Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

Abstract

This paper proposes and validates a modified cellular automata model for determining interaction rate (i.e. number of car-following/overtaking instances) using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter b in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conventional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of b on vehicular interactions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the validity of the model in determining interaction rates. Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles (LMVs) and heavy motor vehicles (HMVs) were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate microscopic traffic flow models.

Selection of open or closed boundaries in a cellular automata model for heterogeneous non-lane-based traffic

“…Once the specific number of vehicles are imported in the system, they start moving based on specified CA rules, and it takes some time to adjust themselves according to surrounding this time is called warmup time. 42 The joining point of the end and beginning of a link is named as “X” (Figure 1(a)). When the vehicles are at location “X,” they calculate the gaps ahead and move to the beginning of the link.…”

Cellular automata models for signalised and unsignalised intersections with special attention to mixed traffic flow: a review

“…Intersections having mixed traffic involve the interaction of different modes such as cars, buses, trucks, motorised three‐wheelers, motorised two‐wheelers etc. As the interaction of vehicles is different for different modes [22], for examples a car tries not to follow a heavy vehicle [24]. Moreover, as discussed above the interactions of vehicles affects safety and capacity of intersections [19].…”

“…No such issue happens while simulating mid‐block with closed boundary conditions. Further, as it takes some time to stabilise the simulation model this time is referred to as warm‐up time in some researches [24]. Thus traffic intersections should be simulated on a long stretch and with a greater number of simulations so that few simulation steps can be ignored for warm‐up.…”

Simulation Strategies for Mixed Traffic Conditions: A Review of Car-Following Models and Simulation Frameworks

“…However, this model does not analyse the lateral distribution of vehicles in the congested traffic, as well as noncongested traffic stream. Pandey et al [70] proposed a modified CA model that considers preference in lateral position, which facilitates the gradual drifting towards the preferred position on the road. e model also considers the safe gap at the front and back of vehicles and the relationship between area of occupancy, interaction rate, and composition of vehicles.…”