Use of IoT-Based Technologies for Determination of Traffic Congestion on Road Infrastructure

Abstract:

Road congestion is mostly common in developing countries where the traffic is heterogeneous and where there is no proper lane discipline. Typical and live traffic state can be identified with the help of IoT-based technologies such as traffic state feature in Google Maps. One can also identify congestion points using this live traffic state which varies through different times of the day and affect road capacity. In this research, the congestion points that were identified by Google Maps are examined by conducting a route-specific survey. The survey is conducted at different times on weekdays and weekends to assess the variably in number of congestion points. In this research, the relationship is established between congestion points and traffic volume at different times of the day and a Chi-square test is performed to check the significance of this relationship. The result shows that the congestion points are significantly related to traffic demand which is higher at one time and lower at another. The result also shows that the trip purpose and trip direction significantly affect the traffic demand at different times of the day.

• Periodical: Construction Technologies and Architecture (Volume 15)
• Pages: 105-114
• DOI: https://doi.org/10.4028/p-j8YWR6
• Online since: January 2025
• Authors: Mirza Asad Ullah Baig*, Ashar Ahmed, Muhammad Owais
• Keywords: Congestion Points, Google Map, Live Traffic, Peak-Hour, Traffic Volume

References

[1] Memon RM, Kumar Khiani R (2020) Traffic Congestion Issues, Perceptions, Experience and Satisfaction of Car Drivers/Owners on Urban Roads. Mehran Univ res j eng technol 39:489–505

DOI: 10.22581/muet1982.2003.04

[2] Lan C-L, Venkatanarayana R, Fontaine MD (2019) Development of a Methodology for Determining Statewide Recurring and Nonrecurring Freeway Congestion: Virginia Case Study. Transportation Research Record 2673:566–578

DOI: 10.1177/0361198119850471

[3] Javaid S, Sufian A, Pervaiz S, Tanveer M (2018) Smart traffic management system using Internet of Things. In: 2018 20th International Conference on Advanced Communication Technology (ICACT). p.393–398

DOI: 10.23919/icact.2018.8323770

[4] Chong HF, Ng DWK (2016) Development of IoT device for traffic management system. In: 2016 IEEE Student Conference on Research and Development (SCOReD). p.1–6

DOI: 10.1109/scored.2016.7810059

[5] Khoo HL, Asitha KS (2016) User requirements and route choice response to smart phone traffic applications (apps). Travel Behaviour and Society 3:59–70

DOI: 10.1016/j.tbs.2015.08.004

[6] Xiong C, Zhou X, Zhang L (2018) AgBM-DTALite: An integrated modelling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12:141–150

DOI: 10.1016/j.tbs.2017.04.004

[7] Zhang L, Levinson D (2008) Determinants of Route Choice and Value of Traveler Information: A Field Experiment. Transportation Research Record 2086:81–92

DOI: 10.3141/2086-10

[8] Abdel-Aty MA, Vaughn KM, Kitamura R, et al Models of Commuters' Information Use and Route Choice: Initial Results Based on a Southern California Commuter Route Choice Survey

[9] Diop EB, Zhao S, Sun B (2015) A Study On Drivers' Route Choice Under The Influence Of ATIS Accuracy. Journal of the Eastern Asia Society for Transportation Studies 11:537–556

[10] Khattak AJ, Schofer JL, Koppelman FS (1995) Effect of traffic information on commuters' propensity to change route and departure time. Journal of Advanced Transportation 29:193–212

DOI: 10.1002/atr.5670290205

[11] Kusakabe T, Sharyo T, Asakura Y (2012) Effects of Traffic Incident Information on Drivers' Route Choice Behaviour in Urban Expressway Network. Procedia - Social and Behavioral Sciences 54:179–188

DOI: 10.1016/j.sbspro.2012.09.737

[12] Ben-Elia E, Di Pace R, Bifulco GN, Shiftan Y (2013) The impact of travel information's accuracy on route-choice. Transportation Research Part C: Emerging Technologies 26:146–159

DOI: 10.1016/j.trc.2012.07.001

[13] Bifulco GN, Simonelli F, di Pace R (2007) Endogenous Driver Compliance and Network Performances under ATIS. In: 2007 IEEE Intelligent Transportation Systems Conference. p.1028–1033

DOI: 10.1109/itsc.2007.4357722

[14] Yang H, Huang H-J (2004) Modeling user adoption of advanced traveler information systems: a control theoretic approach for optimal endogenous growth. Transportation Research Part C: Emerging Technologies 12:193–207

DOI: 10.1016/j.trc.2004.07.004

[15] Mulley C, Clifton GT, Balbontin C, Ma L (2017) Information for travelling: Awareness and usage of the various sources of information available to public transport users in NSW. Transportation Research Part A: Policy and Practice 101:111–132

DOI: 10.1016/j.tra.2017.05.007

[16] Ahmed A, Ngoduy D, Watling D (2016) Prediction of traveller information and route choice based on real-time estimated traffic state. Transportmetrica B: Transport Dynamics 4:23–47

DOI: 10.1080/21680566.2015.1052110

[17] Ezzedine H, Bonte T, Kolski C, Tahon C (2008) Integration of Traffic Management and Traveller Information Systems: Basic Principles and Case Study in Intermodal Transport System Management. International Journal Of Computers Communications & Control 3:281–294

DOI: 10.15837/ijccc.2008.3.2396

[18] Chien SIJ, Liu X, Ozbay K (2003) Predicting Travel Times for the South Jersey Real-Time Motorist Information System. Transportation Research Record 1855:32–40

DOI: 10.3141/1855-04

[19] Long J, Gao Z, Szeto WY (2011) Discretised link travel time models based on cumulative flows: Formulations and properties. Transportation Research Part B: Methodological 45:232–254

DOI: 10.1016/j.trb.2010.05.002

[20] Soriguera F, Robusté F (2011) Highway travel time accurate measurement and short-term prediction using multiple data sources. Transportmetrica 7:85–109. https://doi.org/10.1080/ 18128600903244651

DOI: 10.1080/18128600903244651