Prioritizing Initiatives and Geometric Designing for Improvement the Performance of Intracity Expressways (Case Study: Hemmat Exp)

Document Type : Original Article


Department of Civil Engineering, Malard Branch, Islamic Azad University, Tehran, Iran.


In this study, improvement of the Hemmae Exp-east, between the Haqhani Exp and Sayad Shirazi Exp, was investigated using the smartization implementation and geometric designing. The traffic volumes of passing vehicles through light and heavy detachments were collected, and traffic information involved the PHF and the peak hour were extracted from them. The speed of the vehicles, the number of transmission lanes, and the width of the lanes were taken and the improvement of delay indices, speed, travel time and distance traveled, fuel consumption, and pollutant emissions, were investigated by providing solutions in three scenarios. The first scenario was implemented on-ramp metering. In the second scenario, two lanes were added to the Hemmat Exp, and both of the scenarios were implemented in the third scenario. The Delay index was decreased by 22% in scenario 1. In the third scenario, the speed indicator increased by 77%. Compared to different scenarios, the improvement rate for delay indices, speed, travel time, fuel consumption, and pollutant emissions in scenario 3 had more improvement than the other two scenarios. After the software calibration, in which the results are more reliable, scenario 3 showed better results than the first and second solutions.


Main Subjects

Copyright © 2021 Ali Paydar. This is an open access paper distributed under the Creative Commons Attribution License. Journal of Civil Engineering and Materials Application is published by Pendar Pub; Journal p-ISSN 2676-332X; Journal e-ISSN 2588-2880.

[1] Naikoo AA, Thakur SS, Guroo TA, Lone AA. Development of society under the modern technology-a review. Scholedge International Journal of Business Policy & Governance. 2018 Jan 1;5(1):1-8. [View at Google Scholar] ; [View at Publisher]
[2] Liu ZJ, Tretyakova N, Fedorov V, Kharakhordina M. Digital literacy and digital didactics as the basis for new learning models development. International Journal of Emerging Technologies in Learning (iJET). 2020 Jul 31;15(14):4-18. [View at Google Scholar] ; [View at Publisher]
[3] Kamal MM. The triple-edged sword of COVID-19: understanding the use of digital technologies and the impact of productive, disruptive, and destructive nature of the pandemic. Information systems management. 2020 Oct 1;37(4):310-7. [View at Google Scholar] ; [View at Publisher]
[4] Rachmadi MF, Al Afif F, Jatmiko W, Mursanto P, Manggala EA, Ma'sum MA, Wibowo A. Adaptive traffic signal control system using camera sensor and embedded system. InTENCON 2011-2011 IEEE Region 10 Conference 2011 Nov 21 (pp. 1261-1265). IEEE. [View at Google Scholar] ; [View at Publisher]
[5] Kruszyna M, Śleszyński P, Rychlewski J. Dependencies between demographic urbanization and the agglomeration road traffic volumes: Evidence from Poland. Land. 2021 Jan;10(1):47. [View at Google Scholar] ; [View at Publisher]
[6] Shi Y, Bilal M, Ho HC, Omar A. Urbanization and regional air pollution across South Asian developing countries–A nationwide land use regression for ambient PM2. 5 assessment in Pakistan. Environmental Pollution. 2020 Nov 1;266:115145. [View at Google Scholar] ; [View at Publisher]
[7] Liu Y, Ma X, Shu L, Yang Q, Zhang Y, Huo Z, Zhou Z. Internet of things for noise mapping in smart cities: state of the art and future directions. IEEE Network. 2020 Jun 5;34(4):112-8. [View at Google Scholar] ; [View at Publisher]
[8] Zhang M. Research on Strategies of Low-Carbon City Planning and Construction. InE3S Web of Conferences 2021 (Vol. 248, p. 02037). EDP Sciences. [View at Google Scholar] ; [View at Publisher]
[9] Li HR. Study on green transportation system of international metropolises. Procedia engineering. 2016 Jan 1;137:762-71. [View at Google Scholar] ; [View at Publisher]
[10] Alam I, Farid DM, Rossetti RJ. The prediction of traffic flow with regression analysis. InEmerging Technologies in Data Mining and Information Security 2019 (pp. 661-671). Springer, Singapore. [View at Google Scholar] ; [View at Publisher]
[11] Khan LU. Visible light communication: Applications, architecture, standardization and research challenges. Digital Communications and Networks. 2017 May 1;3(2):78-88. [View at Google Scholar] ; [View at Publisher]
[12] Sun N. Intelligent Transportation System Planning in the Age of Artificial Intelligence. InE3S Web of Conferences 2021 (Vol. 253, p. 01036). EDP Sciences. [View at Google Scholar] ; [View at Publisher]
[13] Wala’a Al-Khrisat NH, Hassan MR. Improving Traffic Incident Management Using Intelligent Transportation Systems, A Case of Amman City. Turkish Journal of Computer and Mathematics Education Vol. 2021;12(12):4343-52. [View at Google Scholar] ; [View at Publisher]
[14] Drakoulelis M, Filios G, Ninos VG, Katsidimas I, Nikoletseas S. Virtual sensors: an industrial application for illumination attributes based on machine learning techniques. Annals of Telecommunications. 2021 Jun 21:1-7. [View at Google Scholar] ; [View at Publisher]
[15] Güçlüer K, Özbeyaz A, Göymen S, Günaydın O. A comparative investigation using machine learning methods for concrete compressive strength estimation. Materials Today Communications. 2021 Jun 1;27:102278. [View at Google Scholar] ; [View at Publisher]
[16] Wang X, Ning Z, Hu X, Ngai EC, Wang L, Hu B, Kwok RY. A city-wide real-time traffic management system: Enabling crowdsensing in social Internet of vehicles. IEEE Communications Magazine. 2018 Sep 17;56(9):19-25. [View at Google Scholar] ; [View at Publisher]
[17] Younes MB, Boukerche A. An intelligent traffic light scheduling algorithm through VANETs. In39th Annual IEEE Conference on Local Computer Networks Workshops 2014 Sep 8 (pp. 637-642). IEEE. [View at Google Scholar] ; [View at Publisher]
[18] Djahel S, Doolan R, Muntean GM, Murphy J. A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Communications Surveys & Tutorials. 2014 Jul 17;17(1):125-51. [View at Google Scholar] ; [View at Publisher]
[19] Barkham R, Bokhari S, Saiz A. Urban big data: city management and real estate markets. GovLab Digest: New York, NY, USA. 2018 Jan. [View at Google Scholar] ; [View at Publisher]
[20] Shehada MK, Kondyli A. Evaluation of ramp metering impacts on travel time reliability and traffic operations through simulation. Journal of Advanced Transportation. 2019 Jan 20;2019. [View at Google Scholar] ; [View at Publisher]
[21] De Souza AM, Yokoyama RS, Maia G, Loureiro A, Villas L. Real-time path planning to prevent traffic jam through an intelligent transportation system. In2016 IEEE symposium on computers and communication (ISCC) 2016 Jun 27 (pp. 726-731). IEEE. [View at Google Scholar] ; [View at Publisher]
[22] Aimsun, N (2012) “AIMSUN user manual”, 2012.[View at Publisher]
Volume 5, Issue 2
June 2021
Pages 93-106
  • Receive Date: 26 January 2021
  • Revise Date: 25 March 2021
  • Accept Date: 02 April 2021
  • First Publish Date: 01 June 2021