Traffic stream models provide relationship among the three basic macroscopic traffic stream characteristics namely traffic speed, volume and density. Traffic stream models are the basic building blocks of traffic flow modeling, design of road systems and traffic management systems. A number of research papers are available proposing different traffic stream models. However, majority of them are based on homogeneous traffic conditions and they may not be directly suitable for the Indian traffic condition. Not much research has been done on this important aspect so far in India. The present study is an attempt to develop a traffic stream model suitable for the heterogeneous traffic flow condition, such as the one existing in India, taking Chennai as case study. Traffic data for the present study are collected using video graphic technique. Flow, speed and occupancy are manually extracted from collected videos and density estimated using the relation connecting occupancy and density. The results show an exponential speed-density relation and parabolic relations for the other two. © 2010 ASCE.

Lelitha Devi Vanajakshi

Department Of Civil Engineering

basic building block

Empirical model

GRAPHIC TECHNIQUES

Heterogeneous traffic

Heterogeneous traffic flow

Research papers

ROAD SYSTEMS

SPEED-DENSITY

TRAFFIC CONDITIONS

TRAFFIC DATA

TRAFFIC FLOW MODELING

TRAFFIC MANAGEMENT SYSTEMS

TRAFFIC SPEED

TRAFFIC STREAMS

Dielectric properties

Hydraulics

Research

Speed

Traffic control

Traffic surveys

Motor transportation

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Traffic Stream Modeling under Heterogeneous Traffic Conditions

Proceedings of the Conference on Traffic and Transportation Studies, ICTTS

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. © 2014 Vilnius Gediminas Technical University (VGTU) Press.

Fulltext

Transport

Significance of incorporating heterogeneity in a non-continuum macroscopic model for density estimation

Traffic data can be classified into spatial data and location-based data depending on the frame of measurement involved. Location-based traffic data pertain to information at a certain point, and spatial traffic data pertain to a longer section of the roadway. This larger coverage makes spatial traffic data better indicators of the traffic condition and the level of service of the roadway section. Therefore, spatial traffic data are preferred in any congestion mitigation program. Because the greater spatial coverage makes measurement of spatial traffic data more difficult than that of location-based data, the spatial data are usually estimated from other easily measurable location-based parameters. This paper proposes a methodology to estimate two of the most important spatial traffic parameters-density and space mean speed (SMS)-from the location-based parameters-flow and time mean speed (TMS)-under Indian traffic conditions. The estimation scheme was based on the Kalman filter and incorporated a dynamic macroscopic traffic flow model formulated in the state-space representation. This framework also included an empirical model calibrated with field data and a parameter α, which was used to relate SMS to TMS. The parameter estimation was formulated as an optimization problem, and the estimation scheme was corroborated with a comparison of density estimates with field data. Because the collection of SMS from the field was difficult, travel time collected from probe vehicle data was used as a surrogate measure to corroborate the SMS estimates. The results were promising and agreed well with the traffic behavior observed in the field.

Transportation Research Record

Estimating spatial traffic states with location-based data under heterogeneous conditions

Journal	Proceedings of the Conference on Traffic and Transportation Studies, ICTTS
Open Access	No