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 Restricted Boltzmann Machine and Recurrent Neural Network 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. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

[1]  Hermann Ney,et al.  Cross-entropy vs. squared error training: a theoretical and experimental comparison , 2013, INTERSPEECH.

[2]  Su Yang,et al.  On feature selection for traffic congestion prediction , 2013 .

[3]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[4]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[5]  Tao Cheng,et al.  Exploratory visualisation of congestion evolutions on urban transport networks , 2013 .

[6]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[7]  Alinda K Aligawesa.  The detection and localization of traffic congestion for highway traffic systems using hybrid estimation techniques , 2009 .

[8]  Peter Stagge,et al.  Recurrent neural networks for time series classification , 2003, Neurocomputing.

[9]  Shengyong Chen,et al.  Traffic Dynamics on Complex Networks: A Survey , 2012 .

[10]  Xue Zhang,et al.  Emergence and disappearance of traffic congestion in weight-evolving networks , 2009, Simul. Model. Pract. Theory.

[11]  Kun He,et al.  Vulnerability Analysis and Passenger Source Prediction in Urban Rail Transit Networks , 2013, PloS one.

[12]  Geoffrey E. Hinton,et al.  The Recurrent Temporal Restricted Boltzmann Machine , 2008, NIPS.

[13]  Nong Ye,et al.  Onset of traffic congestion in complex networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[15]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[16]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..

[17]  Marta C. González,et al.  On the role of spatial dynamics and topology on network flows , 2013, 1501.01275.

[18]  Razvan Pascanu,et al.  Theano: A CPU and GPU Math Compiler in Python , 2010, SciPy.

[19]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[20]  Alexandre M. Bayen,et al.  Understanding Road Usage Patterns in Urban Areas , 2012, Scientific Reports.

[21]  Amitabh Varshney,et al.  High-throughput sequence alignment using Graphics Processing Units , 2007, BMC Bioinformatics.

[22]  Ziyou Gao,et al.  Urban traffic congestion propagation and bottleneck identification , 2008, Science in China Series F: Information Sciences.

[23]  Wenhao Huang,et al.  Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning , 2014, IEEE Transactions on Intelligent Transportation Systems.

[24]  Yoshua Bengio,et al.  Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription , 2012, ICML.

[25]  Geoffrey E. Hinton,et al.  Learning Multilevel Distributed Representations for High-Dimensional Sequences , 2007, AISTATS.

[26]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[27]  Jian Wang,et al.  Congestion analysis of traffic networks with direction-dependant heterogeneity , 2013 .