Abstract
The popular real-time ridesharing service has promoted social and environmental sustainability in various ways. Meanwhile, it also brings some traffic safety concerns. This paper aims to analyze factors affecting real-time ridesharing vehicle crash severity based on the classification and regression tree (CART) model. The Chicago police-reported crash data from January to December 2018 is collected. Crash severity in the original dataset is highly imbalanced: only 60 out of 2624 crashes are severe injury crashes. To fix the data imbalance problem, a hybrid data preprocessing approach which combines the over- and under-sampling is applied. Model results indicate that, by resampling the crash data, the successfully predicted severe crashes are increased from 0 to 40. Besides, the G-mean is increased from 0% to 73%, and the AUC (area under the receiver operating characteristics curve) is increased from 0.73 to 0.82. The classification tree reveals that following variables are the primary indicators of real-time ridesharing vehicle crash severity: pedestrian/pedalcyclist involvement, number of passengers, weather condition, trafficway type, vehicle manufacture year, traffic control device, driver gender, lighting condition, vehicle type, driver age and crash time. The current study could provide some valuable insights for the sustainable development of real-time ridesharing services and urban transportation.
Highlights
In recent years, with the rapid spread of GPS-enabled smartphones and communication technologies, real-time/dynamic ridesharing has become an important transportation mode which is transforming the urban mobility by providing convenient and timely transportation service
Real-time ridesharing is defined by Amey et al [1] as “A single, or recurring rideshare trip with no fixed schedule, organized on a one-time basis, with matching of participants occurring as little as a few minutes before departure or as far in advance as the evening before a trip is scheduled to take place”
The results suggested that the proposed approach outperformed the standard mixed multinomial logit (MNL) model
Summary
With the rapid spread of GPS-enabled smartphones and communication technologies, real-time/dynamic ridesharing has become an important transportation mode which is transforming the urban mobility by providing convenient and timely transportation service. The booming market of real-time ridesharing has brought some concerns, which could potentially hinder its development. One of the greatest concerns regarding this relatively new transportation mode is the potential traffic crash risks. A recent report has argued that the development of real-time ridesharing is associated with a 2–4% increase in the number of fatal crashes [2]. In order to properly regulate the development of real-time ridesharing and fully utilize its potential benefits on urban transportation sustainability, it is critical to thoroughly investigate factors affecting crash severity of real-time ridesharing vehicles
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