NDLI: Historical data based real time prediction of vehicle arrival time

Content Provider	IEEE Xplore Digital Library
Author	Maiti, S. Pal, A. Pal, A. Chattopadhyay, T. Mukherjee, A.
Copyright Year	2014
Description	Author affiliation: Innovation Lab., Tata Consultancy Services, Kolkata, India (Maiti, S.; Pal, A.; Pal, A.; Chattopadhyay, T.; Mukherjee, A.)
Abstract	In recent times, most of the industries provide transportation facility for their employees from scheduled pick-up and drop points. In order to reduce longer waiting time, it is important to accurately predict the vehicle arrival in real time. This paper proposes a simple, lightweight yet powerful historical data based vehicle arrival time prediction model. Unlike previous work, the proposed model uses very limited input features namely vehicle trajectory and timestamp considering the scarcity and unavailability of data in the developing countries regarding traffic congestion, weather, scheduled arrival time, leg time, dwell time etc. Our proposed model is evaluated against standard Artificial Neural Network (ANN) and Support Vector Machine (SVM) regression models using real bus data of an industry campus at Siruseri, Chennai collected over four months of time period. The result shows that proposed historical data based model can predict two and half (approx.) times faster than ANN model and two (approx.) times faster than SVM model while it also achieves a comparable accuracy (75.56%) with respect to ANN model (76%) and SVM model (71.3%). Hence, the proposed historical data based model is capable of providing a real time system by balancing the trade-off between prediction time and prediction accuracy.
Starting Page	1837
Ending Page	1842
File Size	772680
Page Count	6
File Format	PDF
ISBN	9781479960781
DOI	10.1109/ITSC.2014.6957960
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data models Predictive models Artificial neural networks Support vector machines Training Vehicles Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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