TY  - GEN
A1  - Richly, Keven
A1  - Brauer, Janos
A1  - Schlosser, Rainer
T1  - Predicting location probabilities of drivers to improved dispatch decisions of transportation network companies based on trajectory data
T2  - Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät
N2  - 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is challenging as the current location of each available driver is not exactly known since observed locations can be outdated for several seconds.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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 9 
KW  - trajectory data
KW  - location prediction algorithm
KW  - Peer-to-Peer ridesharing
KW  - transport network companies
KW  - risk-aware dispatching
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-524040
IS  - 9
ER  -