The curbside is valuable real estate in cities, providing private vehicle parking, pick-up/drop-off areas, public transit stops, freight loading/unloading zones, and space for pedestrians and bicyclists. Shortages and poor management of curb space can cause congestion and increased emissions due to vehicles searching for parking and can create unsafe conditions from vehicles double parking. Traditional curbside planning strategies have relied on land use–based demand estimates to allocate access priority to the curb, such as pedestrian and transit in residential areas and commercial vehicles in commercial and industrial zones. Recently, pilots in San Francisco, Washington, D.C., and elsewhere have used new technologies to provide information to users about space availability or dynamically price the curb. Researchers at the University of California, Davis conducted a review of practices in curbside management, and they conducted simulations to evaluate the impact of different management and design strategies on travel time, congestion, vehicle travel, and emissions in residential, commercial, and mixed-use neighborhoods in San Francisco. This policy brief summarizes the findings from that research and provides policy implications.

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A number of stakeholders met with the ultimate goal of identifying inefficiencies faced by the freight system and putting forward a set of strategies to achieve a more efficient freight system. In doing so, a key first step was to provide insight as to the possible root cause(s) of major inefficiencies affecting the system. In addition to assessing inefficiencies, this research describes some of the aspects and necessary conditions that need to be considered when defining or identifying remediating strategies. Moreover, the research discusses a number of efficiency improvement strategies. These include:

• Voluntary Off-Hour Delivery Programs.

• Receiver-led Consolidation.

• Development of a Chassis Pool of Pools Fully Integrated System.

• Improvement of Traffic Mitigation Fee Programs.

• Implement Advanced Appointment/ Reservation Systems.

• Developing an Integrated System for Dray Operations and Services.

• Load Matching and Maximizing Capacity.

In light of the Governor’s Executive Order, it is imperative that the various public agencies in the State initiate, continue or reinforce efforts to address some of these issues.

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California has set goals for transitioning the freight sector to near-zero-emission and zero-emission vehicles (ZEVs). California offers several incentive programs to encourage adoption of ZEVs, but purchase decisions are driven by complex factors beyond simple purchase price. Understanding the behaviors and attitudes of freight stakeholders toward ZEVs can inform the design of more effective incentive programs to meet California’s goals. Researchers at the University of California, Davis deployed a stated-preference survey of fleet and carrier companies to collect information about vehicle purchase preferences and how they might be influenced by various incentive programs. This policy brief summarizes the findings from that research and provides policy implications.

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E-commerce can potentially make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities, particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include the use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection-points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.

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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.

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China's road transportation contributed about 8% of the country’s GHG emissions in 2020. Chinese government has made the commitment of reaching “carbon peak in 2030 and carbon neutrality in 2060”. Among all the sectors, transportation is one of the most challenging and essential ones to mitigate GHG emissions. The experienced economic development and improvement in people's living standards have increased vehicle ownership for both passenger vehicles and freight trucks. For the road transportation sector, electrification of the fleet represents one of the most important measures to reduce criteria pollutants and GHG emissions.By modeling passenger and freight vehicle fleets, this study projects the vehicle growth based on economic projections and the changes in demographic characteristics of the population during 2010 to 2050. The study developed a stock and sales model considering vehicle survival rates by vehicle types to reflect the different vehicle retirement and replacement schedules. Moreover, the study designed three sets of fleet electrification scenarios considering different technology penetration and deployment levels for different vehicle type. The scenario analysis extensively explored the energy consumption and GHG emission trajectories for the different vehicle growth scenarios, electrification pathways and renewable energy penetration rates in the grid. The scenarios also considered, as mentioned, changes in the demographics. For instance, in aging society scenario (considering an aging and decreasing fertility demographic), fleet growth rates slow down in the following three decades. This scenario results in a reduction of energy consumption and GHG emission from the road transportation sector. The scenario analysis extensively explored the energy consumption and GHG emission trajectories of the different vehicle growth scenarios, electrification pathways, and renewable energy penetration rates in the grid. From the scenarios analysis, in the aging society vehicle growth scenario, the decelerate vehicle ownership growth rates would bring down the energy consumption and GHG emission from road transportation sector. With the electrification pace stated in the NEV Development Technology Roadmap, the scenarios with truck electrification could bring down the GHG emission 26.6 to 30.4% than the electrification targets solely met through light-duty vehicles electrification, which highlight the need electrify both light-, and medium- and heavy-duty vehicles to achieve the larger reductions. Concentrating on light-duty will be enough to achieve reductions as needed by the country’s reduction targets. However, at present, most of the electrified vehicles are small private light duty passenger vehicles, and the current trends of increased vehicle sizes, would make it impossible to reach the carbon neutrality target. Consequently, a faster electrification and more deployment in the trucking sector would help to significantly bring down the energy consumption and GHG emissions. Furthermore, vehicle electrification must be accompanied by a rapid renewable energy penetration in the power grid to achieve GHG emissions reduction. The models show that if the electricity grid significantly phases out coal fired electricity down to 30% by 2030, GHG emissions from the cleaner fleet could reach carbon neutrality by 2050. To provide some context on the potential feasibility of the scenarios, the study conducted a case study in Shenzhen, which has been able to achieve a very large penetration of the electric vehicles in its transit fleet. The case study included a total cost of ownership analysis of the electric bus fleet, and compare it to the traditional diesel bus. Battery electric bus reached cost parity with the diesel bus with the support of governmental subsidies, and when environmental cost are considered, these buses are preferable to diesel ones.

In the last decade, e‐commerce has grown substantially, increasing business‐to‐business, business‐to‐consumer, and consumer‐to‐consumer transactions. While this has brought prosperity for the e-retailers, the ever-increasing consumer demand has brought more trucks to the residential areas, bringing along externalities such as congestion, air and noise pollution, and energy consumption. To cope with this, different logistics strategies such as the introduction of micro-hubs, alternative delivery points, and use of cargo bikes and zero emission vehicles for the last mile have been introduced and, in some cases, implemented as well. This project, hence, aims to develop an analytical framework to model urban last mile delivery. In particular, this study will build upon the previously developed econometric behavior models that capture e-commerce demand. Then, based on continuous approximation techniques, the authors will model the last-mile delivery operations. And finally, using the cost-based sustainability assessment model (developed in this study), the authors will estimate the economic and environmental impacts of residential deliveries under different city logistics strategies.

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This thesis proposes and implements a novel framework to identify the factors influencing average cruising time (ACT) related to searching for parking and uses multiple machine learning (ML) models to predict ACT and average vehicle emissions (AEM) in New York City (NYC) and Los Angeles (LA). Specifically, the author 1) analyzes NYC datasets by spatial lag models to explore the factors affecting ACT; 2) uses the K-Means method to cluster land use type and vehicle composition, and perform a comparative analysis for different groups; 3) utilizes ML models to predict the land use and vehicle composition in NYC and LA, and use them into a Bayesian Ridge regression model to predict the ACT; and 4) uses a Gaussian-Link generalized linear model to calculate average cruising distance (ACD) based on ACT and other variables to estimate AEM generated by parking events. The major findings include 1) Residential and commercial areas have a significant positive correlation with ACT. The parking hotspots roughly coincide with the distribution of density, land use, and job opportunities, which may be associated to growing residential freight demand and sluggish parking supply. 2) A parking hotspot with high ACT is not necessarily a location with high AEM. Compared to locations with high ACT in the central areas, the heavy-duty-truck- (HDT)-dominated areas at the periphery of the city can generate higher vehicle emissions. 3) AEMs are more affected by the type of dominating vehicles in the grid, and HDT-dominated grids generate four times of pollutants than light-duty-truck (LDT)-dominated ones.