Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management
Publication: International Conference on Transportation and Development 2022
ABSTRACT
Transportation accounts for a significant portion of total global energy consumption. Excessive energy consumption usually occurs in urban traffic environments with congestion and travel delays. With the advancement of remote sensing and computer vision technologies, real-time traffic conditions can be monitored. Therefore, sustainable transportation management strategies can be developed to optimize the overall energy and environment performance and reduce congestion and emissions. This work presents a smart traffic monitoring system based on remote camera sensors. Real-time and historical traffic conditions at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) were monitored and analyzed to develop optimal transportation management strategies for sustainability. Computer vision algorithms were developed and applied to process the real-time camera data to obtain complete traffic information across the ORNL campus. Weeks of historical data were collected and processed to analyze the traffic and identify bottlenecks. The proposed traffic monitoring and management approach can be applied and extended to benefit other campuses or urban areas.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Berres, A. S., LaClair, T. J., Wang, C., Xu, H., Ravulaparthy, S., Todd, A., Tennille, S. A., and Sanyal, J. (2020). “Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic.” Transportation Research Record, SAGE Publications Inc, 2675(6), 23–37.
Blink. (n.d.). “The Power of Blink Charging | EV Charging Station.”
ChargePoint. (n.d.). “ChargePoint as a Service | ChargePoint.”
Chen, N., and Chen, Y. (2018). Smart City Surveillance at the Network Edge in the Era of IoT: Opportunities and Challenges. Springer, Cham, 153–176.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2010). ImageNet: A large-scale hierarchical image database. Institute of Electrical and Electronics Engineers (IEEE), 248–255.
Deter, D., Wang, C., Cook, A., and Perry, N. (2021). “Simulating the Autonomous Future: A Look at Virtual Vehicle Environments and How to Validate Simulation Using Public Data Sets.” IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers Inc., 38(1), 111–121.
Dutta, A., and Zisserman, A. (2019). “The VIA annotation software for images, audio and video.” MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia, Association for Computing Machinery, Inc, 2276–2279.
Econolite. (n.d.). Autoscope - Econolite.
FHWA (Federal Highway Administration). (2012). Manual on Uniform Traffic Control Devices for Streets and Highways.
Feng, Y., Zheng, J., and Liu, H. X. (2018). “Real-Time Detector-Free Adaptive Signal Control with Low Penetration of Connected Vehicles:”, https://doi.org/10.1177/0361198118790860 SAGE Publications Sage CA: Los Angeles, CA, 2672(18), 35–44.
GRIDSMART. (n.d.). “GRIDSMART | Single Camera Solution for Traffic Management.”
Hong, W., Tao, G., Wang, H., and Wang, C. (2022). “Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks.” IEEE Transactions on Neural Networks and Learning Systems, 1–13.
Khan, H. H., Malik, M. N., Zafar, R., Goni, F. A., Chofreh, A. G., Klemeš, J. J., and Alotaibi, Y. (2020). “Challenges for sustainable smart city development: A conceptual framework.” Sustainable Development, John Wiley & Sons, Ltd, 28(5), 1507–1518.
Kochlan, M., Hodon, M., Cechovic, L., Kapitulik, J., and Jurecka, M. (2014). “WSN for traffic monitoring using Raspberry Pi board.” 2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014, Institute of Electrical and Electronics Engineers Inc., 1023–1026.
Kurzhanskiy, A. A., and Varaiya, P. (2015). “Traffic management: An outlook.” Economics of Transportation, Elsevier, 4(3), 135–146.
OpenCV. (2022). “OpenCV.” <https://opencv.org/>.
Shah, A. P., Lamare, J. B., Nguyen-Anh, T., and Hauptmann, A. (2019). “CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis.” Proceedings of AVSS 2018-2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Institute of Electrical and Electronics Engineers Inc.
Shao, Y., and Sun, Z. (2020). “Vehicle Speed and Gear Position Co-optimization for Energy Efficient Connected and Autonomous Vehicles.” IEEE Transactions on Control Systems Technology, 1–12.
Shao, Y., and Sun, Z. (2021a). “Eco-Approach With Traffic Prediction and Experimental Validation for Connected and Autonomous Vehicles.” IEEE Transactions on Intelligent Transportation Systems, Institute of Electrical and Electronics Engineers (IEEE), 22(3), 1562–1572.
Shao, Y., and Sun, Z. (2021b). “Energy-Efficient Connected and Automated Vehicles: Real-Time Traffic Prediction-Enabled Co-Optimization of Vehicle Motion and Powertrain Operation.” IEEE Vehicular Technology Magazine, Institute of Electrical and Electronics Engineers Inc., 16(3), 47–56.
Suh, B., Shao, Y., and Sun, Z. (2020). “Vehicle Speed Prediction for Connected and Autonomous Vehicles Using Communication and Perception.” 2020 American Control Conference (ACC).
US Energy Information Administration. (2022). Use of energy explained: Energy use for transportation.
Vahidi, A., and Sciarretta, A. (2018). “Energy saving potentials of connected and automated vehicles.” Transportation Research Part C: Emerging Technologies, Pergamon, 95, 822–843.
Wang, H., Wang, C., Zhu, M., and Hong, W. (2019). “Globalized Modeling and Signal Timing Control for Large-Scale Networked Intersections.” Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities, SCC ’19, Association for Computing Machinery, New York, NY, USA.
Wang, H., Zhu, M., Hong, W., Wang, C., Tao, G., and Wang, Y. (2022). “Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy.” IEEE Transactions on Intelligent Transportation Systems, IEEE, 23(1), 333–343.
Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., and Girshick, R. (2019). Detectron2.
Information & Authors
Information
Published In
International Conference on Transportation and Development 2022
Pages: 34 - 45
History
Published online: Aug 31, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.