Effects of Data Characteristics on Bus Travel Time Prediction: A Systematic Study
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Published:2023-03-07
Issue:6
Volume:15
Page:4731
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ISSN:2071-1050
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Container-title:Sustainability
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language:en
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Short-container-title:Sustainability
Author:
Shaji Hima1, Vanajakshi Lelitha2, Tangirala Arun3
Affiliation:
1. Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India 2. Department of Civil Engineering/Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai 600036, India 3. Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
Abstract
The prediction of bus travel time with accuracy is a significant step toward improving the quality of public transportation. Drawing meaningful inferences from the data and using these to aid in prediction tasks is always an area of interest. Earlier studies predicted bus travel times by identifying significant regressors, which were identified based on chronological factors. However, travel time patterns may vary depending on time and location. A related question is whether the prediction accuracy can be improved with the choice of input variables. The present study analyzes this question systematically by presenting the input data in different ways to the prediction algorithm. The prediction accuracy increased when the dataset was grouped, and separate models were trained on them, the highest accurate case being the one where the data-derived clusters were considered. This demonstrates that understanding patterns and groups within the dataset helps in improving prediction accuracy.
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference40 articles.
1. Bus Arrival Time Prediction Using Support Vector Machines;Bin;J. Intell. Transp. Syst.,2006 2. Kumar, S.V., Vanajakshi, L., and Subramanian, S.C. (2011, January 5–9). A model based approach to predict stream travel time using public transit as probes. Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany. 3. Zhang, M., Xiao, F., and Chen, D. (2013). Bus Arrival Time Prediction Based on GPS Data. ICTE 2013. 4. Chu, L., Oh, S., and Recker, W. (, January January). Adaptive Kalman filter based freeway travel time estimation. Proceedings of the 96th Annual Meeting of the Transportation Research Board, Washington, DC, USA. 5. Tong, D., Merry, C.J., and Coifman, B. (2005, January 17). Traffic information deriving using GPS probe vehicle data integrated with GIS. Proceedings of the Center for Urban and Regional Analysis and Department of Geography, Columbus, OH, USA.
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