Estimating Taxi Demand-Supply Level Using Taxi Trajectory Data Stream

Estimating Taxi Demand-Supply Level Using Taxi Trajectory Data Stream
Title:
Estimating Taxi Demand-Supply Level Using Taxi Trajectory Data Stream
Other Titles:
2015 IEEE International Conference on Data Mining Workshop (ICDMW)
DOI:
10.1109/ICDMW.2015.250
Publication Date:
14 November 2015
Citation:
D. Shao, W. Wu, S. Xiang and Y. Lu, "Estimating Taxi Demand-Supply Level Using Taxi Trajectory Data Stream," 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, 2015, pp. 407-413.
Abstract:
Taxis provide a flexible and indispensable service to satisfy the urban travel demand of public commuters. Understanding taxi supply and commuter demand, especially the imbalance between the supply and the demand, would directly help to improve the quality of taxi service and eventually increase a city's traffic system efficiency. In this paper, we consider the taxi demand from a region during a period of time to include two parts: satisfied demand, i.e., passengers successfully receive taxi service during this period of time, and unmet demand, i.e., passengers are still waiting for taxi service. To properly estimate the demand-supply level (short for "the level of the taxi demand vs. supply imbalance"), we propose a novel indicator that reflects how fast an available taxi is taken in any given region. Accordingly, we design and implement a taxi analytics system to provide such information in near real time. Finally, we use the passenger waiting time survey data and the taxi streaming data to validate the proposed indicator on the built taxi analytics system.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
2375-9259
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