Other Technologies

Table of Contents:

For the optimal utilization of road network capacity and efficient traffic management during incidents, several countries have adopted a new technology named VMS. These messages appear on electronic displays in the form of text and graphics to notify drivers about any kind of disruption in traffic, such as accidents, obstacles, roadworks, etc. Moreover, the signs can display the warning and advisories about taking alternative routes or limiting travel speed, and disseminate the information about the duration and location of the incidents [59].

Variable speed limits form part of VMS technology, where it indicates a flexible speed limit of the vehicles on the roads [5]. As opposed to the conventional speed limit signs, which are fixed-valued, the variable speed limits are adjusted in accordance with the present environmental and traffic conditions. Therefore, during traffic incidents, bad weather, or roadworks, the maximum speed limits are reduced compared to that of the optimal condition, and commuters are advised to adhere to the limit accordingly.

It has been proven in several studies that VMS can potentially reduce the number of secondary crashes [12] [60]. Moreover, Dos et al. [6] reported that the combination of VMS and variable speed limit techniques can be a potential solution to reduce rear-end collisions. Therefore, apart from predictive solutions, these technologies can also assist drivers in the occurrence of a traffic incident. However, these technologies are still in the nascent stage. The awareness of drivers, which is significantly less than optimal in many cities, compromises the effectiveness of these messages as traffic guidance tools [61]. Therefore, with growing recognition for the efficacy of VMS technology, we hope to see a significant increase in the impact of VMS on overall traffic conditions in the coming years.


For efficient traffic incident management, it is important to adopt new technologies in urban areas. The prediction of parameters, such as incident duration or congestion length, has proved to be highly effective in this regard. However, the incident management authorities need to ensure that commuters and drivers install those applications in their systems and follow the guidance provided. Moreover, the prediction models should be robust, accurate, and adaptive to the varying ground conditions. Apart from that, of late, the VMS system has been an integral part of the dynamic routing guidance system. Therefore, several smart cities across the world are investing a significant amount of resources in installing VMS displays in different locations.

Last but not the least, prevention is better than cure. Hence, road users need to be careful and responsible enough while driving or walking along roads and, thus, play their part in minimizing the risk of incidents on the roads. Moreover, advanced driver-assistance systems (ADAS) are being employed to assist drivers while driving or parking. The safety features of ADAS are designed to alert drivers or take control of vehicles in the occurrence of an incident [62]. Therefore, ADAS have proved to be efficient in reducing road accidents significantly. There are other technologies associated with ADAS as well, such as lane departure warning systems, automatic lane centering, automated lighting, and incorporating traffic warnings, which can aid in minimizing human errors [63].


  • 1. Deo Chimba, Boniphace Kutela, Gary Ogletree, Frank Horne, and Mike Tugwell. Impact of abandoned and disabled vehicles on freeway incident duration. Journal of Transportation Engineering, 140(3):04013013, 2013.
  • 2. Junping Zhang, Fei-Yue Wang, Kunfeng Wang, Wei-Hua Lin, Xin Xu, and Cheng Chen. Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4):1624—1639, 2011.
  • 3. Eleni Petridou and Maria Moustaki. Human factors in the causation of road traffic crashes. European Journal of Epidemiology, 16(9):819-826, 2000.
  • 4. Ali Ahmed Mohammed, Kamarudin Ambak, Ahmed Mancy Mosa, and Deprizon Syamsunur. A review of the traffic accidents and related practices worldwide. The Open Transportation Journal, 13(1), 2019.
  • 5. Ellen F Grumert, Andreas Tapani, and Xiaoliang Ma. Characteristics of variable speed limit systems. European Transport Research Review, 10(2)1-12, 2018.
  • 6. Cristina Dos Santos. Assessment of the safety benefits of vms and vsl using the ucf driving simulator. University of Central Florida, 2007. https://stars.librar y.ucf.edu/cgi/viewcontent.cgi?article=4146&context=etd
  • 7. Bahar N Araghi, Simon Hu, Rajesh Krishnan, Michael Bell, and Washington Ochieng. A comparative study of k-nn and hazard-based models for incident duration prediction. In 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pages 1608-1613. IEEE, 2014.
  • 8. https://en.wikipedia.org/wiki/List_of_countries_by_traffic-related_death_rate.
  • 9. Ahmad Tavassoli Hojati, Luis Ferreira, Simon Washington, Phil Charles, and Ameneh Shobeirinejad. Modelling total duration of traffic incidents including incident detection and recovery time. Accident Analysis & Prevention, 71:296- 305, 2014.
  • 10. Subasish Das, Bradford К Brimley, Tomas E Lindheimer, and Michelle Zupancich. Association of reduced visibility with crash outcomes. IATSS Research, 42(3):143-151,2018.
  • 11. К Huhtala-Jenks and M Forsblom. Mobility as a service-the new transport paradigm. Trafik & Veje: 12-14,2015.
  • 12. P Borrough. Variable speed limits reduce crashes significantly in the UK. Urban Transportation Monitor, 1997.
  • 13. Yuye He, Sebastien Blandin, Laura Wynter, and Barry Trager. Analysis and real-time prediction of local incident impact on transportation networks. In 2014 IEEE International Conference on Data Mining Workshop (ICDMW), pages 158-166. IEEE, 2014.
  • 14. Qing He, Yiannis Kamarianakis, Klayut Jintanakul, and Laura Wynter. Incident duration prediction with hybrid tree-based quantile regression. In Advances in Dynamic Network Modeling in Complex Transportation Systems, pages 287-305. Springer, 2013.
  • 15. Doohee Nam and Fred Mannering. An exploratory hazard-based analysis of highway incident duration. Transportation Research Part A: Policy and Practice, 34(2):85-102, 2000.
  • 16. Xiaolei Ma, Chuan Ding, Sen Luan, Yong Wang, and Yunpeng Wang. Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method. IEEE Transactions on Intelligent Transportation Systems, 18(9): 2303-2310, 2017.
  • 17. Banishree Ghosh, Muhammad Tayyab Asif, Justin Dauwels, Ulrich Fastenrath, and Hongliang Guo. Dynamic prediction of the incident duration using adaptive feature set. IEEE Transactions on Intelligent Transportation Systems, 20(11):4019-4031, 2018.
  • 18. Younshik Chung. Development of an accident duration prediction model on the korean freeway systems. Accident Analysis & Prevention, 42(1):282—289, 2010.
  • 19. Rui Jiang, Ming Qu, Edward Chung, et al. Traffic incident clearance time and arrival time prediction based on hazard models. Mathematical Problems in Engineering, special issue: Transportation Modeling and Management, pages 288-294, 2014.
  • 20. Ruimin Li, Francisco C Pereira, and Moshe E Ben-Akiva. Competing risks mixture model for traffic incident duration prediction. Accident Analysis & Prevention, 75:192-201, 2015.
  • 21. Gaetano Valenti, Maria Lelli, and Domenico Cucina. A comparative study of models for the incident duration prediction. European Transport Research Review, 2(2):103—111, 2010.
  • 22. Francisco C Pereira, Filipe Rodrigues, and Moshe Ben-Akiva. Text analysis in incident duration prediction. Transportation Research Part C: Emerging Technologies, 37:177-192,2013.
  • 23. WW Wu, Shu-yan Chen, and Chang-jiang Zheng. Traffic incident duration prediction based on support vector regression. ICCTP 2011: Towards Sustainable Transportation Systems, ASCE Library, pages 2412-2421,2011.
  • 24. Lei Lin, Qian Wang, and Adel W Sadek. A combined m5p tree and hazard- based duration model for predicting urban freeway traffic accident durations. Accident Analysis & Prevention, 91:114-126,2016.
  • 25. JA Lopes. Traffic Prediction for Unplanned Events on Highways. PhD dissertation, Instituto Superior Tecnico (1ST), 2012.
  • 26. Thomas F Golob, Wilfred W Recker, and John D Leonard. An analysis of the severity and incident duration of truck-involved freeway accidents. Accident Analysis & Prevention, 19(5):375-395,1987.
  • 27. Ruimin Li. Traffic incident duration analysis and prediction models based on the survival analysis approach. IET Intelligent Transport Systems, 9(4):351-358, 2014.
  • 28. Khaled Hamad, Mohamad Ali Khalil, and Abdul Razak Alozi. Predicting freeway incident duration using machine learning. International Journal of Intelligent Transportation Systems Research, 18: 367-380, 2020.
  • 29. J-SR Jang. Anfis: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3):665—685, 1993.
  • 30. Ruimin Li, Francisco C Pereira, and Moshe E Ben-Akiva. Overview of traffic incident duration analysis and prediction. European Transport Research Review, 10(2):22, 2018.
  • 31. Tom V Mathew and KV Krishna Rao. Fundamental relations of traffic flow. https://www.civil.iitb.ac.in/tvm/nptel/512_FundRel/web/web.html#xl- 140006, Indian Institute of Technology Bombay, India.
  • 32. Banishree Ghosh, Muhammad Tayyab Asif, Justin Dauwels, Wentong Cai, Flongliang Guo, and Ulrich Fastenrath. Predicting the duration of non-recurring road incidents by cluster-specific models. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 1522-1527. IEEE, 2016.
  • 33. Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth Silverman, and Angela Y Wu. An efficient к-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 24(7):881-892,2002.
  • 34. Brendan J Frey and Delbert Dueck. Clustering by passing messages between data points. Science, 315(5814):972—976, 2007.
  • 35. Xiong Liu, Li Pan, and Xiaoliang Sun. Real-time traffic status classification based on gaussian mixture model. In IEEE International Conference on Data Science in Cyberspace (DSC), pages 573-578. IEEE, 2016.
  • 36. CP IJ van Hinsbergen, JWC Van Lint, and HJ Van Zuylen. Bayesian committee of neural networks to predict travel times with confidence intervals. Transportation Research Part C: Emerging Technologies, 17(5):498-509,2009.
  • 37. Stephen Boyles, David Fajardo, and S Travis Waller. A naive bayesian classifier for incident duration prediction. In 86th Annual Meeting of the Transportation Research Board, Washington, DC, 2007.
  • 38. Jinyoung Ahn, Eunjeong Ко, and Eun Yi Kim. Highway traffic flow prediction using support vector regression and bayesian classifier. In 2016 International Conference on Big Data and Smart Computing (BigComp), pages 239-244. IEEE, 2016.
  • 39. Banishree Ghosh, Muhammad Tayyab Asif, and Justin Dauwels. Bayesian prediction of the duration of non-recurring road incidents. In 2016 IEEE Region 10 Conference (TENCON), pages 87-90. IEEE, 2016.
  • 40. Jiancheng Long, Ziyou Gao, Xiaomei Zhao, Aiping Lian, and Penina Orenstein. Urban traffic jam simulation based on the cell transmission model. Networks and Spatial Economics, 11(1):43—64, 2011.
  • 41. Chun Liu, Shuhang Zhang, Hangbin Wu, and Qiang Fu. A dynamic spatio- temporal analysis model for traffic incident influence prediction on urban road networks. ISPRS International Journal of Geo-Information, 6(11):362,2017.
  • 42. Taghreed Alghamdi, Khalid Elgazzar, Magdi Bayoumi, Taysseer Sharaf, and Sumit Shah. Forecasting traffic congestion using arima modeling. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pages 1227-1232. IEEE, 2019.
  • 43. S Vasantha Kumar and Lelitha Vanajakshi. Short-term traffic flow prediction using seasonal arima model with limited input data. European Transport Research Review, 7(3):21,2015.
  • 44. Yiannis Kamarianakis and Poulicos Prastacos. Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches. Transportation Research Record, 1857(l):74-84, 2003.
  • 45. Bharti Sharma, Sachin Kumar, Prayag Tiwari, Pranay Yadav, and Marina I Nezhurina. Ann based short-term traffic flow forecasting in undivided two lane highway. Journal of Big Data, 5(1):48, 2018.
  • 46. Manoel Castro-Neto, Young-Seon Jeong, Myong-Kee Jeong, and Lee D Han. Online-svr for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, 36(3):6164-6173, 2009.
  • 47. Banishree Ghosh, Justin Dauwels, and Ulrich Fastenrath. Analysis and prediction of the queue length for non-recurring road incidents. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1-8. IEEE, 2017.
  • 48. Xuchen Dong, Ting Lei, Shangtai Jin, and Zhongsheng Hou. Short-term traffic flow prediction based on xgboost. In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), pages 854-859. IEEE, 2018.
  • 49. Huiwei Xia, Xin Wei, Yun Gao, and Haibing Lv. Traffic prediction based on ensemble machine learning strategies with bagging and lightgbm. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1-6. IEEE, 2019.
  • 50. Guy Leshem and Yaacov Ritov. Traffic flow prediction using adaboost algorithm with random forests as a weak learner. In Proceedings of World Academy of Science, Engineering and Technology, volume 19, pages 193-198. Citeseer, 2007.
  • 51. Muhammad Tayyab Asif, Justin Dauwels, Chong Yang Goh, Ali Oran, Esmail Fathi, Muye Xu, Menoth Mohan Dhanya, Nikola Mitrovic, and Patrick Jaillet. Spatiotemporal patterns in large-scale traffic speed prediction. IEEE Transactions on Intelligent Transportation Systems, 15(2):794-804,2014.
  • 52. Charalampos Bratsas, Kleanthis Koupidis, Josep-Maria Salanova, Konstantinos Giannakopoulos, Aristeidis Kaloudis, and Georgia Aifadopoulou. A comparison of machine learning methods for the prediction of traffic speed in urban places. Sustainability, 12(1):142,2020.
  • 53. Leong Wai Leong, Kelvin Lee, Kumar Swapnil, Xiao Li, Ho Yao Tong Victor, Nikola Mitrovic, Muhammad Tayyab Asif, Justin Dauwels, and Patrick Jaillet. Improving traffic prediction by including rainfall data. In ITS Asia-Pacific Forum, volume 14, 2015.
  • 54. Haiyang Yu, Zhihai Wu, Shuqin Wang, Yunpeng Wang, and Xiaolei Ma. Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors, 17(7):1501,2017.
  • 55. Min Chen, Guizhen Yu, Peng Chen, and Yungpeng Wang. Traffic Congestion Prediction Based on Long-Short Term Memory Neural Network Models. CICTP 2017: Transportation Reform and ChangeEquity, Inclusiveness, Sharing, and Innovation. Reston, VA: American Society of Civil Engineers, 2018. 673-681.
  • 56. Honglei Ren, You Song, Jingwen Wang, Yucheng Hu, and Jinzhi Lei. A deep learning approach to the citywide traffic accident risk prediction. 21st International Conference on Intelligent Transportation Systems (ITSC) pages. 3346- 3351. IEEE.
  • 57. Leonard Kleinrock. Queueing Systems, Volume 2: Journal of Computer Applications, Volume 66. New York: Wiley, 1976.
  • 58. Akintunde A Alonge and Thomas J Afullo. Rainfall time series synthesis from queue scheduling of rain event fractals over radio links. Radio Science, 50(12):1209—1224, 2015.
  • 59. https://en.wikipedia.org/wiki/Variable-message_sign.
  • 60. Chris Lee, Bruce Hellinga, and Frank Saccomanno. Assessing safety benefits of variable speed limits. Transportation Research Record: Journal of the Transportation Research Board, 1897:183-190,2004.
  • 61. Banishree Ghosh, Yuanzheng Zhu, and Ulrich Fastenrath. Effectiveness of vms messages in influencing the motorists' travel behaviour. In 2018 IEEE 22nd International Conference on Intelligent Transportation Systems (ITSC), pages 837- 842. IEEE, 2018.
  • 62. Umar Zakir Abdul Hamid, Fakhrul Razi Ahmad Zakuan, Khairul Akmal Zulkepli, Muhammad Zulfaqar Azmi, Hairi Zamzuri, Mohd Azizi Abdul Rahman, and Muhammad Aizzat Zakaria. Autonomous emergency braking system with potential field risk assessment for frontal collision mitigation. In 2017 IEEE Conference on Systems, Process and Control (ICSPC), pages 71-76. IEEE, 2017.
  • 63. https://en.wikipedia.org/wiki/Advanced_driver-assistance_systems.
< Prev   CONTENTS   Source