Deep Representation of Raw Traffic Data: An Embed-and-Aggregate Framework for High-Level Traffic Analysis

In Intelligent Transportation Systems (ITS), it is widely used to extract a fixed-size feature vector from raw traffic data for high-level traffic analysis. In several existing works, the statistical approach has been used for extracting feature vectors, which directly extracts features by averaging speed or travel time of each vehicle. However, we can achieve a better representation by taking advantage of state-of-the-art machine learning algorithms instead of the statistical approach. In this paper, we propose a two-phase framework named embed-and-aggregate framework for extracting features from raw traffic data, and a feature extraction algorithm (Traffic2Vec) based on our framework exploiting state-of-the-art machine learning algorithms such as deep learning. We also implement a traffic flow prediction system based on Traffic2Vec as a proof-of-concept. We conducted experiments to evaluate the applicability of the proposed algorithm, and show its superior performance in comparison with the prediction system based on the statistical feature extraction method.

[1]  Florent Perronnin,et al.  Fisher Kernels on Visual Vocabularies for Image Categorization , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Martin Treiber,et al.  Traffic Flow Dynamics: Data, Models and Simulation , 2012 .

[3]  Qiuchen Liu,et al.  An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction , 2013 .

[4]  Bin Yu,et al.  Improved k-nn for Short-Term Traffic Forecasting Using Temporal and Spatial Information , 2014 .

[5]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[6]  Cordelia Schmid,et al.  Aggregating Local Image Descriptors into Compact Codes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Nicholas G. Polson,et al.  Deep learning for short-term traffic flow prediction , 2016, 1604.04527.

[8]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  Manoranjan Parida,et al.  Short term traffic flow prediction in heterogeneous condition using artificial neural network , 2013 .

[10]  Hwasoo Yeo,et al.  Real-Time Travel Time Prediction Using Multi-Level k-Nearest Neighbor Algorithm and Data Fusion Method , 2014 .

[11]  Andrew Zisserman,et al.  All About VLAD , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Tieniu Tan,et al.  Similarity based vehicle trajectory clustering and anomaly detection , 2005, IEEE International Conference on Image Processing 2005.

[13]  Xiangjie Kong,et al.  Urban traffic congestion estimation and prediction based on floating car trajectory data , 2016, Future Gener. Comput. Syst..

[14]  Hanghang Tong,et al.  Spatial-temporal traffic flow pattern identification and anomaly detection with dictionary-based compression theory in a large-scale urban network , 2016 .

[15]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Chao Zhang,et al.  Trajectory clustering via deep representation learning , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[17]  Said M. Easa,et al.  Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction , 2013, IEEE Transactions on Intelligent Transportation Systems.

[18]  Martin Treiber,et al.  Traffic Flow Dynamics , 2013 .

[19]  Bin Yu,et al.  k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition , 2016 .

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

[21]  Shin Hyoung Park,et al.  Short-term Traffic States Prediction Using k-Nearest Neighbor Algorithm: Focused on Urban Expressway in Seoul , 2016 .

[22]  Lelitha Vanajakshi,et al.  Short-term traffic flow prediction using seasonal ARIMA model with limited input data , 2015, European Transport Research Review.

[23]  Petros A. Ioannou,et al.  Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data , 2015, IEEE Transactions on Intelligent Transportation Systems.

[24]  Alessandro Farina,et al.  A new urban freight distribution scheme and an optimization methodology for reducing its overall cost , 2015 .

[25]  Yong Gao,et al.  Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality , 2013 .

[26]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.