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dc.contributor.authorAmita J.-
dc.contributor.authorJain S.S.-
dc.contributor.authorGarg, Pradeep Kumar-
dc.identifier.citationTransportation Research Procedia (2016), 17: 263-272-
dc.description.abstractQuality of the bus travel time is improved by the accurate prediction of bus travel time. Accurate travel time information is essential as it attract more commuters and increase commuter's satisfaction. The prime objective of this study is to develop a model that predicts bus travel time based on (Global Positioning System) GPS data using artificial neural network (ANN). The bus travel time prediction model developed in this study includes the number of passengers boarding and alighting, average nonstop trip time, and number of dwells at each at each stop. The real world data collected from route no 832 of Delhi Transport Cooperation (DTC) was used to developed and validate the model. The performance of the developed model is estimated by comparing it with other model using conventional measures such as mean absolute error and root mean square error. Finally, the result indicates that developed model is slightly proficient in achieving predicted travel time with sufficient accuracy. © 2016 The Authors.-
dc.publisherElsevier B.V.-
dc.relation.ispartofTransportation Research Procedia-
dc.subjectTravel Time-
dc.titlePrediction of Bus Travel Time Using ANN: A Case Study in Delhi-
dc.typeConference Paper-
dc.affiliationAmita, J., Centre for Transportation System (CTRANS), IIT Roorkee, Roorkee, Uttarakhand, 247667, India-
dc.affiliationJain, S.S., Transportation Engineering Group, Department of Civil Engineering, Faculty CTRANS, IIT Roorkee, Roorkee, Uttarakhand 247667, India-
dc.affiliationGarg, P.K., Geomatics Engineering Group, Department of Civil Engineering, Faculty CTRANS, IIT Roorkee, Roorkee, Uttarakhand 247667, India-
Appears in Collections:Conference Publications [CE]

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