Simulation Strategies for Mixed Traffic Conditions: A Review of Car-Following Models and Simulation Frameworks

Matcha, Bhargav Naidu; Namasivayam, Satesh; Fouladi, Mohammad Hosseini; Ng, Khai Ching; Sivakumar, S.; Noum, Se Yong Eh

doi:10.1155/2020/8231930

Cited by 38 publications

(11 citation statements)

References 106 publications

(128 reference statements)

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“…e multiple-leader car-following model involves a heterogeneous mixture of vehicle types that lack lane discipline. According to [64][65][66], these traffic conditions lead to a complex driving maneuver that combines vehicle motion in the lateral and longitudinal direction that needed to address multiple-leader following. Papers [66,67] sought to simplify mixed traffic modelling by developing a control technique based on the concept of virtual lane shifts, which entered on identifying significant lateral changes as a signal of a lane-changing situation.…”

Section: 3mentioning

confidence: 99%

Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review

Ozioko

Kunkel

Stahl

2022

Journal of Advanced Transportation

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Autonomous vehicles (AVs) are emerging with enormous potentials to solve many challenging road traffic problems. The AV emergence leads to a paradigm shift in the road traffic system, making the penetration of autonomous vehicles fast and its coexistence with human-driven cars inevitable. The migration from the traditional driving to the intelligent driving system with AV’s gradual deployment needs supporting technology to address mixed traffic systems problems, mixed driving behaviour in a car-following model, variation in-vehicle type control means, the impact of a proportion of AV in traffic mixed traffic, and many more. The migration to fully AV will solve many traffic problems: desire to reclaim travel and commuting time, driving comfort, and accident reduction. Motivated by the above facts, this paper presents an extensive review of road intersection mixed traffic management techniques with a classification matrix of different traffic management strategies and technologies that could effectively describe a mix of human and autonomous vehicles. It explores the existing traffic control strategies and analyses their compatibility in a mixed traffic environment. Then review their drawback and build on it for the proposed robust mix of traffic management schemes. Though many traffic control strategies have been in existence, the analysis presented in this paper gives new insights to the readers on the applications of the cell reservation strategy in a mixed traffic environment. Though many traffic control strategies have been in existence, the Gipp’s car-following model has shown to be very effective for optimal traffic flow performance.

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Section: 3mentioning

confidence: 99%

Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review

Ozioko

Kunkel

Stahl

2022

Journal of Advanced Transportation

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“…From the late 1990's until recently many reports on simulation system comparison have been published, e.g. (Jones et al 2004;Ratrout and Rahman 2009;Kotushevski and Hawick 2009;Matcha et al 2020). Table 7 summarises the most important simulators.…”

Section: Traffic Simulatorsmentioning

confidence: 99%

Solutions to the routing problem: towards trustworthy autonomous vehicles

Varga

2022

Artif Intell Rev

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The general expectation is that the traffic in the cities will be almost optimal when the collective behaviour of autonomous vehicles will determine the traffic. Each member of the collective of autonomous vehicles tries to adapt to the changing environment, therefore together they execute decentralised autonomous adaptation by exploiting real-time information about their environment. The routing of these vehicles needs proper computer science models to be able to develop the best information technology for their control. We review different traffic flow models in computer science, and we evaluate their usefulness and applicability to autonomous vehicles. The classical game theory model implies flow level decision making in route selection. Non-cooperative autonomous vehicles may produce unwanted traffic patterns. Improved decentralised autonomous adaptation techniques try to establish some kind of coordination among autonomous vehicles, mainly through intention awareness. The aggregation of the intentions of autonomous vehicles may help to predict future traffic situations. The novel intention-aware online routing game model points out that intention-awareness helps to avoid that the traffic generated by autonomous vehicles be worse than the traffic indicated by classical traffic flow models. The review helps to make the first steps towards research on global level control of autonomous vehicles by highlighting the strengths and weaknesses of the different formal models. The review also highlights the importance of research on intention-awareness and intention-aware traffic flow prediction methods.

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“…Theorem 2 (Well-posedness for specific input datum). Assume that for the dynamics in Definition 3 with parameters as in Assumption 1, the leading vehicle follows the free flow velocity as defined in (19), and the model parameters and initial datum satisfy (20) and (21), then there exists ε 0 ∈ (0, s 0 ) such that…”

Section: Well-posedness For Specific Initial Datum and All Timesmentioning

confidence: 99%

“…Among all the car-following models introduced so far ( [3,5,6,22,23] to just name a few) it is worth mentioning the Gazis-Herman-Rothery (GHR) model [7] which determines the relative velocity between two-lanes based vehicles, the Safe Distance Model [12], the Optimal Velocity Model [4] in which the acceleration of the single vehicle is controlled according to the velocity of the leading vehicle, and the Intelligent Driver Model (IDM) which is subject of analysis in the present work. For a comprehensive overview of the main car-following models we refer to [28,20,15].…”

Section: Introductionmentioning

confidence: 99%

Limitations and Improvements of the Intelligent Driver Model (IDM)

Albeaik¹,

Bayen²,

Chiri³

et al. 2021

Preprint

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This contribution analyzes the widely used and well-known "intelligent driver model" (briefly IDM), which is a second order car-following model governed by a system of ordinary differential equations. Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of the well-posedness of solutions has, to our knowledge, never been performed. First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to −∞ in finite time, both undesirable properties for a car-following model. Various modifications of the IDM are then proposed in order to avoid such ill-posedness. The theoretical remediation of the model, rather than post facto by ad-hoc modification of code implementations, allows a more sound numerical implementation and preservation of the model features. Indeed, to avoid inconsistencies and ensure dynamics close to the one of the original model, one may need to inspect and clean large input data, which may result practically impossible for large-scale simulations. Although well-posedness issues occur only for specific initial data, this may happen frequently when different traffic scenarios are analyzed, and especially in presence of lane-changing, on ramps and other network components as it is the case for most commonly used micro-simulators. On the other side, it is shown that well-posedness can be guaranteed by straightforward improvements, such as those obtained by slightly changing the acceleration to prevent the velocity from becoming negative.

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Simulation Strategies for Mixed Traffic Conditions: A Review of Car-Following Models and Simulation Frameworks

Cited by 38 publications

References 106 publications

Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review

Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review

Solutions to the routing problem: towards trustworthy autonomous vehicles

Limitations and Improvements of the Intelligent Driver Model (IDM)

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