Linear and nonlinear stability analysis of an extended car-following model considering pedestrians on adjacent lane

Wang, Pengcheng; Yu, Guizhen; Wu, Xinkai; Wang, Yunpeng

doi:10.1007/s11071-016-3275-2

Cited by 8 publications

(4 citation statements)

References 67 publications

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“…[24] described the simultaneous and potentially conflicting propagation of multiple pedestrian group based on the continuum theory of pedestrian and cell transmission model. Wang et al [25] proposed an extended car-following model to describe the dynamic features of mixed traffic with pedestrians walking on adjacent lane, and used the reductive perturbation method to analyze the nonlinear stability of the car flow model. Ma et al [17] studied the stampede at the 2010 German music festival from a perspective of flow pattern, and the result showed that the force generated by the physical contact between pedestrians led to the unsteady crowd movement.…”

Section: Disturbance Propagation In Crowd Flowmentioning

confidence: 99%

See 1 more Smart Citation

Disturbance Propagation Model of Luggage Drifting Motion Based on Nonlinear Pressure in Typical Passenger Corridors of Transportation Hubs

Wei,

Zhao,

et al. 2024

Preprint

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In current transportation hubs, it is a common phenomenon that passengers travel with necessary wheeled-luggage or suitcases. Due to the fact that most luggage occupies certain space within dense passenger crowds with giant mass inertia, its abnormal motions, such as drifting, frequently trig unavoidable local disturbances and turbulence in surrounding pedestrian flows, further increasing security risk. While, current researches primarily concentrate on examining the impact of luggage on crowd evacuation efficiency through scenario-based experiments and analyzing the spatial characteristics of crowd distribution in surveillance videos. There still lacks of the dynamic disturbance mechanisms and crowd stability associated with the combined motion of passengers and luggage. Therefore, this study considers the luggage-laden passenger as a deformable particle, the resulting disturbance on surrounding non-luggage passengers is analyzed and quantified into a nonlinear pressure term. Subsequently, the disturbance propagation model of passenger-luggage is developed by adapting the classical Aw-Rascle traffic flow model with incorporation of the pressure term. Simulation experiments of disturbances caused by luggage drifting and retrograding validate that the proposed propagation model aligns with real-world situations in transportation hubs. Quantitively, when luggage-laden passenger drifted left, the disturbing force of left side crowd can reach a peak of 238N in the passenger density of 3.0 p/m2, and the maximum difference between the left and right sides disturbing force can reach 153N. Furthermore, it is recommended that the proposed model can be applied to crowd flow analysis and risk assessment.

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Section: Disturbance Propagation In Crowd Flowmentioning

confidence: 99%

“…The mass of adult passenger ranges [38,85]kg with a mean of 85kg and a standard deviation of 7.8kg. The mass of luggage ranges [5,25]kg with a mean of 16kg and a standard deviation of 2.3kg. Then, we set the passenger mass and luggage mass to 58kg and 16kg respectively.…”

mentioning

confidence: 99%

Disturbance Propagation Model of Luggage Drifting Motion Based on Nonlinear Pressure in Typical Passenger Corridors of Transportation Hubs

Wei,

Zhao,

et al. 2024

Preprint

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“…Car-following (CF) is an essential element of microscopic traffic flow characteristics in which two vehicles closely follow each other in a single lane [1][2][3][4]. With the development and improvement of CAVs, information can be exchanged between vehicles and vehicles (V2V) and between vehicles and infrastructure (V2I) [5].…”

Section: Introductionmentioning

confidence: 99%

Modeling car-following behavior in heterogeneous traffic mixing human-driven, automated and connected vehicles: considering multitype vehicle interactions

Song

Ding

2023

Nonlinear Dyn

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In this study, a car-following model is put forth to explain the car-following behavior of HDVs (human-driven vehicles), AVs (autonomous vehicles) and CAVs (connected and automated vehicles) in mixed traffic. Based on the IDM (intelligent-driver model) and molecular dynamic theory, the model includes the velocity of surrounding vehicles along with the difference of velocity and the headway between each pair of vehicles. The drivers' sensitivity to the deceleration of the nearest front vehicle was considered in HDV carfollowing modeling and the influences of various kinds of nearest front and rear vehicles were distinguished in the CAV model. Based on the information obtained from the real road test mixed with HDVs, AVs and CAVs, the optimum value of the model parameters was obtained and the accuracy of model was verified with simulation. The results show that the simulation for HDVs', AVs' and CAVs' car-following behavior under the proposed model is more accurate than that for the IDM, ACC (adaptive cruise control) model and CACC (cooperative adaptive cruise control) model. The model can be applied to car-following simulations of HDVs, AVs and CAVs in mixed traffic, in the model the tools and concepts of nonlinear dynamics especially molecular dynamic theory is applied which can benefit road traffic vehicle interaction analysis. In addition, this study provides valuable suggestions and guidance for effectively guiding AVs and CAVs to follow vehicles and improving the stability of CF behavior.

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“…The heterogeneity of modes seems to be increasing. Research has been conducted on mixed traffic conditions, such as bike-pedestrian flow [32,33], vehicle-pedestrian flow [34][35][36], and e-bike-pedestrians flow [5]. Naturally, the comparison between the cyclist/car flow and pedestrian flow is one of the focuses.…”

Section: Introductionmentioning

confidence: 99%

Investigating the influence of a cyclist on crowd behaviors on a shared road

Huang¹,

Wang²,

Yu³

et al. 2021

J. Stat. Mech.

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This study investigated the influence of a cyclist on crowd behaviors on a shared road. A series of mixed traffic experiments of a cyclist and a crowd of pedestrians were conducted in a circular wide track under different densities and moving directions. The influence of the cyclist on the fundamental diagram of pedestrians showed a clear difference under unidirectional and bidirectional scenarios. The cyclist restricted the pedestrian flow evidently in the unidirectional flow, while in bidirectional flow, a critical density around 0.9 ped m−2 was observed, where the restriction on the motion of the crowd was not noticeable unless the critical density was reached. The speed of the cyclist also depended on the relative moving direction, but the discrepancy became negligible when the following effect was eliminated. The lane formation was quantitatively analyzed with Otsu’s method. The results showed that the formed lanes were more concentrated in bidirectional flow than unidirectional flow. When two lanes were formed, the distances between the lanes were larger in unidirectional flow. These differences could be explained by the cognition difference of pedestrians in mixed traffic under unidirectional and bidirectional scenarios. It was observed that a major effect of the cyclist on the crowd behavioral mechanism was the generation of backward propagated density waves. A Y-shaped pattern for the formation of high-density clusters was discovered, which implied that stable high-density clusters were likely to form as the forward propagated and backward propagated density waves merged. Moreover, in bidirectional flow, the generated backward propagated density wave could also promote the fading of stable high-density clusters by reducing the heterogeneity of density distribution. Our study helps to achieve a better understanding and modeling of the dynamics of pedestrian–cyclist in mixed traffic.

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Linear and nonlinear stability analysis of an extended car-following model considering pedestrians on adjacent lane

Cited by 8 publications

References 67 publications

Disturbance Propagation Model of Luggage Drifting Motion Based on Nonlinear Pressure in Typical Passenger Corridors of Transportation Hubs

Disturbance Propagation Model of Luggage Drifting Motion Based on Nonlinear Pressure in Typical Passenger Corridors of Transportation Hubs

Modeling car-following behavior in heterogeneous traffic mixing human-driven, automated and connected vehicles: considering multitype vehicle interactions

Investigating the influence of a cyclist on crowd behaviors on a shared road

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