Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

By Xiaolei Ma, Haiyang Yu, Yunpeng Wang, Yinhai Wang. 2015

484 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Transportation

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “Currently, the integration of DL analytics into IoT applications is limited to RESTful APIs, which are based on the HTTP protocol.”
Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, methodology): “[79] combined deep restricted Boltzmann machines (RBM) with RNN and formed a RBM-RNN model that inherits the advantages of both RBM and RNN.”
Internet of Things (IoT), Applications and Challenges: A Comprehensive Review (2020, methodology): “A methodological Graphical Processing Unit (GPU) based parallel computing environment framework had been proposed by in the study [51].”
An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic (2017, methodology): “Recently, some improve algorithm, such as state-space neural network model [13], Long Short-Term Memory Neural Net-”
Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security (2016, background): “DNN has been shown to be effective for classifying statistical patterns and mapping complex non-linear input-to-output relations in various research fields such as artificial intelligence, multimedia processing, security [37 – 40] as well as in intelligent vehicular systems [41 – 44].”
A Survey on Deep Learning (2018, methodology): “Ma et al. [105] propose a deep learning framework based on the RNN-RBM architecture to predict the transportation network congestion evolution due to the congestion in one location.”
Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, background): “ConvLSTM [1], [89], [152], [186], [142], [66], [189], [63], [138] AE/SDAE [106], [185], [13] [49], [158], [95] [31], [16], [179], [44], [46], [170] RBM/DBN [108] [127] [131], [51], [59] Seq2Seq [75], [161], [20], [162] [82], [81] Hybrid [155], [133], [99] [87], [52] [177], [29], [19], [6], [163], [1”
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks (2017, methodology): “Ma [11] proposed a congestion prediction method that is based on RNN-RBM for a large-scale transportation network that included 515 road links.”
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction (2017, methodology): “RNN can learn the features by unfolding the time series and capturing the pattern through its shared parameters and hidden states at each time step [27].”
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting (2022, unknown): “First, LSTM (Hochre-iter and Schmidhuber 1997) and GRU (Chung et al. 2014) demonstrated superior performance in traffic modeling (Ma et al. 2015a; Lv et al. 2018; Li et al. 2018; Zhao et al. 2019; Wang et al. 2020; Bai et al. 2020; Ye et al. 2021; Shang, Chen, and Bi 2021; Lee et al. 2022) as well…”
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, methodology): “Ma et al. [147] used an RNN and deep RBM to ﬁt a model and predict trafﬁc congestion in China using GPS data from taxis.”
Urban traffic signal control with connected and automated vehicles: A survey (2019, methodology): “So far, most stochastic filtering and particle filter techniques are model-based and try to integrate traffic flow models with data; while the pure data-driven methods such as deep-learning based methods depend solely on data (Lv et al., 2015; Ma et al., 2015, 2017; Polson and Sokolov, 2017).”
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach (2017, unknown): “For example, Ma et al. (2015) employed the long short-term memory (LSTM) neural network to capture the long-term dependences and nonlinear traffic dynamics for short-term traffic speed prediction.”