A microscopic traffic flow model for sharing information from a vehicle to vehicle by considering system time delay effect

Hossain, Md Anowar; Tanimoto, Jun

doi:10.1016/j.physa.2021.126437

Cited by 39 publications

(15 citation statements)

References 30 publications

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“…where 𝑣 𝑚𝑎𝑥 and ℎ 𝑐 are maximal velocity and safety distance, respectively. The OV model can simulate various intricate traffic flow phenomena, including phase transitions and stop-and-go waves, but it will represent unrealistic acceleration and deceleration [33][34][35]. Helbing and Tilch [36] proposed a traffic flow model known as the G.F. model to get around this restriction.…”

Section: Background Of Car-following Modelsmentioning

confidence: 99%

Linear stability analysis of an improved car-following model considering vehicle's inertia effect

Si¹,

Hossain²,

Tanimoto³

2022

Proceedings of International Exchange and Innovation Conference on Engineering &Amp; Sciences (IEICES)

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Car-following model and its variants are proposed to reproduce real traffic flow phenomena, but they ignore that different vehicles have different inertiasm, which is out of touch with reality. In this paper, a heterogeneous vehicular inertia (HVI) model is proposed by considering the vehicle's inertia effect based on the full velocity difference (FVD) model on a single-lane road. The linear stability theory is used to determine the stability condition for this model with time delay. The results demonstrate that the higher inertia effect and lower time delay effect would strengthen the stability of the proposed model.

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Section: Background Of Car-following Modelsmentioning

confidence: 99%

Linear stability analysis of an improved car-following model considering vehicle's inertia effect

Si¹,

Hossain²,

Tanimoto³

2022

Proceedings of International Exchange and Innovation Conference on Engineering &Amp; Sciences (IEICES)

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show abstract

“…Due to development of intelligent transportation technology and vehicle to vehicle (V2V) environment technology, the vehicles are equipped with intelligent devices while moving [35,36] and drivers can obtain information regarding surrounding vehicles more accurately and widely (see figure 1). This has lead to the development of various car-following models based on information interaction between vehicles to obtain information regarding multi-vehicle average velocity, average anticipative velocity, average headway, etc [37,38]. Sometimes the driver of target vehicle may change his or her mind suddenly due to unpredictable situation is created by immediate preceding driver leading to difficulty for following vehicle in handling this critical moment within short notice.…”

Section: Introductionmentioning

confidence: 99%

A new car following model based on weighted average velocity field

Singh,

Kumar

2024

Phys. Scr.

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In real traffic, the motion status of forward moving vehicles play animportant role in influencing the following vehicle’s motion behaviour. Nowadays,autonomous and electric vehicles have been incorporated with car-following modewhen the control process is more complicated and more meaningful. As aresult, researchers are studying car-following control strategies for vehicles toattain stability in traffic. Based on this, we propose a new car following modelconsidering weighted average velocity field to describe the average effect of multivehicleinteraction on the whole road. The weighted average velocity field canbe regarded as the weighted average of velocities of finite number of vehiclespreceding the objective vehicle with more weightage to more preceding vehicle.The stability condition is derived through stability analysis. Subsequently, themodified Korteweg-de Vries (mKdV) equation is formulated to unveil trafficcongestion in the form of the kink-antikink density wave. Theoretical findingsindicate that the weighted average velocity field yields larger stability regionscompared to the average velocity field. Numerical simulations were conducted,and the results obtained demonstrate that the weighted average effect of multivehicleinteractions play a crucial role in suppressing traffic jams faster than asimple average.

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“…The RFM and its generalizations have been developed to understand the complex dynamics of a variety of transport phenomena on a single lane, including translation [35,[39][40][41][42][43][44], transcription [45], motor protein traffic [46,47], phosphorelay [48] and more. There are lattice hydrodynamic models that use ordinary differential equations to model the flow of vehicles along the lanes [49][50][51]. Therefore, the framework of RFM that also describes the flow of particles can serve as the basis for understanding the dynamics of vehicular traffic.…”

Section: Introductionmentioning

confidence: 99%

A mathematical framework for analysing particle flow in a network with multiple pools

Jain,

Gupta

2024

R. Soc. Open Sci.

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In many real-world systems, the entry rate of particles into a lane is affected by the occupancy of nearby pools. For instance, in biological networks, the concentration of molecules on the side of a membrane affects the entry of particles through the membrane. To understand the behaviour of such networks, we develop a network model of ribosome flow models (RFMs) having multiple pools where each RFM captures the dynamics of particle flow in a lane and competes for the finite resources present at the nearby pool. We study a ribosome flow model network with two pools (RFMNTP) and show that the network always admits a steady state. We then analyse the behaviour of the RFMNTP with respect to modifying the transition rate through a theoretical framework. Simulations of the RFMNTP demonstrate a counterintuitive result. For example, increasing any of the transition rates in the presence of a slow site in an RFM can increase the output rate of some RFMs and decrease the output rate of the other RFMs simultaneously. This suggests that the role of local sharing of particles incorporated is non-trivial. Finally, we illustrate how these results can provide insights into studying a network with multiple pools.

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A microscopic traffic flow model for sharing information from a vehicle to vehicle by considering system time delay effect

Cited by 39 publications

References 30 publications

Linear stability analysis of an improved car-following model considering vehicle's inertia effect

Linear stability analysis of an improved car-following model considering vehicle's inertia effect

A new car following model based on weighted average velocity field

A mathematical framework for analysing particle flow in a network with multiple pools

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