Bus travel time analysis is significant to provide useful information to the users for proper journey planning. This study investigated the bus travel time in Klang Valley, Malaysia, which includes the centered city Kuala Lumpur and the state of Selangor. Two bus routes T786 and 851 which cover suburban and urban region have been studied. The data is collected day-to-day. For each link, it is filtered according to morning peak and evening peak, and the travel time is found dual modes, which is well accommodated by the mixture of Burr distribution. The analysis results show that the two-component finite mixture of Burr distribution is viable solution to explain the link travel time. The aim is two-fold. First, the bus travel time data is fitted by the mixture of Burr distribution, subsequently the computation of reliability metrics is carried out. A skew-width method with median based buffer index is considered to measure the reliability. The buffer index of 851 which covers urban region is found relatively high compared to T786. The links which connected by the signalized intersections and junctions tend to show low reliability. For suburban route of T786, the low reliability may be due to the last station. It is found that an additional of 5 minutes waiting time is necessary.
- Aslam, Muhammad, Tahir, Minhas & Hussain, Zawar. (2018). Reliability analysis of 3-component mixture of distributions. Scientia Iranica. 25. 10.24200/sci.2017.4441.
- Büchel, B. & Corman, F. (2020). Review on Statistical Modeling of Travel Time Variability for Road-Based Public Transport. Front. Built Environ. 6:70. doi: 10.3389/fbuil.2020.00070.
- Comi, A., Nuzzolo, A., Brinchi, S. & Verghini, R. (2017). Bus travel time variability: some experimental evidences. Transportation Research Procedia 27, pp101-108.
- Comi, A., Zhuk, M., Kovalyshyn, V., & Hilevych, V. (2020). Investigating bus travel time and predictive models: a time series-based approach. Transportation Research Procedia, 45, 692–699. doi:10.1016/j.trpro.2020.02.109.
- Chalumuri, R.S. & Yasuo, A. (2014). Modelling travel time distribution under various uncertainties on Hanshin expressway of Japan. Eur. Transp. Res. Rev. 6, 85–92. https://doi.org/10.1007/s12544-013-0111-3
- Fan, W. & Gurmu, Z (2015). Dynamic Travel Time Prediction Models for Buses Using Only GPS Data. International Journal of Transportation Science and Technology 4(4), pp 353-366, ISSN 2046-0430, https://doi.org/10.1016/S2046-0430(16)30168-x.
- Guo, J., Li, C., Qin, X., Huang, W., Wei, Y., & Cao, J. (2019). Analyzing distributions for travel time data collected using radio frequency identification technique in urban road networks. Science China Technological Sciences, 62(1), 106-120. doi: 10.1007/s11431-018-9267-4.
- Guessous, Y., Aron, M., Bhouri, N., & Cohen, S. (2014). Estimating Travel Time Distribution under Different Traffic Conditions. Transportation Research Procedia, 3, 339-348. doi:https://doi.org/10.1016/j.trpro.2014.10.014
- Kieu, Le Minh, Bhaskar, Ashish, & Chung, Edward (2015) Public transport travel time variability definitions and monitoring. Journal of Transportation Engineering, 141(1), Article number: 04014068 1-9.
- Liyanage, S. & Dia, H. (2020). An agent-based simulation approach for evaluating the performance of on-demand bus services. Sustainability 12, 4117. doi:10.3390/su12104117
- Low, V. J. M., Khoo, H. L. & Khoo, W. C. (2021). Statistical modelling of bus travel time with Burr distribution. ITM Web of Conference 36, 01011. ICMSA. https://doi.org/10.1051/itmconf/20213601011
- Van Lint, J. W. C., & Van Zuylen, H. J. (2005). Monitoring and Predicting Freeway Travel Time Reliability: Using Width and Skew of Day-to-Day Travel Time Distribution. Transportation Research Record, 1917(1), 54–62. https://doi.org/10.1177/0361198105191700107
- Ma, Z., Ferreira, L., Mesbah, M., & Zhu, S. (2016). Modeling distributions of travel time variability for bus operations. Journal of Advanced Transportation, 50(1), 6-24. doi:10.1002/atr.1314
- Mazloumi, E., Currie, G., & Rose, G. (2010). Using GPS data to gain insight into public transport travel time variability. Journal of Transportation Engineering, 136(7), 623-631.
- Moosavi, S. M. H., Ismail, A. & Yuen, C. W. (2020). Using simulation model as a tool for analysing bus service reliability and implementing improvement strategies. Plos One. https://doi.org/10.1371/journal.pone.0232799
- Md Noor, R., Seong Yik, N., Kolandaisamy, R., Ahmedy, I., Hossain, M.A., Alvin Yau, K., Md Shah, W. & Nandy, T. (2020). Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus. doi: 10.20944/preprints202002.0197.v1
- Polus, A. (1979). A study of travel time and reliability on arterial routes. Transportation, 8(2), 141-151. doi:10.1007/BF00167196
- Shariat Mohaymany, A., Ganjkhanloo, A., Bahaabadi, M. R., & Sayyad, A. (2019). Analysis of Travel Time Distribution for Varying Length of Time Interval. AUT Journal of Civil Engineering, 4(2), 10-10. doi:10.22060/ajce.2019.15596.5545
- Shi, C. Y., Chen, B. Y. & Li, Q. (2017). Estimation of travel time distribution in urban road networks using low-frequency floating car data. ISPRS International Journal of Geo-Information, 6, 253, doi: 10.3390/ijgi6080253
- Susilawati, S., Taylor, M. A. P., & Somenahalli, S. V. C. (2013). Distributions of travel time variability on urban roads. Journal of Advanced Transportation, 47(8), 720-736. doi:10.1002/atr.192
- Taylor, M. (1980). Travel time variability – the case of two public modes. Transportation Science 16(4): 507-521. http://dx.doi.org/10.1287/trsc.16.4.507
- Taylor, M., & Susilawati, S. (2012). Modelling Travel Time Reliability with the Burr Distribution. Procedia - Social and Behavioral Sciences, 54, 75–83. doi:10.1016/j.sbspro.2012.09.727
- Wong, A. C. K. (2009). Travel time prediction model for regional bus transit. Master's thesis. Available from tspace.library.utoronto.ca
- Sun, W., Hu, Z & Hong, L. J. (2018). Gaussian Mixture Model-Based Random Search for Continuous Optimization via Simulation, Winter Simulation Conference (WSC), Gothenburg, Sweden, 2018, pp. 2003-2014, doi: 10.1109/WSC.2018.8632380
