The Cost of Convenience: Ridesharing and Traffic Fatalities (original) (raw)
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The cost of convenience: Ridehailing and traffic fatalities
Journal of Operations Management, 2022
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The effectiveness of ridesharing incentives
Regional Science and Urban Economics, 1992
This paper studies the effects of certain incentives designed to promote ridesharing on work trips to reduce congestion and air pollution Ordered probit discrete choice models of commuters' mode choices (always rideshare, sometimes rideshare, and always drive alone) are estimated using a new study of full-time workers' commutillg behavior in the greater Los Angeles area. We find that women and those who have larger households with multiple workers, longer commutes, and larger worksites are more likely to rideshare. Partial equllibri-m policy simulations with our model indicate that providing all workers with reserved parking, ridesharing subsidies, guaranteed rides home, and high-occupancy vehicle lanes would reduce drive-alone commuting between 11 and 18 percent.
Analysis of the Impact of Ride-Hailing Services on Motor Vehicles Crashes in Madrid
Sustainability, 2021
In most cities, discretionary passenger transport by car is predominantly supplied by taxi services. These services face competition from new digital platforms (UBER, Cabify, etc.) that connect users with the services offered by authorized drivers with a license for rented vehicles with drivers (VTC). However, very little is known about the impacts that these services produce in cities where they operate. So far, most studies on this issue have focused on cities of the United States of America, and they broadly found a positive impact in terms of road safety. Road safety has become one of the priority focuses for ensuring social welfare, to the point of being integrated into the Sustainable Development Goals as a primary value to achieve sustainable, safe and responsible mobility. Within this context, the objective of this paper is to analyze the impact of ride-hailing platforms on the frequency of traffic accidents with at least one fatally or seriously injured person in the munici...
Frontiers in Psychology
IntroductionThe transport and mobility sector is experiencing profound transformations. These changes are mainly due to: environmental awareness, the increase in the population of large urban areas and the size of cities, the aging of the population and the emergence of relevant technological innovations that have changed consumption habits, such as electronic commerce or the sharing economy. The introduction of new services such as Uber or Cabify is transforming urban and metropolitan mobility, which has to adapt to this new scenario and the very concept of mobility.ObjectiveThus, the purpose of this study was to evaluate whether ride-hailing platforms substitute or complement public transport to reduce accident rates, considering the two basic transport zones of Madrid: “The Central Almond” and the periphery.MethodsThe data were collected from the 21 districts of Madrid for the period 2013–2019, and they were analyzed by a Random Effects Negative Binominal model.ResultsThe results...
The determinants of ridesharing: Literature review
1990
Transportation Center (UC'TC) is one of ten regional units mandated by Congress and established in Fall 1988 to support research, education, and training in surface transportation. The UC Center serves federal Region IX and is supported by matching grants from the U.S. Department of Transportation, the California Department of Transportation (Caltrans), and the University. Based on the Berkeley Campus, UCTC draws upon existing capabilities and resources of the Institutes of Transportation Studies at Berkeley, Davis, Irvine, and Los Angeles; the Institute of Urban and Regional Development at Berkeley; and several academic departments at the Berkeley, Davis, Irvine, and Los Angeles campuses. Faculty and students on other University of California campuses may participate in Center activities. Researchers at other universities within the region also have opportunities to collaborate with UC faculty on selected studies.
Modeling determinants of ridesourcing usage: A census tract-level analysis of Chicago
Transportation Research Part C-emerging Technologies, 2020
Ridesourcing services provided by companies like Uber, Lyft, and Didi have grown rapidly over the past decade and now serve a sizable portion of trips in many metropolitan areas. An understanding of these services (e.g. to whom, where, when, and for what purposes do they provide service?) is critical for regulating, planning, and managing urban multi-modal transportation systems effectively. Unfortunately, little is known about ridesourcing travel because private companies providing ridesourcing services were not previously subject to data sharing requirements. Fortunately, the city of Chicago recently collected and released spatially (census tract) and temporally (15-minute interval) aggregated data on ridesourcing trips collected from private companies. This study analyzes the Chicago ridesourcing data to examine factors influencing ridesourcing usage. The study employs a random-effects negative binomial (RENB) regression approach to model ridesourcing usage. Determinants considered in the model include weekend vs. weekday and weather variables as well as census tract socio-demographics and commute characteristics, land-use variables, places of interest, transit supply, parking features, and crime. The model results indicate ridesourcing demand is higher on days when temperatures are lower, there is less precipitation, and on the weekend, as well as in census tracts with (i) higher household incomes, (ii) a higher percentage of workers who carpool or take transit to work, (iii) a higher percentage of households with zero vehicles, (iv) higher population and employment density, (v) higher land-use diversity, (vi) fewer parking spots and higher parking rates, (vii) more restaurants, and (viii) more homicides. The results also demonstrate a non-linear (and insightful) relationship between ridesourcing demand and transit supply variables. The paper discusses the implications of these model results to inform transportation planning and policymaking as well as future research.
Assessing the Impact of Real-time Ridesharing on Urban Traffic using Mobile Phone Data
2015
Recently, smart-phone based technology has enabled ridesharing services to match customers making similar trips in realtime for a reduced rate and minimal inconvenience. But what are the impacts of such services on city-wide congestion? The answer lies in whether or not ridesharing adds to vehicle traffic by diverting non-driving trips like walking, transit, or cycling, or reduces vehicle traffic by diverting trips otherwise made in private, single occupancy cars or taxis. This research explores the impact of rideshare adoption on congestion using mobile phone data. We extract average daily origin-destination (OD) trips from mobile phone records and estimate the proportions of these trips made by auto and other non-auto travelers. Next, we match spatially and temporally similar trips, and assume a range of adoption rates for auto and non-auto users, in order to distill rideshare vehicle trips. Finally, for several adoption scenarios, we evaluate the impacts of congestion network-wide.
Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion
Journal of Advanced Transportation, 2021
Traffic congestion is largely due to the high proportion of solo drivers during peak hours. Ridesharing, in the sense of carpooling, has emerged as a travel mode with the potential to reduce congestion by increasing the average vehicle occupancy rates and reduce the number of vehicles during commuting periods. In this study, we propose a simulation-based optimization framework to explore the potential of subsidizing ridesharing users, drivers, and riders, so as to improve social welfare and reduce congestion. We focus our attention on a realistic case study representative of the morning commute on Sydney’s M4 Motorway in Australia. We synthesize a network model and travel demand data from open data sources and use a multinomial logistic model to capture users’ preferences across different travel roles, including solo drivers, ridesharing drivers, ridesharing passengers, and a reserve option that does not contribute to congestion on the freeway network. We use a link transmission mod...
Ridesharing in North America: Past, Present, and Future
Transport Reviews, 2012
Since the late-1990s, numerous ridematching programs have integrated the Internet, mobile phones, and social networking into their services. Online ridematching systems are employing a range of new strategies to create "critical mass:" 1) regional and large employer partnerships, 2) financial incentives, 3) social networking to younger populations, and 4) real-time ridematching services that employ "smartphones" and automated ridematching software. Enhanced casual carpooling approaches, which focus on "meeting places," are also being explored. Today, ridesharing represents approximately 8 to 11% of the transportation modal share in Canada and the United States, respectively. There are approximately 638 ridematching programs in North America. Ridesharing's evolution can be categorized into five phases: 1) World War II car-sharing (or carpooling) clubs; 2) major responses to the 1970s energy crises; 3) early organized ridesharing schemes; 4) reliable ridesharing systems; and 5) technology-enabled ridematching. While ridesharing's future growth and direction are uncertain, the next decade is likely to include greater interoperability among services, technology integration, and stronger policy support. In light of growing concerns about climate change, congestion, and oil dependency, more research is needed to better understand ridesharing's impacts on infrastructure, congestion, and energy/emissions.
BMC Public Health
Background An emergent group of studies have examined the extent under which ridesharing may decrease alcohol-related crashes in countries such as United States, United Kingdom, Brazil, and Chile. Virtually all existent studies have assumed that ridesharing is equally distributed across socioeconomic groups, potentially masking differences across them. We contribute to this literature by studying how socioeconomic status at the municipal level impacts Uber’s effect on alcohol-related crashes. Methods We use data provided by Chile’s Road Safety Commission considering all alcohol-related crashes, and fatal and severe alcohol-related injuries that occurred between January 2013 and September 2013 (before Uber) and January and September 2014 (with Uber) in Santiago. We first apply spatial autocorrelation techniques to examine the level of spatial dependence between the location of alcohol-related crashes with and without Uber. We then apply random-effects meta-analysis to obtain risk rat...