Predicting Location Probabilities of Drivers to Improve Dispatch Decisions of Transportation Network Companies based on Trajectory Data

Keven Richly, Janos Brauer, Rainer Schlosser

Abstract

The demand for peer-to-peer ridesharing services increased over the last years rapidly. To cost-efficiently dispatch orders and communicate accurate pick-up times is challenging as the current location of each available driver is not exactly known since observed locations can be outdated for several seconds. The developed trajectory visualization tool enables transportation network companies to analyze dispatch processes and determine the causes of unexpected delays. As dispatching algorithms are based on the accuracy of arrival time predictions, we account for factors like noise, sample rate, technical and economic limitations as well as the duration of the entire process as they have an impact on the accuracy of spatio-temporal data. To improve dispatching strategies, we propose a prediction approach that provides a probability distribution for a driver’s future locations based on patterns observed in past trajectories. We demonstrate the capabilities of our prediction results to (i) avoid critical delays, (ii) to estimate waiting times with higher confidence, and (iii) to enable risk considerations in dispatching strategies.

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Paper Citation


in Harvard Style

Richly K., Brauer J. and Schlosser R. (2020). Predicting Location Probabilities of Drivers to Improve Dispatch Decisions of Transportation Network Companies based on Trajectory Data.In Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-396-4, pages 47-58. DOI: 10.5220/0008911100470058


in Bibtex Style

@conference{icores20,
author={Keven Richly and Janos Brauer and Rainer Schlosser},
title={Predicting Location Probabilities of Drivers to Improve Dispatch Decisions of Transportation Network Companies based on Trajectory Data},
booktitle={Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2020},
pages={47-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008911100470058},
isbn={978-989-758-396-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Predicting Location Probabilities of Drivers to Improve Dispatch Decisions of Transportation Network Companies based on Trajectory Data
SN - 978-989-758-396-4
AU - Richly K.
AU - Brauer J.
AU - Schlosser R.
PY - 2020
SP - 47
EP - 58
DO - 10.5220/0008911100470058