Do You Feel Rushing in the Morning Travel? the Evidence of Daily Travel Pattern Activity During Irregular Traffic Conditions in Post Covid-19, Case Study South Bali Indonesia
Abstract
Congestion during rush hours has always been a significant problem for the government, impacting travel time, vehicle operations and air pollution, and health problems. The government has proposed some policies through traffic engineering management and other management applications with the latest technology to overcome such issues. Research related to travel patterns during peak hours is beneficial, which will impact road users' decision-making, particularly during these days when everyone's activities are starting again after COVID-19 restrictions. In this paper, data processing (from Google Maps Distance Matrix API, Typical Google Maps, and Community Mobility Reports) was carried out for almost one month to obtain data on speed and travel time. These data are gathered from four pairs of locations with specific origin/destination of travel patterns in the southern Bali region. The community's travel pattern can be explained based on the one-way ANOVA with Bonferroni and the Kruskal Wallis Test. This paper found that people are in a hurried movement in the morning compared to other busy times, marked by the differences in speed and travel time in the morning commute to afternoon and evening commute during rush hours. This condition illustrates the irregular traffic flow after the Covid-19 restriction rules; thus, drivers can still choose the desired speed to travel. The irregular traffic flow can be seen on community mobility data and typical green traffic during peak hours. This result is significant because of the importance of traffic management during peak hours to optimize the level of road service.
References
Ding, Ling; Zhang, Ning (2016). A Travel Mode Choice Model Using Individual Grouping Based on Cluster Analysis. Procedia Engineering, 137(), 786–795. doi:10.1016/j.proeng.2016.01.317
Basaric, Valentina & Vujičić, Ana & Mitrović Simić, Jelena & Bogdanović, Vuk & Saulic, Nenad. (2016). Gender and Age Differences in the Travel Behavior – A Novi Sad Case Study. Transportation Research Procedia. 14. 4324-4333. 10.1016/j.trpro.2016.05.354.
Peng, Jing & Zhao, Mengxuan & He, Meiling & Chen, Long. (2018). Travel Mode and Travel Route Choice Behavior Based on Random Regret Minimization: A Systematic Review. Sustainability (Switzerland). 10. 10.3390/su10041185.
Schwanen, Tim & Dijst, Martin & Dieleman, F.M.. (2005). The Relationship between Land Use and Travel Patterns: Variations by Household Type. Spatial Planning, Urban Form and Sustainable Transport.
Bonaccorsi, Giovanni; Pierri, Francesco; Cinelli, Matteo; Flori, Andrea; Galeazzi, Alessandro; Porcelli, Francesco; Schmidt, Ana Lucia; Valensise, Carlo Michele; Scala, Antonio; Quattrociocchi, Walter; Pammolli, Fabio (2020). Economic and social consequences of human mobility restrictions under COVID-19. Proceedings of the National Academy of Sciences, (), 202007658–. doi:10.1073/pnas.2007658117
Google Maps Platform. (2019). Google Maps Platform FAQ. Retrieved from https://developers.google.com/maps/documentation/distance-matrix/overview
Google Maps Platform. (2020a). Developer Guide - Directions API. Retrieved from https://developers.google.com/maps/documentation/distance-matrix
Google Maps Platform. (2020b). Developer Guide - Distance Matrix API. Retrieved from https://developers.google.com/maps/documentation/distance-matrix/start
TRB, Highway Capacity Manual 2000, National Research Council Washington D.C., 2000.
A.D. May, Traffic Flow Fundamentals, Traffic Engineering Handbook, Prentice-Hall, Englewood Cliffs, New Jersey (1990)
Pengjun Zheng; McDonad Mike (2012). An Investigation on the Manual Traffic Count Accuracy. , 43(none), –. doi:10.1016/j.sbspro.2012.04.095.
Seedam A, Satiennam T, Radpukdee T, Satiennam W, Ratanavaraha V (2017) Motorcycle on-road driving parameters influencing fuel consumption and emissions on congested signalized urban corridor. J Adv Transp 2017. https://doi.org/10.1155/2017/5859789.
Yan Y, Zhang S, Tang J, Wang X (2017) Understanding characteristics in multivariate traffic flow time series from complex network structure. Physica A: Statistical Mechanics and its Applications 477:149–160. https://doi.org/10. 1016/j.physa.2017.02.040.
Wemegah, T.D., Zhu, S. & Atombo, C. (2018) Modeling the effect of days and road type on peak period travels using structural equation modeling and big data from radio frequency identification for private cars and taxis. Eur. Transp. Res. Rev. 10, 39. https://doi.org/10.1186/s12544-018-0313-9
Wang, Fahui & Xu, Yanqing. (2011). Estimating O-D Travel Time Matrix by Google Maps API: Implementation, Advantages, and Implications. Annals of GIS. 17. 199-209. 10.1080/19475683.2011.625977.
Haitao, Jin & Jin, Fengjun & Qing, Hao & He, Zhu & Xue, Yang. (2019). Measuring Public Transit Accessibility Based On Google Direction API. The Open Transportation Journal. 13. 93-108. 10.2174/1874447801913010093.
Mohan, Dinesh & Tiwari, Geetam & Goel, Rahul & Lahkar, Paranjyoti. (2017). Evaluation of Odd–Even Day Traffic Restriction Experiments in Delhi, India. Transportation Research Record: Journal of the Transportation Research Board. 2627. 9-16. 10.3141/2627-02.
Hananto P. 2020. Tutorial Pelatihan Geospatial dan Big Data Untuk Evaluasi Kinerja Transportasi dan Konektivitas.
Community Mobility Reports. (2022). Community Mobility Reports Help. Retrieved from https://support.google.com/covid19-mobility/answer/overview
Community Mobility Reports. (2022a). Community Mobility Reports Help. Retrieved from https://support.google.com/covid19-mobility/answer/Understand the data
Community Mobility Reports. (2022a). Community Mobility Reports Help. Retrieved from https://support.google.com/covid19-mobility/answer/calibrate your region
