Modelling Critical Meteorological Factors Affecting Public Bus Ridership in the State of Qatar: A Case Study
Pages 106 - 112
Abstract
The paper studies how meteorological factors affect public transportation's ridership by analyzing the significant location of the bus stop stations. By using bus ridership data from the Ministry of Transport and Communications (MTC)- State of Qatar, we conclude that increasing the temperature and decreasing the number of the population reduces public bus ridership while increasing the number of bus stations can attract more car users to utilize the public transportation. Overall, our results indicate that lowering walking time to the destination can attract more car users. Solutions such as increasing the number of stations in the downtown and business areas can attract more car users. The results of this study furnish a valuable insight that should be taken into consideration while implementing and developing Bus-Metro link network stations and proposing policy interpretation for strategic developers in enhancing the accessibility to bus's and metro's stations.
References
[1]
Qatar Meteorology Department. Qatar Weather. [Online] Available at: http://qweather.gov.qa/Index.aspx [Accessed 24 December 2017].
[2]
Weather Spark, 2017. Weather Spark. [Online] Available at: https://weatherspark.com/ [Accessed 05 2017].
[3]
Planning and Statistics Authority. [Online] Available at http://www.psa.gov.qa/en [Accessed 2019]
[4]
Qatar Strategic Transport Model (QSTM), Doha: Ministry of Transport and Communications (MOTC). (2018)
[5]
Mowasalat, 2019. [Online] Available at: www.mowasalat.com [Accessed 2019].
[6]
Bus Stop Guidelines, Doha: Mowasalat. https://motc.gov.qa/en/qptp [online] available [ Accessed 2018]
[7]
Arana, P., Cabezudo, S. & Peñalba, M. 2014. Influence of weather conditions on transit ridership: A statistical study using data from Smartcards". Transportation Research Part A, Volume 59, pp. 1--12. https://doi.org/10.1016/j.tra.2013.10.019
[8]
Li, J., Li, X., Chen, D. & Godding, L. 2018. Assessment of metro ridership fluctuation caused by weather conditions in an Asian context: Using archived weather and ridership data in Nanjing. Journal of Transport Geography, Volume 66, January 2018, pp. 356--368 https://doi.org/10.1016/j.jtrangeo.2017.10.023
[9]
Singhal, A., Kamga, C. & Yazici, A. 2014. Impact of weather on urban transit ridership. Transportation Research Part A: Policy and Practice, Volume 69, pp. 379--391. https://doi.org/10.1016/j.tra.2014.09.008
[10]
Zhou, M., Wang, D., Li, Q., Yue, Y., Tu, W., & Cao, R. 2017. Impacts of weather on public transport ridership: Results from mining data from different sources. Transportation Research Part C: Emerging Technologies. Volume 75, pp. 17--29. https://doi.org/10.1016/j.trc.2016.12.001
[11]
Tao, S., Corcoran, J., Rowe, F. & Hickman, M. 2018. To travel or not to travel: 'Weather' is the question. Modelling the effect of local weather conditions on bus ridership. Transportation Research Part C, Volume 86, pp. 147--167. https://doi.org/10.1016/j.trc.2017.11.005
[12]
Eluru, N., Chakour, V. 2016. Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal. Journal of Transport Geography Volume 51, pp. 205--217. https://doi.org/10.1016/j.jtrangeo.2016.01.007
[13]
Kuby, M., Barranda, A., & Upchurch, C. 2004. Factors influencing light-rail station boardings in the United States. Transportation Research Part A: Policy and Practice, 38(3), 223247. https://doi.org/10.1016/j.tra.2003.10.006
[14]
Hu, N. 2016. Impacts of land use and amenities on public transport use, urban planning and design". Land Use Policy, pp. 356--367. http://dx.doi.org/10.1016/j.landusepol.2016.06.004
[15]
Vale, D. S. 2015. Transit-oriented development, integration of land use and transport, and pedestrian accessibility: Combining node-place model with pedestrian shed ratio to evaluate and classify station areas in Lisbon. Journal of Transport Geography, Volume 45, pp. 70--80. https://doi.org/10.1016/j.jtrangeo.2015.04.009
[16]
Yetiskul, E. & Senbil, M. 2012. Public bus transit travel-time variability in Ankara (Turkey). Transport Policy, Volume 23, pp. 50--59. https://doi.org/10.1016/j.tranpol.2012.05.008
[17]
Kashfi, S.A., Lee, B., Bunker, J. 2013. Impact of rain on daily bus ridership: a Brisbane case study. Australian Transport Research Forum 2013 Proceedings. Australian Transportation Research Forum, Brisbane, Australia. https://doi.org/10.13140/RG.2.1.2256.3289
[18]
Tao, S., Corcoran, J., Hickman, M. & Stimson, R. 2016. The influence of weather on local geographical patterns of bus usage. Journal of Transport Geography. V. 54, Pages 66--80. https://doi.org/10.1016/j.jtrangeo.2016.05.009
[19]
Miaoa, Q., Welchb, E. & Srirajc, P. 2019. Extreme weather, public transport ridership and moderating effect of bus stop shelters. Journal of Transport Geography. V. 74, Pages 125--133. https://doi.org/10.1016/j.jtrangeo.2018.11.007
[20]
Wei, M., Corcoran, J., Sigler, T., & Liu, Y. 2018. Modeling the Influence of Weather on Transit Ridership: A Case Study from Brisbane, Australia. Transportation Research Record, 2672(8), 505--510. https://doi.org/10.1177/0361198118777078
[21]
He, Y. Zhao, Y. & Tsui, K. 2019. Exploring influencing factors on transit ridership from a local perspective. Smart and Resilient Transport. Published by Emerald Publishing Limited. Vol. 1 No. 1, pp. 2--16. https://doi.org/10.1108/SRT-06-2019-0002
[22]
Tang, T., Liu, R. & Choudhury, C. 2020. Incorporating weather conditions and travel history in estimating the alighting bus stops from smart card data. Sustainable Cities and Society. Volume 53, Article number 101927. https://doi.org/10.1016/j.scs.2019.101927
[23]
Ngo, N.S. 2019. Urban bus ridership, income, and extreme weather events. Transportation Research Part D: Transport and Environment. Volume 77, Pages 464--475. https://doi.org/10.1016/j.trd.2019.03.009
[24]
Wei, M., Liu, Y., Sigler, T., Liu, X. & Corcoran, J. 2019. The influence of weather conditions on adult transit ridership in the sub-tropics. Transportation Research Part A: Policy and Practice. V.125, pp. 106--118. https://doi.org/10.1016/j.tra.2019.05.003
[25]
Gong, X., Currie, G., Liu, Z. & Guo, X. 2018. A disaggregate study of urban rail transit feeder transfer penalties including weather effects. Transportation. Volume 45, Issue 5, Pages 1319--1349. https://doi.org/10.1007/s11116-017-9768-0
[26]
Kashfi, S.A., Bunker, J.M. & Yigitcanlar, T. 2016. Modelling and analyzing effects of complex seasonality and weather on an area's daily transit ridership rate. Journal of Transport Geography. Volume 54, Pages 310--324. https://doi.org/10.1016/j.jtrangeo.2016.06.018
[27]
Li, L., Wang, J., Song, Z., Dong, Z. & Wu, B. 2015. Analysing the impact of weather on bus ridership using smart card data. IET Intelligent Transport Systems. Volume 9, Issue 2, Pages 221--229. https://doi.org/10.1049/iet-its.2014.0062
Index Terms
- Modelling Critical Meteorological Factors Affecting Public Bus Ridership in the State of Qatar: A Case Study
Recommendations
Implementation of Bus Rapid Transit System as an Alternative for Public Transportation in Developing Countries Case of Dart System in Dar Es Salaam
ICICTA '10: Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 02Bus Rapid Transit (BRT) combines the benefits of light rail transit with the flexibility and efficiency of bus transit. The goal of BRT development is to enhance ridership and reduce operating costs with increased service levels and quality. A BRT ...
Improving the Quality of Public Transportation by Dynamically Adjusting the Bus Departure Time
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingNowadays, more and more smart cities around the world are being built. As a part of the smart city, intelligent public transportation plays a very important role. Improving the quality of public transportation by reducing crowdedness and total transit ...
Patch-based Modelling of City-centre Bus Movement with Phase-type Distributions
We propose a methodology for constructing a stochastic performance model of a public transportation network using real-world data. Our main data source consists of Automatic Vehicle Location (AVL) measurements of buses in the Edinburgh region. Although ...
Comments
Information & Contributors
Information
Published In
August 2020
120 pages
ISBN:9781450375467
DOI:10.1145/3416028
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Southwest Jiaotong University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 September 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
IMMS 2020
IMMS 2020: 2020 3rd International Conference on Information Management and Management Science
August 7 - 9, 2020
London, United Kingdom
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 53Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in