Communications - Scientific Letters of the University of Zilina 2022, 24(3):D126-D140 | DOI: 10.26552/com.C.2022.3.D126-D140

A Simulation Approach for Evaluating Congestion and Its Mitigation Measures on Urban Arterials Operating with Mixed Traffic Conditions

Chintaman Santosh Bari ORCID..., Tanmayee V. Gunjal, Ashish Dhamaniya ORCID...*
Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India

Present study evaluates the various congestion indexes in terms of speed performance index, volume to capacity ratio and congestion index based on travel time on the urban roadway network of 5.7 km in length, operating with mixed traffic conditions (traffic with varying static and dynamic characteristics of vehicles). The mitigations measures have been proposed to cater to the present traffic demand by converting the uncontrolled intersection to a partially controlled or signal-controlled intersection. In order to check the effectiveness of such mitigation measures, various scenarios have been generated in the developed and a well calibrated simulation model. It has also been observed that the speed performance index has improved by 8.88 percent while providing channelized intersection at one of the point elements and 11.52 %when the two-point elements have converted to channelizing intersection.

Keywords: congestion intersection simulation speed
Grants and funding:

The authors would like to thank TEQIP-III, a Government of India initiative, for sponsoring this project. The project is entitled “Operational Efficiency of Urban Roadway Links using Performance Box” (Project number SVNIT/CED/AD/TEQIPIII/2119/2019). The present study is a part of the project.

Received: October 25, 2021; Accepted: February 7, 2022; Prepublished online: May 4, 2022; Published: July 1, 2022  Show citation

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Bari, C.S., Gunjal, T.V., & Dhamaniya, A. (2022). A Simulation Approach for Evaluating Congestion and Its Mitigation Measures on Urban Arterials Operating with Mixed Traffic Conditions. Communications - Scientific Letters of the University of Zilina, 24(3), D126-140. doi: 10.26552/com.C.2022.3.D126-D140
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    References

    1. SHI, G., SHAN, J., DING, L., YE, P., LI, Y., JIANG, N. Urban road network expansion and its driving variables: a case study of Nanjing city. International Journal of Environmental Research and Public Health [online]. 2019, 16(2318), p. 1-16. ISSN 1660-4601. Available from: https://doi.org/10.3390/ijerph16132318 Go to original source...
    2. COHEN, B. Urbanization in developing countries: current trends, future projections and key challenges for sustainability. Technology in Society [online]. 2006, 28, p. 63-80. ISSN 0160-791X. Available from: https://doi.org/10.1016/j.techsoc.2005.10.005 Go to original source...
    3. CLEVELAND, T., DEC, P., RAINHAM, D. Shorter roads go a long way: the relationship between density and road length per resident within and between cities. Canadian Planning and Policy / Amenagement et Politique au Canada [online]. 2020, 2020, p. 71-89. ISSN 2562-122X. Available from: https://doi.org/10.24908/cpp-apc.v2020i0.13406 Go to original source...
    4. GLOVER, D. R., SIMON, J. L. The effect of population density on infrastructure: the case of road building. Economic Development and Cultural Change. 1975, 23(3), p. 453-468. ISSN 0013-0079, eISSN 1539-2988. Go to original source...
    5. KARTHIK, S., LOUI, T. R. Development of traffic congestion model to evaluate the level of congestion and its associative cost for an urban arterial road: a case study. Journal of The Institution of Engineers (India): Series A [online]. 2021, 102(2), p. 623-632. ISSN 2250-2157. Available from: https://doi.org/10.1007/s40030-021-00526-z Go to original source...
    6. RAO, A. M., RAO, R. K. Identification of traffic congestion on urban arterials for heterogeneous traffic. Transport Problems [online]. 2016, 11(3), p. 131-142. ISSN 1896-0596. Available from: https://doi.org/10.20858/tp.2016.11.3.13 Go to original source...
    7. OLAYODE, I. O., SEVERINO, A., CAMPISI, T., TARTIBU, L. K. Prediction of vehicular traffic flow using Levenberg-Marquardt artificial neural network model: Italy road transportation system. Communications - Scientific Letters of the University of Zilina [online]. 2022, 24(2), p. E74-E86. ISSN 1335-4205, eISSN 2585-7878. Available from: https://doi.org/10.26552/com.C.2022.2.E74-E86 Go to original source...
    8. JANDACKA, D, DURCANSKA, D., KOVALOVA, D. Concentrations of traffic related pollutants in the vicinity of different types of urban crossroads. Communications-Scientific Letters of the University of Zilina [online]. 2019, 21(1), p. 49-58. ISSN 1335-4205, eISSN 2585-7878. Available from: https://doi.org/10.1088/1757-899X/661/1/012152 Go to original source...
    9. SAMAL, S. R., KUMAR, P. G., SANTHOSH, C. J., SANTHAKUMAR, M. Analysis of traffic congestion impacts of urban road network under Indian condition. IOP Conference Series: Materials Science and Engineering [online]. 2020, 1006(1), 012002. ISSN 1757-899X. Available from: https://doi.org/10.1088/1757-899X/1006/1/012002 Go to original source...
    10. BARI, CH. S., NAVANDAR, Y. V., DHAMANIYA, A. Vehicular emission modeling at Toll Plaza using performance box data. Journal of Hazardous, Toxic and Radioactive Waste [online]. 2020, 24(4), p. 1-19. ISSN 2153-5493, eISSN 2153-5515. Available from: https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000550 Go to original source...
    11. JI, W., WANG, Y., ZHUANG, D., SONG, D., SHEN, X., WANG, W., LI, G. Spatial and temporal distribution of expressway and its relationships to land cover and population: a case study of Beijing, China. Transportation Research Part D: Transport and Environment [online]. 2014, 32, p. 86-96. ISSN 1361-9209. Available from: https://doi.org/10.1016/j.trd.2014.07.010 Go to original source...
    12. TRIPATHI, S. Relationship between infrastructure and population agglomeration in urban India: an empirical assessment. ADBI Working Paper Series. 2017, 731, p. 1-25. ISSN 1655-5252.
    13. DINGIL, A. E., SCHWEIZER, G., RUPI, F., STASISKIENE, Z. Transport indicator analysis and comparison of 151 urban areas, based on open source data. European Transport Research Review [online]. 2018, 10(58), p. 1-9. ISSN 1866-8887. Available from: https://doi.org/10.1186/s12544-018-0334-4 Go to original source...
    14. DAS, A., SAW, K. Traffic congestion modelling under mixed traffic conditions through fuzzy logic approach: an Indian case study of arterial road. 2004.
    15. ASAITHAMBI, G., KANAGARAJ, V., SRINIVASAN, K. K., SIVANANDAN, R. Characteristics of mixed traffic on urban arterials with significant volumes of motorized two-Wheelers: role of composition, intraclass variability and lack of lane discipline. Transportation Research Record [online]. 2012, 2317(2317), p. 51-59. ISSN 0361-1981. Available from: https://doi.org/10.3141/2317-07 Go to original source...
    16. HE, F., YAN, X., LIU, Y., MA, L. A traffic congestion assessment method for urban road networks based on speed performance index. Procedia Engineering [online]. 2016, 137, p. 425-433. ISSN 1877-7058. Available from: https://doi.org/10.1016/j.proeng.2016.01.277 Go to original source...
    17. GAUTAM, L., JAIN, K. Study on mixed traffic flow behavior on arterial road. International Journal of Engineering Research and Technology (IJERT). 2018, 6(11), p. 1-7. ISSN 2278-0181.
    18. WANG, W.-X., GUO, R.-J., YU, J. Research on road traffic congestion index based on comprehensive parameters: taking Dalian city as an example. Advances in Mechanical Engineering [online]. 2018, 10(6), p. 1-8. ISSN 1687-8132, eISSN 1687-8140. Available from: https://doi.org/10.1177/1687814018781482 Go to original source...
    19. MADE, D., WEDAGAMA, P. The influence of mixed traffic on congestion level. International Journal of GEOMATE. 2019, 17(64), p. 18-25. ISSN 2186-2982, eISSN 2186-2990. Go to original source...
    20. AFRIN, T., YODO, N. A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability [online]. 2020, 12(11), p. 1-23. ISSN 2071-1050. Available from: https://doi.org/10.3390/su12114660 Go to original source...
    21. FELLENDORF, M., VORTISCH, P. Validation of the microscopic traffic flow model VISSIM in different real-world situations. In: 80th Annual Meeting of Transportation Research Board: proceedings. 2001. p. 1-9.
    22. JIE, L., FANGFANG, Z., VAN ZUYLEN, H., SHOUFENG, L. Calibration of a micro simulation program for a Chinese city. Procedia - Social and Behavioral Sciences [online]. 2011, 20, p. 263-272. ISSN 1877-0428. Available from: https://doi.org/10.1016/j.sbspro.2011.08.032 Go to original source...
    23. SUN, D. J., LIU, X., NI, A., PENG, CH. Traffic congestion evaluation method for urban arterials: case study of Changzhou, China. Transportation Research Record [online]. 2014, 2461, p. 9-15. ISSN 0361-1981. Available from: https://doi.org/10.3141/2461-02 Go to original source...
    24. CELKO, J., KOVAC, M., HUSZAROVA, K. Influence of selected vehicle maneuvers on reduction of the urban roads capacity. Communications - Scientific Letters of the University of Zilina [online]. 2019, 21(4), p. 81-89. ISSN 1335-4205, eISSN 2585-7878. Available from: https://doi.org/10.26552/com.C.2019.4.81-89 Go to original source...
    25. INDO-HCM. Indian Highway Capacity Manual (Indo-HCM). 2017.
    26. CHARLY, A., MATHEW, T. V. Evaluation of driving performance in relation to safety on an expressway using field driving data. Transportation Letters [online]. 2020, 12(5), p. 1-9. ISSN 1942-7875. Available from: https://doi.org/10.1080/19427867.2019.1591075 Go to original source...
    27. Racelogic Limited. Performance Box-III 100Hz GPS Data Logger User Guide. Unit 10, Swan Business Centre, Osier Way, Buckingham MK18 1TB. United Kingdom.: Racelogic Limited., 2014.
    28. PAUL, G., RAJU, N., ARKATKAR, S., EASA, S. Can segregating vehicles in mixed-traffic stream improve safety and throughput? implications using simulation. Transportmetrica A: Transport Science [online]. 2021, 17(4), p. 1-25. ISSN 2324-9935. Available from: https://doi.org/10.1080/23249935.2020.1826595 Go to original source...
    29. MITKAS, D. Z., POLITIS, I. Evaluation of alternative ramp metering scenarios on freeway on-ramp with the use of microscopic simulation software Vissim. Transportation Research Procedia [online]. 2020, 45(2019), p. 483-490. ISSN 2352-1465. Available from: https://doi.org/10.1016/j.trpro.2020.03.042 Go to original source...
    30. BARI, CH S., GUPTA, U., CHANDRA, S., ANTONIOU, C., DHAMANIYA, A. Examining effect of electronic toll collection (ETC) system on queue delay using microsimulation approach at Toll Plaza -a case study of Ghoti Toll Plaza, India. In: 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems: proceedings. 2021. p. 1-6. Go to original source...
    31. MATHEW, T. V., RAO, K. V. K. Traffic signal design-II. In: Introduction to Transportation Engineering. NPTEL, 2007.

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