Gowri Asaithambi

Trajectory Data and Flow Characteristics of Mixed Traffic

Models of driving behavior (e.g., car following and lane changing) describe the longitudinal and lateral movements of vehicles in the traffic stream. Calibration and validation of these models require detailed vehicle trajectory data. Trajectory data about traffic in cities in the developing world are not publicly available. These cities are characterized by a heterogeneous mix of vehicle types and by a lack of lane discipline. This paper reports on an effort to create a data set of vehicle trajectory data in mixed traffic and on the first results of analysis of these data. The data were collected through video photography in an urban midblock road section in Chennai, India. The trajectory data were extracted from the video sequences with specialized software, and the locally weighted regression method was used to process the data to reduce measurement errors and obtain continuous position, speed, and acceleration functions. The collected data were freely available at http://toledo.net.technion.ac.il/downloads. The traffic flow characteristics of these trajectories, such as speed, acceleration and deceleration, and longitudinal spacing, were investigated. The results show statistically significant differences between the various vehicle types in travel speeds, accelerations, distance keeping, and selection of lateral positions on the roadway. The results further indicate that vehicles, particularly motorcycles, move substantially in the lateral direction and that in a substantial fraction of the observations, drivers are not strictly following their leaders. The results suggest directions for development of a driving behavior model for mixed traffic streams.

Study of traffic flow characteristics using different vehicle-following models under mixed traffic conditions

Abstract 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.

Characteristics of Mixed Traffic on Urban Arterials with Significant Volumes of Motorized Two-Wheelers: Role of Composition, Intraclass Variability, and Lack of Lane Discipline

Mixed traffic in the cities of many developing countries is characterized by a lack of lane discipline, varying compositions of constituent vehicle types, and significant intraclass variability in static and dynamic characteristics. However, the influence of these factors on traffic flow parameters is not well understood. This study addressed the influence of lane discipline, intraclass variability, and composition on traffic flow characteristics under heterogeneous traffic conditions in Chennai, India. A microscopic traffic simulation model was calibrated and validated with field data from a four-lane divided urban arterial road in Chennai. The preliminary analysis indicated that factors such as composition, intraclass variability, and lane discipline had a statistically significant effect on stream speed. Speed–flow and speed–density relationships were developed on the basis of simulation results. These results showed a clear influence of lack of lane discipline, variability, and composition on stream speed. The influence varied depending on volume level and type of subject vehicle. The effect of composition on capacity was quantified. When two-wheelers had a predominant share, they enjoyed better performance in the absence of lane discipline. However, when cars and heavy vehicles had a significant presence, the impact of the lack of lane discipline was much smaller. The simulation model was applied to evaluate a range of traffic control measures based on vehicle type and lane. The results showed the promise of some measures based on vehicle class, namely, the exclusion of autorickshaws or autorickshaws and heavy vehicles. The findings have interesting implications for efficiency, user experience, and equity in mixed traffic.

Modeling Free-flow Speeds on Undivided Roads in Mixed Traffic with Weak Lane Discipline

In developing countries like India, transportation systems are characterized by limited roadway infrastructure and lack of operation and management experience. Hence, there exists a need to evaluate a performance indicator that reflects the current level of service (LOS) of a road facility. Free-flow speed (FFS) is a key parameter used to express LOS assessment. The objective of this study is to develop FFS prediction models for undivided roads with mixed traffic conditions in both urban and rural settings in India. Traffic data were collected from two-way two-lane undivided roads in southern India during free-flow traffic conditions using videographic method. Various class-specific and site-specific characteristics, such as vehicle class, subclass, carriageway width, link length, number of side roads, lateral clearance, land use type, and area type, were investigated and their influence on FFS evaluated. Statistical tests assessed the variations of obtained FFS with different vehicle-specific and site-specific factors. Free-flow prediction models were developed using linear regression method. The developed models show that FFS increases with greater carriageway width, lateral clearance, and link length, and decreases with increase in number of side roads. In general, FFS is higher in rural areas than urban areas. Similarly, open areas have higher FFS than residential, institutional, and commercial areas. The model can be used to predict FFS of undivided roads if site-specific and vehicle-specific data are known. This study finds interesting applications in capacity and LOS analysis, accident analysis, and before-and-after studies of road improvement schemes.

Simulation framework for modeling bidirectional mixed traffic

Most of the Indian urban roads are bi-directional in nature consists of mix up of different vehicle types with weak lane discipline. A mathematical or analytical treatment of such condition is found infeasible due to its complex nature. Hence, simulation has become inevitable tool for analysis and interpretation of such real world situations. There are only few studies which focuses exclusively on developing a bidirectional traffic simulation model considering the longitudinal and lateral behaviour of vehicles for urban undivided roads. With the above motivation, the present study focuses on development of simulation models for bi-directional mixed traffic flow using object oriented programming (OOP) concepts. The proposed model would be of significant assistance to traffic engineers while making key decisions in traffic control and management policies.