Data Collection in Countries with Extreme Vehicle Heterogeneity and Weak Lane Disciplined Traffic

Abstract

Understanding driver behavior is extremely important for the design and analysis of any transportation infrastructure. Several methodologies exist to collect information on driver behavior. However, most of these apply to homogeneous traffic with lane discipline. In India, like in most of the low- and middle-income countries, the traffic is highly heterogeneous and exhibits poor lane discipline. In this case, the vehicles interact not only longitudinally, but also laterally. Hence, these traditional methodologies may not work in such a scenario. The objective of this paper is to present a data collection method which will help to collect information about individual vehicles in highly heterogeneous traffic with poor lane discipline. Instrumented vehicles help to observe individual driver behavior accurately and precisely. Although such vehicles are present in various universities in the USA (such as University of Michigan, Southampton, Texas A&M University, to name a few), their purpose of such vehicles is different. However, the major challenges are associated with data processing and extraction. Since these sensors give large data, its processing is not easy, and it offers challenges. This paper discusses the opportunity such a vehicle offers to understand driving conditions and the challenges the researchers might face. This paper also presents some simple applications of the data.