No abstract

Traffic stream models provide relationships among the three basic traffic variables namely speed, flow and density under steady-state conditions. Since reported stream models are mainly developed for homogeneous traffic conditions, they may not be directly suitable for Indian traffic condition which is heterogeneous and lacks lane discipline. Only very limited studies have been reported from India in this respect and the present study develops an optimal speed-density relation and from that derive theoretically the speed-flow and flow density relations that are suitable for the study stretch under consideration. The results indicate that the developed model is able to represent the steady-state macroscopic behavior of the traffic stream with reasonable accuracy. An application of such a stream model for a real time application is also demonstrated. The results obtained are promising showing the potential for the use of such stream models for real time application such as a congestion information system.

Dynamic traffic flow models are essential for obtaining information about the time evolution of variables describing the traffic flow phenomena and have a critical role in the development and implementation of real-time applications such as Intelligent Transportation Systems. Macroscopic traffic flow models that treat the traffic as a continuum are preferred for such applications. But, existing macroscopic models characterize homogeneous traffic, and may not be directly applicable to capture the vehicle heterogeneity seen on Indian roads. To address this issue, a non-continuum macroscopic dynamic traffic flow model based on the lumped-parameter approach was developed in this study. The model was developed based on the conservation of vehicles equation and a dynamic speed equation, incorporating an empirically developed traffic stream model, which is an important contribution of this study. Using this model, an estimation scheme has been developed based on the Kalman filtering technique to estimate traffic states in real time. The proposed scheme was implemented and corroborated for the heterogeneous traffic conditions existing in India. The performance of this scheme has been evaluated and the results obtained have been found to be promising.

A comprehensive understanding of speed–flow–density relations is an essential requirement in planning, design and operation of transportation systems. Many researchers have investigated the nature of speed–flow–density relationships, commonly known as stream models, under homogeneous traffic conditions. However, those models may not be the best under Indian traffic conditions. Indian traffic differs significantly from homogeneous traffic and may need a different treatment for analysis. Not much research has been reported on this important aspect so far from India. The present study is an attempt to develop an optimal traffic stream model suitable for the heterogeneous traffic flow condition existing in India, taking Chennai as a case study. A two-regime speed–density relationship was developed using a conventional optimisation algorithm. Using this, the speed–flow and flow–density relationships were theoretically derived. The results indicate that the proposed model is able to represent the steady-state macroscopic behaviour of the heterogeneous and less lane-disciplined traffic stream under study with reasonable accuracy.

The heterogeneity of traffic and the lack of lane discipline on the roads in India and other developing countries add complexity to the analysis and modeling of traffic. It is generally believed that it is important to take heterogeneity into account in traffic modeling. The aim of the present study is to check the validity of this assumption by analyzing the effect of incorporating heterogeneity in a macroscopic level traffic flow analysis. The application considered is real-time congestion analysis on Indian roads. Traffic density is considered as the congestion indicator. The measurement of density is difficult since it is a spatial parameter. It is usually estimated from other traffic parameters that can be readily measured using available sensors. A model-based estimation scheme using Kalman filtering has been employed to estimate traffic density. A non-continuum macroscopic model was attempted based on the lumped parameter approach. All the traffic variables were quantified without considering traffic lanes in order to take into account the lack of lane discipline. The effect of heterogeneity has been studied by incorporating static values of Passenger Car Units (PCU), dynamic values of Two Wheeler Units (TWU) and considering different classes of vehicles explicitly in the modeling process. The proposed estimation schemes without and with heterogeneity have been compared. The results have been corroborated using data collected from a road stretch in Chennai, India. The study shows that the significance of incorporating heterogeneity into the modeling of mixed traffic at the macroscopic level was not very significant.