Public transit ridership has declined in major US cities over the past decade. Integrating traditional fixed-route transit with flexible microtransit has been proposed to enhance ridership, mobility, accessibility, and sustainability. This project surveyed California transit agencies on their microtransit services to identify challenges to integrating them with fixed-route services. An agent-based model combining the two modes of transit was developed to evaluate different operational designs. FleetPy, an open-source simulation tool, modeled microtransit dynamics. The study examined design impacts, such as fixed route headways and microtransit fleet size, in downtown San Diego and Lemon Grove, California. Results showed that while microtransit reduces fixed-route ridership and requires higher subsidies, it significantly boosts job accessibility.
Charging-as-a-Service (CaaS) is an innovative electric vehicle (EV) charging station model that allows customers access to EV chargers through a contract with a provider responsible for design, deployment, operations, and maintenance. Little is known about the motivations and experiences of stakeholders involved in CaaS operations, including providers, electric utilities, and customers. A grey literature review identified CaaS services, provider-described benefits, and utility-provided CaaS and charging services. Then, we conducted semi-structured interviews with 13 stakeholders to identify critical themes on interactions between stakeholders and the perceptions, challenges, and opportunities of the CaaS business model in addressing charging station needs in California. CaaS may have structural benefits to customer-owned chargers and could improve charger reliability, provide scalable solutions, and reduce customer fatigue with EV charging deployment. However, CaaS faces the same challenges present in the broader charging industry. The findings in this study can guide policymakers in supporting maintenance-related workforce development and streamlining and crafting EV charging infrastructure-informed subsidy programs. Additionally, stakeholders recommend municipal-led EV infrastructure planning and funding for chargers in disadvantaged communities. These interviews clarify the role of CaaS within the EV charging industry and confirm the need for engaged policymaker support to clear roadblocks, support investment, and educate customers about decision-making, which benefits all EV charging stakeholders.
As online shopping nears its third decade, it is clear that its impacts on urban goods flow are profound. Increased freight traffic and related negative externalities such as greenhouse gas emissions and local air pollution can impede sustainability goals. In response, e-retailers are exploring innovative distribution strategies to enhance last-mile delivery sustainability and efficiency. They use urban consolidation centers with light-duty vehicles like electric vans and cargo bikes, establish alternative customer pickup points, and deploy crowdsourced delivery networks. Advanced technologies that may streamline deliveries, such as autonomous delivery robots and unmanned aerial vehicles, are being tested. University of California Davis and Irvine researchers have investigated these strategies under economic viability, environmental efficiency, and social equity frameworks. Different modeling approaches were implemented to evaluate last-mile network designs and the potential for decarbonizing delivery fleets by switching to electric vehicles. Key findings suggest that while these innovative strategies offer substantial environmental benefits and reduce operational costs, they also present challenges like higher initial investments and operational hurdles. The study emphasizes the need for ongoing innovation and careful strategy implementation to balance sustainability with urban delivery systems’ economic and service reliability demands.
In 2017, California passed Senate Bill 1 (SB1) to bolster transportation infrastructure funding. Using data primarily from the California Department of Transportation (Caltrans)’s official SB1 progress reports, we analyze the severity of cost overruns, delays, and cancellations across SB1 Transportation Projects. Although events such as the COVID-19 pandemic likely caused some of these negative outcomes, our statistical models show consistent patterns of overruns associated with fiscal periods, programs, and geographic locations. Our results indicate that the common 20% contingency is generally insufficient, indicating the need for better risk estimation in project planning. We also suggest amplifying data transparency on project performance and re-evaluating project selection criteria to avoid rewarding underestimation of project costs and duration and penalizing accurate estimation.
While transportation infrastructure and efficiency should inform where to build more housing, little is known about how housing allocation and development processes can be coordinated more systematically with transportation. To date, transportation-housing coordination has often relied on the densification of areas near rail transit stations, putting heavy burdens on these locations and their residents. Much less attention has been paid to how densification can be achieved in a more equitable manner by encompassing other sites. This report directs attention to non-rail locations, specifically low vehicle miles traveled (VMT) areas and bus corridors, and examines the challenges that can arise in promoting densification more broadly. It shows that data uncertainties can make it challenging to identify low VMT locations and that prioritizing only low VMT locations for residential development may have limited effectiveness in expanding housing opportunities in high opportunity areas. The report further explores ways to achieve more inclusive densification of non-rail transit areas and highlights the importance of anti-displacement strategies.
Autonomous vehicles (AVs) heavily rely on machine learning-based perception models to accurately interpret their surroundings. However, these crucial perception components are vulnerable to a range of malicious attacks. Even though individual attacks can be highly successful, the actual security risks such attacks can pose to our daily life are unclear. Various factors, such as lack of stealthiness, cost-effectiveness, and ease of deployment, can deter potential attackers from employing certain attacks, thereby reducing the actual risk. This research report presents the first quantitative risk assessment for physical adversarial attacks on AVs. The specific focus is on attacks on AV’s perception components due to their highly critical function and representation in existing research. The report defines the daily-life risk as the likelihood that a given type of attack will be employed in real life and the authors develop a problem-specific risk scoring system and accompanying metrics. They perform an initial evaluation of the proposed risk assessment method for all the reported attacks on AVs from 2017 to 2023. They quantitatively rank the daily-life risks posed by each of eight different categories of attacks s and find three attacks with the highest risks: 2D printed images, 2D patches, and coated camouflage stickers, which deserve more focused attention for potential future mitigation strategy development and policy making.
The COVID-19 pandemic has generated renewed interest in how telecommuting can alter the workings of our cities and regions, but there is little guidance on how to align planning practice with the new reality. This report synthesizes the research on telecommuting and its consequences to help planners better understand what effects may occur from the proliferation of telecommuting and what lessons can be drawn from research findings. Emphasis is on the broad relevance of telecommuting to many domains of planning, including housing, land use, community development, and inclusive place-making, while attention is paid to changes in travel demand, vehicle miles traveled (VMT), and greenhouse gas emissions. The research suggests that telecommuting can occur in a variety of ways, and its impacts are largely dependent not only on the type/schedule of telecommuting but on the built environment, transit accessibility, and other amenities/opportunities the location provides. The varying impacts reported in the research can be seen as an encouragement for planners to actively create a better future rather than merely responding to the rise of telecommuting. Given the breadth of telecommuting’s impacts, systematic coordination across various planning domains will be increasingly important. This report also calls for collaboration across cities to guide the ongoing transformation induced by telecommuting not in a way that leads to more residential segregation but in a way that provides more sustainable and inclusive communities.
Transportation pricing policies aim to manage vehicular demand for parking, dense urban areas, roadways, and highway lanes. Although pricing policies take various forms, most were designed in a world before the sharing economy and ride-sourcing companies. Hence, the efficacy of existing pricing policies in a world with shared mobility services requires further consideration. Moreover, future pricing policies designed to handle private vehicles and shared ride-sourcing vehicles must consider the behavior of both sets of travelers and vehicle fleets. This study develops a conceptual framework to support systems level analysis of pricing policies for a world with private and shared vehicle usage. It qualitatively analyzes the impact of shared vehicles on the effectiveness of various pricing policies, while also considering the role of vehicle-to-infrastructure technology. This conceptual framework will support future research that uses activity-based travel demand and dynamic network assignment models to evaluate congestion pricing policies in an era of shared mobility. Additionally, the study presents a detailed review of the literature related to transportation pricing together with a trend analysis on congestion pricing policies in Transportation Research Board annual meeting titles and abstracts.
The upcoming Connected Vehicles (CV) technology shows great promise in effectively managing traffic congestion and enhancing mobility for users along transportation corridors. Data analysis powered by sensors in CVs allows us to implement optimized traffic management strategies optimizing the efficiency of transportation infrastructure resources. In this study, we introduce a novel Integrated Corridor Management (ICM) methodology, which integrates underutilized Park-And-Ride (PAR) facilities into the global optimization strategy. To achieve this, we use vehicle-to-infrastructure (V2I) communication protocols, namely basic safety messages (BSM) and traveler information messages (TIM) to help gather downstream traffic information and share park and ride advisories with upstream traffic, respectively. Next, we develop a model that assesses potential delays experienced by vehicles in the corridor. Based on this model, we employ a novel centralized deep reinforcement learning (DRL) solution to control the timing and content of these messages. The ultimate goal is to maximize throughput, minimize carbon emissions, and reduce travel time effectively. To evaluate our ICM strategy, we conduct simulations on a realistic model of Interstate 5 using the Veins simulation software. The DRL agent converges to a strategy that marginally improves throughput, travel speed, and freeway travel time, at the cost of a slightly higher carbon footprint.
Connected Vehicles (CV) technology offers significant potential for managing traffic congestion and improving mobility along transportation corridors. This report presents a novel approach using integrated corridor management (ICM) technology to divert CVs to underutilized park-and-ride facilities where drivers can park their vehicle and access public transportation. Using vehicle-to-infrastructure (V2I) communication protocols, the system collects data on downstream traffic and sends messages regarding available park-and-ride options to upstream traffic. A deep reinforcement learning (DRL) program controls the messaging, with the objective of maximizing traffic throughput and minimizing CO2 emissions and travel time. The ICM strategy is simulated on a realistic model of Interstate 5 using Veins simulation software. The results show marginal improvement in throughput, freeway travel time, and CO2 emissions, but increased travel delay for drivers choosing to divert to a park-and-ride facility to take public transportation for a portion of their travel.
