The travel time to proceed from one location to another in a network is an important consideration in many urban transportation settings ranging from the planning of delivery routes in freight transportation to the determination of shortest itineraries in advanced traveler information systems. Accordingly, accurate travel time predictions are of foremost importance. In an urban environment, vehicle speeds, and consequently travel times, can be highly variable due to congestion caused, for instance, by accidents or bad weather conditions. At another level, one also observes daily patterns (e.g., rush hours), weekly patterns (e.g., weekdays versus weekend), and seasonal patterns. Capturing these time-varying patterns when modeling travel speeds can provide an immediate benefit to commercial transportation companies that distribute goods, since it allows them to better optimize their routes and reduce their environmental footprint. This paper presents the first part of a project aimed at optimizing time-dependent delivery routes in an urban setting. It focuses on the prediction of travel speeds using as input GPS traces of commercial vehicles collected over a significant period of time. The proposed algorithmic framework is made of a number of macro-steps where different machine learning and data mining methods are applied. Computational results are reported on real data to empirically demonstrate the accuracy of the obtained predictions.
https://doi.org/10.1016/j.ejtl.2020.100006Cite as:
@article{Gmira_2020,
doi = {10.1016/j.ejtl.2020.100006},
url = {https://doi.org/10.1016%2Fj.ejtl.2020.100006},
year = 2020,
month = {dec},
publisher = {Elsevier {BV}},
volume = {9},
number = {4},
pages = {100006},
author = {Maha Gmira and Michel Gendreau and Andrea Lodi and Jean-Yves Potvin},
title = {Travel speed prediction based on learning methods for home delivery},
journal = {{EURO} Journal on Transportation and Logistics}
}