Journal Screenshot

International Journal of Academic Research in Economics and Management Sciences

Open Access Journal

ISSN: 2226-3624

Traffic Flow Optimization: Modeling the Inter-Arrival Times for Simulation Model

Muhammad Zulqarnain Hakim Abd Jalal, Mohd Kamal Mohd Nawawi, Wan Laailatul Hanim Mat Desa, Muhammad Yassar Yusri, Nur Fatihah Shaari

http://dx.doi.org/10.6007/IJAREMS/v11-i3/14631

Open access

With the growing number of vehicles on the road, traffic flow problems are no longer a local issue; instead, traffic flow optimization has drawn significant interest from researchers all over the world. Studies of discrete-event simulation have been widely used to encounter problems related to traffic flow. Researchers of discrete-event simulation modeling typically tend to use statistical distributions for inter-arrival and process times based on the simulation software's built-in tools. The software tools include Input Analyzer in Arena, Stat::Fit for Promodel, and ExpertFit for FlexSim. However, there are other numerical metrics and concerns that researchers should examine while deciding on the best distribution. This research explores the exponential distribution and compares it to distributions generated by software, then to real data. The square error value is the focus of the comparison. There were 5404 data points collected for the vehicle arriving at six lanes at selected traffic junctions. According to the findings of this study, the commonly used exponential distribution can be utilized to depict the distribution of inter-arrival times as there is no significant difference from the more complex distribution. In future study, researcher can comfortably use exponential distribution instead of using complex distribution.

Adams, W. F. (1950) “Road Traffic Considered as a Random Series,” Oper. Res. Q., vol. 1, no. 1, p. 9, 1950.
Desa, W. L. H. M., Kamaruddin, S., Nawawi, M. K. M., and Zulkepli, J (2015) “Evaluation on absenteeism effect in production line at aircraft composite manufacturer,” J. Teknol., vol. 77, no. 5, pp. 63–67, 2015, doi: 10.11113/jt.v77.6120.
El-Hadidy, M. A. A., and Alfreedi, A. A. (2021) “Detection of an appropriate pharmaceutical company to get a suitable vaccine against COVID-19 with minimum cost under the quality control process,” Qual. Reliab. Eng. Int., vol. 37, no. 6, pp. 2646–2664, 2021, doi: 10.1002/qre.2881.
Frough, O., Khetwal, A., and Rostami, J. (2019) “Predicting TBM utilization factor using discrete event simulation models,” Tunn. Undergr. Sp. Technol., vol. 87, pp. 91–99, 2019, doi: 10.1016/j.tust.2019.01.017.
Hashim, S., Tahar R. M., Bakar E. M. N. E. A. (2003) “Simulation study for improving patient treatment services,” J. ICT, 2003.
Jalal, M. Z. H. A., Nawawi, M. K. M., Desa, W. L. H. M., Khalid, R., Abduljabbar, W. K., and Ramli, R. (2017) “Green supply chain: Simulating road traffic congestion,” in Journal of Physics: Conference Series, 2017, vol. 890, no. 1, doi: 10.1088/1742-6596/890/1/012111.
Jalal, M. Z. H. A., Desa, W. L. H. M., Nawawi, M. K. M., Khalid, R. (2018) “Discrete-event simulation of road traffic congestion to support green supply chain,” Int. J. Eng. Technol., vol. 7, no. 3.20 Speci, pp. 377–380, 2018.
Jose, J. K., and Deepthi, V. (2021) “M/PH/1 queueing model with re-servicing,” Commun. Stat. Simul. Comput., 2021, doi: 10.1080/03610918.2021.1921209.
Kelton, D. W., Sadowski, R. P., and Zupick, N. B. (2015) Simulation with Arena, 6th ed. New York: McGraw-Hill, 2015.
Kumar, B. (2022) “Transient analysis of M/M/C queuing model with reneging, finite capacity and population,” Int. J. Math. Oper. Res., vol. 21, no. 1, pp. 1–25, 2022, doi: 10.1504/ijmor.2022.120339.
Meng, Q., and Khoo, H. L. (2009) “Self-similar characteristics of vehicle arrival pattern on highways,” J. Transp. Eng., 2009, doi: 10.1061/(ASCE)0733-947X(2009)135:11(864).
Neeraj, R. R., Nithin, R. P., Niranjhan, P., Sumesh, A., and Thenarasu, M. (2018) “Modelling and simulation of discrete manufacturing industry,” in Materials Today: Proceedings, 2018, pp. 24971–24983, doi: 10.1016/j.matpr.2018.10.298.
Saritha, S., Mamatha, E., Reddy, C. S., and Rajadurai, P. (2022) “A model for overflow queuing network with two-station heterogeneous system,” Int. J. Process Manag. Benchmarking, vol. 12, no. 2, pp. 147–158, 2022, doi: 10.1504/IJPMB.2022.121592.
Sumaryo, S., Halim, A., and Ramli, K. (2015) “Simulation and analysis of traffic flow models with emergency vehicles distortion on a single road,” 2015, doi: 10.1109/TIME-E.2014.7011611.
Wang, C., Chen, W., Xu, Y., and Ye, Z. (2021) Modeling bus capacity for bus stops using queuing theory and diffusion approximation, vol. 2675, no. 12. 2021.
Yemane, A., Gebremicheal, G., Meraha, T., and Hailemicheal, M. (2020) “Productivity improvement through line balancing by using simulation modeling (case study almeda garment factory),” J. Optim. Ind. Eng., vol. 13, no. 1, pp. 153–165, 2020, doi: 10.22094/JOIE.2019.567816.1565.
Zulkepli, J., Khalid, R., Nawawi, M. K. M., and Hamid, M. H. (2017) “Developing a discrete event simulation model for university student shuttle buses,” in AIP Conference Proceedings, 2017, vol. 1905, doi: 10.1063/1.5012146.

In-Text Citation: (Jalal et al., 2022)
To Cite this Article: Jalal, M. Z. H. A., Nawawi, M. K. M., Desa, W. L. H. M., Yusri, M. Y., & Shaari, N. F. (2022). Traffic Flow Optimization: Modeling the Inter-Arrival Times for Simulation Model. International Journal of Academic Research in Economics and Managment and Sciences, 11(3), 313– 323.