Analytical Modeling Framework to Assess the Economic and Environmental Impacts of Residential Deliveries, and Evaluate Sustainable Last-Mile Strategies

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.View the NCST Project Webpag

Automated Vehicles are Expected to Increase Driving and Emissions Without Policy Intervention

Researchers at UC Davis explored what an automated vehicle future in the San Francisco Bay Area might look like by simulating:1) A 100% personal automated vehicle future and its effects on travel and greenhouse emissions.2) The introduction of an automated taxi service with plausible per-mile fares and its effects on conventional personal vehicle and transit travel.The researchers used the Metropolitan Transportation Commission’s activity-based travel demand model (MTC-ABM) and MATSim, an agent-based transportation model, to carry out the simulations. This policy brief summarizes the results, which provide insight into the relative benefits of each service and automated vehicle technology and the potential market for these services.View the NCST Project Webpag

Bikesharing and other micromobility services can improve connectivity between affordable housing communities and transit

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 Webpag

The Colombian Strategic Freight Transport Model Based on Product Analysis

Freight transport modelling at interregional scale is relevant for planning issues. However, freight modelling processes are complex because it is not easy to define the relevant variables in the analysis, and to obtain the required information on freight movements through the network. These facts raise the need to adapt the modelling framework to each context.This paper proposes a strategic national freight transport modelling framework developed as a variant of the traditional four-step modelling process with additional steps to estimate traffic flows from freight flows and to consider empty trips. The country of Colombia is used as the case study to implement and calibrate the proposed model. The data, data sources, and modelling methodologies used for each step are explained. In addition, data limitations and measures taken to complement the available data are discussed. From the implementation, the authors identify a set of advantages derived from the modelling approaches considered and suggestions for improvement

The Impacts of Automated Vehicles on Center City Parking Demand

The potential for automated vehicles (AVs) to reduce parking in city centers has generated much excitement among urban planners. AVs could drop-off (DO) and pick-up (PU) passengers in areas where parking costs are high: personal AVs could return home or park in less expensive locations, and shared AVs could serve other passengers. Reduced on-street and off-street parking present numerous opportunities for redevelopment that could improve the livability of cities, for example, more street and sidewalk space for pedestrian and bicycle travel. However, reduced demand for parking would be accompanied by increased demand for curbside DO/PU space with related movements to enter and exit the flow of traffic. This change could be particularly challenging for traffic flows in downtown urban areas during peak hours, where high volumes of DOs and PUs are likely to occur. Only limited research examines the travel effects of a shift from parking to DO/PU travel and the impact of changes in parking supply. This study uses a microscopic road traffic model with local travel activity data to simulate personal AV parking scenarios in San Francisco's downtown central business district. These scenarios vary (1) the demand for DO and PU travel versus parking, (2) the supply of on-street and off-street parking, and (3) the total demand for parking and DO/PU travel due to an increase in the cost of travel to the central business district.View the NCST Project Webpag

Cost-Benefit Analysis of Novel Access Modes: A Case Study in the San Francisco Bay Area

The first-mile, last-mile problem is a significant deterrent for potential transit riders, especially in suburban neighborhoods with low density. Transit agencies have typically sought to solve this problem by adding parking spaces near transit stations and adding stops to connect riders to fixed-route transit. However, these measures are often only short-term solutions. In the last few years, transit agencies have tested whether new mobility services, such as ridehailing, ridesharing, and microtransit, can offer fast, reliable connections to and from transit stations. However, there is limited research that evaluates the potential impacts of these projects. Concurrently, there is growing interest in the future of automated vehicles (AVs) and the potential of AVs to solve this first-mile problem by reducing the cost of providing these new mobility services to promote access to transit. This paper expands upon existing research to model the simulate the travel and revenue impacts of a fleet of automated vehicles that provide transit access services in the San Francisco Bay Area offered over a range of fares. The model simulates a fleet of AVs for first-mile transit access at different price points for three different service models (door-to-door ridehailing and ridesharing and meeting point ridesharing services). These service models include home-based drop-off and pick-up for single passenger service (e.g., Uber and Lyft), home-based drop-off and pick-up for multi-passenger service (e.g.,microtransit), and meeting point multi-passenger service (e.g., Via)

Development of a Logistics Decision Support Tool for Small and Medium Companies to Evaluate the Impacts of Environmental Regulations in California

UC-ITS-2020-43Satisfying the demand for goods requires the movement of commercial and private vehicles, which are responsible for multiple negative impacts including noise, emissions, and traffic congestion. While efficiency is crucial for sustainable and profitable freight transportation, operations are typically inefficient with respect to emissions and social impacts. The reasons for such inefficiency are diverse, including the need for several attempts to complete a delivery and the under usage of vehicle capacity. The arrival of zero-emission and near-zero-emission medium- and heavy-duty vehicles may reduce (tailpipe) emissions. Multiple government agencies have supported the development and promotion of cleaner vehicles through strategies such as economic incentives to support purchases and disincentives to using internal combustion engine vehicles. However, small- and medium-sized companies face challenges in adopting cleaner vehicles, either because of high purchase costs or because the volume of their operations may not justify the expense. To address this issue, this work evaluates cooperative strategies between noncompeting companies that would exploit economies of scale through the sharing of vehicle capacities in joint routing. The work develops a decision support tool named Cargo Aggregator Beta 1.0, which provides companies willing to cooperate with an efficient joint route to pick-up and deliver cargo from different origins and destinations. The tool, based on an extension of the vehicle routing problem, allows users to consider different vehicle capacities, decide on charging and/or refueling points, consider multiple depots, and guarantee the completion of all deliveries in a general time window. The tool can be used to better understand the impact of sustainability policies that would limit the amount of pollutant emissions generated, or policies that seek to restrict fleet composition. Numerical analyses using study cases in California show the potential benefits of implementing these collaborations in reducing both costs and emissions

Coping with the Rise of E-commerce Generated Home Deliveries through Innovative Last-Mile Technologies and Strategies

Caltrans 65A0686 Task Order 066USDOT Grant 69A3551747114E-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

Optimizing Bikeshare Service to Connect Affordable Housing Units with Transit Service

Caltrans 65A0686 Task Order 058USDOT Grant 69A3551747114This 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

Future Connected and Automated Vehicle Adoption Will Likely Increase Car Dependence and Reduce Transit Use without Policy Intervention [Policy Brief]

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