Volume List  / Volume 7 (4)



DOI: 10.7708/ijtte.2017.7(4).04

7 / 4 / 443-460 Pages


Srijith Balakrishnan - Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, USA -

Ramaswamy Sivanandan - Department of Civil Engineering, Indian Institute of Technology Madras, India -


Free-flow speed (FFS) is the desired speed that drivers choose when no (or very less number of) vehicles are present in the road segment. FFS is an important parameter of traffic flow that decides the level of service and capacity aspects of various types of highway facilities. Estimation of FFS is extremely time consuming and requires extensive human resource and capital. Hence, a FFS model can be a solution to bring down the above difficulties while ensuring satisfactory prediction of FFS. In countries like India, a widely used method of estimating FFS is to collect vehicle speeds from field during low volume hours. However, this method requires significant amount of time, human resource and capital for studies on large road networks. Hence, it is essential to develop models to predict free-flow speeds. It is important that models are capable of capturing the free-flow speed variations due to local road factors. Majority of the existing free-flow speed models are developed under homogeneous traffic conditions, in which passenger cars dominate the vehicle composition. However, the traffic condition in emerging economies like India is heterogeneous in nature characterized by the presence of multiple vehicle categories with varying physical and dynamic characteristics. The present paper attempts to investigate the influence of different road factors on FFS on urban roads of Chennai, India. The paper also tries to capture the FFS variations across different classes of vehicles and develop FFS prediction models. The typical Indian traffic comprises significant percentage of slow moving vehicles like three-wheelers as well as fast moving sedans and SUVs. Composition of traffic and corresponding proportions of different classes are important factors that differentiate heterogeneous and homogeneous traffic. The presented models could explore the driver speed behavior with respect to the aforementioned factors into consideration.

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The support for this work by the Centre of Excellence in Urban Transport at Indian Institute of Technology Madras, India, funded by the Ministry of Urban Development, Government of India, is gratefully acknowledged.


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