TY - GEN
T1 - Forecasting Sparse Traffic Congestion Patterns Using Message-Passing RNNS
AU - Iyer, Shiva R.
AU - An, Ulzee
AU - Subramanian, Lakshminarayanan
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Road traffic
KW - deep learning
KW - message passing
KW - recurrent neural networks
UR - http://www.scopus.com/inward/record.url?scp=85089209826&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089209826&partnerID=8YFLogxK
U2 - 10.1109/ICASSP40776.2020.9052963
DO - 10.1109/ICASSP40776.2020.9052963
M3 - Conference contribution
AN - SCOPUS:85089209826
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3772
EP - 3776
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -