DocumentCode :
154755
Title :
Historical data based real time prediction of vehicle arrival time
Author :
Maiti, Santa ; Pal, Arnab ; Pal, Arnab ; Chattopadhyay, Taraprasad ; Mukherjee, Arjun
Author_Institution :
Innovation Lab., Tata Consultancy Services, Kolkata, India
fYear :
2014
fDate :
8-11 Oct. 2014
Firstpage :
1837
Lastpage :
1842
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.
Keywords :
neural nets; regression analysis; road traffic; support vector machines; traffic engineering computing; transportation; ANN model; Chennai; SVM regression model; Siruseri; artificial neural network model; dwell time; historical data based real time prediction; industry campus; leg time; prediction accuracy; prediction time; scheduled arrival time; scheduled drop points; scheduled pick-up points; support vector machine regression model; timestamp; traffic congestion; transportation facility; vehicle arrival time prediction; vehicle trajectory; waiting time; Artificial neural networks; Data models; Predictive models; Support vector machines; Testing; Training; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on
Conference_Location :
Qingdao
Type :
conf
DOI :
10.1109/ITSC.2014.6957960
Filename :
6957960
Link To Document :
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