Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow

Zheng, Fangfang; Liu, Can; Liu, Xiaobo; Jabari, Saif Eddin; Lu, Liang

doi:10.1016/j.trc.2020.01.017

Cited by 142 publications

(51 citation statements)

References 38 publications

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“…Bahrami and Roorda formulated the multi-class traffic assignment problem as a nonlinear complementarity problem to find optimal traffic management policies [20]. Zheng et al proposed a stochastic model for mixed traffic flow [21]. They used the proposed model to investigate the interaction between CAVs and HDVs.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Huang¹,

Hu²,

Ho³

et al. 2021

IJCIS

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Traffic congestion in urban areas has become a critical problem that municipal governments cannot overlook. Meanwhile, mixed traffic systems containing both autonomous and human-driven electric vehicles ramp up the challenge for traffic management in urban areas. Although numerous researchers have proposed traffic control heuristics to alleviate traffic congestion problems in the recent literature, scant research has addressed the joint problems of route and charging strategies for electric vehicles along with urban traffic congestion prevention. Accordingly, this work tackles the complex task of traffic management in urban areas during peak periods by using practical congestion prevention strategies that consider the characteristics of mixed traffic flows and the charging demands of electric vehicle users. Notably, we apply support vector regressions to compute the charging time at each charging point and the traverse time of an electric vehicle at each road segment/intersection, based on historical traffic data. The simulation results reveal that the proposed algorithms are feasible because they can avoid possible occurrences of traffic congestion during rush hours and provide the routes and charging options that are chosen by electric vehicle users.

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Section: Related Workmentioning

confidence: 99%

“…where Eqs. (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) are iterated until the traffic density is set for the road segment connecting l and m, and approval statuses are marked for the EVs in the prerouting and rerouting reservation queues.…”

Section: Real-time Congestion Control Of the Urban Traffic Control Centermentioning

confidence: 99%

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Huang¹,

Hu²,

Ho³

et al. 2021

IJCIS

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show abstract

“…When exploring AVs, the authors mainly use microsimulation software to simulate their behaviour, and those simulations are based on the assumptions regarding the behaviour of autonomous vehicles. Zheng et al [48] proposed a stochastic model for mixed traffic flow with Human-driven Vehicles (HVs) and Auto-model is challenging due to determining the perceptual thresholds of all the drivers observed in the model. Fuzzy logic-based models allow variations in the driver behaviour in the traffic flow, instead of assigning the same parameter values to all drivers.…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Modelling of Driver and Pedestrian Behaviour – A Historical Review

Babojelić

Novačko

2020

PROMET

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Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.

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“…In the case of a traffic environment which includes only RVs, the stochastic nature of human driving behavior is critical to determining traffic flow efficiency and road operational capacity [13] and traffic stability [19]. The reaction times, desired speed, selected headways, and safe distance in addition to their heterogeneous reactions to traffic disturbances, such as performing lane changes or applying sudden brakes, are examples of such behaviors [13,20].…”

Section: Introductionmentioning

confidence: 99%

“…Some studies showed that AVs have negligible reaction time delays as compared to RVs [25]. Furthermore, different reports showed that the AV implementation in the traffic flow might harmonize and increase the mean speed in the traffic flow [19,25] and decrease the speed variability [23]. When the desired speed is set, the AV will barely deviate from this speed [22].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives

et al. 2021

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Vehicle automation and communication technologies are considered promising approaches to improve operational driving behavior. The expected gradual implementation of autonomous vehicles (AVs) shortly will cause unique impacts on the traffic flow characteristics. This paper focuses on reviewing the expected impacts under a mixed traffic environment of AVs and regular vehicles (RVs) considering different AV characteristics. The paper includes a policy implication discussion for possible actual future practice and research interests. The AV implementation has positive impacts on the traffic flow, such as improved traffic capacity and stability. However, the impact depends on the factors including penetration rate of the AVs, characteristics, and operational settings of the AVs, traffic volume level, and human driving behavior. The critical penetration rate, which has a high potential to improve traffic characteristics, was higher than 40%. AV’s intelligent control of operational driving is a function of its operational settings, mainly car-following modeling. Different adjustments of these settings may improve some traffic flow parameters and may deteriorate others. The position and distribution of AVs and the type of their leading or following vehicles may play a role in maximizing their impacts.

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Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow

Cited by 142 publications

References 38 publications

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Modelling of Driver and Pedestrian Behaviour – A Historical Review

Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives

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