When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
Source abstract
Abstract
The widespread use of mobile devices and sensors has motivated data-driven applications that can leverage the power of big data to benefit many aspects of our daily life, such as health, transportation, economy, and environment. Under the context of smart city, intelligent transportation systems (ITS), as a main building block of modern cities, and edge computing (EC), as an emerging computing service that targets addressing the limitations of cloud computing, have attracted increasing attention in the research community in recent years. It is well believed that the application of EC in ITS will have considerable benefits to transportation systems regarding efficiency, safety, and sustainability. Despite the growing trend in ITS and EC research, a big gap in the existing literature is identified: the intersection between these two promising directions has been far from well explored. In this paper, we focus on a critical part of ITS, i.e., sensing, and conducting a review on the recent advances in ITS sensing and EC applications in this field. The key challenges in ITS sensing and future directions with the integration of edge computing are discussed.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Air Quality Monitoring and Forecasting · Smart Parking Systems Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Department of Civil Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
References (100+)
Show source-provided references
1. Edge Computing: Vision and Challenges10.1109/JIOT.2016.2579198 ↗
2. Edge intelligence: Paving the last mile of artificial intelligence with edge computing
3. Mobile edge computing: A survey10.1109/JIOT.2017.2750180 ↗
4. A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices10.1109/TITS.2020.2984197 ↗
5. Real-time video analytics: The killer app for edge computing10.1109/MC.2017.3641638 ↗
6. Ji, B., Han, Y., Wang, Y., Cao, D., Tao, F., Fu, Z., Li, P., and Wen, H. (2021). Relay Cooperative Transmission Algorithms for IoV Under Aggregated Interference. IEEE Trans. Intell. Transp. Syst., 1–14.
7. Artificial Intelligence Empowered Edge Computing and Caching for Internet of Vehicles10.1109/MWC.2019.1800411 ↗
8. Smart parking in IoT-enabled cities: A survey10.1016/j.scs.2019.101608 ↗
9. Ke, R. (2020). Real-Time Video Analytics Empowered by Machine Learning and Edge Computing for Smart Transportation Applications, University of Washington.
10. Ban, X.J., Herring, R., Margulici, J.D., and Bayen, A.M. (2009). Optimal Sensor Placement for Freeway Travel Time Estimation. Transportation and Traffic Theory 2009: Golden Jubilee, Springer.10.1007/978-1-4419-0820-9_34 ↗
11. Input-output and hybrid techniques for real-time prediction of delay and maximum queue length at signalized intersections10.3141/2035-08 ↗
12. Freeway traffic speed estimation with single-loop outputs10.3141/1727-15 ↗
13. Traffic measurement and vehicle classification with single magnetic sensor10.1177/0361198105191700119 ↗
14. Wireless magnetic sensors for traffic surveillance10.1016/j.trc.2007.10.004 ↗
15. A review of computer vision techniques for the analysis of urban traffic10.1109/TITS.2011.2119372 ↗
16. A Survey of Vision-Based Traffic Monitoring of Road Intersections10.1109/TITS.2016.2530146 ↗
17. Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors10.1109/TITS.2017.2727224 ↗
18. Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors10.1016/j.trc.2019.01.007 ↗
19. Sen, R., Siriah, P., and Raman, B. (2011, January 27–30). RoadSoundSense: Acoustic sensing based road congestion monitoring in developing regions. Proceedings of the 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, Salt Lake City, UT, USA.10.1109/SAHCN.2011.5984883 ↗
20. Analysis of pedestrian travel with static bluetooth sensors10.3141/2299-15 ↗
21. Estimation of origin and destination information from bluetooth and wi-fi sensing for transit10.3141/2595-02 ↗
22. Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment10.1016/j.trc.2009.10.006 ↗
23. Machine learning approach to forecast work zone mobility using probe vehicle data10.1177/0361198120927401 ↗
24. ITS devices used to collect truck data for performance benchmarks10.1177/0361198106195700107 ↗
25. Real time queue length estimation for signalized intersections using travel times from mobile sensors10.1016/j.trc.2011.01.002 ↗
26. Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison10.1109/JSEN.2020.3007809 ↗
27. Online Map Matching with Route Prediction10.1109/TITS.2018.2812147 ↗
28. Goh, C.Y., Dauwels, J., Mitrovic, N., Asif, M.T., Oran, A., and Jaillet, P. (2012, January 16–19). Online map-matching based on hidden Markov model for real-time traffic sensing applications. Proceedings of the 2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012, Anchorage, AK, USA.10.1109/ITSC.2012.6338627 ↗
29. Accurate Real-Time Map Matching for Challenging Environments10.1109/TITS.2016.2591958 ↗
30. An efficient realization of deep learning for traffic data imputation10.1016/j.trc.2016.09.015 ↗
31. Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks10.1109/TITS.2019.2910295 ↗
32. Innovative method for traffic data imputation based on convolutional neural network10.1049/iet-its.2018.5114 ↗
33. Asadi, R., and Regan, A. (2019). A convolution recurrent autoencoder for spatio-temporal missing data imputation. arXiv.
34. A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation10.1016/j.trc.2020.102673 ↗
35. A generative adversarial network for travel times imputation using trajectory data10.1111/mice.12595 ↗
36. Torres, R., Ohashi, O., and Pessin, G. (2019). A machine-learning approach to distinguish passengers and drivers reading while driving. Sensors, 19.10.3390/s19143174 ↗
37. Using truck probe gps data to identify and rank roadway bottlenecks10.1061/(ASCE)TE.1943-5436.0000444 ↗
38. Hsu, Y.W., Chen, Y.W., and Perng, J.W. (2020). Estimation of the number of passengers in a bus using deep learning. Sensors, 20.10.3390/s20082178 ↗
39. Sargolzaei, A., Crane, C.D., Abbaspour, A., and Noei, S. (2016, January 18–20). A machine learning approach for fault detection in vehicular cyber-physical systems. Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA.10.1109/ICMLA.2016.0112 ↗
40. Transportation Mode Recognition Using Mobile Phone Sensor Data10.1109/TITS.2015.2405759 ↗
41. Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features from Time and Frequency Domains
42. Chen, Z., Wang, S., Shen, Z., Chen, Y., and Zhao, Z. (2013, January 12–15). Online sequential ELM based transfer learning for transportation mode recognition. Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems (CIS), Manila, Philippines.10.1109/ICCIS.2013.6751582 ↗
43. Jeyakumar, J.V., Sandha, S.S., Lee, E.S., Tausik, N., Xia, Z., and Srivastava, M. (2018, January 8–12). Deep convolutional bidirectional LSTM based transportation mode recognition. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Singapore.10.1145/3267305.3267529 ↗
44. (Kouros) Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis10.1016/j.aap.2019.105405 ↗
45. (Kouros) Real-time accident detection: Coping with imbalanced data10.1016/j.aap.2019.05.014 ↗
46. A deep learning approach for detecting traffic accidents from social media data10.1016/j.trc.2017.11.027 ↗
47. Dogru, N., and Subasi, A. (2018, January 25–26). Traffic accident detection using random forest classifier. Proceedings of the 15th Learning and Technology Conference (L&T), Jeddah, Saudi Arabia.10.1109/LT.2018.8368509 ↗
48. Zhang, Z., Li, M., Lin, X., Wang, Y., and He, F. (2018). Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism. arXiv.
49. Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-Wide Traffic State with Missing Values10.1016/j.trc.2020.102674 ↗
50. Traffic Flow Prediction with Big Data: A Deep Learning Approach
51. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data10.1016/j.trc.2015.03.014 ↗
52. Yu, H., Wu, Z., Wang, S., Wang, Y., and Ma, X. (2017). Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors, 17.10.3390/s17071501 ↗
53. Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact10.1177/0361198120911052 ↗
54. Traffic Flow Prediction using Kalman Filtering Technique10.1016/j.proeng.2017.04.417 ↗
55. Real time transit demand prediction capturing station interactions and impact of special events10.1016/j.trc.2018.10.023 ↗
56. Zhang, J., Chen, F., Cui, Z., Guo, Y., and Zhu, Y. (2020). Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit. IEEE Trans. Intell. Transp. Syst., 1–11.10.1109/TITS.2020.3000761 ↗
57. Yao, H., Wu, F., Ke, J., Tang, X., Jia, Y., Lu, S., Gong, P., Ye, J., and Li, Z. (2018). Deep multi-view spatial-temporal network for taxi demand prediction. arXiv Prepr.10.1609/aaai.v32i1.11836 ↗
58. Geng, X., Li, Y., Wang, L., Zhang, L., Yang, Q., Ye, J., and Liu, Y. (February, January 27). Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.
59. Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach10.1016/j.trc.2017.10.016 ↗
60. The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets10.1016/j.trc.2018.07.013 ↗
61. Predicting station-level hourly demand in a large-scale bike- sharing network: A graph convolutional neural network approach10.1016/j.trc.2018.10.011 ↗
62. A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources10.1016/j.trc.2019.08.010 ↗
63. Ridel, D., Rehder, E., Lauer, M., Stiller, C., and Wolf, D. (2018, January 4–7). A literature review on the prediction of pedestrian behavior in urban scenarios. Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8569415 ↗
64. Lane-changes pre diction base d on adaptive fuzzy neural network10.1016/j.eswa.2017.09.025 ↗
65. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting10.1109/TITS.2019.2950416 ↗
66. Trafficgan: Network-scale deep traffic prediction with generative adversarial nets10.1109/TITS.2019.2955794 ↗
67. Cui, Z., Ke, R., Pu, Z., Ma, X., and Wang, Y. (2020). Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction. Transp. Res. Part C Emerg. Technol., 115.10.1016/j.trc.2020.102620 ↗
68. Event-based data collection for generating actuated controller performance measures10.3141/2035-11 ↗
69. Freeway ramp metering: An overview10.1109/TITS.2002.806803 ↗
70. Freeway performance measurement system: Mining loop detector data10.3141/1748-12 ↗
71. Comparison of vehicle detection techniques applied to IP camera video feeds for use in intelligent transport systems10.1016/j.trpro.2020.02.069 ↗
72. Controller area network reliability: Overview of design challenges and safety related perspectives of future transportation systems10.1049/iet-its.2019.0565 ↗
73. Comparing INRIX speed data against concurrent loop detector stations over several months10.1016/j.trc.2014.10.002 ↗
74. Big Data Applications for Managing Roadways
75. Jeske, T. (2013, January 12–15). Floating car data from smartphones: What google and waze know about you and how hackers can control traffic. Proceedings of the BlackHat Europe, Amsterdam, The Netherlands.
76. Vehicle-to-everything (V2X) services supported by LTE-based systems and 5G10.1109/MCOMSTD.2017.1700015 ↗
77. Interworking of DSRC and cellular network technologies for V2X communications: A survey10.1109/TVT.2016.2591558 ↗
78. A vision of C-V2X: Technologies, field testing, and challenges with chinese development10.1109/JIOT.2020.2974823 ↗
79. Shaon, M.R.R., Li, X., Wu, Y.-J., and Ramos, S. (2021). Quantitative Evaluation of Advanced Traffic Management Systems using Analytic Hierarchy Process. Transp. Res. Rec., 03611981211030256.10.1177/03611981211030256 ↗
80. Modeling individual travel time with back propagation neural network approach for advanced traveler information systems10.1061/JTEPBS.0000359 ↗
81. Autonomous vehicle perception: The technology of today and tomorrow10.1016/j.trc.2018.02.012 ↗
82. Qi, C.R., Liu, W., Wu, C., Su, H., and Guibas, L.J. (2017). Frustum PointNets for 3D object detection from RGB-D data. arXiv.10.1109/CVPR.2018.00102 ↗
83. Allodi, M., Broggi, A., Giaquinto, D., Patander, M., and Prioletti, A. (2016, January 19–22). Machine learning in tracking associations with stereo vision and lidar observations for an autonomous vehicle. Proceedings of the IEEE Intelligent Vehicles Symposium, Gothenburg, Sweden.10.1109/IVS.2016.7535456 ↗
84. Jung, J., and Bae, S.H. (2018). Real-time road lane detection in Urban areas using LiDAR data. Electronics, 7.10.3390/electronics7110276 ↗
85. Robust Traffic-Sign Detection and Classification Using Mobile LiDAR Data with Digital Images10.1109/JSTARS.2018.2810143 ↗
86. Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7–10). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.
87. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.
88. MIO-TCD: A new benchmark dataset for vehicle classification and localization10.1109/TIP.2018.2848705 ↗
89. Chang, M.C., Chiang, C.K., Tsai, C.M., Chang, Y.K., Chiang, H.L., Wang, Y.A., Chang, S.Y., Li, Y.L., Tsai, M.S., and Tseng, H.Y. (2020, January 14–19). AI city challenge 2020 - Computer vision for smart transportation applications. Proceedings of the IEEE/CVF Conference On Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.10.1109/CVPRW50498.2020.00318 ↗
90. Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16–21). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the IEEE/CVF Conference On Computer Vision and Pattern Recognition, Providence, RI, USA.10.1109/CVPR.2012.6248074 ↗
91. Xu, H., Gao, Y., Yu, F., and Darrell, T. (2017, January 21–26). End-to-end learning of driving models from large-scale video datasets. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.376 ↗
92. A UAV Video Dataset for Mosaicking and Change Detection from Low-Altitude Flights10.1109/TSMC.2018.2804766 ↗
93. Li, S., and Yeung, D.Y. (2017, January 4–9). Visual object tracking for unmanned aerial vehicles: A benchmark and new motion models. Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.10.1609/aaai.v31i1.11205 ↗
94. An edge traffic flow detection scheme based on deep learning in an intelligent transportation system10.1109/TITS.2020.3025687 ↗
95. Haferkamp, M., Al-Askary, M., Dorn, D., Sliwa, B., Habel, L., Schreckenberg, M., and Wietfeld, C. (2017, January 4–7). Radio-based traffic flow detection and vehicle classification for future smart cities. Proceedings of the IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, Australia.10.1109/VTCSpring.2017.8108633 ↗
96. Ho, T.-J., and Chung, M.-J. (2016, January 26–30). An approach to traffic flow detection improvements of non-contact microwave radar detectors. Proceedings of the 2016 International Conference on Applied System Innovation (ICASI), Okinawa, Japan.10.1109/ICASI.2016.7539785 ↗
97. New framework for automatic identification and quantification of freeway bottlenecks based on wavelet analysis10.1061/JTEPBS.0000168 ↗
98. Liu, Z., Zhang, W., Gao, X., Meng, H., Tan, X., Zhu, X., Xue, Z., Ye, X., Zhang, H., and Wen, S. (2020, January 14–19). Robust movement-specific vehicle counting at crowded intersections. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.10.1109/CVPRW50498.2020.00315 ↗
99. Traffic flow detection using distributed fiber optic acoustic sensing10.1109/ACCESS.2018.2868418 ↗
100. Djenouri, Y., Zimek, A., and Chiarandini, M. (2018, January 17–20). Outlier detection in urban traffic flow distributions. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), Singapore.10.1109/ICDM.2018.00114 ↗