Driver behaviour modelling of vehicles at signalized intersection with heterogeneous traffic

Roads network is composed of mid-blocks and intersections. The part where two roads cross is called an intersection whereas the straight sections without intersection or any other interuptions is called mid-block. It can be observed that the vehicles on the mid-blocks tend to achieve their free-flow speeds while those at the intersections are forced to decelerate. Modelling of these sections needs to separate the intersections from mid-block. Further, drivers behave differently at these two locations. Present study attempts to separate the intersection zone of influence (IZOI) and mid-block using the manoeuvring characteristics of drivers in terms of acceleration/deceleration. These were captured through a global positioning system (GPS) device in the vehicle after sighting a red signal at the intersection. Further, this study also tried to observe whether different classes of drivers such as aggressive, normal or timid drivers, based on acceleration/deceleration behaviour exists. A junction with 1-km straight stretch in R. K. Puram New Delhi (India) was chosen for the study to find the IZOI. After identifying IZOI a video data was collected in Mumbai (India) for a stretch more than 200-m long near intersection where the red signal was visible; This enabled observing the driver behaviour more closely. Around 900 drivers of different modes were analysed to understand their behaviour. It was found that cars start reducing its speed at 160 m, motorized three-wheelers at 124 m and buses start reducing their speeds at 98 m distance from the intersection. The driver behaviours were distinct in each of the mode (Bus, Car and motorized-there-wheelers), but it emerges that the drivers cannot be classified into finite number of clusters based on the fitted normal distribution. Thus it can be seen that there are no clearly demcarcated driver behaviours irrespective of the vehicle type, such as aggressive, normal and timid categories as the intersection approaches. A normal distribution model can classify the drivers satisfactorily.