Traffic Speed Prediction and Congestion Source Exploration: A

Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method Jingyuan Wangy, Qian Guy, Junjie Wuz*, Guannan Liuz, Zhang Xiongyx y School of Computer Science and Engineering, Beihang University, Beijing, China z School of Economics and Management, Beihang University, Beijing, China x Research Institute of Beihang University in Shenzhen, Shenzhen, China Email: fjywang, gqian, wujj, liugn, [email protected], *Corresponding author Abstract—Traffic speed prediction is a long-standing and mostly concerned with traffic flow and congestion predic- critically important topic in the area of Intelligent Trans- tions [4], [5]. Traffic speed prediction, therefore, is still an portation Systems (ITS). Recent years have witnessed the open problem in the deep-learning era, with two notable encouraging potentials of deep neural networks for real- life applications of various domains. Traffic speed prediction, challenges as follows: however, is still in its initial stage without making full use • How to characterize the latent interactions of road seg- of spatio-temporal traffic information. In light of this, in this ments in traffic speeds so as to improve the predictive paper, we propose a deep learning method with an Error- feedback Recurrent Convolutional Neural Network structure performance of a deep neural network? (eRCNN) for continuous traffic speed prediction. By integrating • How to model the abrupt changes of traffic speeds in the spatio-temporal traffic speeds of contiguous road segments case of emerging events such as morning peaks and as an input matrix, eRCNN explicitly leverages the implicit traffic accidents? correlations among nearby segments to improve the predictive accuracy. By further introducing separate error feedback These indeed motivate our study. Specifically, in this neurons to the recurrent layer, eRCNN learns from prediction paper, we propose a deep learning method using an Error- errors so as to meet predictive challenges rising from abrupt feedback Recurrent Convolutional Neural Network (eRC- traffic events such as morning peaks and traffic accidents. NN) for continuous traffic speed prediction. The novel Extensive experiments on real-life speed data of taxis running on the 2nd and 3rd ring roads of Beijing city demonstrate the contributions of our study are summarized as follows. strong predictive power of eRCNN in comparison to some state- First, we take the matrix containing the spatio-temporal of-the-art competitors. The necessity of weight pre-training traffic speeds of contiguous road segments as the input of using a transfer learning notion has also been testified. More eRCNN. By this means, the complicated interactions of interestingly, we design a novel influence function based on traffic speeds among nearby road segments can be captured the deep learning model, and showcase how to leverage it to recognize the congestion sources of the ring roads in Beijing. by eRCNN naturally without elaborative characterization, which is crucial to the high-accuracy prediction of eRCNN. Keywords-Intelligent transportation systems; Deep learning; Spatio-temporal; Time series prediction; Convolutional neural Second, we introduce separate error-feedback neurons to network; the recurrent layer of eRCNN, for the purpose of capturing the prediction errors from the output layer. This empowers eRCNN the ability to model the abrupt changes in traffic I. INTRODUCTION speeds due to some emerging traffic events like the morning Traffic speed prediction, as a sub-direction of traffic peaks and traffic accidents. prediction in the area of Intelligent Transportation Systems Third, we put forward a novel weight pre-training method, (ITS), has long been regarded as a critically important which adopts a transfer-learning notion by clustering similar way for decision making in transportation navigation, travel yet contiguous road segments into a group for the generation scheduling, and traffic management. Traditional models, in- of a same set of initial weights. This “sharing scheme” not cluding auto-regression methods [1] and supervised learning only helps to reduce the learning process of eRCNN for methods such as support vector regression [2] and artificial every road segment, but also improves the chance of finding neural networks [3], all treat traffic speed prediction as better optimal solutions. a time-series forecasting problem, and thus run into the Finally, we design a novel influence function based on bottleneck gradually. the deep learning model, and illustrate how to leverage it to In recent years, with the rapid development of deep recognize the congestion sources of the ring roads in Beijing. learning techniques, more and more researchers in ITS began To the best of our knowledge, we are among the earliest to to adopt deep neural networks for high-accuracy traffic explore how to learn road congestion sources from deep prediction. The rich studies along this line, however, are learning models. downstream of the segment s at the time t, and the row vector vs;: contains traffic speed data of the segment s from time t to t − n. In this way, the input matrix V contains all the speed information that is spatially and temporally adjacent to the variate to be predicted, i.e., vs;t+1. B. The CNN-based Feature Extracting In the eRCNN model, we adopt a CNN-based network structure to extract features from the spatio-temporal input matrix. The CNN structure contains a convolution layer and Figure 1. The framework of the eRCNN model. a pooling layer, and then we introduce the two layers in this subsection. 1) The Convolution Layer: The convolution layer is a Extensive experiments on real-life speed data of taxis core part of the CNN model [6]. The convolution layer con- running on the 2nd and 3rd ring roads of Beijing city demon- nects the spatio-temporal input matrix with several trainable strate the strong predictive power of eRCNN, even with filters, with each being a i × i weight matrix. We define the the presence of state-of-the-art competitors. The inclusion (C) (C) k-th filter as Wk . The convolution layer uses the Wk of spatio-temporal information of contiguous segments, the to zigzag scan the input matrix to calculate a convolution introduction of error-feedback neurons to the recurrent layer, neuron matrix. The (p; q) element of the convolution neuron and the weight pre-training of similar segments, all give a matrix generated by the filter k is calculated by positive boost to the high accuracy of eRCNN. ! Xi Xi p;q x;y p+x;q+y II. THE ERCNN FRAMEWORK ck = sigmoid bk + wk m ; (2) Fig. 1 shows the framework of the eRCNN model con- x=0 y=0 x;y taining five network layers, including the input layer (LI), where bk is a bias for the filter k, wk is the (x; y) (C) the convolution layer (LC), the pooling layer (LP), the p+x;q+y element of Wk , m is the (p + x; q + y) element error-feedback recurrent layer (LeR), and the output layer of the spatio-temporal matrix V. More details about the (LO). The function of the input layer is to organize the implementation of the CNN’s convolution layer could be original traffic speed data as a spatio-temporal input matrix, found in [7]. which can be processed by the CNN layers of eRCNN. The 2) The Pooling Layer: The pooling layer is another function of the convolution layer and the pooling layer is important component of the CNN model, which is used to extract features from the spatio-temporal input matrix. to reduce the dimension of the convolution neuron matrix The function of the error-feedback recurrent layer is to through an average down sampling method. In the proposed compensate prediction errors using predicting results of eRCNN model, the pooling layer divides the convolution previous periods. The output layer uses a modified rectified neuron matrix into j × j disjoint regions, and uses the linear unit to generate the predictions of traffic speeds. averages of each region to represent the characteristic of the A. The Spatio-Temporal Input Matrix convolution neurons in the region. Through the processing of the pooling layer, the dimension of the spatio-temporal In order to exploit spatial and temporal correlation infor- matrix is reduced as about 1/(j × j) of its original size. The mation, we construct a spatio-temporal input matrix in the output of the pooling layer is a feature vector generated input layer. Given a road segment s, we define the traffic through vectoring the down sampled convolution neuron speed of the segment s at the time t as vs;t. When we use matrix, which is denoted as p. the proposed model to predict vs;t+1, the spatio-temporal matrix for the input layer is defined as C. The Error-Feedback Recurrent Layer 2 3 An important characteristic of traffic speed data is the vs−m;t vs−m;t−1 ··· vs−m;t−n 6 7 abrupt change of speed within a short time period. For 6 . 7 6 . ··· . 7 example, during the beginning 30 minutes of morning peaks, 6 7 6 vs−1;t vs−1;t−1 ··· vs−1;t−n 7 the traffic speed of the Beijing ring roads could drop 6 7 V = 6 vs;t vs;t−1 ··· vs;t−n 7 : (1) from 70km/h to 30km/h; while after a rear-end collision 6 7 6 vs+1;t vs+1;t−1 ··· vs+1;t−n 7 traffic accident, the traffic speed could drop from 50km/h to 6 . 7 20km/h. In general, it is hard to predict the traffic conditions 4 . ··· . 5 with these abrupt speed changes using traditional neural v v − ··· v − s+m;t s+m;t 1 s+m;t n network structures. In this way, we introduce an error- The column vector v:t contains traffic speed data of all feedback recurrent layer to improve prediction performance the segments in a range that m segments upstream and of our model in the above scenarios.

Load more