{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T18:51:09Z","timestamp":1716576669491},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"Investigating travel time variability is critical for pre-trip planning, reliable route selection, traffic management, and the development of control strategies to mitigate traffic congestion problems cost-effectively. Hence, a large number of studies are available in the literature which determine the most suitable distribution to fit the travel time data, but these studies recommend different distributions for the travel time data, and there is a disagreement on the best distribution option for fitting to the travel time data. The present study proposes a novel framework to determine the best distribution to represent the travel time data obtained from probe vehicles by using the modern machine learning technique. This study employs vast travel time data collected by fitting GPS tracking units on the probe vehicles and offers a comprehensive investigation of travel time distribution in different scenarios generated due to spatiotemporal variation of the travel time. The study also considers the effect of weather and uses the three most commonly used non-parametric goodness-of-fit tests (namely, Kolmogorov\u2013Smirnov test, Anderson\u2013Darling test, and chi-squared test) to fit and rank a comprehensive set of around 60 unimodal statistical distributions. The framework proposed in the study can determine the travel time distribution with 91% accuracy. Additionally, the distribution determined by the framework has an acceptance rate of 98.4%, which is better than the acceptance rates of the distributions recommended in existing studies. Because of its robustness and applicability in many different traffic situations, the proposed framework can also be used in developing countries with heterogeneous disordered traffic conditions to evaluate the road network\u2019s performance in terms of travel time reliability.<\/jats:p>","DOI":"10.3390\/data8030060","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T07:29:16Z","timestamp":1678778956000},"page":"60","source":"Crossref","is-referenced-by-count":1,"title":["Development of a Machine-Learning-Based Novel Framework for Travel Time Distribution Determination Using Probe Vehicle Data"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-3825-819X","authenticated-orcid":false,"given":"Gurmesh","family":"Sihag","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2463-5532","authenticated-orcid":false,"given":"Praveen","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0554-7155","authenticated-orcid":false,"given":"Manoranjan","family":"Parida","sequence":"additional","affiliation":[{"name":"CSIR-Central Road Research Institute (CRRI), New Delhi 110025, India"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"ref_1","first-page":"74","article-title":"Exploring Route Choice Behavior Using Geographic Information System-Based Alternative Routes and Hypothetical Travel Time Information Input","volume":"1493","author":"Kitamura","year":"1995","journal-title":"Transp. Res. Rec."},{"key":"ref_2","first-page":"123","article-title":"A Rank-Dependent Scheduling Model","volume":"46","author":"Koster","year":"2012","journal-title":"J. Transp. Econ. Policy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.tra.2016.05.014","article-title":"Integrating the Mean\u2013Variance and Scheduling Approaches to Allow for Schedule Delay and Trip Time Variability under Uncertainty","volume":"89","author":"Li","year":"2016","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.3141\/1783-04","article-title":"Travel Time Reliability with Risk-Sensitive Travelers","volume":"1783","author":"Chen","year":"2002","journal-title":"Transp. Res. Rec."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1287\/trsc.2013.0476","article-title":"A Robust Scenario Approach for the Vehicle Routing Problem with Uncertain Travel Times","volume":"48","author":"Han","year":"2013","journal-title":"Transp. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.trb.2005.09.008","article-title":"The Impact of Stop-Making and Travel Time Reliability on Commute Mode Choice","volume":"40","author":"Bhat","year":"2006","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.jtrangeo.2010.11.009","article-title":"Travel-Time Reliability Impacts on Railway Passenger Demand: A Revealed Preference Analysis","volume":"19","author":"Rietveld","year":"2011","journal-title":"J. Transp. Geogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S1366-5545(00)00011-9","article-title":"The Valuation of Reliability for Personal Travel","volume":"37","author":"Bates","year":"2001","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/BF00167196","article-title":"A Study of Travel Time and Reliability on Arterial Routes","volume":"8","author":"Polus","year":"1979","journal-title":"Transportation"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1061\/(ASCE)TE.1943-5436.0000126","article-title":"Using GPS Data to Gain Insight into Public Transport Travel Time Variability","volume":"136","author":"Mazloumi","year":"2009","journal-title":"J. Transp. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1080\/15472450802644439","article-title":"Using Bus Probe Data for Analysis of Travel Time Variability","volume":"13","author":"Uno","year":"2009","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1002\/atr.192","article-title":"Distributions of Travel Time Variability on Urban Roads","volume":"47","author":"Susilawati","year":"2013","journal-title":"J. Adv. Transp."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.trc.2014.09.019","article-title":"A Travel Time Reliability Model of Urban Expressways with Varying Levels of Service","volume":"48","author":"Lei","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"04014068","DOI":"10.1061\/(ASCE)TE.1943-5436.0000724","article-title":"Public Transport Travel-Time Variability Definitions and Monitoring","volume":"141","author":"Kieu","year":"2015","journal-title":"J. Transp. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1002\/atr.1314","article-title":"Modeling Distributions of Travel Time Variability for Bus Operations","volume":"50","author":"Ma","year":"2016","journal-title":"J. Adv. Transp."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3747632","DOI":"10.1155\/2018\/3747632","article-title":"Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing","volume":"2018","author":"Chen","year":"2018","journal-title":"J. Adv. Transp."},{"key":"ref_17","first-page":"30","article-title":"Examining Travel Time Reliability under Mixed Traffic Conditions: A Case Study of Urban Arterial Roads in Indian Cities","volume":"5","author":"Chepuri","year":"2018","journal-title":"Asian Transp. Stud."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1177\/0361198118770175","article-title":"Performance Comparison of Bus Travel Time Prediction Models across Indian Cities","volume":"2672","author":"Jairam","year":"2018","journal-title":"Transp. Res. Rec."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.trc.2017.11.023","article-title":"Analysis of Bus Travel Time Distributions for Varying Horizons and Real-Time Applications","volume":"86","author":"Rahman","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1007\/s11431-018-9267-4","article-title":"De Analyzing Distributions for Travel Time Data Collected Using Radio Frequency Identification Technique in Urban Road Networks","volume":"62","author":"Guo","year":"2018","journal-title":"Sci. China Technol. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/978-981-15-3742-4_11","article-title":"Empirical Travel Time Reliability Assessment of Indian Urban Roads","volume":"69","author":"Amrutsamanvar","year":"2020","journal-title":"Lect. Notes Civ. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ijtst.2019.10.001","article-title":"Analyzing Travel Time Distribution Based on Different Travel Time Reliability Patterns Using Probe Vehicle Data","volume":"9","author":"Chen","year":"2020","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1080\/19427867.2019.1595356","article-title":"Development of New Reliability Measure for Bus Routes Using Trajectory Data","volume":"12","author":"Chepuri","year":"2019","journal-title":"Transp. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"05020001","DOI":"10.1061\/JTEPBS.0000351","article-title":"Applying Finite Mixture Models to New York City Travel Times","volume":"146","author":"Xu","year":"2020","journal-title":"J. Transp. Eng. Part A Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s00779-020-01369-4","article-title":"Estimation of Travel Time Distributions for Urban Roads Using GPS Trajectories of Vehicles: A Case of Athens, Greece","volume":"25","author":"Adnan","year":"2021","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1016\/j.ijtst.2021.10.006","article-title":"Probability Distributions Analysis of Travel Time Variability for the Public Transit System","volume":"11","author":"Harsha","year":"2021","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"05021012","DOI":"10.1061\/JTEPBS.0000624","article-title":"A Random Effects Model for Travel-Time Variability Analysis Using Wi-Fi and Bluetooth Data","volume":"148","author":"Ghavidel","year":"2022","journal-title":"J. Transp. Eng. Part A Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sihag, G., Parida, M., and Kumar, P. (2022). Travel Time Prediction for Traveler Information System in Heterogeneous Disordered Traffic Conditions Using GPS Trajectories. Sustainability, 14.","DOI":"10.3390\/su141610070"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"05020003","DOI":"10.1061\/JTEPBS.0000357","article-title":"Travel-Time Variability Analysis of Bus Rapid Transit System Using GPS Data","volume":"146","author":"Kathuria","year":"2020","journal-title":"J. Transp. Eng. Part A Syst."},{"key":"ref_30","unstructured":"Kieu, L.M., Bhaskar, A., and Chung, E. (2012, January 26\u201328). Benefits and Issues of Bus Travel Time Estimation and Prediction. Proceedings of the Australasian Transport Research Forum, ATRF 2012, Perth, Australia."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/8\/3\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T10:57:15Z","timestamp":1678791435000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/8\/3\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,14]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["data8030060"],"URL":"https:\/\/doi.org\/10.3390\/data8030060","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,14]]}}}