Forecasting Sparse Traffic Congestion Patterns Using Message-Passing RNNS

Shiva R. Iyer, Ulzee An, Lakshminarayanan Subramanian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The ability to forecast traffic congestion ahead of time given road conditions has remained a prominent problem in road traffic analysis. In this work, we leverage mobility traces of public transport vehicles tracked by the New York City MTA and formulate Message-Passing Recurrent Neural Nets (MPRNN) to produce long-term traffic forecasting on data that is sparse but wide in coverage. We model the interactions among road segments spread over the entirety of Manhattan, New York over a period of 3 months, such that traffic conditions can be propagated to > 90% of examined segments from just a few observations. In comparison to other competing algorithms, MPRNN achieves the lowest mean error of < 0.3 mph when predicting ahead in 10 minute intervals, for up to 3 road segments ahead (message passing across 3 hops). The MPRNN model further offers compelling results when forecasting traffic speeds several hours ahead given distant observations up to approximately 1 kilometer away (three consecutive bus stops) with a mean error of about 2 mph.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3772-3776
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

Keywords

  • deep learning
  • message passing
  • recurrent neural networks
  • Road traffic

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Iyer, S. R., An, U., & Subramanian, L. (2020). Forecasting Sparse Traffic Congestion Patterns Using Message-Passing RNNS. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 3772-3776). [9052963] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9052963