Toward the improvement of traffic incident management systems using Car2X technologies


In addition to their environmental impact, road traffic congestions have been recognized to seriously affect commuters’ safety as well as the performance of transportation systems. To address these issues, various Traffic Incident Management (TIM) systems have been implemented. Recent systems are particularly focusing on the integration of promising emergent technologies such as the Internet of Things. However, thorough studies are still necessary to make sure that these technologies are compatible with existing systems and effective within their context of use. The main goal of this research is to develop a smart TIM system which is based on Car2X communications and which aims at improving both traffic safety, commuters’ mobility, and gas emissions. To assess the effectiveness of our solution, we use the following measures: stops delay, stops all, vehicle delay, travel time, gas emissions, and fuel consumption. This paper also outlines how we use our simulation platform (which was developed based on VISSIM and Python) to quantify the benefits of using Car2X communications. We run our simulations on Muscat Expressway in the Sultanate of Oman. Results are promising and include (1) the travel time decreased by 6%; (2) the average stop delay and vehicle stops were reduced by at least 9% and 27% respectively; and (3) there is a total decrease in fuel consumption and carbon monoxide emissions by approximately 16%.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. 1.

    Erick N (2013) Traffic jam and its social impacts: the case of Dar Es Salaam City. MSC thesis , Institute of Development Studies Mzumbe University.[available on line].

  2. 2.

    Carson J (2010) Best practices in traffic incident management. Report 9WA-HOP-1—050, FHWA, U.S. Department of Transportation.

  3. 3.

    Angela K, R. Irina, and Jesuk, K. Towards automated road information framework. A case study of Tanzania. Transport and telecommunication (15)(1)pp, 12–19 Transport and Telecommunication Institute, Lomonosova 1, Riga, LV-1019, Latvia.

  4. 4.

    OECD Organization for Economic Cooperation and Development. Better use of infrastructures to reduce environmental and congestion costs. In: OECD Economic Surveys: Belgium 2013. OECD Publishing, Paris, pp 11–13

  5. 5.

    Das D, Keetse M. Assessment of traffic congestion in the central areas (CBD) of South African cities-a case study of Kimberley City. Proceedings of the 34th Southern African Transport Conference (SATC 2015). ISBN Number: 978–1–920017-63-7

  6. 6.

    David S, Bill E and Tim L. TTI’s 2012 Urban Mobility Report. INRIX Traffic Data Texas A&M Transportation Institute the Texas A&M University System [available on line]:

  7. 7.

    Zhang G (2015) Connected autonomous vehicle control optimization at intersections. In VE Balas, LC Jain, & X Zhao (Eds) Information technology and intelligent transportation systems: Volume 1, Proceedings of the 2015 International Conference on Information Technology and Intelligent Transportation Systems ITITS 2015, held December 12-13, Xi’an China. Cham: Springer International Publishing, pp 5-6

  8. 8.

    Pettigrew S, Fritschi L, Norman R (2018) The potential implications of autonomous vehicles in and around the workplace. Int J Environ Res Public Health 15(9):1876. Published 2018 Aug 30. 10th. International Scientific Conference Transbaltica 2017: Transportation Science and Technology Connected Vehicles: an Innovative Transport Technology

    Article  Google Scholar 

  9. 9.

    NCST (2017) Examining the safety, mobility and environmental sustainability co-benefits and tradeoffs of intelligent transportation systems

  10. 10.

    Daniel K, Laura B, J’erôme H, Robbin B (2012) Simulation of V2X applications with the iTETRIS system. Proc-Soc Behav Sci 48:1482–1492

    Article  Google Scholar 

  11. 11.

    Daniel J and Luca D (2008) IEEE 802.11 p: towards an international standard for wireless access in vehicular environments. In Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE, pages 44. 2036–2040. IEEE

  12. 12.

    Singh V, Kumar P (2011) Web-based advanced traveler information system for developing countries. J Transp Eng 136(9):836–845

    Article  Google Scholar 

  13. 13.

    Choudhury C, Maszczyk M, Math Li and Dauwels F. An integrated simulation environment for testing V2X protocols and applications. ICCS 2016. The International Conference on Computational Science.

  14. 14.

    Eichler SU (2007) Performance evaluation of the IEEE 802.11 p WAVE communication standard. In Vehicular Technology Conference, 2007. VTC-2007 Fall. 2007 IEEE 66th, pages 2199–2203. IEEE

  15. 15.

    Jodi L (2010) Best practices in traffic incident management. U.S. Department of Transportation Federal Highway Administration. Office of Operations 21st Century Operations Using 21st Century Technologies Texas Transportation Institute

  16. 16.

    Singh B, Gupta A and Suman S (2014) Framework for development of advanced traveler information system: a case study for Chandigarh City. Proceedings of International Conference on Transportation Planning and implementation Methodologies for Developing Countries (TPMDC-2014), Submission Id. 54

  17. 17.

    Low, N. & Gleeson, B., 2015. Impact of congestion growth in Muscat, England : UK Essays

  18. 18.

    NCSI. National Centre for Statistics and Information. [Online]. Available:

  19. 19.

    PTV,AG, accessed December 2016

  20. 20.

    Honda project report: V2X connected vehicle early deployment application analysis. Sep 2016

  21. 21.

    Khair J, Sana Z, Yazan A (2017) Connected vehicles: an innovative transport technology. Procedia Eng 187:641–648 U. S. DOT, Jan. 20, 2017. [Online]. Available:

    Article  Google Scholar 

  22. 22.

    Uhlemann E (2016) Connected-vehicles applications are emerging. IEEE Vehicular Technol. Mag., vol. 11, issue 1, pp 25–28

  23. 23.

    ITERIS Connected vehicle reference implementation architecture. [Online]. Available:

  24. 24.

    EC European Commission, “FP7-COOPERATION-EUROPA Research Transport, [Online]. Available:

  25. 25.

    Chen C, Zhao X, Yao Y, Zhang Y, Rong J, Liu X (2018) Driver’s eco-driving behavior evaluation modeling based on driving events. 2018. J Adv Transp:1–12.

  26. 26.

    Giovanni N, Laurent Th, Antonio S. A model-based eco-routing strategy for electric vehicles in large urban networks. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Nov 2016, Rio de Janeiro, Brazil. pp 2301–2306,

  27. 27.

    Bierstedt J, Gooze A, Gray C, Peterman J, Raykin L, Walters J Effects of next-generation vehicles on travel demand and highway capacity. FP Think Working Group, 10–11 2014

  28. 28.

    Bohm F, Häger K (2015) Introduction of autonomous vehicles in the Swedish traffic system: effects and changes due to the new self-driving car technology. UPPSALA UNIVERSITET. Retrieved from

  29. 29.

    Nowakowski C, O’Connell J, Shladover SE, Cody D (2010) Cooperative adaptive cruise control: driver acceptance of following gap settings less than one second. Proc Hum Factors Ergonomics Soc Annu Meet 54(24):2033–2037.

    Article  Google Scholar 

  30. 30.

    Sivaraman S and Trivedi MM. Towards cooperative, predictive driver assistance. 16th International IEEE Annual Conference on Intelligent Transportation Systems, pp. 1719–1924, 6-9 10 2013

  31. 31.

    Fullerton M, Leonhardt A, Busch F, Maag C, Mark C, Kruger H-P (2010) Modeling cooperative on road systems. International Workshop on Intelligent Transportation (WIT), (in press)

  32. 32.

    Pragathi R (2017) Improvements of dilemma zone operation at high-speed intersections in mixed traffic conditions. A Thesis Presented to The Graduate Faculty of The University of Akron

  33. 33.

    Xie Y, Zhang H, Gartner NH, Arsava T (2017) Collaborative merging strategy for freeway ramp operations in a connected and autonomous vehicles environment. J Intell Transp Syst 21(2):136–147.

    Article  Google Scholar 

  34. 34.

    Sangung P, Kima J, Leea S, Hwang K. Experimental analysis on control constraints for connected vehicles using Vissim. 2016 International Symposium of Transport Simulation (ISTS’16 Conference)

  35. 35.

    Zhao L, Sun J Simulation framework for vehicle platooning and car-following behaviors under connected-vehicle environment. Procedia Soc Behav Sci 96:914–924

  36. 36.

    Leyn U, Vortisch P Calibrating VISSIM for the German highway capacity manual. Transp Res Rec J Transp Res Board (2483):74–79.

  37. 37.

    Mehar A, Chandra S, Velmurugan S Highway capacity through VISSIM calibrated for mixed traffic conditions. KSCE J Civ Eng 18(2):639–645.

  38. 38.

    Aria E, Olstam J, Schwietering C (2016) Investigation of automated vehicle effects on driver’s behavior and traffic performance. Transp Res Procedia 15:761–770.

    Article  Google Scholar 

  39. 39.

    Futurism, [Online]. Available:

  40. 40.

    Himani A (2016) Enhancing the side to main street merging using autonomousvehicle technology. A Thesis Presented to The Graduate Faculty of The University of Akron

  41. 41.

    FHWA- Traffic analysis toolbox volume III: guidelines for applying traffic microsimulation software. Final Report 2004 HRT-04-040

  42. 42.

    Yang N, Chang G, Kang K (2009) Simulation-based study on a lane-based signal system for merge control at freeway work zones. J Transp Eng 135(1):9–17

    Article  Google Scholar 

  43. 43.

    Muscat Municipality (2018) Traffic Study Report. Design Of Widening Of Muscat Expressway “Muscat Municipality

  44. 44.

    NTS National Transport Survey. Oman National Spatial Strategy. National Transport Survey (NTS) Final Draft Inception Report. Supreme Council for Planning. November 2017

  45. 45.

    Dong, J., Houchin N, Shafieirad C, Lu N, Hawkins, and Knickerbocker S (2015) VISSIM calibration for urban freeways. Center for Transportation Research and Education, Institute for Transportation, Iowa State University, Ames, IA

  46. 46.

    WSDOT Washington State Department of Transportation 2015.. Protocol for Vissim simulation, C. Mai, C. McDaniel-Wilson, D. Noval, et al. Traffic Incident Management Program Plan For the Topeka Metropolitan Area 2015

  47. 47.

    ITU-R M.2445-0 Report. Intelligent transport systems (ITS) usage. 2019. International Telecommunication Union. available from

  48. 48.

    Xie Y, Nathan HG, Polichronis S, Tianzhu R, and Gustavo S (2018) Optimizing future work zones in New England for improved safety and mobility. Report No. NETCR104

  49. 49.

    Grillo LF, Datta TK, Hartner C (2008) Dynamic late lane merge system at freeway construction work zones. Transp Res Rec:3–10, 2055

Download references


The authors acknowledge the research support provided by GUTech Oman and IMOB Hasselt University Belgium, Middle East College, Directorate General of Traffic, Royal Oman Police (ROP), Supreme Council for Planning, and Muscat Municipality for their support and providing the data that makes this research viable and effective.


This research received funding from Zayed University, UAE, under the Cluster Research Grant No. R17075.

Author information



Corresponding author

Correspondence to Siham G. Farrag.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Farrag, S.G., Outay, F., Yasar, A.UH. et al. Toward the improvement of traffic incident management systems using Car2X technologies. Pers Ubiquit Comput 25, 163–176 (2021).

Download citation


  • ITS
  • Safety
  • Connected vehicle (CV)