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Finding ways to boost transportation access for underserved populations can unlock broad social benefits. Micromobility programs, including bikesharing, offer scalable solutions. National, state, and regional housing and urban development agencies promote affordable housing and transit-accessible developments by funding programs such as the Low-Income Housing Tax Credit and Community Development Block Grants. However, these efforts are not always coordinated and the physical distance between affordable housing and transit access continues to grow. The problem is compounded by low car ownership rates in lower income urban communities. These circumstances have led to inequitable mobility access. To correct course, pairing affordable housing developments with reliable transit services is essential. This practice can increase equity and accessibility. A team at the University of California, Davis, conducted a case study in Sacramento, California, to explore bikesharing as an option for connecting affordable housing residents with transit services. This brief summarizes the findings from that research and provides implications for the field. View the NCST Project Webpage

The recent worldwide SARS-CoV-2 (COVID-19) pandemic has reshaped the way people live, how they access goods and services, and how they perform various activities. For public transit, there have been health concerns over the potential spread to transit users and transit service staff, which prompted transportation agencies to make decisions about the service, e.g., whether to reduce or temporarily shut down services. These decisions had substantial negative consequences, especially for transit-dependent travelers, and prompted transit users to explore alternative transportation modes, e.g., bikeshare. However, local governments and the public in general have limited information about whether and to what extent bikeshare provides adequate accessibility and mobility to those transit-dependent residents. To fill this gap, this study implemented spatial and visual analytics to identify how micro-mobility in the form of bikesharing has addressed travel needs and improved the resilience of transportation systems. The study analyzed the case of San Francisco in California, USA, focusing on three phases of the pandemic, i.e., initial confirmed cases, shelter-in-place, and initial changes in transit service. First, the authors implemented unsupervised machine learning clustering methods to identify different bikesharing trip types. Moreover, through spatiotemporally matching bikeshare ridership data with transit service information (i.e., General Transit Feed Specification, GTFS) using the tool called OpenTripPlanner (OTP), the authors studied the travel behavior changes (e.g., the proportion of bikeshare trips that could be finished by transit) for different bikeshare trip types over the three specified phases. This study revealed that during the pandemic, more casual users joined bikeshare programs; the proportion of recreation-related bikeshare trips increased; and routine trips became more prevalent considering that docking-station-based bikeshare trips increased. More importantly, the analyses also provided insights about mode substitution, because the analyses identified an increase in dockless bikeshare trips in areas with no or limited transit coverage.

This research studies the potential of bikeshare services to bridge the gap between Affordable Housing Communities (AHC) and transit services to improve transport accessibility of the residents. In doing so, the study develops an agent-based simulation optimization modeling (ABM) framework for the optimal design of the bikesharing station network considering improving accessibility as the objective. The study discusses measures of accessibility and uses travel times in a multi-modal network. Focusing on the city of Sacramento, CA, the study gathered information related to affordable housing communities, detailed transit services, demographic information, and other relevant data. This ABM framework is used to run three stages of travel demand modelling: trip generation, trip distribution and mode split to find the travel time differences under the availability of new bikesharing stations. The model is solved with a genetic algorithm approach. The results of the optimization and ABM-based simulation indicate the share of bike and bike & transit trips in the network under different scenarios. Key results indicate that about 60% of the AHCs are within 25-minute active travel time when the number of stations range from 25 to 75, and when the number of stations is increased to 100, most AHCs are within 40 mins of active mode distance and all of them are less than an hour away. In terms of accessibility, for example, having a larger network of stations (e.g., 100) increases by 70% the number of Points of Interest (for work, health, recreation, and other) within a 30-minute travel time. This report then provides some general recommendations for the planning of the bikesharing network considering information about destination choices as well as highlighting the past and current challenges in housing and transit planning.View the NCST Project Webpage

Motor vehicle crashes are the leading cause of accidental deaths in the US. In 2020, 38,824 people lost their lives in car-related crashes. Bicyclists and pedestrians are particularly susceptible—7,448 of these “vulnerable road users” were killed nationwide in 2020, and 29% of all reported crash-related fatalities in California were vulnerable road users. A variety of intelligent vehicle technologies hold promise for improving bicycle and pedestrian safety. Sensors in vehicles and/or used by vulnerable road users themselves could alert travelers of potential conflicts, giving them more time to react. However, these technologies all have unique technical, operational, and financial characteristics, and they might perform differently in different environmental conditions and at different levels of deployment. Little research has been done on how these technologies might affect safety. Researchers at the University of California, Davis combined aggregate historical crash data analysis and micro transportation simulation to examine the safety impacts of four different intelligent vehicle technologies: blind spot detection, a vulnerable-road-user beacon system carried by bicyclists or pedestrians, bicycle/pedestrian-to-vehicle communication, and intersection safety.View the NCST Project Webpage

This project aims to know how the Intelligent Vehicle Technologies (IVT) can improve Vulnerable Road Users’ (VRU) safety in different environments and conditions (e.g., sight distance and traffic flow) at signalized intersections. For the statistical analysis on historical aggregate crash data, the project studied risk factors on crash injury severity for VRU-related crashes at signalized intersections in California cities. The researchers summarize seven critical crash types for the micro-level traffic safety simulation. For the traffic safety simulation part, it is found that Intersection Safety (INS) is empowered to be the most efficient technology to significantly reduce average collision counts for passenger cars under all seven collision types of interest. Blind Spot Detection (BSD) has the most minimal effects on those types. The safety improvement of VRU Beacon Systems (VBS) and Bicycle/Pedestrian to Vehicle Communication (BPTV) are between INS and BSD. Results show that under a certain threshold of sight distance, IVT can significantly reduce the collision probability and IVT can still improve safety under good sight condition if collisions happen in front of vehicles. In the end, the project conducted sensitive analyses of sight distance and traffic volume. For some collision types, INS and BPTV can only reduce ~50% of collision at extremely high traffic volume conditions.View the NCST Project Webpage

California sits at the epicenter of self-driving vehicle technology development, with numerous companies testing connected and automated vehicles (CAVs) in the state. CAVs have the potential to improve safety and increase mobility for children, the elderly, and people with disabilities. These vehicles will operate more efficiently, use less space on the roadway, and cause fewer crashes, all of which are expected to relieve traffic congestion. However, CAVs will also likely bring about complex changes to travel demand, urban design, and land use. The degree to which these changes will affect vehicle miles traveled, energy use, and air pollution in California is unknown and could have wideranging implications for the state’s ability to meet its climate goals.Researchers at the University of California, Davis investigated the range of potential impacts that rapid adoption of CAVs in California might have on vehicle miles traveled and emissions. The researchers estimated the vehicle miles traveled and emissions of each scenario using a statewide travel demand model, emissions factors from California agencies, and assumptions derived from the scientific literature and expert input. This policy brief summarizes the findings from that research and provides policy implications.View the NCST Project Webpage

Bikeshare is an increasingly prevalent transportation option that offers users access to a bicycle without owning it. Both dockbased (requiring users to return bicycles to a fixed station) and dockless (free-floating) services have grown significantly over the past decade. Previous research has found that a well-designed bikeshare system has great potential to improve accessibility for disadvantaged communities. However, systems currently underserve these communities. Moreover, there is a lack of research about the performance and impacts of dock-based versus dockless bikeshare systems in terms of providing equitable access to disadvantaged communities.Researchers at the University of California, Davis analyzed the difference in service levels among dock-based and dockless systems in the cities of San Francisco and Los Angeles. The researchers analyzed the spatial distribution of service areas, availability of bikes and bike idle times, trip statistics, rebalancing, and other metrics to understand how well or poorly these systems serve designated “communities of concern” (CoCs).  Finally, using crowdsourced suggestions from online platforms, the researchers conducted a comparative assessment of actual station locations with the users’ suggestions of potential station locations. These analyses can help planning agencies and local governments to better understand and manage these systems. This policy brief summarizes the findings from that research and provides policy implications.View the NCST Project Webpage

This project aims to provide recommendations on the methodology and design specifications for the travel demand model to be built for the Link21 program in the Northern California megaregion. The Link21 program is a major rail investment program that will considerably improve and upgrade the passenger rail services in the Northern California megaregion, centered around the Transbay Corridor between Oakland and San Francisco in the San Francisco Bay Area. To support this effort, we reviewed the current and potential travel markets for the Link21 program, assessed the available travel demand models that could be used to support the modeling efforts for the Link21 program, and conducted interviews with experts from academic institutions, metropolitan planning organizations, state and federal agencies, and US DOE national labs. Considering the goals and objectives of the Link21 program, a list of 20 critical, important, and optional modeling features were identified, which should be considered for the Link21 program. We reviewed 11 existing travel demand models based on the evaluation of their modeling features, and present four proposed modeling approaches which could be considered to support the Link21 program. For each modeling approach, we summarize pros and cons in terms of fulfilling the requirements of the Link21 program. The four modeling approaches include: 1) building on the Metropolitan Transportation Commission (MTC) TM 2.1 regional travel demand model without a dedicated long-distance travel model component; 2) building on the MTC TM 2.1 regional travel demand model with a dedicated long-distance travel model component; 3) building on the San Francisco County Transportation Authority (SFCTA) regional travel demand model with or without a dedicated long-distance travel model component; and 4) building on the California High Speed Rail (CHSR) or the new statewide rail model that is currently under development. The study also discusses some sources of uncertainties that might affect future travel demand and the modeling practice in the Link21 regions. These include the impacts of the COVID-19 pandemic on work patterns and activity/travel choices, the introduction of shared mobility services, micromobility, the potential deployment of Mobility as a Service (MaaS) solutions, and the forthcoming deployment of connected and automated vehicles (CAVs). Given the complexity of the Link21 program and the requested 18-month timeline for developing a new travel demand model to support the program, we recommend that the model development for the Link21 program build on an existing modeling framework and adopt a modular system, which can be updated over time. An initial model release would become available in the proposed timeline of 18 months, while future updates and improvements in the model components could be added in future model updates. This process also would be well-suited to address eventual modeling issues that could arise with the initial model release, and it would benefit from the development and updates of other models in the Northern California megaregion that are being carried out in parallel.

Dockless bikeshare systems show potential for replacing traditional dock-based systems, primarily by offering greater flexibility for bike returns. However, many cities in the US currently regulate the maximum number of bikes a dockless system can deploy due to bicycle management issues. Despite inventory management challenges, dockless systems offer two main advantages over dock-based systems: a lower (sometimes zero) membership fee, and being free-range (or, at least free-range within designated service areas). Moreover, these two advantages may help to solve existing access barriers for disadvantaged populations. To date, much of the research on micro-mobility options has focused on addressing equity issues in dock-based systems. There is limited knowledge of whether, and the extent to which dockless systems might help mitigate barriers to bikeshare for disadvantaged populations. Using San Francisco and Los Angeles as case studies, because both cities have both dock-based and dockless systems running concurrently, the research team quantified bikeshare service levels for communities of concern (CoCs) by analyzing the spatial distribution of service areas, available bikes and bike idle times, trip data, and rebalancing among the dock-based and dockless systems. They found that dockless systems can provide greater availability of bikes for CoCs than for other communities, attracting more trip demand in these communities because of a larger service area and frequent bike rebalancing practices. More importantly, they noticed that the existence of electric bikes helps mitigate the bikeshare usage gap between CoCs and other tracts. Besides the data analyses for bikeshare trips, the research team also studied the spatial distribution of online suggested station locations and find that the participants’ desired destinations for work/school purposes have not been covered to the same extent in CoCs as in other communities. The results provide policy insights to local municipalities on how to properly regulate and develop dockless bikeshare systems to improve mobility equity.View the NCST Project Webpage

This study helps understand how the anticipated emergence of autonomous vehicles will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. The study begins with a literature review on connected and automated vehicle (CAV) technology for light-duty vehicles, the factors likely to affect CAV adoption, expected impacts of CAVs, and approaches to modeling these impacts. The study then uses a set of modifications in the California Statewide Travel Demand Model (CSTDM) to simulate the following scenarios for the deployment of passenger light-duty CAVs in California by 2050: (0) Baseline (no automation); (1) Private CAV; (2) Private CAV + Pricing; (3) Private CAV + Zero emission vehicles (ZEV); (4) Shared CAV; (5) Shared CAV + Pricing; (6) Shared CAV + ZEV. The modified CSTDM is used to forecast travel demand and mode share for each scenario, and this output is used in combination with the emission factors from the EMission FACtor model (EMFAC) and Vision model to calculate energy consumption and criteria pollutant emissions. The modeling results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled and emissions will likely increase, due to the relative convenience of CAVs. The study also reveals limitations in models like the CSTDM that primarily use sociodemographic factors and job/residence location as inputs for the simulation of activity participation and tour patterns, without accounting for some of the disruptive effects of CAVs. The study results also show that total vehicle miles traveled and vehicle hours traveled could be substantially impacted by a modification in future auto travel costs. This means that the eventual implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help curb tailpipe pollutant emissions in future scenarios, though they may not be able to completely offset the increases in travel demand and road congestion that might result from CAV deployment. Such policies should be considered to counteract and mitigate some of the undesirable impacts of CAVs on society and on the environment.

Prior to the last decade, the logistics industry trended towards the development of mega-warehouse facilities in suburban settings far from core markets, creating a phenomenon known as logistics sprawl. This trend is particularly prominent in Southern California (Figure 1). Since the 2008-2009 economic crisis, however, the trend has shifted. The rise of e-commerce may have influenced supply chain decisions to locate warehouses and distribution centers closer to denser urban areas to enable faster deliveries to consumers. The changes in size and spatial distribution of warehouses and distribution centers as well as the environmental and equity implications of these changes are not fully understood. As warehouses and distribution centers locate in denser urban areas, they may introduce additional diesel truck traffic into disadvantaged and low-income communities.To gain a better understanding of how the spatial distribution and size of freight facilities are changing and the implications of these changes for disadvantaged and low-income communities, researchers at the University of California, Davis analyzed aggregate data about the number of warehouses and distribution centers and disaggregate real estate data of purchases and leases during the last three decades in California. They also analyzed the relationship between freight facilities and communities of concern using the California Environmental Protection Agency’s CalEnviroScreen 3.0 tool. The research focused on the San Diego, Los Angeles, San Francisco, San Joaquin Valley, and Sacramento regions. Key findings from the research are presented in this brief.

This work addresses the distribution of warehouses and distribution centers (W&DCs) influenced by e-commerce, through spatial analysis and econometric modelling. Specifically, this work analyzes the concentration of W&DCs in various metropolitan planning organizations (MPOs) in California between 1989 and 2016-18; and studies the spatial relationships between W&DC distribution and other demographic and environmental factors through econometric modeling techniques. The work conducts analyses to uncover common trends in W&DC distribution. The analyses used aggregate establishment, employment, and other socio-economic information, complemented with transportation related variables. The results: 1) confirm that the weighted geometric centers of W&DCs have shifted slightly towards city central areas in all five MPOs; 2) W&DCs show a non-decreasing trend between 2008 and 2016; and 3) areas with more serious environmental problems are more likely to have W&DCs. A disaggregate analyses of properties sold and leased in one of the study regions shows a trend where businesses are buying or leasing smaller facilities, closer to the core of consumer demand. Among other factors, the growth of e-commerce sales, and expedited delivery services, which require proximity to the customers, may explain these trends. The study results provide insights for planners and policy decision makers, and will be of interest to practitioners, public and private entities, and academia. Caltrans, MPOs, and affiliated institutions of the National Center for Sustainable Transportation will directly benefit from the results as they want to avoid equity issues brought by the fast development of e-commerce, and its potential impact on W&DC distribution.

It is a key avenue of carbon peaking and carbon neutrality goals to reduce energy consumption of buildings， and distribution system planning of buildings plays an essential role in it. According to the characteristic of each part of source-network-load-storage， a total cost model of energy consumption and distribution for buildings is established. An energy-limited “photovoltaics plus storage” planning problem with optimum cost is proposed and solved by Benders decomposition. Given alternatives of design parameters， the optimum selection of parameters is realized by the sensitivity model of the initial planning. Besides， two iterative procedures of planning and parameter adjustment are formulated. Finally， a case study of a single-story exhibition hall reveals that the planning method is practicable and effective； it can curtail energy consumption of operation and costs and help promote buildings with low energy consumption.

Bikeshare programs are increasingly popular in the United States and they are an important part of sustainable transportation systems, offering a viable mode choice for many types of last-mile trips. This popularity means that an increasing number of people can enjoy the convenience of cycling and the associated physical health benefits without actually owning a bike (or having access to their own bikes). However, bikeshare systems have not captured high levels of ridership from disadvantaged populations. Many barriers exist that prohibit residents from disadvantaged communities from accessing bikeshare services. These barriers include absence of bikeshare stations within walking distances, lack of financial resources, cultural barriers, and/or unsafe cycling environments. Most of the current research on bikeshare programs focuses on societal benefits (e.g. reducing greenhouse gas emissions by replacing auto trips with bike trips) and bikeshare system management (e.g., bike repositioning between stations). There is some emerging research focused on equity issues in developing bikeshare. However, far less attention has been paid to bikeshare programs’ potential benefits for disadvantaged communities and virtually no quantitative research on how to design bikeshare systems to improve access for these populations. This dissertation work addresses three fundamental bikeshare equity problems.

Bikeshare programs are increasingly popular in the United States, and they are an important part of sustainable transportation systems. They offer an alternative mode choice for many types of last-mile trips. Most of the current research on bikeshare focuses on benefits (e.g., how to replace auto trips with bike trips and reduce greenhouse gas emissions) and system management (e.g., bike repositioning between stations). Far less attention has been paid to the potential for bikeshare programs to provide greater access to jobs and essential services for underserved communities. To date, there is virtually no quantitative research aimed at designing bikeshare systems for underserved communities. To address this research gap, this study of two cities (Chicago and Philadelphia) first, examines whether bikeshare systems have targeted specific populations, and second, quantitatively assesses the potential for bikeshare systems to provide greater accessibility for underserved communities.View the NCST Project Webpage

AbstractBikeshare system operators have leveraged online platforms to gather user feedback on station experiences and service quality. This crowdsourced data presents a valuable opportunity to enhance bikeshare planning and operations. However, planners have limited methods to analyze those qualitative data given the sparse nature of this data, which is insufficient for training comprehensive machine learning models. Addressing this challenge, our study employs a novel approach combining spatial analysis and Factor Analysis of Mixed Data (FAMD) to delve into bikeshare users’ perceptions and expectations. Focusing on Chicago’s bikeshare system, we utilize its limited online comments to demonstrate the robustness of our algorithm and its applicability to both large and small datasets. Our findings reveal a geographic pattern in feedback: negative comments on bike rebalancing, station locations, and facilities are concentrated in the city center, while dissatisfaction with the cycling environment is consistent across both urban and peripheral areas. Moreover, we discovered that the demographic and employment characteristics of areas surrounding bikeshare stations significantly influence positive feedback, overshadowing the impact of station design and local infrastructure. Overall, this study offers a quantitative framework for leveraging limited crowdsourced feedback effectively, providing strategic insights for refining bikeshare planning and operational decisions. [ABSTRACT FROM AUTHOR]

Bikeshare programs are increasingly popular in the United States, and they are an important part of sustainable transportation systems. They offer an important alternative mode choice for many types of last mile trips. Most of the current research on bikeshare focuses on bikeshare benefits (e.g., how to replace auto trips with bike trips and reduce greenhouse-gas emissions) and bikeshare system management (e.g., bike repositioning between stations). Far less attention has been paid to the programmatic potential for providing greater access to jobs and essential services for underserved communities. To date, there is virtually no quantitative research aimed at designing bikeshare systems for underserved communities. The authors develop a new spatial index that identifies bikeshare station locations exhibiting a high potential for providing service for underserved communities. The index can: 1) facilitate the identification of priority areas for bikeshare investment based on current infrastructure and the potential for increased job or essential service access; 2) inform the siting of bikeshare stations and investment in bike infrastructure to better assist underserved populations, and finally 3) provide an estimate of the potential for improved job and social services access via bike‐to‐transit.View the NCST Project Webpage