A new cellular automaton model accounting for stochasticity in traffic flow induced by heterogeneity in driving behavior*

Ni, Xiaoyong; Huang, H. Z.

doi:10.1088/1674-1056/ab343b

Cited by 7 publications

(3 citation statements)

References 46 publications

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“…), and an ego vehicle acceler-ating according to the states of surrounding vehicles. Major CF models [9] include the Gipps model, GM model, and intelligent driver model (IDM). [10] The IDM, which may be one of the most famous models, is used in major traffic flow simulation software.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics

Ying

2023

Chinese Phys. B

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The law of vehicle movement has long been studied under the umbrella of microscopic traffic flow models, especially for car-following (CF) models. These models of the movement of vehicles serve as the backbone of traffic flow analysis, simulation, autonomous vehicle development, etc. Two-dimensional vehicular movement is basically stochastic and is the result of interactions between a driver’s behavior and a vehicle’s characteristics. Current microscopic models either neglect two-dimensional noise, or overlook vehicle dynamics. The modeling capabilities, thus, are limited, so that stochastic lateral movement cannot be reproduced. This research extends an intelligent driver model (IDM) by explicitly considering both vehicle dynamics and two-dimensional noises to formulate a stochastic two-dimensional IDM model, with vehicle dynamics based on the stochastic differential equation (SDE) theory. Control inputs from the vehicle include the steer rate and longitudinal acceleration, both of which are developed based on an idea from a traditional intelligent driver model. The stochastic stability condition is analyzed, based on the Lyapunov theory. Numerical analysis is used to assess the cases: 1) when a vehicle accelerates from a standstill and 2) a platoon of vehicles follow a leader with a stop-and-go speed profile, in a situation that mimics the formation of congestion and subsequent dispersion. The results show that the model can reproduce the stochastic two-dimensional trajectories of the vehicle and the marginal distribution of lateral movement. The proposed model can be used in both a simulation platform, or for a behavioral analysis of a human driver in traffic flow.

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Section: Introduction 1backgroundmentioning

confidence: 99%

A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics

Ying

2023

Chinese Phys. B

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“…[19] To enrich the traffic flow theory, some scholars proposed extended traffic flow models. [20] However, most models only consider the driver's perception of the leading vehicle's motion status information and completely ignore the stimulation of rear vehicle's motion. In an actual driving environment, the driver will also observe the movement of rear vehicle through the rearview mirror and use it as a basis for adjusting the speed of own vehicle to prevent the rear vehicle from rear-end collision due to its own emergency braking.…”

Section: Introductionmentioning

confidence: 99%

Modeling and analysis of car-following behavior considering backward-looking effect*

Han

et al. 2021

Chinese Phys. B

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The car-following behavior can be influenced by its driver’s backward-looking effect. Especially in traffic congestion, if vehicles adjust the headway by considering backward-looking effect, the stability of traffic flow can be enhanced. A model of car-following behavior considering backward-looking effect was built using visual information as a stimulus. The critical stability conditions were derived by linear and nonlinear stability analyses. The results of parameter sensitivity analysis indicate that the stability of traffic flow was enhanced by considering the backward-looking effect. The spatiotemporal evolution of traffic flow of different truck ratios and varying degrees of backward-looking effect was determined by numerical simulation. This study lays a foundation for exploring the complex feature of car-following behavior and making the intelligent network vehicles control rules more consistent with human driver habits.

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“…Modeling the mixed traffic flow is a feasible way to solve this problem. Many scholars have carried out in-depth research on the car-following models of vehicular platoons, which are mainly divided into the following categories: stimulus-response models [11], safety distance models [12], psycho-physical models [13], artificial intelligence model [14], optimal velocity model [15], intelligent driver model [16], and cellular automata model [17]. e advantages of these car-following models have been widely recognized in the field of transportation.…”

Section: Introductionmentioning

confidence: 99%

Risk Modeling and Quantification of a Platoon in Mixed Traffic Based on the Mass-Spring-Damper Model

Jiang

Ren

et al. 2020

Journal of Advanced Transportation

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Connected and automated vehicle (CAV) technologies have great potential to improve road safety. However, an emerging type of mixed traffic flow with human-driven vehicles (HDVs) and CAVs has also arisen in recent years. To improve the overall safety of this mixed traffic flow, a novel car-following model is proposed to control the driving behaviors of the above two types of vehicles in a platoon from the perspective of a mechanical system, mass-spring-damper (MSD) system. Furthermore, a quantitative index is proposed by incorporating the psychological field theory into the MSD model. The errors of spacing and speed in the car-following processes can be expressed as the accumulation of the virtual total energy, and the magnitude of the energy is used to reflect the danger level of vehicles in the mixed platoon. At the same time, the optimization model of minimum total energy is solved under the constraints of vehicle dynamics and the mechanical characteristics of the MSD system, and the optimal solutions are used as the parameters of the MSD car-following model. Finally, a mixed platoon composed of 3 CAVs and 2 HDVs without performing lane changing is tested using the driver-in-the-loop test platform. The test results show that, in the mixed platoon, CAVs can optimally adjust the intervehicle spacing by making full use of the braking distance, which also provides sufficient reaction time for the driver of HDV to avoid rear-end collisions. Furthermore, in the early stage of the emergency braking, the spacing error is the dominant factor influencing the car-following behaviors, but in the later stage of emergency braking, the speed error becomes the decisive factor of the car-following behaviors. These results indicate that the proposed car-following model and quantitative index are of great significance for improving the overall safety of the mixed traffic flow with CAVs and HDVs.

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A new cellular automaton model accounting for stochasticity in traffic flow induced by heterogeneity in driving behavior*

Cited by 7 publications

References 46 publications

A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics

A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics

Modeling and analysis of car-following behavior considering backward-looking effect*

Risk Modeling and Quantification of a Platoon in Mixed Traffic Based on the Mass-Spring-Damper Model

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