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Car-following models are the cornerstones of microscopic traffic simulation tools and intelligent transportation systems, but the applicability of car-following models to disordered traffic have not been investigated in detail with longer trajectory dataset. To address this gap, two car-following models namely, Intelligent Driver Model (IDM) and Full Velocity Difference Model (FVDM) are calibrated using trajectory data collected on an urban arterial road in Chennai, India using Unmanned Aerial Vehicles. The raw data are smoothed for noise removal and the car-following pairs are identified based on the lateral overlap and following duration. The models are calibrated by minimizing the deviations between the observed and simulated longitudinal gaps between leader and follower using genetic algorithm. The obtained errors are between 2.5% and 19.5%, which are comparable with standard ranges of error reported in literature. The optimal parameter values represent the distinct characteristics of disordered traffic in comparison with the homogeneous traffic. [ABSTRACT FROM AUTHOR]

The behaviour of traffic in the heterogeneous environment of an urban signalized intersection is complex and difficult to model. This paper presents the development of a simulation model to imitate the flow of heterogeneous traffic through a signalized intersection. It discusses the validation of the proposed model in terms of queue density and dissipation of vehicles at an intersection approach and found to be satisfactorily replicating the field conditions. In this study, the model was extended to examine the effects of left turn channelization on vehicle waiting times. Sensitivity analysis was carried out to study the variation of vehicle waiting times. Analysis estimated that vehicle waiting times were reduced if a channelization was provided for a high traffic volume and certain proportions of left turn vehicles in the intersection approach. The length of channelisation has marginal impacts on vehicle waiting times. First published online: 27 Oct 2010

Representation of traffic in terms of Passenger Car Unit (PCU) is imperative to estimate capacity in disordered traffic. Many studies have been conducted on investigation of impacts of traffic and geometric conditions on traffic characteristics and PCUs. However, the sensitivity of PCUs due to roadside frictions are not adequately studied. To address this gap, this study aims to estimate PCU values for vehicles under the influence of curbside bus stop, which is the most common roadside friction in developing countries. Lack of space for providing exclusive bus bays and higher demand for public transport buses in urban roads justify the need for this study. Methodology of this study involves development and validation of a microscopic simulation model to quantify the impact of curbside bus stop on PCU as well as capacity. The results indicate the significant differences in PCU values due to the presence of curbside bus stop with varying traffic volume and composition. [ABSTRACT FROM AUTHOR]

To understand the congestion problem and the occurrence of bottlenecks and to formulate solutions for it, a thorough study of vehicle-to-vehicle interactions is necessary. Car-following models replicate the behavior of a driver following another vehicle. These models are widely used in the development of traffic simulation models, and in analysis of safety and capacity. In India, traffic on roads is mixed in nature with wide variations in physical dimensions and other vehicular and traffic characteristics with loose lane discipline. In mixed traffic conditions, leader-follower vehicle types are not only car-car cases but also there are different combinations of vehicles (e.g. car-two wheeler, two wheeler-auto rickshaw, and heavy vehicle-two wheeler). The present study focuses on evaluation of different vehicle-following models under mixed traffic conditions. The car-following models such as Gipps, Intelligent Driver Model (IDM), Krauss Model and Das and Asundi were selected for this study. These models were implemented in a microscopic traffic simulation model for a mid-block section. The performance of different vehicle-following models was evaluated based on different Measure of Effectiveness (MoE) using field data collected from a four-lane divided urban arterial road in Chennai city. Speed-concentration and flow-concentration relationships for different vehicle-following models were developed and analyzed for different compositions. Capacity is higher when the proportion of smaller size vehicles is higher, since these vehicles use longitudinal and lateral gaps effectively. The simulation model was also applied to evaluate a range of traffic control measures based on vehicle type and lane (Ex: exclusion of auto-rickshaws, heavy vehicles, auto-rickshaws + heavy vehicles, etc.). The results showed the promise of some measures based on vehicle class, namely, the exclusion of auto rickshaws or auto rickshaws and heavy vehicles. The findings have interesting implications for capacity and PCU estimation and Level of Service (LoS) Analysis. [ABSTRACT FROM AUTHOR]