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Enhancing Expressiveness of Models for Static Route-Free Estimation of Time of Arrival in Urban Environments

EasyChair Preprint no. 6558

9 pagesDate: September 7, 2021


Scheduling of taxis can reduce cost and potentially decreases CO2 emissions. However, with a rising number of taxis or travel requests, the time for computing schedules increases. A promising alternative is to estimate trip durations based on historical trip data without calculating routes. Based on an analysis of the state of the art, in this paper we identify and investigate two limitations of route-free Estimated Time of Arrival (ETA) models: First, the overall set of features considered by state-of-the-art models is limited. For instance, some potential relevant features (such as weather-related ones) are not considered at all. Also, different models use different sets of features, such as the linear distance between pickup and dropoff location, in diverse and partly inconsistent ways. For those features generally considered, we find different representations, e.g., for trip start time. Second, while discretization of degree-based coordinates for pickup/dropoff locations via spatial binning is very common in state-of-the-art ETA models, the chosen grid cell sizes vary widely and apparently arbitrarily. The contribution of this paper is threefold: First, we propose to enhance route-free ETA models by additional features and investigate the influence of the feature representation on the estimation precision based on a benchmark dataset. Second, we compare different grid cell topologies and sizes as regards their effect on the prediction precision of ETA. Third, we construct and evaluate three types of Machine Learning (ML) models. Our findings indicate that the results outperform state-of-the-art static route-free ETA estimation models.

Keyphrases: estimated time of arrival, machine learning, Taxi Fleet Management, travel time estimation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sören Schleibaum and Jörg P. Müller and Monika Sester},
  title = {Enhancing Expressiveness of Models for Static Route-Free Estimation of Time of Arrival in Urban Environments},
  howpublished = {EasyChair Preprint no. 6558},

  year = {EasyChair, 2021}}
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