Complex Event Analysis for Traffic Risk Prediction based on 3D-CNN with Multi-sources Urban Sensing Data

Predictive analytics are concerned as a type of complex event processing where a complex event can be predicted by utilizing insights extracted from a set of related events. This paper introduces a new complex event analysis for traffic risk prediction using 3D-CNN and a set of related events detected from multi-sources urban sensing data (e.g., congestion, traffic accident, precipitation). The contribution of this paper involves (1) the spatio-temporal information of multi-sources urban sensing data is reserved and wrapped into 3D raster images towards being able to leverage recent developments of 3D-CNN to conduct predictive analytics, (2) the imbalanced data problem which could severely affect the performance of deep learning models is tackled by straightening curved geographic chains, (3) traffic risks can be predicted well in both short-term and medium-term time horizons, and (4) The influence of related events detected from extra factors on a complex event can be explained explicitly. The proposed method is evaluated on the real dataset collected in Kobe, Japan during 2014 and 2015. The comparisons to baseline methods such as historical average and 2D-CNN show the advantage of the proposed method as well.

[1]  Carla P. Gomes,et al.  Understanding Batch Normalization , 2018, NeurIPS.

[2]  Yang Deng,et al.  Traffic Congestion Prediction by Spatiotemporal Propagation Patterns , 2019, 2019 20th IEEE International Conference on Mobile Data Management (MDM).

[3]  Yang Liu,et al.  PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion Prediction , 2018, IEEE Transactions on Intelligent Transportation Systems.

[4]  Eneko Osaba,et al.  A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy , 2016, IEEE Transactions on Intelligent Transportation Systems.

[5]  Jiannong Cao,et al.  Exploring traffic congestion correlation from multiple data sources , 2017, Pervasive Mob. Comput..

[6]  Yuliang Shi,et al.  Data Driven Congestion Trends Prediction of Urban Transportation , 2018, IEEE Internet of Things Journal.

[7]  Li-Der Chou,et al.  Congestion Prediction With Big Data for Real-Time Highway Traffic , 2018, IEEE Access.

[8]  Xiao Liu,et al.  Straightening Caenorhabditis elegans images , 2007, Bioinform..

[9]  Alexander Artikis,et al.  Complex event recognition in the Big Data era: a survey , 2019, The VLDB Journal.

[10]  Eleni I. Vlahogianni,et al.  Short-term traffic forecasting: Where we are and where we’re going , 2014 .

[11]  Koji Zettsu,et al.  Complex Event Analysis of Urban Environmental Data based on Deep CNN of Spatiotemporal Raster Images , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[12]  Rui Xu,et al.  Integrating heterogeneous data sources for traffic flow prediction through extreme learning machine , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[13]  Ying Liu,et al.  Short-Term Traffic Congestion Forecasting Using Attention-Based Long Short-Term Memory Recurrent Neural Network , 2019, ICCS.

[14]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[15]  Li Kuang,et al.  Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning , 2019, Remote. Sens..

[16]  Eleni I. Vlahogianni,et al.  Road Traffic Forecasting: Recent Advances and New Challenges , 2018, IEEE Intelligent Transportation Systems Magazine.

[17]  Xuan Song,et al.  Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference , 2016, AAAI.

[18]  Yunxiang Liu,et al.  Prediction of Road Traffic Congestion Based on Random Forest , 2017, 2017 10th International Symposium on Computational Intelligence and Design (ISCID).