VEHICLE TRAVEL TIME ESTIMATION USING SEQUENCE PREDICTION

Creative Commons License

Gunduz G., ACARMAN T.

PROMET-TRAFFIC & TRANSPORTATION, cilt.32, sa.1, ss.1-12, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.7307/ptt.v32i1.3008
  • Dergi Adı: PROMET-TRAFFIC & TRANSPORTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Geobase, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1-12
  • Anahtar Kelimeler: travel time estimation, recurrent neural networks, sequence prediction, intelligent transportation systems, TRAFFIC SPEED PREDICTION, LOOP DETECTOR, NETWORK
  • Galatasaray Üniversitesi Adresli: Evet

Özet

This paper proposes a region-based travel time and traffic speed prediction method using sequence prediction. Floating Car Data collected from 8,317 vehicles during 34 days are used for evaluation purposes. Twelve districts are chosen and the spatio-temporal non-linear relations are learned with Recurrent Neural Networks. Time estimation of the total trip is solved by travel time estimation of the divided sub-trips, which are constituted between two consecutive GATSS measurement data. The travel time and final speed of sub-trips are learned with Long Short-term Memory cells using sequence prediction. A sequence is defined by including the day of the week meta-information, dynamic information about vehicle route start and end positions, and average travel speed of the road segment that has been traversed by the vehicle. The, final travel time is estimated for this sequence. The sequence-based prediction shows promising results, outperforms function mapping and non-parametric linear velocity change based methods in terms of root-mean-square error and mean absolute error metrics.