Next Article in Journal
Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion
Next Article in Special Issue
Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture
Previous Article in Journal
A Representation Method for Complex Road Networks in Virtual Geographic Environments
Previous Article in Special Issue
Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(11), 373; https://doi.org/10.3390/ijgi6110373

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

1
School of Information Engineering, China University of Geosciences, Wuhan 430074, China
2
National Engineering Research Center for GIS, Wuhan 430074, China
3
Department of Urban and Regional Planning, State University of New York, Buffalo, NY 14214, USA
*
Author to whom correspondence should be addressed.
Received: 26 September 2017 / Revised: 2 November 2017 / Accepted: 13 November 2017 / Published: 19 November 2017
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
Full-Text   |   PDF [7864 KB, uploaded 21 November 2017]   |  

Abstract

Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation. View Full-Text
Keywords: trajectory data mining; taxi planning; spatio-temporal trajectory model; load balance trajectory data mining; taxi planning; spatio-temporal trajectory model; load balance
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wu, L.; Hu, S.; Yin, L.; Wang, Y.; Chen, Z.; Guo, M.; Chen, H.; Xie, Z. Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model. ISPRS Int. J. Geo-Inf. 2017, 6, 373.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top