N R Madhuri Kashyap

Bio: N R Madhuri Kashyap is an academic researcher. The author has co-authored 1 publications.

Analysis of Vehicle-Following Behavior in Mixed Traffic Conditions using Vehicle Trajectory Data:

Abstract: In mixed traffic streams without lane discipline, driving behaviors are complex and difficult to model. However, limited attempts have been made to study the characteristics of these maneuvers usin...

Vehicle-following behaviour in mixed traffic – role of lane position and adjacent vehicle

Abstract: In mixed traffic condition, varying vehicle dimensions and lack of lane discipline lead to parallel movement of vehicles in the same lane or between lanes. This results in a condition where the longitudinal response of vehicles gets affected by adjacent vehicles and their configurations. Correspondingly, the adjacent vehicle configurations are significantly influenced by the lane position of subject vehicle. To accommodate this scenario, the study formulates longitudinal acceleration models from trajectory data considering the influence of subject vehicle’s lane position along with adjacent vehicle characteristics. The response under different cases of subject vehicle’s lane position and adjacent vehicle configurations are evaluated. The statistical analysis indicates that the following behavior varies based on lane position of subject vehicle and adjacent vehicle attributes. It is also found that disregarding these attributes can produce significantly erroneous acceleration estimates. These features can improve existing following behavior models and can enhance the realism of the microscopic modeling scheme for mixed traffic conditions.

Effect of vehicle size on crash risk in a heterogeneous traffic scenario: a bivariate extreme value approach

Abstract: Most traffic conflict indicators are defined for car-following scenarios where a follower vehicle interacts with a leader vehicle in one-dimensional space. However, vehicles do interact in a two-dimensional space especially in a heterogeneous traffic environment. Further, designating an interaction as risky depends on the interacting leader-follower (LF) pairs.Conflict indicators namely Time-to-Collision (TTC) and lateral gap which quantifies longitudinal and lateral interactions respectively, were computed from video recordings at four accident black spots on four-lane divided highways. Conflict in two-dimensional space was modelled for various LF-pairs using the Bivariate Extreme Value function of these two conflict indicators. Crash risk was estimated for each LF-pairs separately. Results show that cars and light commercial vehicles exhibit higher crash risk as compared to two-wheelers and motorized three-wheelers. The proposed framework can be used for more accurate risk assessment and calibration of collision warning systems in lane free mixed traffic conditions.

Development of Car-Following Models with Multiple Leader Vehicles for Mixed Traffic Conditions in Urban Areas

Abstract: For quick and efficient implementation of transport policies, the assessment of traffic conditions with high precision techniques namely, microscopic simulation models is important. However, the models that are developed for homogeneous traffic conditions would not yield realistic estimations for Indian conditions which are highly heterogeneous and lane indiscipline. Further car-following model being core part of simulation considers single leader vehicle which is not the case especially in mixed traffic condition. In the present study, an attempt has been done to consider all the surrounding vehicles as influencing variables in developing car-following model apart from driver’s behaviour. For this, vehicle trajectory data consist of time wise positions, speed and acceleration has been extracted from the video graphic data collected. Multiple linear regression models are developed for three vehicles types, i.e. cars, two-wheelers and three-wheelers and subsequently compared with General Motor car-following using RSME and MAPE values. It was found that developed model is able to reduce these values by 44% and 26%, respectively, for cars similar observation are observed for two-wheeler and three-wheeler which demonstrate the suitability for the mixed traffic condition. Further the developed model is implemented in a microscopic traffic simulation to study Impact of traffic composition of heavy vehicles on road capacity and free speed.