Assessing Survey Data to Study Traffic Flow Characteristics: An in-depth analysis of King Fahad Road, Al-Ahsa, Saudi Arabia

Authors

  • Md Kamrul Islam Department of Civil & Environmental Engineering, College of Engineering King Faisal University, Al-Ahsa
  • Abdulaziz Ibrahim Mohammed Al-Muaybid Department of Civil & Environmental Engineering, College of Engineering King Faisal University, Al-Ahsa
  • Muath Fahad Abdullah Al-Saqer Department of Civil & Environmental Engineering, College of Engineering King Faisal University, Al-Ahsa
  • Mohammed Saleh Rashid Al-Nagada Department of Civil & Environmental Engineering, College of Engineering King Faisal University, Al-Ahsa
  • Khaled Saleh Abdulaziz Al-Newaihel Department of Civil & Environmental Engineering, College of Engineering King Faisal University, Al-Ahsa
  • Rocksana Akter Department of Civil Engineering, Dhaka University of Engineering and Technology, Gazipur
  • Muhammad Aniq Gul Department of Civil & Environmental Engineering, College of Engineering King Faisal University, Al-Ahsa
  • Muhammad Muhitur Rhaman Department of Civil & Environmental Engineering, College of Engineering King Faisal University, Al-Ahsa
  • Ziad Shatnawi Department of Civil & Environmental Engineering, College of Engineering King Faisal University, Al-Ahsa

DOI:

https://doi.org/10.23917/forgeo.v38i2.4629

Keywords:

Traffic survey, data classification, measured density, calculated density, non-intrusive data collection techniques

Abstract

Traffic volume studies are crucial for understanding vehicle quantity, movements, and classifications at specific sites. This study aims to establish a correlation between flow rate and density, providing in-sights into traffic flow characteristics such as density, velocity, and flow, essential for effective road design. The proposed method combines automated (mobile phones and cars) and manual counts on separate sheets, offering a compelling alternative to traditional traffic study methods. The collected da-ta can be utilized for various purposes including estimating fuel consumption, road pricing, road user cost, and planning road network improvements. Acquiring precise traffic data using cost-effective, low-tech solutions is vital for comprehending urban traffic dynamics. Evaluating collected data, which encompasses traffic parameters like flow, density, and speed, is crucial for informing urban road de-sign and planning. For instance, traffic flow indicates throughput, density reflects traffic conditions, and speed is essential for calculating travel times. This investigation focused on King Fahad Road in Al-Ahsa, Saudi Arabia, chosen among three alternative urban roads based on varying traffic condi-tions. Smartphones and cars were used to collect traffic data during weekday evening peak hours, ana-lyzing the interrelation between traffic flow, density, and vehicle speed. Manual traffic counts were conducted to determine measured density and speed, which were then used to estimate calculated den-sity. Additionally, a statistical t-test was performed to validate measured density against calculated den-sity at a 5% significance level. The data collection systems utilized in this research provide a cost-effective solution, considering capital, operational, and maintenance expenses, while remaining por-table and non-intrusive to road users during surveys. These characteristics make the system a practical choice for implementation in developing nations where resources are constrained, rendering costlier al-ternatives economically unfeasible.

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References

Al Kherret, A. (2015). Video-Based Detection and Tracking Model for Acquiring Traffic Data (Doctoral dissertation). Cairo University.

Al Kherret, A., Al Sobky, A., & Mousa, R. (2015). Video-based detection and tracking model for traffic surveilance. Pre-sented at the 94th TRB Annual Meeting, Washington, DC, 15-1465.

Al-Gamdhi. (1998). Spot speed analysis on urban roads in Riyadh. Transportation Research Record, 162, 162-170. [Google Scholar]

Ali, A. T., Flannery, A., & Venigalla, M. M. (2007). Prediction Models for Free Flow Speed on Urban Streets. Transporta-tion Research Record, 1992, 199–207. [Google Scholar]

Al-Sayed Ahmed Al-Sobky, & Mousa, R. M. (2016). Traffic density determination and its applications using smart-phone. Alexandria Engineering Journal, 55(1), 513–523. doi: 10.1016/j.aej.2015.12.010 [Google Scholar] [CrossRef]

Arasan, V. T., & Arkatkar, S. S. (2011). Derivation of Capacity Standards for Intercity Roads Carrying Heterogeneous Traffic using Computer Simulation. Procedia, 16, 218–229. doi: 10.1016/j.sbspro.2011.04.444 [Google Scholar] [CrossRef]

Asaithambi, G., Kanagaraj, V., Srinivasan, K. K., & Sivanandan, R. (2018). Study of traffic flow characteristics using different vehicle-following models under mixed traffic conditions. Transportation Letters, 10(2), 92-103. doi: 10.1080/19427867.2016.1190887 [Google Scholar] [CrossRef]

Chakravorty, S. (2019). A Report on Traffic Volume Study. Academia. Retrived From https://www.academia.edu/33942140/A_Report_on_Traffic_Volume_Study.

Fitzpatrick, K., Carlson, P., Brewer, M., Wooldridge, M., & Miaou, S. (2003). Design speed, Operating Speed, and Posted Speed Practices. NCHRP 504.

Ghosh, P. (2019). Traffic Volume Study. Academia. Retrived From https://www.academia.edu/Documents/in/Traffic_Volume.

Guido, G., Vitale, A., Saccomanno, F. F., Festa, D. C., Astarita, V., Rogano, D., & Gallelli, V. (2013). Using smartphones as a tool to capture road traffic attributes. Applied Mechanics and Materials, 432, 513-519. doi: 10.4028/www.scientific.net/AMM.432.513 [Google Scholar] [CrossRef]

Haefner, L. E., Li, M.-S., Porrello, L. A., Blanco, M. V., & Van Pelt, D. B. (1998). Examination of fundamental traffic characteristics and implications to ITS. Transportation Conference Proceedings, 12–17.

Hasanpour, M. Z., Ahadi, M. R., Moghadam, A. S., & Behzadi, G. A. (2017). Variable Speed Limits: Strategies to Im-prove Safety and Traffic Parameters for a Bottleneck. Engineering, Technology & Applied Science Research, 7(2), 1535–1539. doi: 10.48084/etasr.831 [Google Scholar] [CrossRef]

Hazim, N., Shbeeb, L., & Abu Salem, Z. (2020). Impact of Roadside Fixed Objects in Traffic Conditions. Engineering, Technology & Applied Science Research, 10(2), 5428–5433. [Google Scholar]

Jain, K., Jain, S. S., & Singh, M. (2016). Traffic Flow Characteristics for Multilane Highways in India. Transportation Research Procedia, 17, 468-477. doi: 10.1016/j.trpro.2016.11.092 [Google Scholar] [CrossRef]

Kumar, V. M., & Rao, S. K. (1998). Headway and speed studies on two-lane highways. Indian Highways, 26(5), 23–36. [Google Scholar]

Massaro, E., Ahn, C., Ratti, C., Santi, P., Stahlmann, R., Lamprecht, A., ... Huber, M. (2016). The car as an ambient sen-sing platform. Proceedings of the IEEE, 105(1), 3–7. doi: 10.1109/JPROC.2016.2634938 [Google Scholar] [CrossRef]

May, A. D. (1990). Traffic Flow Fundamentals. Prentice Hall, Englewood Cliffs. Retrived From https://www.academia.edu/80158143/Traffic_flow_fundamentals_adolf_d_may_pdf.

May, A. D. (1990). Traffic Flow Fundamentals. Prentice Hall, Englewood Cliffs. Retrived From https://trid.trb.org/View/356201

National Research Council. (1992). Urban Streets. Transportation Research Record, 1992(199), 199–207. [Google Scholar]

Roshandeh, A. M., Nesheli, M. M., & Puan, O. C. (2009). Evaluation of traffic characteristics: A case study. Internatio-nal Journal of Recent Trends in Engineering, 1(6), 62–68. [Google Scholar]

Semeida, A. M. (2013). Impact of Highway Geometry and Posted Speed on Operating Speed at Multilane Highways in Egypt. Journal of Advanced Research, 4, 515-523. doi: 10.1016/j.jare.2012.08.014 [Google Scholar] [CrossRef]

Shen, L., & Stopher, P. R. (2014). Review of GPS travel survey and GPS data-processing methods. Transport Reviews, 34(3), 316–334. doi: 10.1080/01441647.2014.903530 [Google Scholar] [CrossRef]

Srivastava, K., & Kumar, A. (2023). Critical Analysis of Road Side Friction on an Urban Arterial Road. Engineering, Technology & Applied Science Research, 13(2), 10261–10269. [Google Scholar]

Tseng, P.-Y., Lin, F.-B., & Shieh, S.-L. (2005). Estimation of free-flow speeds for multilane rural and suburban highways. Journal of the Eastern Asia Society for Transportation Studies, 6, 1484–1495. doi: 10.11175/easts.6.1484 [Google Scholar] [CrossRef]

Wang, W., Jin, J., Ran, B., & Guo, X. (2011). Large-scale freeway network traffic monitoring: A map-matching algo-rithm based on low-logging frequency GPS probe data. ASCE Journal of Intelligent Transportation Systems, 15(2), 63–74. doi: 10.1080/15472450.2011.570103 [Google Scholar] [CrossRef]

Wen, T.-H., Chin, W.-C.-B., & Lai, P.-C. (2017). Understanding the topological characteristics and flow complexity of urban traffic congestion. Physica A: Statistical Mechanics and its Applications. Scienderect, Elsivier, 473, 166-177. doi: 10.1016/j.physa.2017.01.035 [Google Scholar] [CrossRef]

Wolshon, B., & Hatipkarasulu, Y. (2000). Results of car following analyses using global positioning system. ASCE Jour-nal of Transportation Engineering, 126(4), 324–331. doi: 10.1061/(ASCE)0733-947X(2000)126:4(324) [Google Scholar] [CrossRef]

Wong, K. I., Lee, T. C., & Chen, Y. Y. (2016). Traffic characteristics of mixed traffic flows in urban arterials. Asian Transport Studies, 4(2), 379-391. doi: 10.11175/eastsats.4.379 [Google Scholar] [CrossRef]

Zhao, N., Qi, T., Yu, L., Zhang, J., & Jiang, P. (2014). A Practical Method for Estimating Traffic Flow Characteristic Pa-rameters of Tolled Expressway Using Toll Data. Procedia - Social and Behavioral Sciences, 138, 632-640. doi: 10.1016/j.sbspro.2014.07.250 [Google Scholar] [CrossRef]

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Published

2024-07-23

Issue

Vol. 38 No. 2 (2024): August 2024

Section

Research article

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Copyright (c) 2024 Md Kamrul Islam

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