National Center for Sustainable Transportation

Planners and engineers need to know how to assess the impacts of proposed cycling infrastructure projects, so that projects that have the greatest potential impact on the actual and perceived cycling safety are selected over those that would be less effective. Planners also need to be able to communicate these impacts to decision-makers and the public. This research addresses these problems using the BikewaySim cycling shortest path model. BikewaySim uses link impedance functions to account for link attributes (e.g., presence of a bike lane, steep gradients, the number of lanes) and find the least impedance path for any origin-destination pair. In this project, BikewaySim was used to assess the impacts of using time-only and time with attribute impedances, as well as two proposed cycling infrastructure projects, on 28,392 potential trips for a study area in Atlanta, Georgia. These impacts were visualized through bikesheds, individual routing, and betweenness centrality. Two metrics, percent detour and change in impedance, were also calculated. Results demonstrate that BikewaySim can effectively visualize potential improvements of cycling infrastructure and has additional applications for trip planning. An expanded study area was also used to demonstrate bike + transit mode routing for four study area locations. Visualizations examine the accessibility to TAZs, travel time, and the utilized transit modes for each location. Compared to the walk + transit mode, the bike + transit mode provided greater access to other TAZs and reached them in a shorter amount of time. The locations near the center of the transit network where many routes converge offered the greatest accessibility for both the bike + transit and walk + transit modes. The difference in accessibility was greatest for locations near fewer transit routes. This research demonstrated how BikewaySim can be used to both examine the current cycling network and show changes in accessibility likely to result from new infrastructure. Both BikewaySim and TransitSim are open-source Python based tools that will be made available for practitioners to use in bicycle network planning. Future research will focus on calibrating link impedance functions with revealed preference data (cycling GPS traces) and survey response data (surveys on user preference for cycling infrastructure).

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As governing bodies continue to explore mileage fees as an alternative to the gas tax, the uncertainty surrounding public support remains a critical barrier to policy uptake. This study examines the extent to which public perceptions of mileage fees are guided by misinformation or lack of information using a national, internet-based survey. Hypothetical voting opportunities were used to gather respondent support for mileage fees, coupled with educational treatments that address mileage fee fairness, privacy, and costs. The findings indicate that respondents are largely misinformed or lack information about mileage fees and the gas tax. Pre-education, only 32% of respondents supported the policy, but post-education, 46% of respondents supported the policy. Through binomial, multinomial, and fixed effect modeling, we examined the factors associated with policy support, changes in policy support, and the educational treatments. Ultimately, our findings indicate that education can play a key role in increasing support for a mileage fee policy as an alternative to the gas tax.

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Lithium-ion batteries (LIBs) are the enabling technology for modern electric vehicles (EVs), allowing them to reach driving ranges and costs comparable to internal combustion engine vehicles, an important development with EVs being integral to greenhouse gas mitigation efforts. However, LIB advancements include the use of rapidly evolving and chemically diverse batteries as well as larger battery packs, raising concerns about battery production sustainability as well as battery end-of-life (EoL). This study seeks to respond to these concerns by analyzing potential pathways for EoL EV batteries, quantifies flows of retiring EV battery materials, proposes economically and environmentally preferable LIB EoL strategies, and recommends pertinent policies with an emphasis on environmental justice. The researchers used a loosely coupled dynamic systems model that utilized life cycle assessment and material flow analysis and a mixed methods research approach. They find that the U.S. can make significant gains in securing supply chains for critical materials and decrease life cycle environmental impacts through the adoption of Recycled Content Standard policies similar to those found in the European Union. In addition, they examine the currently understood waste hierarchy in the context of LIB technology. Comparing immediate recycling to repurposing and reusing, they find that repurposing and reusing reduces life cycle environmental impacts relative to recycling. This project also includes an investigation of EoL battery collection and transportation and the vehicle afterlife ecosystem, as well as general stakeholders in the LiB life cycle, informed by expert interviews and a case study of a developing lithium industry in Imperial, California.

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The adoption of battery electric trucks (BETs) as a replacement for diesel trucks has potential to significantly reduce greenhouse gas (GHG) emissions from the freight transportation sector. However, BETs have shorter driving range and lower payload capacity, which need to be taken into account when dispatching them. This paper addresses the energy-efficient dispatching of BET fleets, considering backhauls and time windows. To optimize vehicle utilization, customers are categorized into two groups: linehaul customers requiring deliveries and backhaul customers requiring pickups, where the deliveries need to be made following the last-in-first-out principle. The objective is to determine a set of energy-efficient routes that integrate both linehaul and backhaul customers, while considering factors such as limited driving range, payload capacity of BETs and the possibility of en route recharging. The problem is formulated as a mixed-integer linear programming (MILP) model and propose an adaptive large neighborhood search (ALNS) metaheuristic algorithm to solve it. The effectiveness of the proposed strategy is demonstrated through extensive experiments using a real-world case study from a logistics company in Southern California. The results indicate that the proposed strategy leads to a significant reduction in total energy consumption compared to the baseline strategy, ranging from 7% to 40%, while maintaining reasonable computational time. This research contributes to the development of sustainable transportation solutions in the freight sector by providing a practical and more efficient approach for dispatching BET fleets. The findings emphasize the potential of BETs in achieving energy savings and advancing the goal of green logistics. 

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Ridesharing can help reduce traffic congestion, greenhouse gas emissions and increase accessibility to transportation in major metropolitan areas across the United States. A robust rideshare system needs to take uncertainties such as traffic congestion and passenger cancellations into account. In this report, the authors propose a data-driven stochastic rideshare system that integrates those sources of uncertainties. Instead of assuming a probability distribution, the approach learns the underlying distribution in travel times and passenger cancellations from historical data. The authors first provide a mathematical model of the problem. Later they propose a stochastic average approximation approach for solving the routing and flexible pickup and drop-off selection problem. They also propose a Branch-and-Price heuristic and Adaptive Large Neighborhood Search-basedmetaheuristic to solve the underlying rideshare routing problem. To validate the approach, the authors construct test cases based on the New York City taxicab dataset. Numerical results show that the proposed branch and price-based solution approach can efficiently solve small instances while being close to the true optimum. On the other hand, the ALNS-based approach can solve medium to large instances with a small computational time budget while being robust to uncertainties. The proposed approach can help transportation officials and rideshare planners design more robust rideshare systems to alleviate traffic congestion in California.

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This paper describes a model for developing highly resolved, time-of-day specific electric vehicle charging demand profiles from travel survey data. Since timing of vehicle charging is dependent on electric vehicle supply equipment (EVSE) availability, four EVSE scenarios are considered: 1) home only, 2) home and workplace only, 3) universal EVSE, and 4) a probabilistic scenario where EVSE availability varies by location. To illustrate the implications of differing demand profiles on power grid operation with high renewable generating capacity, the profiles are in a typical regional economic dispatch model. The results provide a valuable approach for understanding the interactions between vehicle electrification and renewable energy deployment while exploring an updated range of assumptions about EVSE availability and charging behaviors for New York and the six New England states. All scenarios result in increased peak demand and increased generation by non-renewable generating sources. This indicates that incentive mechanisms that influence charging decisions are necessary to attain lower emissions outcomes.

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Pooled trips in private vehicles, or pooling, can lead to smaller environmental impacts and more efficient use of the limited roadway capacity, especially during peak hours. However, pooling has not been well adopted in part because of difficulties in coordinating schedules among various travelers and the lack of flexibility to changes in schedules and locations. In the meantime, ridehailing (RH) provides pooled services at a discounted fare (compared to the single-travel-party option) via advanced information and communication technology. This study examines individuals’ preferences for/against pooled RH services using information collected among travelers answering a set of questions related to their last RH trip. In doing so, both trip attributes and rider characteristics are considered. Taste heterogeneity is modeled in a way that assumes the presence of unobserved groups (i.e., latent classes), each with unique preferences, in a given sample of RH riders (N=1,190) recruited in four metropolitan regions in Southern U.S. cities from June 2019 to March 2020. The researchers find two latent classes with qualitatively different preferences, choosy poolers and non-selective poolers, regarding their choice in favor of/against pooling based on wait time, travel costs, purpose, and travel party size of the last RH trip. Personal characteristics are also identified, specifically age and three attitudes (travel satisfaction, environmentalism, and travel multitasking), which account for individuals’ class membership. This research contributes to the literature by explicitly modeling taste heterogeneity towards pooled ridehailing. In addition, unlike existing studies either at the person level or employing stated-preference data, a trip-level analysis is performed in connection with revealed preferences, which generates more realistic and relevant implications to policy and practice.

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Vehicle electrification has attracted strong policy support in California due to its air quality and climate benefits from adoption. However, it is unclear whether these benefits are equitable across the state’s sensitive populations and socioeconomic groups and whether disadvantaged communities are able to take advantage of the emission savings and associated health benefits of electric vehicle (EV) adoption. In this study, we analyze the statewide health impacts from the reduction of on-road emissions reduction (from reducing gasoline powered cars) and the increase in power plant emissions (from EV charging) across disadvantaged communities (DACs) detected by using the environmental justice screening tool CalEnviroScreen. The results indicate that EV adoption will reduce statewide primary PM2.5 emissions by 24.02-25.05 kilotonnes and CO2 emissions by 1,223-1,255 megatonnes through 2045, and the overall monetized emission-related health benefits from decreased mortality and morbidity can be 2.52-2.76 billion dollars overall. However, the average per capita per year air pollution benefit in DACs is about $1.60 lower than that in the least 10% vulnerable communities in 2020, and this disparity expands to over $31 per capita per year in 2045, indicating that the benefits overlook some of the state's most vulnerable population, and suggesting clear distributive and equity impacts of existing EV support policies. This study contributes to our growing understanding of environmental justice rising from vehicle electrification, underscoring the need for policy frameworks that create a more equitable transportation system.

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Freeway and arterial transportation networks in most districts are managed separately without any coordination. This lack of coordination increases the severity of traffic congestion when one or both systems reach their capacities. Some field studies have observed reductions in travel time by coordinating freeway ramps with adjacent arterial signals. To advance the investigation, we propose an integrated traffic management strategy that involves variable speed limit, lane change, ramp metering for freeway traffic flow control, and a traffic-responsive signal control scheme for adjacent traffic light intersections.The variable speed limit and lane change control are designed to alleviate congestion at a lane-drop bottleneck in an arbitrary section, and reject potential uncertainties from measurements or model parameters. The ramp metering algorithm takes advantage of the signal plan of neighboring arterial intersection when estimating on-ramp demands. The signal plan for each arterial intersection is determined by a simulation-based cycle length model and estimated demands of all directions, part ofwhich depend on off-ramp flow measurements. The above data sharing mechanism strengthens the connection between thefreeway and arterial networks and enhances the control performance. We demonstrate the effectiveness and quantify the benefits provided by the proposed system in terms of traffic mobility, safety and emission using microscopic traffic simulations.

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In partnership with the Southern California Regional Transit Training Consortium (SCRTTC), the California State University at Long Beach (CSULB) expanded potential audiences and offered program support for the Electric Vehicle Transit Bus High Voltage Safety Awareness class, which was previously developed under the National Center for Sustainable Transportation (NCST). CSULB expanded both the number of online offerings and the geographic reach by opening the class to transit agencies and campus fleet operators within the NCST network. The course was designed to enhance a technician's basic electrical skills and 2-circuit diagnosis, while teaching students how to work with a Digital Volt-Ohm Meter (DVOM). The effort supports the broader goal of building the workforce needed to support the transition to alternative energy and zero emission bus fleets.

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