Smart Vehicle Technologies and Connected Automated Vehicles

Fatalities from motor vehicle crashes lead all accident deaths in the US. In 2017, 34,247 people lost their lives in car-related crashes and there were 121,000 injured vulnerable road users (VRUs). There are three main categories of factors influencing crashes between vehicles and VRUs: VRUs’ demographic information, drivers’ behavior, intersection condition, and weather. Studies have shown that cyclists are willing to equip bicycles with specific technology considering the potential improvement of personal safety. As reflected by the literature, several sensor technologies in personal vehicles, freight trucks, and other users (e.g., VRUs) could help improve safety. Moreover, the incoming 5G Era will mitigate telecommunication challenges for some of these advanced intelligent vehicle technologies (IVTs).

This research will develop a simulation tool to help quantify safety improvements from various IVTs, including Vehicle-to-Infrastructure (V2I). In particular, the work will contribute to filling the knowledge gap on IVT’s influence on VRUs’ safety. The analysis of these technologies can help both public and related stakeholders to better understand how different IVTs will improve the safety of cyclists and pedestrians under various conditions at intersections. Please read this report for more detailed information.

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.

Micro-mobility, Transit, and Equity

Currently, affordable housing units have become an important part of urban housing planning, and both Federal and local governments seek to provide more affordable housing for low-income populations. Additionally, transit-oriented development (TOD) has attracted increased interest from local transit agencies. Overall, these two developments have been addressed separately resulting in a spatial gap between affordable housing units and transit services including fixed-route bus/rail and para transit bus. Bikeshare, as an emerging micro-mobility service, shows a great potential to increase accessibility, especially for disadvantaged populations. Thus, this project will develop a framework to optimize the location of bikeshare stations to mitigate the barriers between affordable housing and transit services.

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. We have limited knowledge of the extent to which dockless systems can help mitigate barriers to bikeshare for disadvantaged populations. Using San Francisco as a case study, because the city has both dock-based and dockless systems running concurrently, we find 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. Please refer to this paper for more information.

In our research, we chose the Divvy bikeshare system in Chicago since it is currently one of the biggest systems in the USA and its trip data are open. By incorporating emission and air dispersion modeling tools, we first estimated the high-resolution air pollution concentration level in the city. Then, we quantified the trip-based PM2.5 exposure by including every trip route and duration time. Finally, we conducted a spatial analysis for health exposure related to bikeshare trips in disadvantaged areas.

In Chicago, most of routes with high PM2.5 exposure index are distributed in the southwest of Chicago, where there are more minority populations or low-income communities. From the station level, most of the stations in disadvantaged areas have a high level of PM2.5 exposure index on average.

Our research has clearly shown that users from disadvantaged areas are more likely to take a risk of absorbing more PM2.5, especially when traveling to other areas with more job opportunities and other essential services by bikeshare. In summary, our research points out an ignored aspect in planning bikeshare. Bicycle infrastructure design and air pollution control should be integrated into the process of bikeshare promotion in disadvantaged areas. Please refer to this paper.

We develop a model to estimate the potential demand (i.e., bikeshare trip production and attraction) and its distribution, and evaluate performance over a set of objectives (e.g., maximization of annual revenue, accessibility improvements) to find the most equitable distribution of stations. We build a genetic algorithm to solve this multi-objective optimization. The study uses the Divvy bikeshare system in Chicago as a case study, and compares the solutions of the model with the system's expansion (new stations added) in 2016, which targeted disadvantaged areas. When selecting accessibility as the main objective, the results indicate the need to provide more stations in disadvantaged areas and those results overlap with the system's expansion in 2016. On the contrary, the goal of revenue maximization results in a smaller network of stations and fewer accessibility improvements, especially in disadvantaged communities. A sensitivity analysis uncovers the greatest obstacle (i.e., station cost) to adding more stations in disadvantaged areas. More importantly, a Pareto frontier of this multi-objective optimization supports several policy suggestions for incentivizing private bikeshare companies to target more disadvantaged populations. Our results show the importance of considering accessibility and other equity constraints in developing a more inclusive, equitable and sustainable transportation system, and we provide several planning suggestions. Please refer to this paper.

Exploring the Potential Role of Bikeshare to Complement Public Transit: The Case of San Francisco Amid the Coronavirus Crisis

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. Specifically, the study analyzed the case of San Francisco in California, USA, and selected 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 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; recreation-related bikeshare trips increased in terms of the proportions; 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 paper uses two case study cities (Chicago and Philadelphia) to first, examine whether bikeshare systems have targeted specific populations, and to second, quantitatively assess the potential for bikeshare systems to provide greater accessibility for disadvantaged communities. Our results demonstrate that a well-designed bikeshare system can generate greater accessibility improvements for disadvantaged communities than the same system would produce for other populations. Using a newly developed spatial index that combines the potential for increased access to jobs and essential services, the level of bike infrastructure, and the disadvantaged population shares, we also find evidence that existing bikeshare systems have been specifically designed to target certain ridership. We find that locating stations in proximity to disadvantaged communities has the potential to increase household access (by bike and by bike-to-transit) to jobs and essential services and can close accessibility gaps between mobility constrained populations and critical services. The spatial index can be applied to identify potential locations to locate bikeshare stations (dock-based bikeshare systems) or rebalance bikes (dockless bikeshare systems) to address bikeshare equity issues. Please refer to this paper.

Bicycle paths or even bicycle lanes have not emerged as key priorities in traditional pavement systems analysis. Most cities rely on route preferences (e.g., common school routes) or visual checks to prioritize pavement conditions on bicycle facilities. We used 31 bike path sections with a representative range of pavement surface conditions to collect acceleration data, GPS location data, bicycle steering angle, surface displacement data, and mean texture depth (MTD) data. We also recruited cyclists to complete a post-ride survey on ride quality. Using these data, we specified two ordered logit regression models to separately examine the relationships between bicycle ride quality and traditional pavement roughness measurement (or surface defect density on trajectories) while holding other parameters (e.g., bicycle accelerations and steering angle) constant. Our study shows that a surface defect index can replace the MTD test for bicycle facilities and can produce better performance in predicting ride quality, especially when pavement condition needs moderate repair to avoid becoming much worse. We also examine ride quality, specifically the vertical acceleration effect on ride experience, for different types of bicycles (e.g., a mountain bike with a suspension system versus a touring bike). Please refer to this paper.

Freight and Equity

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. The study results provide insights for planners and policy decision makers, and will be of interest to practitioners, public and private entities, and academia. Please refer to this report.

Natural Hazard and Resilience

No-notice wildfires pose a serious threat to the safety of residents, where the notification time and departure time may all be within a matter of minutes. Here, we examine the factors associated with the initial wildfire awareness time, the evacuation preparation time, and finally the departure time. We use unique interview and survey data gathered in Red Cross shelters just weeks after evacuation for the 2018 Camp Fire in Northern California. We specify models for awareness time, departure time, and preparation time and find that quicker awareness is associated with seeing the fire first-hand, familiarity with local evacuation protocol, smartphone ownership, among others. We find that higher incomes are associated with quicker awareness, but had no effect on departure or preparation times. Longtime residents had longer preparation and departure times. Taken together, our results suggest new pathways for better understanding how to plan and prepare for no-notice disaster events. Please refer to this paper for more information.