Predicting location probabilities of drivers to improved dispatch decisions of transportation network companies based on trajectory data
- 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is challenging as the current location of each available driver is not exactly known since observed locations can be outdated for several seconds.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 waitingThe 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is challenging as the current location of each available driver is not exactly known since observed locations can be outdated for several seconds.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.…
Verfasserangaben: | Keven RichlyORCiD, Janos Brauer, Rainer SchlosserORCiDGND |
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URN: | urn:nbn:de:kobv:517-opus4-524040 |
DOI: | https://doi.org/10.25932/publishup-52404 |
Titel des übergeordneten Werks (Deutsch): | Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät |
Schriftenreihe (Bandnummer): | Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät (9) |
Publikationstyp: | Postprint |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 03.11.2021 |
Erscheinungsjahr: | 2020 |
Veröffentlichende Institution: | Universität Potsdam |
Datum der Freischaltung: | 03.11.2021 |
Freies Schlagwort / Tag: | Peer-to-Peer ridesharing; location prediction algorithm; risk-aware dispatching; trajectory data; transport network companies |
Ausgabe: | 9 |
Seitenanzahl: | 14 |
Quelle: | Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - ICORES, ISBN 978-989-758-396-4; ISSN 2184-4372, pages 47-58. DOI: 10.5220/0008911100470058 |
Organisationseinheiten: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC-Klassifikation: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer Review: | Referiert |
Publikationsweg: | Open Access / Green Open-Access |
Lizenz (Deutsch): | ![]() |
Externe Anmerkung: | Bibliographieeintrag der Originalveröffentlichung/Quelle |