Car-Following Models based on Driving Strategies

The models introduced in this chapter are derived from assumptions about real driving behavior such as keeping a “safe distance” from the leading vehicle, driving at a desired speed, or preferring accelerations to be within a comfortable range. Additionally, kinematical aspects are taken into account, such as the quadratic relation between braking distance and speed. We introduce two examples: The simplified Gipps model, and the Intelligent Driver Model. Both models use the same input variables as the sensors of adaptive cruise control (ACC) systems, and produce a similar driving behavior. Characteristics that are specific to the human nature, like erroneous judgement, reaction time, and multi-anticipation, are discussed in the next chapter. 11.1 Model Criteria The models introduced in this chapter are formally identical to the minimal models presented in the previous chapter. They are defined by an acceleration function amic (see Eq. (10.3)) or a speed function vmic (see Eq. (10.7)). In contrast to the minimal models, the acceleration or speed functions encoding the driving behavior should at least model the following aspects: 1. The acceleration is a strictly decreasing function of the speed. Moreover, the vehicle accelerates towards a desired speed v0 if not constrained by other vehicles or obstacles: ∂amic(s,v,vl) ∂v < 0, lim s→∞ amic(s,v0,vl) = 0 for all vl . (11.1) 2. The acceleration is an increasing function of the distance s to the leading vehicle: ∂amic(s,v,vl) ∂ s ≥ 0, lim s→∞ ∂amic(s,v,vl) ∂ s = 0 for all vl . (11.2) 181 Sample Chapter from "Traffic Flow Dynamics" written by M.Treiber and A.Kesting More information: http://www.traffic-flow-dynamics.org By courtesy of Springer publisher, http://www.springer.com