A simulation of real-time ridesharing model with taxi data

Authors: Meilin Shi*, University of California, Santa Barbara, Bingzheng Xu, University of California, Davis
Topics: Transportation Geography
Keywords: ridesharing, mobility, trajectory, spatial and temporal GIS
Session Type: Paper
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Congressional B, Omni, West
Presentation File: No File Uploaded


Real-time ridesharing is a burgeoning travel mode in the field of public transportation. By combing two or more rides on short notice, bringing travelers with similar itineraries and time schedules together, we can reduce the amount of traffic and carbon footprint. In this paper, we simulate ridesharing model with New York City and San Francisco taxi data and we aim to find out how many trips can be saved by ridesharing. Two patterns of ridesharing are considered here: identical trips, that two trips share the similar origins and destinations; inclusive trips, that one trip’s origin and destination are on the way of another trip. For identical trips, we match trips by similar origins and destinations within certain time range. For inclusive trips, we use trajectory data, set buffer along the trip and then match trips within the buffer based on time range and trip direction. After filtering out the trips that can be combined, we use Google Map API to generate the new trip distance and calculate cost based on the new distance. The results show that by matching rides, number of trips and cost can be significantly saved. However, time is the trade-off factor here.

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