Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

The technological landscape of intelligent transport systems (ITS) has been radically transformed by the emergence of the big data streams generated by the Internet of Things (IoT), smart sensors, surveillance feeds, social media, as well as growing infrastructure needs. It is timely and pertinent that ITS harness the potential of an artificial intelligence (AI) to develop the big data-driven smart traffic management solutions for effective decision-making. The existing AI techniques that function in isolation exhibit clear limitations in developing a comprehensive platform due to the dynamicity of big data streams, high-frequency unlabeled data generation from the heterogeneous data sources, and volatility of traffic conditions. In this paper, we propose an expansive smart traffic management platform (STMP) based on the unsupervised online incremental machine learning, deep learning, and deep reinforcement learning to address these limitations. The STMP integrates the heterogeneous big data streams, such as the IoT, smart sensors, and social media, to detect concept drifts, distinguish between the recurrent and non-recurrent traffic events, and impact propagation, traffic flow forecasting, commuter sentiment analysis, and optimized traffic control decisions. The platform is successfully demonstrated on 190 million records of smart sensor network traffic data generated by 545,851 commuters and corresponding social media data on the arterial road network of Victoria, Australia.

[1]  Zhen Qian,et al.  Road Traffic Congestion Monitoring in Social Media with Hinge-Loss Markov Random Fields , 2014, 2014 IEEE International Conference on Data Mining.

[2]  Moamar Sayed-Mouchaweh,et al.  Learning in Dynamic Environments , 2016 .

[3]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[4]  John Davies,et al.  Real time road traffic monitoring alert based on incremental learning from tweets , 2014, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS).

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

[6]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[7]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[8]  Chetan Gupta,et al.  Forecasting Spatiotemporal Impact of Traffic Incidents on Road Networks , 2013, 2013 IEEE 13th International Conference on Data Mining.

[9]  Charu C. Aggarwal,et al.  Addressing Concept-Evolution in Concept-Drifting Data Streams , 2010, 2010 IEEE International Conference on Data Mining.

[10]  João Gama,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[11]  Ashish Bhaskar,et al.  Noisy Bluetooth traffic data , 2014 .

[12]  Ashish Bhaskar,et al.  Fundamental understanding on the use of Bluetooth scanner as a complementary transport data , 2013 .

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

[14]  Javier Del Ser,et al.  DRED: An evolutionary diversity generation method for concept drift adaptation in online learning environments , 2017, Appl. Soft Comput..

[15]  Geoff Holmes,et al.  New ensemble methods for evolving data streams , 2009, KDD.

[16]  P. I. Richards.  Shock Waves on the Highway , 1956 .

[17]  P. Ekman.  An argument for basic emotions , 1992 .

[18]  Michael Gertz,et al.  EvenTweet: Online Localized Event Detection from Twitter , 2013, Proc. VLDB Endow..

[19]  Ashish Bhaskar,et al.  Bluetooth Data in an Urban Context: Retrieving Vehicle Trajectories , 2017, IEEE Transactions on Intelligent Transportation Systems.

[20]  Kyung Sup Kwak,et al.  Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling , 2017, ArXiv.

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

[22]  Abdul Hanan Abdullah,et al.  A Survey on Intelligent Transportation Systems , 2013 .

[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]  Bala Srinivasan,et al.  Dynamic self-organizing maps with controlled growth for knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..

[25]  Chandra Khatri,et al.  Real-time Road Traffic Information Detection Through Social Media , 2015, ArXiv.

[26]  Xinghuo Yu,et al.  Intelligent Detection of Driver Behavior Changes for Effective Coordination Between Autonomous and Human Driven Vehicles , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[27]  R. M. Tobin,et al.  Measuring emotional expression with the Linguistic Inquiry and Word Count. , 2007, The American journal of psychology.

[28]  S. Chandana Wirasinghe,et al.  DETERMINATION OF TRAFFIC DELAYS FROM SHOCK-WAVE ANALYSIS , 1978 .

[29]  Z. Papp,et al.  Traffic control and intelligent vehicle highway systems: a survey , 2011 .

[30]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[31]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[32]  Xinghuo Yu,et al.  A cognitive data stream mining technique for context-aware IoT systems , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.

[33]  Maxim Raya,et al.  TraCI: an interface for coupling road traffic and network simulators , 2008, CNS '08.

[34]  Daswin De Silva,et al.  Incremental knowledge acquisition and self learning from text , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[35]  Ugur Demiryurek,et al.  Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting , 2017, SDM.

[36]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[37]  Benjamin Schrauwen,et al.  Training and Analysing Deep Recurrent Neural Networks , 2013, NIPS.

[38]  Ryszard S. Michalski,et al.  Incremental learning with partial instance memory , 2002, Artif. Intell..

[39]  Tsvi Kuflik,et al.  Enhancing transport data collection through social media sources: methods, challenges and opportunities for textual data , 2015 .

[40]  David Silver,et al.  Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.

[41]  Alina Campan,et al.  Adaptive Clustering Algorithms , 2006, Canadian Conference on AI.

[42]  Ana L. C. Bazzan,et al.  The Role of Context in Transport Prediction , 2014, IEEE Intelligent Systems.

[43]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[44]  Carlos F. Daganzo,et al.  The Cell Transmission Model. Part I: A Simple Dynamic Representation Of Highway Traffic , 1992 .

[45]  Alexander Mendiburu,et al.  A Review of Travel Time Estimation and Forecasting for Advanced Traveler Information Systems , 2012 .

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

[47]  Axel Klar,et al.  Derivation of Continuum Traffic Flow Models from Microscopic Follow-the-Leader Models , 2002, SIAM J. Appl. Math..