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Volume List  / Volume 8 (1)

Article

TRAVEL TIME PREDICTION MODELLING IN MIXED TRAFFIC CONDITIONS

DOI: 10.7708/ijtte.2018.8(1).10


8 / 1 / 135-147 Pages

Author(s)

Jaya Krishna Jammula - Transportation Division, Department of Civil Engineering, National Institute of Technology, Warangal-506004, Telangana, India -

Rajesh Bera - Transportation Division, Department of Civil Engineering, National Institute of Technology, Warangal-506004, Telangana, India -

Kodavanti Venkata Raghavendra Ravishankar - Transportation Division, Department of Civil Engineering, National Institute of Technology, Warangal-506004, Telangana, India -


Abstract

Over the past few decades, exponential increase in vehicle ownership resulted in issues of traffic control and management. Intelligent Transportation System (ITS) is one of the solutions for the intelligent management of traffic. ITS applications like Advanced Traveler Information Systems and Advanced Traffic Management Systems need travel time as a major input. The estimation of travel time in urban network became more complicated because of the rapid change in the system and traffic. This study is done in order to assess the impact of different travel modes on travel time. Data has been collected on a stretch of 14 km length in Warangal city, India using GPS probe vehicle along with video camera. Different private modes of transportation such as 2 wheeler, passenger car and 3 wheeler have been used as test vehicles for the collection of data in different traffic flow scenarios. Artificial Neural Network and a multi linear regression model have been developed to compare the estimated travel times with the field data. Two combinations of ANN model using single hidden layer, different numbers of neurons and epochs have been compared. The travel time of different modes has been compared and the effect of vehicle composition on travel time has been analyzed. The ANN model perform better than the regression model.


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Keywords:
travel time; ANN; GPS; regression; vehicle mode; mixed traffic;


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