifting drivers who are not willing to pay the congestion charge to public transport. When 20% of car drivers shifted to public transport, carbon emissions were reduced by up to 30% and network performance improved by 45%, compared to the baseline scenario without pricing. The findings of this research provide important directions for policymakers in deciding on the type and scope of charging schemes to use and how these could reshape transportation taxation systems by moving away from taxes on vehicles through registration fees and towards user-pay taxations where travelers pay for the amount of travel they do or the pollution and emissions they are responsible for.

Green Vehicles’ Responses to an Expiring Congestion Toll Exemption: Findings from a Natural Experiment in Stockholm, Sweden

Stockholm established time-varying congestion pricing in 2007, and adopted a toll exemption as a temporary incentive for green vehicles (GVs) that ended in 2012. We examine the behavioral effects of phasing out the exemption by studying the change in cordon crossing events for GV morning commuters between May 2012 and May 2013, with a random sample of conventional vehicles (CVs) as control. The results suggest i) a significant drop in the total number of crossings; ii) a slight shift towards later journeys in the morning; and iii) a reduction in the ratio of peak-toll period crossings to other ones.

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.

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, in a simple one corridor scenario 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, we then examine the impacts of three traffic regulation policies, i.e., road space rationing, congestion pricing and tradable credit schemes, and investigate the existence of traffic regulation policies that can achieve system optimum in the presence of ride-hailing.

Collaborative road pricing strategy for heterogeneous vehicles considering emission constraints

Urban transportation problems, primarily characterized by traffic congestion and exhaust emissions, continue to worsen, significantly impeding the economic and social development of cities. Road pricing strategies have emerged as an effective approach to tackle urban transportation problems, and their successful implementation in numerous cities have garnered significant attention. In this context, this study introduces a collaborative road pricing strategy tailored for mixed traffic flow comprising both conventional vehicles (CVs) and new energy vehicles (NEVs). CVs and NEVs are considered as two distinct types of cars with heterogeneous characteristics. The collaborative road pricing strategy consists of two components: congestion pricing and emission pricing. A bi-level programming model for the collaborative road pricing strategy is built. Exhaust emissions within the low-emission zone (LEZ) are used as constraints for the model, while the optimization objective of the model involves minimizing the sum of total travel time and total emissions of the road network. The viability of the proposed collaborative road pricing scheme is demonstrated through numerical tests within the second ring road in Beijing, China. The results imply that the service level of the road network can be improved by implementing the collaborative road pricing scheme. It also concludes that, increasing the penetration of new energy vehicles would help to decrease traffic congestion and emissions.

The elasticity and efficiency of carbon reduction strategies in transportation

ABSTRACT Transportation significantly contributes to carbon emissions, prompting the need for effective mitigation policies. This study addresses the knowledge gaps in assessing the effectiveness of transport carbon policies and offers the lack of a holistic comparative overview. The study used a model composed of a mixed-effects meta-regression and carbon elasticity to investigate policies, like shared bikes, mobility hubs, low emission zones, congestion pricing, electric vehicles, and hydrogen vehicles. This model included seven control variables: year, GDP, implementation costs, geographic scale, environmental benefits, and transport share of energy consumption and carbon emissions. Mobility hubs and electric vehicles ranked are top effective policies with carbon elasticities of 3.73 and 3.72, effect sizes of 127.47 and 86.73, and confidence intervals of [65.55, 107.93] and [106.17, 148.78], respectively. Followed by the low emission zone of 16.3 carbon elasticity, proving its cost-effectiveness, effect size of 10.16, and a confidence interval of [−2.48, 22.80]. Congestion pricing, despite having the highest effect size of 873.39, its confidence interval [−354.01, 2100.80] is wide, indicating the uncertainty of this effect. Shared bikes and hydrogen vehicles ranked lowest, suggesting a need for deeper life cycle-based analysis. Although this model displayed high accuracy, the findings’ interpretation should consider the inherent data limitations.

FACTORS AFFECTING THE DEVELOPMENT OF AN INTEGRATED TOLL TRANSACTION SYSTEM TO IMPROVE TRAFFIC VOLUME DISTRIBUTION

The Indonesian government has attempted to reduce traffic congestion on toll roads by implementing a non-cash toll transaction system that has been valid on all Indonesian toll roads since October 2017. However, heavy traffic that causes traffic congestion on toll roads in urban areas often occurs. This study aimed to find out the factors that affect the development of the toll transaction system and the extent to which vehicles will be distributed through the preferences of road users to choose to enter the toll road or not. This research method used a quantitative descriptive approach with online and offline surveys through Stated Preference. Respondents were class I – V vehicle users who crossed Prof. Dr. Ir. Soedijatmo Toll Road with the direction of travel from the Jakarta Intra Urban Toll Road. The results of the study indicated that the implementation of contactless cashless toll transactions (Electronic Toll Collection (ETC)) can reduce the interest of road users to enter toll roads if fines are imposed, but on the other hand road users are still highly interested in entering toll road because there is a toll transaction time cut service. In the congestion pricing scenario, there was an increase in the distribution of traffic volume because the number of vehicles decreased along with the reduced interest of road users to enter the toll road due to the higher toll rate increase.

Costs and Benefits of Regional Connectivity: An Analysis from Bangladesh Perspective

Aims: This paper aims to investigate the costs and benefits of regional connectivity in Bangladesh from the economic perspective through using the analytical tools of benefit-cost analysis.
 Study design: The study is conducted based on forecasted secondary data from different related published sources. A qualitative stakeholder survey is conducted to evaluate the intended impact of regional connectivity on the economy of Bangladesh.
 Methodology: The study uses qualitative research tools to assess the impact of bilateral or multilateral connectivity and uses the tools of benefits-costs analysis (NPV, BCR, and IRR) applied to evaluate the effectiveness of regional connectivity from bilateral and multilateral perspectives. Our key hypothesis is that ‘regional connectivity improves benefits’.
 Results: The study shows that Bangladesh can gain from connectivity. However, gains from regional connectivity, especially, bilateral connectivity is very sensitive to the changes in the variables particularly, transshipment charge, volume of transshipment, and cost of building physical infrastructure. With modest values of the parameters, we find that connectivity generates a positive net present value, if Bangladesh can charge rationally against the cost of building infrastructure. From the stakeholders’ point, connectivity is beneficial, particularly multilateral connectivity generates more net benefit than the bilateral connectivity. Besides, the economic costs, the security concern can play a big effect on the social cost of such connectivity. Meanwhile, grave concern is aired on incurring a huge project cost, if Bangladesh implements the project with a higher interest tide loan from India. Moreover, the imposition of congestion fees, water pollution, and traffic fees derive the benefit from transit facilities.
 Conclusion: The net gains from regional connectivity depend on trading patterns’ transshipment of goods, trade creation, and trade diversion among the partners through the connectivity process. Bangladesh needs to adopt gains-optimizing strategies for fixing the direct and indirect fees. Bangladesh should prioritize the silk-route connectivity more than the Asian Highway to attract FDI and sunset industry from China.

How does the dominant stakeholder strategically manage an innovative public policy? Evidence from the London congestion charge

AbstractPrior studies have examined various economic, environmental, and social impacts of the London congestion charge (LCC). However, few studies have investigatedhowthe Transport for London (TfL) managed the LCC in all stages of its policy. Without active, strategic management of policies, stakeholders affected may doubt the policies’ legitimacy and reduce policy effectiveness. Thus, examining the means utilized to achieve effective policy management is critical and can potentially influence the development and management of future policies. This study adapts Mitchell et al.’s stakeholder typology to studyhowthe TfL, as part of the management of, and as the dominant stakeholder in, environmental regulations in London, strategically managed the LCC via their own efforts to exert power, establish legitimacy, and claim urgency. Relying on Yin, we analyze publicly available data to show how the TfL actively managed the LCC to ensure its sustainability as policy. The TfL primarily used tactics to maintain and enhance the LCC's legitimacy with authoritative power and fears of unproven urgency to influence other stakeholders. Results have practical implications for policymakers contemplating policy reforms and shed additional light on less‐discussed latent factors regarding policy management. This study contributes to the literature by applying stakeholder theory to the domain of policy reform, administration, and management. Our findings have policy implications as policymakers may benefit from learning the tactics examined in this study to assess their policy management and administration. Lastly, through our discussion and conclusions, we reflect on our findings to encourage research on policy management.

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.

ASSESSING THE PUBLIC AND EXPERTS’ PERCEPTION TOWARDS THE PROPOSED CONGESTION PRICING SCHEME IN JORDAN

This study aims to investigate the attitudes of both the public and the experts towards the Congestion Pricing (CP) scheme by considering the case of Amman City in Jordan. A combination of qualitative and quantitative methodologies was adopted. A predesigned survey with 600 respondents was the primary source of data in addition to semi-structured comprehensive interviews conducted with knowledgeable individuals experienced in the related fields including wholesalers. The analysis of the collected data was performed using the Statistical Package for Social Science SPSS IBM software together with the SPSS AMOS (Analysis of Moments Structural Equation Modeling Software) and Excel spreadsheets programs to carry out CFA, RII, descriptive statistics, and socio-economic index. The results showed that more than half of the respondents (58%) have never heard of CP. 47% of those who are aware of CP were in favor of implementing it. Greater support was given to the CP implementation during the Peak hours (55%) compared to their support of all-hour implementation (26%). The RII results revealed that CP can reduce road congestion (RII=69.8%), helps protect the environment (RII=70%), and limit recreation and shopping preferences (RII=64.7%) and this aspect could be better publicized as an effective means of maintaining social distance and reducing the risk of infection during Covid-19 pandemic. Moreover, people with greater socio-economic educational class tended to perceive a higher personal change in travel habits when CP is implemented. People’s level of acceptance of the implementation of CP was found to be affected by several meaningful factors. The CFA revealed that a more significant factor was the "potential benefits and economic revenue of implementing CP". The main causes of traffic congestion in Amman, the capital of Jordan, were identified through interviews with experts from various concerned parties which included poor public transport system and parking management, increasing car ownership, and inadequate policies to control traffic congestion. The experts advised that the CP implementation should be linked with other related strategies and be evaluated periodically. They also suggested a minimum of 3.5 years’ time framework in case of introducing the CP scheme. This period is needed for issuing new CP legislation and developing an acceptable level of public awareness of the system. Furthermore, the implementation of CP might not always seriously damage the wholesaler's own business or economic competitiveness. Based on the results of the study, recommendations for developing and implementing an effective CP system in Amman were outlined.

Public acceptability of policies to reduce urban air pollution: A population-based survey experiment

Low-emission zones, congestion charges and pedestrian areas have the potential to improve urban air quality. However, the lack of public and political support for these policies is often a significant obstacle to their introduction. In this article, we present the results of two studies. First, we investigated the public acceptability of three policies to reduce urban air pollution: pedestrianization, low emission zones and congestion charges. Second, based on an experimental online survey, we examined the effect of various framing conditions on the acceptability of congestion charges. The results show, first, that public acceptability is higher for pedestrianization and low emission zones and lower for congestion charging. Second, we find a positive effect on acceptability of information emphasizing the benefits of congestion charges and of positive labelling, comparing the wording “environmental contribution” with “urban toll”; and a negative effect of making political ideology prominent. Our findings suggest that while some interventions aimed at mitigating urban air pollution may be more readily supported than others, their acceptability can be enhanced (or diminished) through careful design and effective communication.

The distributed ownership of on-demand mobility service

Mobility as a service (MaaS) is an innovative and value-laden method to operate on-demand mobility services within the existing transport infrastructures. A big challenge for the transport system is that the transport infrastructure cannot be restructured as quickly as a digital platform like cloud computing infrastructure services, which is a constraint that limits the balance between mobility demand and supply in real-time. The ownership of mobility services needs to be redefined to establish legal binds between service vendors and commuters, further demonstrating the capacity to balance the demand and supply of mobility services in real-time. The ownership of a mobility service is defined as the commuter's right to use the mobility service provided by the service vendor under agreed terms and conditions. This is facilitated by MobiToken in this study. An eMarket-place is proposed for commuters, mobility service vendors and other stakeholders to exchange MobiToken, where the marketplace demonstrates the potential use of blockchain tools, methods and algorithms, where a specialised capability for reliably link stakeholders is proposed through a nonrevertible blockchain dataset. The bidden price of MobiToken reflects the demand for mobility services in the real world, where congestion charges are incorporated within the bidden price. The performance of the preliminary platform is evaluated with three testing scenarios, and the results of performance testing indicate the stability and reliability of the Raft consensus algorithm for the blockchain database.

Assessing public transport passenger attitudes towards a dynamic fare model based on in-vehicle crowdedness levels and additional waiting time

Public Transport (PT) provides passenger mobility and contributes to sustainable transportation. To achieve this a PT system must provide continuous accessible service and connections for passengers. PT reliability is considered a major obstacle to growing its market share. Current solutions primarily address travel time reliability through methods like priority lanes and traffic signal priority. Dwell time reliability improvement, in turn, can be achieved by the use of smart cards which reduce the variability in boarding and alighting times. Another factor affecting reliability is in-vehicle crowdedness which causes delays and increases dwell time variability. To mitigate crowdedness, we propose a monetary approach that dynamically changes the fare based on the in-vehicle crowdedness level in a manner similar to congestion pricing. This approach would shift some passengers from boarding the over-crowded vehicle to waiting for the next, less crowded vehicle, while compensating them for the additional waiting. Passengers unwilling to wait might pay a penalty if the additional waiting time is reasonable. To assess the attitude of passengers towards a dynamic fare model, a stated preference questionnaire was developed to assess the factors that affect the choice of whether or not to board an over-crowded vehicle. Based on panel data and the fixed effect logit model it was revealed that the higher the waiting time, the lower the willingness to board the next vehicle. However, monetary schemes (penalties or discounts) increased the willingness to wait and board the next vehicle. Moreover, the willingness to wait was higher when a penalty was introduced compared to a discount, which is in line with the prospect theory. The results suggest that it is possible to construct a dynamic fare model that using data on vehicle crowdedness levels and waiting times obtained from advanced data collection systems, which is integrated within a mobile payment application. This approach could reduce crowdedness and increase reliability.

Congestion and incentives in the age of driverless fleets

The diffusion of autonomous vehicles (AVs) will expand the tools to manage congestion. Differently than fleets of traditional vehicles, operators of fleets of AVs will be able to assign different travelers to different routes, potentially inducing different congestion levels (and speed). We look at the effects of the technological transition from traditional to autonomous vehicles. Our model exhibits a unit mass of heterogeneous individuals. Some of them use the services of a fleet, while others do not, and travel independently. With few fleet users, the fleet technology (traditional vs automated vehicles) is immaterial to welfare. On the contrary, when there are many fleet users, we show that, if fleets do not price any individuals out of the market, the differentiation in congestion across routes under the automated fleet is welfare-reducing. When, instead, fleets price some individuals out of the market, the welfare effects of the transition are ambiguous and depend on the interplay between the extent of rationing by both types of fleets and the extent of differentiation by the AVs fleet. Finally, we characterize the tax restoring the first best with AVs. It involves charging different taxes across lanes, starkly different between independent travelers and the fleet. While independent travelers should be charged lane-specific congestion charges, the fleet should be imposed a scheme involving a congestion-based tax and a subsidy.

Top Down Thinking

Pay-per-mile tolling using satellites is under close scrutiny – it’s already in use for trucks in Europe and pilots for cars are underway, it is an option for road user charging (RUC) subscribers in Oregon, and Singapore is poised to switch its cordon-based congestion charging to a GNSS system (with RUC capability built in) later this year. Christopher Court-Dobson investigates the challenges and benefits of such systems and asks, how far can we go?

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.