@article{RichlyBrauerSchlosser2020,
  author    = {Richly, Keven and Brauer, Janos and Schlosser, Rainer},
  title     = {Predicting location probabilities of drivers to improved dispatch decisions of transportation network companies based on trajectory data},
  series = {Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - ICORES},
  journal   = {Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - ICORES},
  publisher = {Springer},
  address   = {Berlin},
  pages     = {12},
  year      = {2020},
  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is challenging as the current location of each available driver is not exactly known since observed locations can be outdated for several seconds.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.},
  language  = {en}
}
@misc{RichlyBrauerSchlosser2020,
  author    = {Richly, Keven and Brauer, Janos and Schlosser, Rainer},
  title     = {Predicting location probabilities of drivers to improved dispatch decisions of transportation network companies based on trajectory data},
  series = {Postprints der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Postprints der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {9},
  doi       = {10.25932/publishup-52404},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-524040},
  pages     = {14},
  year      = {2020},
  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.},
  language  = {en}
}