Speed dependent stochasticity capacitates Newell model for synchronized flow and oscillation growth pattern

TL;DR

This study enhances the Newell car-following model by incorporating speed-dependent stochastic factors, successfully replicating empirical traffic oscillations and synchronized flow patterns observed in real-world data.

Contribution

The paper introduces a stochastic extension to the Newell model with speed-dependent randomness, improving the simulation of traffic oscillations and flow patterns.

Findings

Simulated traffic oscillations match empirical NGSIM data.

Model captures concave growth pattern of traffic oscillations.

Stochasticity significantly influences traffic flow dynamics.

Abstract

This paper has incorporated the stochasticity into the Newell car following model. Three stochastic driving factors have been considered: (i) Driver's acceleration is bounded. (ii) Driver's deceleration includes stochastic component, which is depicted by a deceleration with the randomization probability that is assumed to increase with the speed. (iii) Vehicles in the jam state have a larger randomization probability. Two simulation scenarios are conducted to test the model. In the first scenario, traffic flow on a circular road is investigated. In the second scenario, empirical traffic flow patterns in the NGSIM data induced by a rubberneck bottleneck is studied, and the simulated traffic oscillations and synchronized traffic flow are consistent with the empirical patterns. Moreover, two experiments of model calibration and validation are conducted. The first is to calibrate and validate using experimental data, which illustrates that the concave growth pattern has been quantitatively simulated. The second is to calibrate and cross validate vehicles' trajectories using NGSIM data, which exhibits that the car following behaviors of single vehicles can be well described. Therefore, our study highlights the importance of speed dependent stochasticity in traffic flow modeling, which cannot be ignored as in most car-following studies.