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Open Access Publications from the University of California
Cover page of Spatial Scenarios for Market Penetration of Plug-in Battery Electric Trucks in the U.S.

Spatial Scenarios for Market Penetration of Plug-in Battery Electric Trucks in the U.S.

(2022)

Carbon emissions targets require large reductions in greenhouse gases (GHGs) in the near-to mid-term, and the transportation sector is a major emitter of GHGs. To understand potential pathways to GHG reductions, this project developed the U.S. Transportation Transitions Model (US TTM) to study various scenarios of zero-emission vehicle (ZEV) market penetration in the U.S. The model includes vehicle fuel economy, vehicle stock and sales, fuel carbon intensities, and costs for vehicles and fuels all projected through 2050. Market penetration scenarios through 2050 are input as percentages of sales for all vehicle types and technologies. Three scenarios were developed for the U.S.: a business as usual (BAU), low carbon (LC), and High ZEV scenario. The LC and High ZEV include rapid penetration of ZEVs into the vehicle market. The introduction of ZEVs requires fueling infrastructure to support the vehicles. Initial deployments of ZEVs are expected to be dominated by battery electric vehicles. To estimate the number and cost of charging stations for battery electric trucks in the mid-term, outputs were used from a California Energy Commission (CEC) study projecting the need for chargers in California. The study used the HEVI-Pro model to estimate electrical energy needs and number of chargers for the truck stock in several California cities. The CEC study outputs were used along with the TTM model outputs from this study to estimate charger needs and costs for six U.S. cities outside California. The LC and High ZEV scenarios reduced carbon emissions by 92% and 94% in the U.S. by 2050, respectively. Due to slow stock turnover, the LC and High ZEV scenarios contain significant numbers of ICE trucks. The biomass-based liquid volume reaches 70 (High ZEV) to 80 (LC) billion GGE by 2045. For the cities in this study, the charger cost ranges from $5 million to $2.6 billion in 2030 and from roughly $1 billion to almost $30 billion in 2040.

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Cover page of Case Studies of Socio-Economic and Environmental Life Cycle Assessment of Complete Streets

Case Studies of Socio-Economic and Environmental Life Cycle Assessment of Complete Streets

(2022)

“Complete streets” is a design concept for primarily urban streets and intersections (existing and/or new) intended to encourage active transportation by bicyclists and pedestrians by making streets safer, convenient, and attractive for active transportation; motorized transportation and parking are also accommodated in the design concept. The social and economic performance indicators included in the social life cycle assessment (SLCA) framework that was used in this project provide a great deal of insight into specific and different potential benefits of a given complete streets project. The SLCA framework is based on five categories of concerns and 17 performance measures or indicators. The indicators were tested in the project and evaluated for final recommendations for use in future studies. The results are compared with the existing streets that were configured to be vehicle-centric. The case studies were solicited in more and less advantaged neighborhoods so that the framework could also be evaluated in different contexts. Use of the CalEnviroScreen tool from the California Environmental Protection Agency was also investigated to assess the exposure of neighborhoods and their vulnerability to environmental impacts in conjunction with the performance indicators when evaluating the potential benefits for disadvantaged neighborhoods (also called priority population areas). As was found in the preceding study, the primary environmental impacts come from the use stage, namely changes in vehicle travel and changes in vehicle speeds from complete street design features. Recommendations are made for dropping some indicators because of difficulties collecting data or interpreting the results, modifications of other indicators, and adding some new indicators to fill important gaps.

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Cover page of Georgia Express Lane Corridors Vehicle Occupancy and Throughput Study 2018-2020 - Volume I: Vehicle and Person Throughput Analysis Before and After the I-75 Northwest Corridor and I-85 Express Lanes Extension

Georgia Express Lane Corridors Vehicle Occupancy and Throughput Study 2018-2020 - Volume I: Vehicle and Person Throughput Analysis Before and After the I-75 Northwest Corridor and I-85 Express Lanes Extension

(2022)

Ongoing assessment of system performance monitoring is critical to successful and efficient transportation planning, ensuring that infrastructure investments provide a desired return on investment. As with any new transportation facility, it is important to understand how Express Lane facilities affect travel behavior, resulting on-road vehicle activity, and subsequent person-throughput (a function of vehicle occupancy) as part of the facility performance assessment. This report summarizes the vehicle and person throughput analysis for the I-75 Northwest Corridor (NWC) and I-85 Express Lanes in Atlanta, GA, undertaken by the Georgia Institute of Technology research team for the State Road and Tollway Authority (SRTA). The research team tracked changes in observed vehicle throughput on four managed lane corridors and collected vehicle occupancy (persons per vehicle) data to assess changes in both vehicle throughput and person throughput associated with the opening of new Express Lane facilities. The team collected traffic volumes by video observation (GDOT’s Georgia NaviGAtor machine vision system and SRTA’s vehicle activity monitoring system). The team implemented a large-scale data collection effort for vehicle occupancy across all general purpose freeway lanes and from SRTA’s Express Lanes over a two-year period (before-and-after the opening of the Express Lanes). Between the baseline year (2018) and post-opening year (2019), the team observed a decrease in average vehicle occupancy (persons/vehicle), coupled with a significant increase in traffic volumes, especially on the NWC. The combined effect of increased traffic volumes and decreased occupancy still led to an overall increase in person throughput at all sites. Vehicle throughput on the I-85 corridor increased by about 5-7% and person throughput increased by 1-2% in the morning peak, and increased by around 10% for vehicles and 5% for persons in the evening peak. Vehicle throughput increased by more than 35% on I-575 in the AM and PM peaks, and by the same on I-75 in the AM peaks (only minor increases were noted in the PM peaks), likely due to the diversion of commute traffic from arterials onto the freeway corridor once the Express Lanes opened and congestion declined. Based upon vehicle throughput and occupancy distributions, the largest share of the increase in vehicle throughput in the peak periods came from an influx of single-occupant vehicle activity onto the corridor. Even though the number of carpools traversing the I-575 corridor increased slightly during the morning peak, the overall carpool mode share (percentage of carpools) decreased after the significantly greater numbers of single-occupant vehicles began using the corridor.

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Cover page of A Before and After Evaluation of Shared Mobility Projects in the San Joaquin Valley

A Before and After Evaluation of Shared Mobility Projects in the San Joaquin Valley

(2022)

In rural areas, cost-effective transit service is challenging due to greater travel distances, lower population densities, and longer travel times than in cities. As a result, the people who rely on public transit contend with infrequent and slow service, and keeping a sufficient number of personal vehicles in reliable working order can be prohibitively expensive for low-income families. UC Davis partnered with the eight San Joaquin Valley Metropolitan Planning Organizations to identify and support development of three innovative mobility pilot concepts for the region. The first pilot is an electric vehicle (EV) carsharing service known as Míocar, located in affordable housing complexes in eight rural communities in Tulare and Kern counties. The second is a volunteer ridesharing service, known as VOGO, which supplements existing transit services in transport-disadvantaged rural areas in San Joaquin and Stanislaus counties. The third is a Mobility-as-a-Service (MaaS) platform that allows planning and payment for fixed and demand-responsive transit services, including VOGO, in San Joaquin and Stanislaus counties. These pilots seek to (a) provide improved access to destinations for individuals with limited transportation alternatives, (b) and achieve greenhouse gas reductions through mode shifts from traditional internal combustion vehicles to EVs, ridesharing, and fixed transit. This report presents the methods and results for “before” and “after” evaluations conducted by UC Davis researchers to assess the performance and impacts of each pilot. The evaluations incorporate service usage data including telematics and MaaS application data, and survey data collected from pilot participants, to assess the programs beginning with pilot launch (2019 and2020) until November 2021. The results provide insights into participant characteristics and barriers to transportation, travel behavior, trip planning activities, and the extent to which the pilots addressed the travel needs of their target populations region.

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Cover page of Exploring Solar Charging Station Design for Electric Bicycles

Exploring Solar Charging Station Design for Electric Bicycles

(2022)

Electric bicycle charging facilities that support active mobility and public transit connectivity can play a significant role in the global transition to low-carbon energy. Design of an electric bicycle solar charging station can combine solar technology, light transportation infrastructure, and civic place-making to provide the public an opportunity to recharge their mobile electronics, e-bikes, or e-scooters. The proposed station design reimagines public space by providing a shaded seating area during the day and a vibrant, LED-lit space at night. Four solar panels and a battery bank extend the station’s charging capacity into the night. The goal of this project is to serve as an off-grid energy power supply and environmental information center, with interactive displays of the solar station operation and an LED display of local air quality.

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Cover page of Exploring the Consumer Market of Microtransit Services in the Sacramento Area, California

Exploring the Consumer Market of Microtransit Services in the Sacramento Area, California

(2022)

Microtransit is an emerging, technology-enabled, on-demand transportation mode whereby small shuttles provide shared rides through flexible routing and scheduling in response to customers’ requests for rides. Given its potential to address the equity and accessibility needs of the public, public transportation agencies are experimenting with this service to fill gaps in traditional transportation in the US. However, why some people are interested in microtransit while others are not remains an open question. For people who have never used it, what factors could work as facilitators or barriers in their willingness to adopt microtransit? Who are the early adopters of microtransit? Guided by the theory of planned behavior, this study aims to fill the gap in knowledge by conducting a large-scale survey of microtransit adopters and users of other means of transportation in the Sacramento area of California in 2021. This study focuses on the microtransit service SmaRT Ride (SR), operated by the Sacramento Regional Transit District (SacRT). Focus groups and interviews were conducted before the largescale online survey to gather preliminary information, help develop survey questions, and improve understanding of research findings, given the novelty of microtransit. Discrete choice models, including binary logit and ordered logit models and latent class analysis, were employed to explore barriers to and facilitators of SR adoption, willingness to use it, and underlying subgroups of early adopters. Important findings include that people who like fixed-route transit are less likely to adopt microtransit. Social support plays an important role in explaining the willingness to use microtransit. The analysis reveals three salient classes of microtransit users: travel time savers with environmental awareness, riders with a neutral mindset, and pro-SR and travel cost savers.

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Cover page of New Open-Source Analyses of Transit Job Access and Transit Ridership

New Open-Source Analyses of Transit Job Access and Transit Ridership

(2022)

This research project examines the link between job access and stop/station level transit ridership. Job access, following recent literature, is measured as the number of jobs that can be reached within a 30-minute transit travel time, including transfers and walk time to access jobs once exiting a transit station. Cumulative opportunity job access measures of this sort—i.e., the number of jobs that can be reached within 30 minutes—have become common in the recent access literature, and those measures have often focused on access via transit. Yet there have been few studies that examine the link between transit job access and transit ridership, and of those none that examine the link at a station or stop level. This study uses station and stop level ridership data for the Los Angeles Metro bus and rail system and the BART rail system in the San Francisco Bay Area. The research team calculated transit job access as jobs that can be reached within 30 minutes, using the Remix software tool. Regression analysis of 1,000 randomly selected Los Angeles bus stops reveals a robust relationship between stop-level ridership and job access. The association between transit job access and bus stop ridership (embarkations and disembarkations at the stop) is statistically significant. Converting that association into an elasticity, if the number of jobs accessible within 30-minutes were to increase by 1 percent, on average stop-level ridership would increase between 0.6 to 0.8 percent. The same association, with similar magnitudes, exists for Metro rail stations and BART rail stations, but due the smaller sample sizes, those relationships are not statistically significant when control variables are added to the regression. The findings show that job access is closely related to ridership at the bus stop level, suggesting transit agencies can increase job access by increasing bus frequency, reducing transfers, siting lines that connect job concentrations to residents, and by improving bus stop/rail station access/egress times.

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Cover page of Integrating Zero Emission Vehicles into the Caltrans Fleet

Integrating Zero Emission Vehicles into the Caltrans Fleet

(2022)

This report details the development and application of a spreadsheet tool which enables the evaluation and use of electric and hydrogen Zero Emission Vehicles (ZEVs) within the Caltrans fleet. The spreadsheet tool assists with both the placement of ZEVs and determining placement of new fueling stations to obtain the maximum benefit. The ZEV tool created as a result of this project allows Caltrans to maximize the usage of ZEVs that will be procured within Caltrans. The ZEV tool enables a strategic adoption of ZEVs within the Caltrans fleet by analyzing: fleet parameters, vehicle technology, vehicle usage, refueling infrastructure development, and operational needs. This report summarizes how available information, trip activity, and EVSE have been integrated within a database tool to analyze ZEV integration possibilities. The ZEV tool development provides Caltrans an architecture to integrate evolving data and fleet characteristics while optimizing ZEV placement and utilization into the future. Caltrans staff will be able to utilize the ZEV tool to strategically select vehicles as ZEV capable based on vehicle specifications, refueling infrastructure, and prior vehicle activity. The ZEV tool provides evaluation techniques by vehicle classification, region or refueling/charging capabilities. Utilization of the tool will assist California in the transition to ZEV platforms that are either battery electric or hydrogen fuel cell. The report serves as a guide to utilize the ZEV tool for deployment of ZEVs in the Caltrans fleet while maintaining or improving the effectiveness of operations. The optimized deployment strategy includes the operational and performance criteria of ZEVs while considering the location of refueling/recharging stations for EVSE (electric vehicle supply equipment) and hydrogen ZEVs.

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Cover page of Optimizing Fuel Consumption and Pollutant Emissions in Truck Routing with Parking Availability Prediction and Working Hours Constraints

Optimizing Fuel Consumption and Pollutant Emissions in Truck Routing with Parking Availability Prediction and Working Hours Constraints

(2022)

The transportation sector is responsible for a significant part of the U.S.’s greenhouse emissions, with a considerable amount being generated by medium-and heavy-duty trucks. However, when it comes to the trucking industry, ‘green’ routing studies do not consider other critical practical factors, like working hours regulations and parking availability. Due to parking shortages, routes and schedules that do not account for parking availability may lead to last-minute changes that make them more polluting than expected. Similarly, working hours regulations influence the timing of required rest stops, which may force drivers to deviate from initially selected routes and schedules with negative consequences to fuel consumption and emissions.

This study addresses a variant of the shortest path and truck driver scheduling problem under parking availability constraints which focuses on optimizing fuel consumption and emissions by controlling the truck's travel speed and accounting for time-dependent traffic conditions. As it is impossible to be absolutely certain about the future parking availability of any location during planning, the case of stochastic parking availability was also studied. When studying the trade-offs between prioritizing emissions reduction or trip duration, it was found that although focusing on emissions reduction can increase trip duration significantly, this impact is greatly reduced when considering scenarios with limited parking availability. The problem formulation was further extended to model drivers’ possible recourse actions when unable to find parking and the ensuing costs. This formulation was used to study how the solutions are affected by the level of information provided to drivers. It was found that ignoring uncertainty in parking availability results in inconsistent performance even when restricting parking to periods when probability of finding parking is high. Furthermore, results might not reflect the intent of the cost function used, e.g., minimizing illegal parking events and/or the priority assigned to emissions reduction. Giving drivers full information about the probability of finding parking at any time/location significantly improves performance and reduces illegal parking-related risks, but also substantially increase problem complexity and computation time. Using full information regarding parking availability but restricting the parking times to high availability time-windows can reduce complexity while maintaining consistent, although reduced, performance.

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Cover page of Balancing of Truck Parking Demand by a Centralized Incentives/Pricing System

Balancing of Truck Parking Demand by a Centralized Incentives/Pricing System

(2022)

Due to hours-of-service (HOS) regulations, commercial drivers are required to stop and rest regularly, thus reducing fatigue-related crashes. Nevertheless, if the parking infrastructure cannot cope with the demand generated by these required stops, new issues arise. In particular, this is the case for long-haul trucking, which is the focus of this work. Drivers often have difficulty finding appropriate parking, leading to illegal parking, safety risks, and increased pollution and costs. In this project, the researchers consider the issue of coordinating the parking decisions of a large number of long-haul trucks. More specifically, how to model the behavior of a region’s driver population and how it could be influenced. Understanding how truck parking demand is affected by the interaction of individual drivers’ selfish planning behaviors (in the sense that they minimize their own costs, not the overall system cost) and how parking prices affect optimal schedules are important steps in developing a system able to balance demand. The study presents a formulation that uses a modified TDSP (Truck Driver Scheduling Problem) mixed-integer programming model which tracks parking usage by dividing time into time-slots and charging drivers per time slot used. Results show that if truck drivers are following optimal schedules, then parking prices would be effective in changing which locations and time slots would be chosen by each driver. However, price adjustments can cause demand to shift in unexpected and not always beneficial ways, likely due to HOS regulations and client constraints limiting the possible alternative schedules. Therefore, further study is required to better understand the system’s properties and how to avoid or dampen these oscillations. Furthermore, due to HOS rules and client constraints, it might be impossible to divert demand from specific time slots and locations sufficiently. Nevertheless, this model could still aid in identifying these spots and contribute to the evaluation of infrastructure investment needs.

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