DeepTRANS: A Deep Learning System for Public Bus Travel Time Estimation using Traffic Forecasting
In the public transportation domain, accurate estimation of travel times helps to manage rider expectations as well as to provide a powerful tool for transportation agencies to coordinate the public transport vehicles. Although many statistical and machine learning methods have been proposed to estimate travel times, none of the methods consider utilizing predicted traffic information. Forecasting how congestion is going to evolve is critical for accurate travel time estimations. In this paper, we present DeepTRANS, which incorporates traffic forecasting information to our prior Deep Learning based Bus Estimated Time of Arrival (ETA) model, increasing its accuracy by 21% in estimating bus travel time. PVLDB Reference Format: Luan Tran, Min Y. Mun, Matthew Lim, Jonah Yamato, Nathan Huh, Cyrus Shahabi. DeepTRANS: A Deep Learning System for Public Bus Travel Time Estimation using Traffic Forecasting. PVLDB, 13(12) 2957 2960, 2020. DOI: https:/doi.org/10.14778/3415478.3415518
Proc. VLDB Endow.