The Lord of the Ring Road: A Review and Evaluation of Autonomous Control Policies for Traffic in a Ring Road

This study focuses on the comprehensive investigation of stop-and-go waves appearing in closed-circuit ring road traffic wherein we evaluate various longitudinal dynamical models for vehicles. It is known that the behavior of human-driven vehicles, with other traffic elements such as density held constant, could stimulate stop-and-go waves, which do not dissipate on the circuit ring road. Stop-and-go waves can be dissipated by adding automated vehicles (AVs) to the ring. Thorough investigations of the performance of AV longitudinal control algorithms were carried out in Flow, which is an integrated platform for reinforcement learning on traffic control. Ten AV algorithms presented in the literature are evaluated. For each AV algorithm, experiments are carried out by varying distributions and penetration rates of AVs. Two different distributions of AVs are studied. For the first distribution scenario, AVs are placed consecutively. Penetration rates are varied from 1 AV (5%) to all AVs (100%). For the second distribution scenario, AVs are placed with even distribution of human-driven vehicles in between any two AVs. In this scenario, penetration rates are varied from 2 AVs (10%) to 11 AVs (50%). Multiple runs (10 runs) are simulated to average out the randomness in the results. From more than 3,000 simulation experiments, we investigated how AV algorithms perform differently with varying distributions and penetration rates while all AV algorithms remained fixed under all distributions and penetration rates. Time to stabilize, maximum headway, vehicle miles traveled, and fuel economy are used to evaluate their performance. Using these metrics, we find that the traffic condition improvement is not necessarily dependent on the distribution for most of the AV controllers, particularly when no cooperation among AVs is considered. Traffic condition is generally improved with a higher AV penetration rate with only one of the AV algorithms showing a contrary trend. Among all AV algorithms in this study, the reinforcement learning controller shows the most consistent improvement under all distributions and penetration rates.

Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled

In this study, gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models were constructed to systematically ascertain the influencing factors and electric vehicle (EV) use action laws from the perspective of travelers. The use intensity of EVs was represented by electric vehicle miles traveled (eVMT); variables such as the charging time, travel preference, and annual income were used to describe the travel characteristics. Seven variables, including distance to the nearest business district, road density, public transport service level, and land use mix were extracted from different dimensions to describe the built environment, explore the influence of the travel behavior mode and built environment on EV use. From the eVMT survey data, points of interest (POI) data, urban road network data, and other heterogeneous data from Chongqing, an empirical analysis of EV usage intensity was conducted. The results indicated that the deviation of the GBDT model (9.62%) was 11.72% lower than that of the OLS model (21.34%). The charging time was the most significant factor influencing the service intensity of EVs (18.37%). The charging pile density (15.24%), EV preference (11.52%), and distance to the nearest business district (10.28%) also exerted a significant influence.

Safeguarding Equity in Off-Site Vehicle Miles Traveled (VMT) Mitigation in California

Historically, the State of California assessed the environmental impacts of proposed developments based on how it was projected to affect an area’s level of service (LOS). However, as LOS focused on traffic delays, many agencies simply widened roads, which was an ineffective way to reduce greenhouse gas emissions (GHGs). With the passage of Senate Bill (SB)743 in 2013, LOS was replaced by Vehicle Miles Traveled (VMT) as a more appropriate metric by which to gauge the environmental impacts of proposed development. Additionally, SB 743 presented an opportunity for off-site VMT mitigation strategies through banking and exchanges– allowing multiple development projects to fund a variety of strategies to reduce VMT elsewhere in the city or region. While the shift from LOS to VMT has generally been lauded, concerns remain about how to apply SB 743 effectively and equitably. This study aimed to: 1) understand how local governments are addressing this shift toward VMT while ensuring equity, including its approaches to off-site VMT mitigation; and 2) evaluate the various built environment factors that impact VMT, which should be considered by local governments, using both qualitative and quantitative research designs. The study posited that both micro and macro level aspects of the built environment needed to be considered when evaluating the impacts of proposed development on VMT, not only to ensure higher accuracy VMT models, but also because of the potential equity implications of off-site mitigation measures. Using multiple linear regression, the study shows that macroscale built environment features such as land use, density, housing, and employment access have a statistically significant impact on reducing VMT (35%), along with transit access (15%), microscale features such as sidewalks, benches, and trees (13%), and income (6%). More notably, a four-way interaction was detected, indicating that VMT is dependent on the combination of macro and micro level built environment features, public transit access, and income. Additionally, qualitative interviews indicate that transportation practitioners deal with three types of challenges in the transition to VMT impact mitigation: the lack of reliable, standardized VMT measure and evaluation tools; the lack of a strong legal foundation for VMT as a component of the California Environmental Quality Act (CEQA); and the challenge of distributing off-site VMT mitigation equitably. Overall, findings support a nuanced, multi-factor understanding of the context in which new developments are being proposed, both in terms of modeling VMT, but also when considering whether offsite mitigation would be appropriate. The results of this study can help California ensure equitable VMT mitigation that better aligns with the state’s climate goals.

Glimpse of the Future: Simulating Life with Personally Owned Autonomous Vehicles and Their Implications on Travel Behaviors

To explore potential travel behavior shifts induced by personally owned, fully autonomous vehicles (AVs), we ran an experiment that provided personal chauffeurs to 43 households in the Sacramento region to simulate life with an AV. Like an advanced AV, the chauffeurs took over driving duties. Households were recruited from the 2018 Sacramento household travel survey sample. Sampling was stratified by weekly vehicle miles traveled (VMT), and households were selected to be diverse by demographics, modal preferences, mobility barriers, and residential location. Thirty-four households received 60 h of chauffeur service for 1 week, and nine households received 60 h per week for 2 weeks. Smartphone-based travel diaries were recorded for the chauffeur week(s), 1 week before, and 1 week after. During the chauffeur week, the overall systemwide VMT (summing across all sampled households) increased by 60%, over half of which came from “zero-occupancy vehicle” (ZOV) trips (when the chauffeur was the only occupant). The number of trips made in the system increased by 25%, with ZOV trips accounting for 85% of these additional trips. There was a shift away from transit, ridehailing, biking, and walking trips, which dropped by 70%, 55%, 38%, and 10%, respectively. Households with mobility barriers and those with less auto dependency had the greatest percent increase in VMT, whereas higher VMT households and families with children had the lowest. The results highlight how AVs can enhance mobility, but also caution against the potential detrimental effects on the transportation system and the need to regulate AVs and ZOVs.

Land use uncertainty in transportation forecast

Using an integrated land use and travel model system implemented for the Puget Sound region in Washington state, a Bayesian Melding technique is applied to represent variations in land use outcomes, and is propagated into travel choices across a multi-year agent-based simulation. A scenario is considered where zoned capacity is increased around light rail stations. Samples are drawn from the posterior distribution of households to generate travel model inputs. They allow for propagation of land use uncertainty into travel choices, which are themselves assessed for uncertainty by comparing against observed data. Resulting travel measures of zonal vehicle miles traveled (VMT) per capita and light rail station boardings indicate the importance of comparing distributions rather than point forecasts. Results suggest decreased VMT per capita in zones near light rail stations and increased boardings at certain stations with existing development, and less significant impacts around stations with lower initial development capacity. In many cases, individual point level comparisons of scenarios would lead to very different conclusions. Altogether, this finding adds to a line of work demonstrating the policy value of incorporating uncertainty in integrated models and provides a method for assessing these variations in a systematic way.

Understanding the Temporal, Regional, Demographic, and Policy Factors Influencing Counties’ Daily Traffic Volume Reductions in Response to COVID-19

This research aims to understand temporal, regional, demographic, and policy factors that influenced travel reduction within the contiguous United States during the early period of the COVID-19 pandemic. Particularly, this research combines U.S. Census data, infection rates, and state-level mandates to determine their effects on daily, county-level vehicle miles traveled (VMT) estimations from March 1, 2020 to April 21, 2020. Specifically, this work generates metrics of VMT per capita, daily change in VMT, and VMT immediate reaction rates for every county in the U.S.A. and develops regression models to determine how these factors influence VMT rates over time. Results show that state-mandated orders were deployed in a pattern relative to their expected economic impact. Model results showed infection rates may have had a greater influence on forcing state policy adoption, ensuring reduced VMT, rather than the number of cases directly influencing individual travel to a significant degree. Additionally, counties with higher populations or labeled as urban counties saw a greater reduction in VMT across all three models compared with lower population and rural counties. Planners and policy makers in the future can utilize the results of this research to make better informed responses as well as to know the expected results of their actions.

Does Car-Sharing Reduce Car Ownership? Empirical Evidence from Germany

The sharing economy is making its way into our everyday lives. One of its business models, car-sharing, has become highly popular. Can it help us increase our sustainability? Besides emissions and vehicle miles traveled, one key aspect in the assessment regards the effect of car-sharing on car ownership. Previous studies investigating this effect have relied almost exclusively on surveys and come to very heterogeneous results, partly suggesting spectacular substitution rates between shared and private cars. This study empirically explores the impact of car-sharing on noncorporate car ownership and car markets in 35 large German cities. The analysis draws on publicly available data for the years 2012, 2013, 2015, and 2017, including, among others, the number of shared cars per operating mode (free-floating and station-based) and the number of cars owned and registered by private individuals (i.e., excluding company cars). We find that one additional station-based car is associated with a reduction of about nine private cars. We do not find a statistically significant relation between car ownership and free-floating car-sharing. Neither type of car-sharing appears to impact the markets for used and new cars significantly. Given the measurable impacts on car ownership levels, this result is surprising and invites future research to study car-sharing’s impact on the dynamics of car markets.

Peer Review: The Volpe/Federal Highway Administration National Vehicle Miles Traveled Forecasting Models

This paper describes the findings of an independent peer review of the modeling tools used by the Volpe National Transportation Systems Center to forecast national vehicle miles traveled (VMT) over the next 30 years. Overall, the VMT forecasting models, which use autoregressive distributed lag models for light-duty vehicle, single-unit truck, and combination truck VMT, work well to estimate travel demand. All model estimations were reviewed, and all models perform well against several validation and testing techniques. The study team was supported by an expert panel selected from academia, government, and industry with experience in econometric methods, transportation and economic data, and modeling methods. The panel reviewed model documentation as well as the report assessing the VMT forecasting models and provided insight into alternative model research. The paper is an effort to synthesize the approaches and the validation methods used. A complementary literature search was also conducted to test the validity and comparability of several estimated variable coefficients. The paper concludes by summarizing the key findings and making recommendations on future model improvements.