Large-scale transportation network congestion evolution prediction using deep learning theory

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis.

A deep Recurrent Neural Network and Restricted Boltzmann Machine (RNN-RBM) architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. Compared with BP Neural Network and Support Vector Machine algorithms, the prediction accuracy for RNN-RBM increases by at least 17%, while the algorithm execution time reduces by 98%.

The finding of this study is important for large-scale roadway network planning, operations, and investment decisions. Understanding which congested locations are autonomously generated and how congestion propagates over transportation network will allow researchers and practitioners to focus the limited resources on the primary congestion locations, and adopt the proactive countermeasures to mitigate congestion.

Figure 1: Deep Recurrent Neural Network and Restricted Boltzmann Machine (RNN-RBM) architecture

Figure 2: Transportation network congestion evolution prediction

Xiaolei Ma, associate professor, school of transportation science and engineering, Beihang University,

[1]Xiaolei Ma, Haiyang Yu, Yunpeng Wang and Yinhai Wang, Large-scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory, PLOS ONE, 10(3): e0119044, 2015.
[2]Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu and Yunpeng Wang, Long Short-term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data, Transportation Research Part C: Emerging Technologies, 54: pp.187-197, 2015.