Quantitative explanation of circuit experiments and real traffic using the optimal velocity model

Nakayama, Akihiro; Kikuchi, Macoto; Shibata, Akihiro; Sugiyama, Yūki; Tadaki, Shin–ichi; Yukawa, Satoshi

doi:10.1088/1367-2630/18/4/043040

Cited by 21 publications

(13 citation statements)

References 23 publications

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“…Without loss of generality, only the first three terms of Taylor expansion expression are retained. Setting Y i � V n (Δx(t)), we obtain the approximate expression of equation (6) in the following:…”

Section: Benchmark Model Calibrationmentioning

confidence: 99%

“…ese models could largely be grouped into two classi cations with modeling concepts stemming from engineering and driver behavior perspectives [3,4]. e optimal velocity (OV) model that was initiated by Bando et al [5] is a notable example of the engineering-based car-following model [6]. It assumes that each vehicle has an optimal car-following speed (CFS) dependent on spacing between the lead and lag vehicles.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them

Jiao

Zhang

et al. 2020

Journal of Advanced Transportation

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This paper introduces an improved car-following speed (CFS) model that simultaneously considers speed of the lead vehicle, vehicle spacing, and driver’s sensitivity to them. Specifically, the proposed model extends the Helbing-Tilch model and Yang et al. model developed based on the principle of grey relational analysis where vehicle spacing is considered as the primary factor contributing to car-following speed choices. A computational experiment is conducted for model calibration using vehicle spacing, speed, and acceleration data derived from vehicle trajectory data of the Next Generation Simulation (NGSIM) project sponsored by the Federal Highway Administration (FHWA). It shows that speed of the lead vehicle and vehicle spacing significantly affect speed of the lag vehicle. Further, model validation is carried out using an independent NGSIM dataset by comparing vehicle speed predictions made by the calibrated CFS model with Helbing-Tilch model and Yang et al. model as benchmarks. Compared with speed prediction results of the benchmark models, mean relative errors, root mean square errors, and equal coefficient of speed predictions of the CFS model have reduced by 72.41% and 61.85%, 70.14% and 57.99%, and 33.15% and 14.48%, respectively. The findings of model validation reveal that the CFS model could improve the accuracy of speed predictions in the car-following process.

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Section: Benchmark Model Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them

Jiao

Zhang

et al. 2020

Journal of Advanced Transportation

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show abstract

“…In fact, the optimal velocity (OV) can represent the motion patterns of dissipative systems, where each particle aims to preserve an adequate velocity depending on its distance to neighbouring particles. For instance, the one-dimensional OV model, where particles reproduce a moving cluster, can be used to identify a traffic congestion 19 and several related properties 20 . Likewise, the 2d-OV model can describe a variety of macroscopic patterns formed by moving particles, which can emulate the collective motion of living organisms 14 , 16 .…”

Section: Introductionmentioning

confidence: 99%

Analysis of dynamically stable patterns in a maze-like corridor using the Wasserstein metric

Ishiwata

Kinukawa

Sugiyama

2018

Sci Rep

Self Cite

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The two-dimensional optimal velocity (2d-OV) model represents a dissipative system with asymmetric interactions, thus being suitable to reproduce behaviours such as pedestrian dynamics and the collective motion of living organisms. In this study, we found that particles in the 2d-OV model form optimal patterns in a maze-like corridor. Then, we estimated the stability of such patterns using the Wasserstein metric. Furthermore, we mapped these patterns into the Wasserstein metric space and represented them as points in a plane. As a result, we discovered that the stability of the dynamical patterns is strongly affected by the model sensitivity, which controls the motion of each particle. In addition, we verified the existence of two stable macroscopic patterns which were cohesive, stable, and appeared regularly over the time evolution of the model.

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“…Perhaps the most impactful traffic model is the optimal velocity model (OVM) pioneered by Bando et al, where the acceleration and deceleration forces of each individual car are a function of the spacing between cars, the speed limit of the road, and the sensitivity of the drivers [16,17,[20][21][22][23][24]. The OVM has been correlated with experiments of single-lane traffic on circuits [25,26] or freeways [27][28][29][30][31], but nearly always in the context of beginning with flow in the liquid phase and identifying critical conditions for jamming to occur.…”

Section: Introductionmentioning

confidence: 99%

Latent heat of traffic moving from rest

et al. 2017

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Contrary to traditional thinking and driver intuition, here we show that there is no benefit to ground vehicles increasing their packing density at stoppages. By systematically controlling the packing density of vehicles queued at a traffic light on a Smart Road, drone footage revealed that the benefit of an initial increase in displacement for close-packed vehicles is completely offset by the lag time inherent to changing back into a 'liquid phase' when flow resumes. This lag is analogous to the thermodynamic concept of the latent heat of fusion, as the 'temperature' (kinetic energy) of the vehicles cannot increase until the traffic 'melts' into the liquid phase. These findings suggest that in situations where gridlock is not an issue, drivers should not decrease their spacing during stoppages in order to lessen the likelihood of collisions with no loss in flow efficiency. In contrast, motion capture experiments of a line of people walking from rest showed higher flow efficiency with increased packing densities, indicating that the importance of latent heat becomes trivial for slower moving systems.

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Quantitative explanation of circuit experiments and real traffic using the optimal velocity model

Cited by 21 publications

References 23 publications

An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them

An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them

Analysis of dynamically stable patterns in a maze-like corridor using the Wasserstein metric

Latent heat of traffic moving from rest

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