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The trucking industry is expected to be an early adopter of self-driving technology which could have a major impact on the truck-driving profession.
This report explores peer-to-peer carsharing, its impacts on travel behavior, and how it can be incorporated with other shared mobility services.
"Researchers at the Transportation Sustainability Research Center (TSRC) at UC Berkeley conducted an evaluation of the RideKC: Bridj pilot program operating in Kansas City, MO. RideKC: Bridj is a public‐private partnership with the goal to enhance existing public transit options in Kansas City through a flexible microtransit service offered by Bridj. TSRC UC Berkeley’s goal in this evaluation is to assess the travel behavior impacts of the service, as well as to provide operational and institutional analysis."
"This Future of Mobility White Paper is intended to inform and guide policymakers and modelers developing the next iteration of the CTP –CTP 2050 –by presenting updated descriptions and analyses of developments impacting California’s transportation system."
This document provides background on micromobility and what it is, answers the question "Who uses shared micromobility?" and identifies current policies and practices.
Concerns over rising fuel prices and greenhouse-gas emissions have prompted research into the influences of built environments on travel, notably vehicle miles traveled (VMT). Accessibility to basic employment has comparatively modest effects, as do size of urbanized area, and rail-transit supplies and usage. Nevertheless, urban planning and city design should be part of any strategic effort to shrink the environmental footprint of the urban transportation sector.
This is a review of what research is saying about the negative impacts of autonomous vehicles are on public health issues specifically.
This paper discusses the history of shared mobility within the context of the urban transportation landscape, first in Europe and Asia, and more recently in the Americas, with a specific focus on first- and last-mile connections to public transit. The authors discuss the known impacts of shared mobility modes—carsharing, bikesharing, and ridesharing—on reducing vehicle miles/kilometers traveled (VMT/VKT), greenhouse gas (GHG) emissions, and modal splits with public transit. The future of shared mobility in the urban transportation landscape is discussed, as mobile technology and public policy continue to evolve to integrate shared mobility with public transit and future automated vehicles.
In this study, we present exploratory evidence of how “ridesourcing” services (app-based, on-demand ride services like Uber and Lyft) are used in San Francisco. We explore who uses ridesourcing and for what reasons, how the ridesourcing market compares to that of traditional taxis, and how ridesourcing impacts the use of public transit and overall vehicle travel. In spring 2014, 380 completed intercept surveys were collected from three ridesourcing “hot spots” in San Francisco. We compare survey results with matched-pair taxi trip data and results of a previous taxi user survey. We also compare travel times for ridesourcing and taxis with those for public transit.
"Automated driving technologies are currently penetrating the market, and the coming fully autonomous cars will have far-reaching, yet largely unknown, implications. A critical unknown is the impact on traveler behavior, which in turn impacts sustainability, the economy, and well-being. Most behavioral studies, to date, either focus on safety and human factors (driving simulators; test beds), assume travel behavior implications (microsimulators; network analysis), or ask about hypothetical scenarios that are unfamiliar to the subjects (stated preference studies). Here we present a different approach, which is to use a naturalistic experiment to project people into a world of self-driving cars. We mimic potential life with a privately-owned self-driving vehicle by providing 60 h of free chauffeur service for each participating household for use within a 7-day period. We seek to understand the changes in travel behavior as the subjects adjust their travel and activities during the chauffeur week when, as in a self-driving vehicle, they are explicitly relieved of the driving task. In this first pilot application, our sample consisted of 13 subjects from the San Francisco Bay area, drawn from three cohorts: millennials, families, and retirees. We tracked each subject’s travel for 3 weeks (the chauffeur week, 1 week before and 1 week after) and conducted surveys and interviews. During the chauffeur week, we observed sizable increases in vehicle-miles traveled and number of trips, with a more pronounced increase in trips made in the evening and for longer distances and a substantial proportion of “zero-occupancy” vehicle-miles traveled."
"This paper advances understanding of modal shifts caused by bikesharing through a geographic evaluation of survey data collected through recently completed research. Working with surveys in two of the cities surveyed in the United States, the authors analyze the attributes of individuals who increased and decreased their rail and bus usage in a geospatial context along with the population density of respondent home and work locations. The results inform the nuances of bikesharing impacts on the modal shift of urban residents with respect to public transportation."
This white paper presents a generalized evaluation framework that can be used for assessing project impacts within the context of transportation-related city projects. In support of this framework, we discuss a selection of metrics and data sources that are needed to evaluate the performance of smart city innovations. We first present a collection of projects and applications from near-term smart city concepts or actual pilot projects underway (i.e., Smart City Challenge, Federal Transit Administration (FTA) Mobility on Demand (MOD) Sandbox, and other pilot projects operating in the regions of Los Angeles, Portland, and San Francisco). These projects are identified and explained in Section 2 of this report. Using these projects as the basis for hypothetical case studies, we present selected metrics that would be necessary to evaluate and monitor the performance of such innovations over time. We then identify the data needs to compute those metrics and further highlight the gaps in known data resources that should be covered to enable their computation. The objective of this effort is to help guide future city planners, policy makers, and practitioners in understanding the design of key metrics 3 and data needs at the outset of a project to better facilitate the establishment of rigorous and thoughtful data collection requirements.
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