Effectiveness of Flexible Working Hours on Traffic Index, a Case Study for Tehran

Document Type : Original Article

Authors

1 Ph.D. candidate in industrial Engineering, Tehran University, Tehran, Iran

2 Ph.D. candidate in Transportation Engineering, Iran University of Science and Technology, Tehran, Iran

3 Transportation Engineering, Tarbiat Modares University, Tehran, Iran

4 Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Nowadays, traffic congestion is a major problem in metropolises that affect daily life. The imbalance between supply and demand causes congestion. Demand management is used to balance demand and supply since has more reasonable costs and fewer restrictions in comparison with increasing transportation supply. One policy in order to manage the demand and reduce Tehran traffic congestion is to implement flexible working hours since October 2022. In this study, the effect of flexible working hours on traffic congestion has been investigated by using the length of Tehran's congested roads. The linear regression has been modeled based on the length of traffic congestion of the second week of October 2019 before the Covid-19 epidemic in comparison with the second week of October 2022. By flexible working hours, the graphs of the average hourly queue length have become smoother. In 2022 this chart has two prominent peaks as morning peak and evening peak. In 2022, the height of these two peaks has decreased and the trips have been more evenly distributed over time. In 2022, the height of these two peaks has decreased and the trips have been more evenly distributed over time. It was expected to face more road congestion due to the traditional education after the virtual education during the Covid-19 epidemic, user behavior changes, and an increase in the usage of private cars. Nevertheless, by implementing flexible working hours, Tehran's traffic congestion has been far more suitable except for the Limited traffic zone than the urban management's prediction.

Keywords


1- Koźlak, A., and Wach, D., 2018, Causes of traffic congestion in urban areas. Case of Poland, SHS Web Conf., 57, doi: 10.1051/shsconf/20185701019.
2- El-Hansali, Y., Farrag, S., Yasar, A., Malik, H., Shakshuki, E., and Al-Abri, K., 2021 Assessment of the traffic enforcement strategies impact on emission reduction and air quality, in Procedia Computer Science, 184, doi: 10.1016/j.procs.2021.03.068.
3- Ghodsi, M., Ardestani, A., Rasaizadi, A., Ghadamgahi,  S., and Yang, H., 2021, How covid19 pandemic affected urban trips? Structural interpretive model of online shopping and passengers trips during the pandemic, Sustain., 13, 21, doi: 10.3390/su132111995.
4- Shahdani, F. J., Rasaizadi, A., and Seyedabrishami, S., 2021, The interaction between activity choice and duration: Application of copula-based and nested-logit models, Sci. Iran., 28, 4, doi: 10.24200/SCI.2020.56380.4701.
5- Seyedabrishami, S., and Izadi, A. R., 2019, A Copula-Based Joint Model to Capture the Interaction between Mode and Departure Time Choices in Urban Trips, in Transportation Research Procedia, 41, doi: 10.1016/j.trpro.2019.09.120.
6-Rasaizadi, A., and Kermanshah, M., 2018, Mode choice and number of non-work stops during the commute: Application of a copula-based joint model, Sci. Iran., 25, 3A, doi: 10.24200/sci.2017.4194.
7-Dizaho, E., Salleh, R., and Abdullah, A., 2017, Achieveing Work Life Balance Through Flexible Work Schedule and Arrangements.
8- Yu, R., Burke, M., and Raad, N., 2019, Exploring impact of future flexible working model evolution on urban environment, economy and planning, J. Urban Manag., 8, 3, doi: 10.1016/j.jum.2019.05.002.
9- Hostettler Macias, L., Ravalet, E., and Rérat, P., 2022, Potential rebound effects of teleworking on residential and daily mobility, Geography Compass., doi: 10.1111/gec3.12657.
10- Yildirimoglu, M., Ramezani, M., and Amirgholy, M., 2021, Staggered work schedules for congestion mitigation: A morning commute problem, Transp. Res. Part C Emerg. Technol., 132, doi: 10.1016/j.trc.2021.103391.
11- Van der Loop, H., Haaijer, R., and Willigers, J., 2019, The impact of various forms of flexible working on mobility and congestion estimated empirically, in Autonomous Vehicles and Future Mobility, doi: 10.1016/B978-0-12-817696-2.00010-X.
12- van der Loop, H., Willigers, J., and Haaijer, R., 2019, Empirical Estimation of Effects of Flexible Working on Mobility and Congestion in the Netherlands 2000 to 2016, Transp. Res. Rec., 2673, 6, doi: 10.1177/0361198119845889.
13-Mutlu, O., Durak, Z., and Akyer, H., 2020, Staggered working hours in order to reduce traffic congestion, Pamukkale Univ. J. Eng. Sci., 26, 4, doi: 10.5505/pajes.2019.90922.
14- Zong, F., Juan, Z., and Jia, H., 2013, Examination of staggered shifts impacts on travel behavior: A case study of Beijing, China, Transport, 28, 2, doi: 10.3846/16484142.2013.803263.
15- Huang, L., and Li, J., 2011, Applicability of staggered work hours for urban traffic: Case of guangzhou, doi: 10.1061/41184(419)67.
16- Gatersleben, B., and Uzzell, D., 2007, Affective appraisals of the daily commute: Comparing perceptions of drivers, cyclists, walkers, and users of public transport, Environ. Behav., 39, 3, doi: 10.1177/0013916506294032.
17- Metz, D., 2018, Tackling urban traffic congestion: The experience of London, Stockholm and Singapore, Case Studies on Transport Policy, 6, 4, doi: 10.1016/j.cstp.2018.06.002.
18- Rahman, M., Gurumurthy, K. M., and Kockelman, K. M., 2022, Impact of Flextime on Departure Time Choice for Home-Based Commuting Trips in Austin, Texas, in Transportation Research Record, 2676, 1, doi: 10.1177/03611981211035757.
19- Cho, S. J., Bellemans, T., C., Joh, H., and Choi, K., 2017, A Study on the Transport-related Impacts of Flexible Working Policy using Activity-Based Model, J. Korean Soc. Transp., 35, 6, 2017, doi: 10.7470/jkst.2017.35.6.511.
20- Yang, y., Steiner, R. L., and Srinivasan, S., 2016, The Impact of Flexible Work Hours on Trip Departure Time Choices in Metropolitan Miami, doi: 10.1061/9780784479896.199.
21-Kumarage, S., Yildirimoglu, M., Ramezani, M., and Zheng, Z., 2021, Schedule-constrained demand management in two-region urban networks, Transp. Sci., 55, 4, doi: 10.1287/trsc.2021.1052.
22- Kunsch, P., Johan, S., and Brans, J. P., 2000, Traffic crowding in urban areas: Control with urban tolls and flexible working hours, JORBEL-Belgian J. Oper. Res. Stat. Comput. Sci., 40(1–2), 81–90.
23- Il Mun, S., and Yonekawa, M., 2006, Flextime, traffic congestion and urban productivity, J. Transp. Econ. Policy, 40, 3.
24- He, S. Y., 2013, Does flexitime affect choice of departure time for morning home-based commuting trips? Evidence from two regions in California, Transp. Policy, 25, doi: 10.1016/j.tranpol.2012.11.003.
25- Wöhner, F., 2022, Work flexibly, travel less? The impact of telework and flextime on mobility behavior in Switzerland, J. Transp. Geogr., 102, doi: 10.1016/j.jtrangeo.2022.103390.
26- Ecke, L., Magdolen, M., Chlond, B., and Vortisch, P., 2022, How the COVID-19 pandemic changes daily commuting routines – Insights from the German Mobility Panel,” Case Stud. Transp. Policy, 10, 4, doi: 10.1016/j.cstp.2022.10.001.
27- James, R., Witten, G., Hastie, D., and Tibshirani, T., 2013, An Introduction to Statistical Learning - with Applications in R, Gareth James, Springer,.
28- Moghadami, M., Rasaizadi, A., and Askari, M., 2020, The effect of coronavirus restrictions on air quality and exiting daily traffic,” Int. J. Hum. Cap. Urban Manag.
29- Williams, M. N., Grajales, C. A. G., and Kurkiewicz, D., 2013, Assumptions of multiple regression: Correcting two misconceptions,” Pract. Assessment, Res. Eval., 18, 9.