DOI: 10.5937/jaes0-32627
This is an open access article distributed under the CC BY 4.0
Volume 20 article 901 pages: 29-40
Importance of detailed traffic flow characterization is immense for achieving an intelligent transportation system. As such, great efforts in existing literature have gone into proposing different solutions for traffic flow characterization. Among these, first generation intrusive sensors such as pneumatic tube, inductive loop, piezoelectric and magnetic sensors were both labor intensive and expensive to install and maintain. These sensors were able to provide only vehicle count and classification under homogeneous traffic conditions. Second generation non-intrusive sensors based solutions, though a marked improvement over intrusive sensors, have the capability to only measure vehicle count, speed and classifications. Furthermore, both intrusive and non-intrusive sensor based solutions have limitations when employed under congested and heterogeneous traffic conditions. To overcome these limitations, a compute vision based solution has been proposed for traffic flow characterization under heterogeneous traffic behaviour. The proposed solution was field tested on a complex road configuration, consisting of a two-way multi-lane road with three U-turns. Unlike both intrusive and non-intrusive sensors, the proposed solution can detect pedestrians, two/three wheelers and animal/human driven carts. Furthermore, detailed flow parameters such as vehicle count, speed, spatial/temporal densities, trajectories and heat maps were measured.
This work has been funded by the Higher Education Commission, Pakistan to establish a National Center for Big Data and Cloud Computing, University of Engineering and Technology, Peshawar.
1. WHO. Ambient (Outdoor) Air Pollution, from https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health, accessed on 2019-06-07.
2. Khan, N., Khattak, K. S., Ullah, S., Khan. Z. H. (2019). A Low-Cost IoT Based System for Environmental Monitoring.International Conference on Frontiers of Information Technology (FIT), 173-1735.IEEE,DOI:10.1109/FIT47737.2019.00 041
3. Sohail, A. M., Khattak, K.S., Iqbal, A., Khan, Z.H., Ahmad, A., (2019). Cloud-based Detection of Road Bottlenecks Using OBD-II Telematics.22nd International Multitopic Conference(INMIC),1-7. IEEE,DOI:10.1109/ INMIC48123.2019.9022754
4. Guerrero-Ibáñez, J.A., Zeadally,S., Contreras-Castillo,J. (2018). Sensor Technologies for Intelligent Transportation Systems.Sensors 18, no. 4: 1212, DOI: 10.3390/s18041212
5. Ullah, R., Khattak, K. S., Khan, Z. H., Khan, M. A., Minallah, N., Khan, A.N. (2021). Vehicular Traffic Simulation Software: A Systematic Comparative Analysis. Pakistan Journal of Engineering and Technology,vol. 4, no. 1 (2021): 66-78
6. Khan, Z. H., Gulliver, T. A., Imran, W., Khattak, K.S., Altamimi, A, Qazi, A., (2021). A macroscopic traffic model based on relaxation time, Alexandria Engineering Journal, vol. 61,585-596,ISSN1110-0168, DOI: 10.1016/j.aej.2021.06.042
7. Waheed, I., Khan, Z.H., Gulliver, T.A., Khattak, K.S., Saeed, S., and Aslam, M.S, (2021). "Macroscopic Traffic Flow Characterization for Stimuli Based on Driver Reaction.Civil Engineering Journal 7, no. 1, 1-13. DOI: 10.28991/cej-2021-03091632
8. Khan, Z. H., Gulliver, T.A., Azam, K., and Khattak, K.S., (2019). Macroscopic Model on Driver Physiological and Psychological Behavior at changes in Traffic.Journal of Engineering and Applied Sciences,vol. 38, no. 2, 57-66, DOI: 10.25211/jeas.v38i2.3150
9. Khan, Z. H., Imran, W., Gulliver, T.A., Khattak, K.S., Wadud, Z., Khan, A.N., (2020). An Anisotropic Traffic Model Based on Driver Interaction. IEEE Access 8, 66799-66812, DOI: 10.28991/cej-2021-03091632
10. Iftekhar, A., Khan, Z.H., Khattak, K.S, Gulliver, T.A., Minallah, N., (2020). A macroscopic traffic flow characterization at bottlenecks.Civil Engineering Journal 6, no. 6, 848-859, DOI: 10.28991/cej-2020-03091543.
11. Jinturkar, S.P.,Pawar, S. (2016). Vehicle Detection and Parameter Measurement using Smart Portable Sensor System. Communications on Applied Electronics,vol. 4, 5-9, DOI: 10.5120/CAE2016652132.
12. Jeon, S., Kwon, E. , Jung, I. (2014). Traffic Measurement on Multiple Drive Lanes with Wireless Ultrasonic Sensors. Sensors (Basel, Switzerland). vol. 14. 22891-906, DOI: 10.3390/s141222891.
13. Hussain, T., Saadawi, T., Ahmed, S.A. (1993). Overhead infrared sensor for monitoring vehicular traffic. IEEE Transactions on Vehicular Technology, vol. 42, 477-483, DOI: 10.1109/25.260764.
14. Handscombe, J., Hong Q.Y. (2019). Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System. Sensors 19, no.2, DOI: 10.3390/s19020347.
15. Hostettler,R., Birk,W. (2011).Analysis of the Adaptive Threshold Vehicle Detection Algorithm Applied to Traffic Vibrations. IFAC Proceedings Volumes, vol. 44, 2150-2155,DOI:10.3182/20110828-6-IT-1002.014 84.
16. Ki, Y., Baik, D. (2006). Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks. Vehicular Technology, IEEE Transactions, vol. 55, 1704–1711,DOI: 10.1109/TVT.2006.883726.
17. Rajab, S. , Al Kalaa, M.O. , Refai, H. (2016). Classification and speed estimation of vehicles via tire detection using single-element piezoelectric sensor. Journal of advanced transportation, vol. 50, DOI: 10.1002/atr.1406.
18. Rajab, S. A, Othman, A. S, Refai, H.H. (2012). Novel vehicle and motorcycle classification using single element piezoelectric sensor.15th International IEEE Conference on Intelligent Transportation Systems,496-501,DOI: 10.1109/ITSC.2012.6 338778.
19. Zhu, H., Yu, F. (2016). A Cross-Correlation Technique for Vehicle Detections in Wireless Magnetic Sensor Network. IEEE Sensors Journal, vol. 16, no. 11, pp. 4484-4494, DOI: 10.1109/JSEN.2016.2523601.
20. Zhang, L. , Wang, R., Cui, L. (2011). Real-time Traffic Monitoring with Magnetic Sensor Networks. J. Inf. Sci. Eng. vol. 27, 1473-1486.
21. Hostettler, R., Birk, W., Nordenvaad, M.L. (2010). Feasibility of road vibrations-based vehicle property sensing. Iet Intelligent Transport Systems, vol. 4, 356-364, DOI: 10.1049/IET-ITS.2010.0046.
22. Hostettler, R. , Birk, W., Nordenvaad, M. L. (2012). Extended Kalman filter for vehicle tracking using road surface vibration measurements. IEEE 51st IEEE Conference on Decision and Control (CDC), 5643-5648,DOI: 10.1109/CDC.2012.6426451.
23. Hostettler, R., Birk, W. (2011). Analysis of the Adaptive Threshold Vehicle Detection Algorithm Applied to Traffic Vibrations. IFAC Proceedings Volumes, vol. 44,2150-2155,DOI:10.3182/20110828-6-IT-1002.014 84.
24. Rivas, J.,Wunderlich, R., Heinen, S. J. (2017). Road Vibrations as a Source to Detect the Presence and Speed of Vehicles. IEEE Sensors Journal, vol. 17, no. 2, 377-385, DOI: 10.1109/JSEN.2016.2628858.
25. Severdaks, A., Liepins, M. (2013). Vehicle Counting and Motion Direction Detection Using Microphone Array. Elektronika Ir Elektrotechnika,vol. 19, 89-92. DOI: 10.5755/j01.eee.19.8.5400.
26. Na, Y. , Guo, Y., Fu, Q.,Yan, Y. (2015). An Acoustic Traffic Monitoring System: Design and Implementation. IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), 119-126,DOI:10.1109/UIC-ATC-ScalCom CBD Com-IoP.2015.41.
27. George, J. , Cyril, A. , Koshy, B., Mary, L. (2013). Exploring Sound Signature for Vehicle Detection and Classification Using ANN. International Journal on Soft Computing.vol.4.DOI:10.5121/ijsc.2013.4203.
28. Forren, J. F.,Jaarsma, D. (1997). Traffic monitoring by tire noise. Proceedings of Conference on Intelligent Transportation Systems,177-182,DOI: 10.1109/ITSC.1997.6 60471.
29. Chen, S., Sun, Z. P.,Bridge, B. (1997). Automatic traffic monitoring by intelligent sound detection. Proceedings of Conference on Intelligent Transportation Systems , 171-176, DOI: 10.1109/ITSC.1997.660470.
30. Oudat, E.,Mousa, M., Claudel, C. (2015). Vehicle Detection and Classification Using Passive Infrared Sensing. 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems, 443-444, DOI: 10.1109/MASS.2015.62.
31. Hussain, T. M., Baig, A. M., Saadawi T. N. and Ahmed,S.A.(1995). Infrared pyroelectric sensor for detection of vehicular traffic using digital signal processing techniques. IEEE Transactions on Vehicular Technology, vol. 44, no. 3, 683-689, DOI: 10.1109/25.406637.
32. Fang, J., Meng, H., Zhang, H., Wang, X. (2007). A Low-cost Vehicle Detection and Classification System based on Unmodulated Continuous-wave Radar", IEEE Intelligent Transportation Systems Conference, 715-720, DOI: 10.1109/ITSC.2007.4357739.
33. Zwahlen, H. T., Russ, A., Oner, E., Parthasarathy, M. (2005). Evaluation of Microwave Radar Trailers for Nonintrusive Traffic Measurements. Transportation Research Record, vol. 1917(1), 127–140. DOI: 10.1177/0361198105191700115.
34. Samczynski, P. , Kulpa, K., Malanowski, M., Krysik, P., Maślikowski, Ł. (2011). A concept of GSM-based passive radar for vehicle traffic monitoring. Microwaves, Radar and Remote Sening Symposium, 271-274, DOI: 10.1109/MRRS.2011.6053652.
35. Felguera-Martin, D., Gonzalez-Partida, J., Almorox-Gonzalez, P., Burgos-García, M. (2012). Vehicular Traffic Surveillance and Road Lane Detection Using Radar Interferometry.IEEE Transactions on Vehicular Technology, vol. 61, no. 3, pp. 959-970, DOI: 10.1109/TVT.2012.2186323.
36. Odat, E. , Shamma, J. S., Claudel, C. (2018). Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors. IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 5, pp. 1593-1606,DOI: 10.1109/TITS.2017.272722 4.
37. Won, M., Zhang, S., Son, S. H. (2017).WiTraffic: Low-Cost and Non-Intrusive Traffic Monitoring System Using WiFi. 26th International Conference on Computer Communication and Networks (ICCCN),1-9,DOI: 10.1109/ICCCN.2017.80 38380.
38. Roy, S., Sen, R., Kulkarni, S., Kulkarni, P., Raman, B., Singh, L. K. (2011). Wireless across road: RF based road traffic congestion detection. Third International Conference on Communication Systems and Networks (COMSNETS),1-6,DOI: 10.1109/COMSNE TS.2011.5716525.
39. Lewandowski, M., Płaczek, B., Bernaś, M., Szymała, P. (2018). Road Traffic Monitoring System Based on Mobile Devices and Bluetooth Low Energy Beacons, Wireless Communications and Mobile Computing, 1-12, DOI: 10.1155/2018/3251598.
40. Horvat, G., Šoštarić, D., Žagar, D.(2012). Using radio irregularity for vehicle detection in adaptive roadway lighting. Proceedings of the 35th International Convention MIPRO, 748-753.
41. Kulkarni, A.P., Baligar, V. (2020). Real Time Vehicle Detection, Tracking and Counting Using Raspberry-Pi. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 603-607, DOI: 10.1109/ICIMIA48430.2020.9074944.
42. Sundararajan, M. (2018). Counting and Classification of Highway Vehicles by Using Raspberry Pi. International Journal of Pure and Applied Mathematics,vol. 118, no. 18: 193-201.
43. Iszaidy,I., Alias, A., Ngadiran, R., Ahmad,R., Jais,M.I., Shuhaizar, D. (2016). Video size comparison for embedded vehicle speed detection & travel time estimation system by using Raspberry Pi. International Conference on Robotics, Automation and Sciences (ICORAS),1-4,DOI: 10.1109/ICORAS.2016. 7872631.
44. Jiménez, A., Gacia-Díaz, V., Bolaños, S. (2018). A Decentralized Framework for Multi-Agent Robotic Systems. Sensors. vol. 18(2), 417, DOI: 10.3390/s18020417.
45. Traffic Vison. Traffic Monitoring Software, from http://www.trafficvision.com/ ,accessed on 2019/10/17.
46. Picomixer. Traffic Monitoring Software, from https://www.picomixer.com/STA.html ,accessed on 2019/10/15.
47. Traffic Monitoring Software, from http://www.autostradetech.it/en/solutions/traffic-management/automated-traffic-monitoring.html, accessed on 2019/10/17.
48. Camlytics. Traffic Monitoring Software ,from https://camlytics.com/help/index.html ,accessed on 2019/10/19.