Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems

The Intelligent Transportation System (ITS) is an important part of modern transportation infrastructure, employing a combination of communication technology, information processing and control systems to manage transportation networks. This integration of various components such as roads, vehicles, and communication systems, is expected to improve efficiency and safety by providing better information, services, and coordination of transportation modes. In recent years, graph-based machine learning has become an increasingly important research focus in the field of ITS aiming at the development of complex, data-driven solutions to address various ITS-related challenges. This chapter presents background information on the key technical challenges for ITS design, along with a review of research methods ranging from classic statistical approaches to modern machine learning and deep learning-based approaches. Specifically, we provide an in-depth review of graph-based machine learning methods, including basic concepts of graphs, graph data representation, graph neural network architectures and their relation to ITS applications. Additionally, two case studies of graph-based ITS applications proposed in our recent work are presented in detail to demonstrate the potential of graph-based machine learning in the ITS domain.

[1]  S. Loke,et al.  Context-Aware Machine Learning for Intelligent Transportation Systems: A Survey , 2023, IEEE Transactions on Intelligent Transportation Systems.

[2]  Mingming Liu,et al.  A Survey on Graph Neural Networks for Microservice-Based Cloud Applications , 2022, Sensors.

[3]  H. M. Al-Ahmadi,et al.  Implementing the Maximum Likelihood Method for Critical Gap Estimation under Heterogeneous Traffic Conditions , 2022, Sustainability.

[4]  Pablo Rodrigo Gantier Cadena,et al.  Pedestrian Graph +: A Fast Pedestrian Crossing Prediction Model Based on Graph Convolutional Networks , 2022, IEEE Transactions on Intelligent Transportation Systems.

[5]  H. Koutsopoulos,et al.  Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks , 2022, KDD.

[6]  Anshul Gandhi,et al.  B-MEG: Bottlenecked-Microservices Extraction Using Graph Neural Networks , 2022, ICPE.

[7]  Mingming Liu,et al.  Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention , 2022, 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).

[8]  Chaofeng Sha,et al.  DeepTraLog: Trace-Log Combined Microservice Anomaly Detection through Graph-based Deep Learning , 2022, 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE).

[9]  Sen Yan,et al.  Parking Behaviour Analysis of Shared E-Bike Users Based on a Real-World Dataset - A Case Study in Dublin, Ireland , 2022, 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring).

[10]  D. Ioannidis,et al.  Utilizing machine learning on freight transportation and logistics applications: A review , 2022, ICT Express.

[11]  Said Gadri,et al.  An Efficient System to Predict Customers’ Satisfaction on Touristic Services Using ML and DL Approaches , 2021, 2021 22nd International Arab Conference on Information Technology (ACIT).

[12]  Chunghan Lee,et al.  GRAF: a graph neural network based proactive resource allocation framework for SLO-oriented microservices , 2021, CoNEXT.

[13]  Deepan Muthirayan,et al.  Spatiotemporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction , 2021, IEEE Internet of Things Journal.

[14]  Carina Goldbach.  Towards autonomous public transportation: Attitudes and intentions of the local population , 2021, Transportation Research Interdisciplinary Perspectives.

[15]  N. Shiwakoti,et al.  Review on Lane Detection and Tracking Algorithms of Advanced Driver Assistance System , 2021, Sustainability.

[16]  Myoungho Sunwoo,et al.  Vehicle Trajectory Prediction Using Hierarchical Graph Neural Network for Considering Interaction among Multimodal Maneuvers , 2021, Sensors.

[17]  Hong-Ning Dai,et al.  Graph Neural Networks for Anomaly Detection in Industrial Internet of Things , 2021, IEEE Internet of Things Journal.

[18]  Khac-Hoai Nam Bui,et al.  Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues , 2021, Applied Intelligence.

[19]  Noel E. O'Connor,et al.  A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes , 2021, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC).

[20]  Michel Bierlaire,et al.  A systematic review of machine learning classification methodologies for modelling passenger mode choice , 2021 .

[21]  Noel E. O'Connor,et al.  An Intelligent Multi-Speed Advisory System using Improved Whale Optimisation Algorithm , 2021, 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring).

[22]  Scott Weaven,et al.  A systematic literature review of AI in the sharing economy , 2021, Journal of Global Scholars of Marketing Science.

[23]  Weiwei Jiang,et al.  Graph Neural Network for Traffic Forecasting: A Survey , 2021, Expert Syst. Appl..

[24]  Yanjie Fu,et al.  Coupled Layer-wise Graph Convolution for Transportation Demand Prediction , 2020, AAAI.

[25]  Azzedine Boukerche,et al.  Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems , 2020, Comput. Networks.

[26]  Francisco Martínez-Álvarez,et al.  Deep Learning for Time Series Forecasting: A Survey , 2020, Big Data.

[27]  Thierry Turletti,et al.  Machine learning for next‐generation intelligent transportation systems: A survey , 2020, Trans. Emerg. Telecommun. Technol..

[28]  Yingshi Guo,et al.  Improving the User Acceptability of Advanced Driver Assistance Systems Based on Different Driving Styles: A Case Study of Lane Change Warning Systems , 2020, IEEE Transactions on Intelligent Transportation Systems.

[29]  Ta-Sung Lee,et al.  A Survey on Deep Learning-Based Vehicular Communication Applications , 2020, Journal of Signal Processing Systems.

[30]  Yasaman Esfandiari,et al.  Applications of Deep Learning in Intelligent Transportation Systems , 2020, Journal of Big Data Analytics in Transportation.

[31]  Medhat Moussa,et al.  Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends , 2020, IEEE Transactions on Intelligent Transportation Systems.

[32]  Xin Zhao,et al.  Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network , 2020, Sensors.

[33]  Pei Xu,et al.  Application on traffic flow prediction of machine learning in intelligent transportation , 2020, Neural Computing and Applications.

[34]  Yasin Yilmaz,et al.  Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey , 2020, IEEE Transactions on Intelligent Transportation Systems.

[35]  Wang Senzhang,et al.  Convolutional LSTM based transportation mode learning from raw GPS trajectories , 2020 .

[36]  Masayoshi Tomizuka,et al.  Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network , 2020, ArXiv.

[37]  Jinjun Tang,et al.  Traffic flow prediction based on combination of support vector machine and data denoising schemes , 2019, Physica A: Statistical Mechanics and its Applications.

[38]  Cheng Wang,et al.  GMAN: A Graph Multi-Attention Network for Traffic Prediction , 2019, AAAI.

[39]  Feng Xue,et al.  Passenger Flow Prediction in Bus Transportation System using ARIMA Models with Big Data , 2019, 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC).

[40]  So Young Sohn,et al.  Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects , 2019, PloS one.

[41]  Freddy Lecue,et al.  A Distributed Markovian Parking Assist System , 2019, IEEE Transactions on Intelligent Transportation Systems.

[42]  Jing Jiang,et al.  Graph WaveNet for Deep Spatial-Temporal Graph Modeling , 2019, IJCAI.

[43]  Lina Yao,et al.  STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting , 2019, IJCAI.

[44]  Ruili Wang,et al.  A Survey on an Emerging Area: Deep Learning for Smart City Data , 2019, IEEE Transactions on Emerging Topics in Computational Intelligence.

[45]  Sotiris Karabetsos,et al.  A Review of Machine Learning and IoT in Smart Transportation , 2019, Future Internet.

[46]  Ahmad F. Klaib,et al.  Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques: A Comprehensive Study , 2019, IEEE Access.

[47]  Divya Jayakumar Nair,et al.  Characterizing multicity urban traffic conditions using crowdsourced data , 2019, PloS one.

[48]  Zhiyuan Liu,et al.  Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.

[49]  Cheng Zhang,et al.  Short-term Prediction of Bike-sharing Usage Considering Public Transport: A LSTM Approach , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[50]  Kenli Li,et al.  Exploiting Spatio-Temporal Correlations with Multiple 3D Convolutional Neural Networks for Citywide Vehicle Flow Prediction , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[51]  Robert Shorten,et al.  Bayesian classifier for Route prediction with Markov chains , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[52]  Qiang Yang,et al.  Bike flow prediction with multi-graph convolutional networks , 2018, SIGSPATIAL/GIS.

[53]  Gholamreza Haffari,et al.  Graph-to-Sequence Learning using Gated Graph Neural Networks , 2018, ACL.

[54]  Raia Hadsell,et al.  Graph networks as learnable physics engines for inference and control , 2018, ICML.

[55]  Riza Sulaiman,et al.  Clustering of public transport operation using K-means , 2018, 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA).

[56]  Kevin Heaslip,et al.  Inferring transportation modes from GPS trajectories using a convolutional neural network , 2018, ArXiv.

[57]  R. Zemel,et al.  Neural Relational Inference for Interacting Systems , 2018, ICML.

[58]  Lei Lin,et al.  Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach , 2017, Transportation Research Part C: Emerging Technologies.

[59]  Yao-Jan Wu,et al.  Short-Term Traffic Flow Forecasting for Urban Roads Using Data-Driven Feature Selection Strategy and Bias-Corrected Random Forests , 2017 .

[60]  Yichen Wei,et al.  Relation Networks for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[61]  Di Wang,et al.  Real-Time Traffic Event Detection From Social Media , 2017, ACM Trans. Internet Techn..

[62]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[63]  Zhanxing Zhu,et al.  Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017, IJCAI.

[64]  Zhonghui Chen,et al.  Short-term traffic flow prediction with optimized Multi-kernel Support Vector Machine , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[65]  Fei-Yue Wang,et al.  Long short-term memory model for traffic congestion prediction with online open data , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[66]  Huadong Ma,et al.  A vehicle classification system based on hierarchical multi-SVMs in crowded traffic scenes , 2016, Neurocomputing.

[67]  Feng Chen,et al.  From Twitter to detector: real-time traffic incident detection using social media data , 2016 .

[68]  Li Pan,et al.  Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network , 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

[69]  Shuicheng Yan,et al.  Scale-Aware Fast R-CNN for Pedestrian Detection , 2015, IEEE Transactions on Multimedia.

[70]  Robert Shorten,et al.  A Distributed and Privacy-Aware Speed Advisory System for Optimizing Conventional and Electric Vehicle Networks , 2015, IEEE Transactions on Intelligent Transportation Systems.

[71]  Jianhua Guo,et al.  Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification , 2014 .

[72]  Joan Bruna,et al.  Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.

[73]  Josef F. Krems,et al.  Workload-adaptive cruise control – A new generation of advanced driver assistance systems , 2013 .

[74]  John Krumm,et al.  From destination prediction to route prediction , 2013, J. Locat. Based Serv..

[75]  Jin Wang,et al.  Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory , 2013 .

[76]  Eleni I. Vlahogianni,et al.  Testing and Comparing Neural Network and Statistical Approaches for Predicting Transportation Time Series , 2013 .

[77]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[78]  Abbas Fotouhi,et al.  Traffic condition recognition using the k-means clustering method , 2011 .

[79]  William Whittaker,et al.  Self-Driving Cars and the Urban Challenge , 2008, IEEE Intelligent Systems.

[80]  Zhirui Ye,et al.  Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition , 2007, Comput. Aided Civ. Infrastructure Eng..

[81]  Markos Papageorgiou,et al.  Real-time freeway traffic state estimation based on extended Kalman filter: a general approach , 2005 .

[82]  Timothy C. Coburn,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2004, Technometrics.

[83]  Azim Eskandarian,et al.  Research advances in intelligent collision avoidance and adaptive cruise control , 2003, IEEE Trans. Intell. Transp. Syst..

[84]  Adnan Shaout,et al.  Cruise control technology review , 1997 .

[85]  David Watling,et al.  MAXIMUM LIKELIHOOD ESTIMATION OF AN ORIGIN-DESTINATION MATRIX FROM A PARTIAL REGISTRATION PLATE SURVEY , 1994 .

[86]  H. Spiess.  A MAXIMUM LIKELIHOOD MODEL FOR ESTIMATING ORIGIN-DESTINATION MATRICES , 1987 .

[87]  I Okutani,et al.  Dynamic prediction of traffic volume through Kalman Filtering , 1984 .

[88]  Estefania Munoz Diaz,et al.  Survey of Machine Learning Methods Applied to Urban Mobility , 2022, IEEE Access.

[89]  Binh Minh Nguyen,et al.  EO-CNN: An Enhanced CNN Model Trained by Equilibrium Optimization for Traffic Transportation Prediction , 2020, KES.

[90]  Zhiyuan Liu,et al.  A Two-Stage Destination Prediction Framework of Shared Bicycles Based on Geographical Position Recommendation , 2019, IEEE Intelligent Transportation Systems Magazine.

[91]  Takayoshi Yoshimura,et al.  Online Map Matching With Route Prediction , 2019, IEEE Transactions on Intelligent Transportation Systems.

[92]  Kanika Chourasia,et al.  Autonomous vehicles: challenges, opportunities, and future implications for transportation policies , 2019 .

[93]  Kil To Chong,et al.  Vehicle Detection and Counting in High-Resolution Aerial Images Using Convolutional Regression Neural Network , 2018, IEEE Access.

[94]  Kumar Molugaram,et al.  Statistical Techniques for Transportation Engineering , 2015 .

[95]  Henry Leung,et al.  Data fusion in intelligent transportation systems: Progress and challenges - A survey , 2011, Inf. Fusion.

[96]  R. Lyman Ott.,et al.  An introduction to statistical methods and data analysis , 1977 .