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Tytuł artykułu

Measurement of delay using travel time reliability statistics in an urban outer corridor

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Unexpected delay on freeways is the prime cause of dissatisfaction in road users. Increasing traffic, adverse environmental conditions, accidents, time, season, location and many more factors influence travel time and cause delay. There is no direct method to estimate delay. It is calculated from trip time estimates. Thus, it is a very big challenge for transportation professionals to develop a model that accurately estimates the trip time for a trip at a particular time, by a specific mode of transport. Subsequently, the reliability of the delay calculated from those trip time estimates is often doubtful. Further, the measurement of delay using the trip time data is another big thing. This paper is a step toward measuring the delay in an accurate way using travel time reliability measures. The study was conducted on the two modes of public transportation (City bus and Auto) in an urban corridor of length 16.3 km, in Hyderabad city, India. In this study, a license plate survey was conducted for data collection, travel time-based statistical analysis was employed for estimation of trip time and by making use of travel time measures, the delay was measured. The approach was validated graphically to portray its accuracy.
Rocznik
Tom
Strony
143--154
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
  • Civil Engineering, K. L. University, Green Fields, Vaddeswaram, Guntur-522502, Andhra Pradesh State, India
  • Civil Engineering, K. L. University, Green Fields, Vaddeswaram, Guntur-522502, Andhra Pradesh State, India
Bibliografia
  • 1. Chen Peng, Rui Tong, Guangquan Lu, Yunpeng Wang. 2018. „Exploring travel time distribution and variability patterns using probe vehicle data: case study in Beijing”. Journal of Advanced Transportation. Article ID 3747632. DOI: https://doi.org/10.1155/2018/3747632.
  • 2. Ahmad Tavassoli Hojati, LuisFerreira, SimonWashington, PhilCharles, Ameneh Shobeirinejad. 2016. „Modelling the impact of traffic incidents on travel time reliability”. Transportation Research Part C: Emerging Technologies 65: 49060. DOI: http://doi.org/10.1016/j.trc.2015.11.017.
  • 3. Kwon Jaimyoung, Tiffany Barkley, Rob Hranac, Karl Petty, Nick Compin. 2011. „Decomposition of travel time reliability into various sources: incidents, weather, work zones, special events, and base capacity”. Transportation Research Record: Journal of the Transportation Research Board 2229(1): 28-33. DOI: https://doi.org/10.3141/2229-04.
  • 4. Chen Anthony, Zhaowang Ji, Will Recker. 2003. „Effect of route choice models on estimation of travel time reliability under demand and supply variations”. In: 1st International Symposium „Transportation Network Reliability”: 93-117. 2001, Kyoto, Japan. ISBN: 1786359545. DOI: https://doi.org/10.1108/9781786359544-006.
  • 5. Lyman Kate, Robert L. Bertini. 2008. „Using travel time reliability measures to improve regional transportation planning and operations”. Transportation Research Record: Journal of the Transportation Research Board 2046(1): 1-10. DOI: https://doi.org/10.3141/2046-01.
  • 6. Emam B. Emam, Haitham Al-Deek. 2006. „Utilizing a Real Life Dual Loop Detector Data to Develop a New Methodology for Estimating Freeway Travel Time Reliability”. Transportation Research Record: Journal of the Transportation Research Board 1959(1): 140-150. DOI: https://doi.org/10.1177/0361198106195900116.
  • 7. Economic Development Research Group. 2005. The Cost of Congestion to the Economy of the Portland Region. Portland: Portland Business Alliance.
  • 8. Cambridge Systematics and Texas A&M Transportation Institute. 2005. Traffic Congestion and Reliability: Trends and Strategies for Advanced Mitigation. Washington DC: Federal Highway Administration (FHWA).
  • 9. Lomax T., D. Schrank, S. Turner, R. Margiotta. 2007. Selecting Travel Reliability Measures. Texas Transportation Institute. Texas: Transport Research International Documentation (TRID).
  • 10. Nam Doohee, Dongjoo Park, Apichat Khamkongkhun. 2006. „Estimation of Value of Travel Time Reliability”. Journal of Advanced Transportation 39(1): 39-61. DOI: https://doi.org/10.1002/atr.5670390105.
  • 11. Shao H., W.H.K. Lam, Q. Meng, M.L. Tam. 2006. „A Demand Driven Travel Time Reliability-Based Traffic Assignment Problem”. In: 85th Annual Meeting „Transportation Research Board”. Transportation Research Board, Washington DC, 22-26 January 2006, Washington DC, USA. Available at: http://hdl.handle.net/10397/56005.
  • 12. Hani S. Mahmassani, Jing Dong, Chung-Cheng Lu. 2006. „How Reliable is this Route? Predictive Travel Time and Reliability for Anticipatory Traveler Information Systems”. Transportation Research Record: Journal of the Transportation Research Board 1980(1): 117-125. DOI: https://doi.org/10.3141/1980-17.
  • 13. Chen Zhen, Wei Fan. 2019. „Data analytics approach for travel time reliability pattern analysis and prediction”. Journal of Modern Transportation 27: 250-265. DOI: https://doi.org/10.1007/s40534-019-00195-6.
  • 14. Thomas Williams R., Billy M. Rouphail, Chase Jr., Nagui M. 2013. Detailed Analysis of Travel Time Reliability Performance Measures from Empirical Data. Washington DC: Transport Research International Documentation (TRID).
  • 15. Isukapati Isaac K., George F. List. 2016. „Using Travel Time Reliability Measures with Individual Vehicle Data”. In: IEEE 19th International Conference „Intelligent Transportation Systems” IEEE Xplore, 1-4 November 2016, Rio de Janeiro, Brazil. DOI: https://doi.org/10.1109/ITSC.2016.7795901.
  • 16. Chepuri Akhilesh, Jairam Ramakrishnan, Shriniwas Arkatkar, Gaurang Joshi, Srinivas S. Pulugurtha. 2018. „Examining Travel Time Reliability-Based Performance Indicators for Bus Routes Using GPS-Based Bus Trajectory Data in India”. ASCE Journal of Transportation Engineering, Part A: Systems 144(5). ISSN: 2473-2907. DOI: https://doi.org/10.1061/JTEPBS.0000109.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5ac1a1d7-2010-438a-b98b-19d7d6d1647f
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