A Safety Index for Traffic with Linear Spacing

Kometani, Eiji; Sasaki, Tsuna

doi:10.1287/opre.7.6.704

Cited by 81 publications

(23 citation statements)

References 2 publications

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“…(1). Then Kometani and Sasaki (1959) proposed the first model of avoidance collision. This model aims to transcribing the trajectory of a vehicle according to a minimal safe distance.…”

Section: Microscopic Approachmentioning

confidence: 99%

A Review Analysis of Optimal Velocity Models

Lazar

Rhoulami

Rahmani

2016

Period. Polytech. Transp. Eng.

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“…(1). Then Kometani and Sasaki (1959) proposed the first model of avoidance collision. This model aims to transcribing the trajectory of a vehicle according to a minimal safe distance.…”

Section: Microscopic Approachmentioning

confidence: 99%

A Review Analysis of Optimal Velocity Models

Lazar

Rhoulami

Rahmani

2016

Period. Polytech. Transp. Eng.

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“…The CA model, which is known as the safe distance model was introduced in 1959 by Kometani and Sasaki (Kometani and Sasaki, 1959). The equation of this model is as follows:…”

Section: Collision Avoidance Model (Ca)mentioning

confidence: 99%

“…At the first time, this model was proposed by Reuschel (Reuschel, A, 1950) and Pipes (Pipes, Louis A, 1953) and had been extensively refined by Herman et al (Herman and Potts, 1900;Herman et al 1959;Rothery, 1965, Herman andRothery, 1967). Among the most prominent models widely used in many simulation software, several models can be mentioned, including GHR (Gazis, 1959), Collision Avoidance (CA) (Kometani and Sasaki, 1959), Gipps (Gipps, 1981), Helly (Helly, 1961), Fuzzy logic (Chakroborty and Kikuchi, 1999), and neural network (Panwai and Dia, 2007) models. Due to the multi-disciplinary study scope, all of the above mentioned have many parameters to calibrate.…”

Section: Introductionmentioning

confidence: 99%

Moving Objects Trajectoty Prediction Based on Artificial Neural Network Approximator by Considering Instantaneous Reaction Time, Case Study: Car Following

Moghadam

Pahlavani

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Car following models as well-known moving objects trajectory problems have been used for more than half a century in all traffic simulation software for describing driving behaviour in traffic flows. However, previous empirical studies and modeling about car following behavior had some important limitations. One of the main and clear defects of the introduced models was the very large number of parameters that made their calibration very time-consuming and costly. Also, any change in these parameters, even slight ones, severely disrupted the output. In this study, an artificial neural network approximator was used to introduce a trajectory model for vehicle movements. In this regard, the Levenberg-Marquardt back propagation function and the hyperbolic tangent sigmoid function were employed as the training and the transfer functions, respectively. One of the important aspects in identifying driver behavior is the reaction time. This parameter shows the period between the time the driver recognizes a stimulus and the time a suitable response is shown to that stimulus. In this paper, the actual data on car following from the NGSIM project was used to determine the performance of the proposed model. This dataset was used for the purpose of expanding behavioral algorithm in micro simulation. Sixty percent of the data was entered into the designed artificial neural network approximator as the training data, twenty percent as the testing data, and twenty percent as the evaluation data. A statistical and a micro simulation method were employed to show the accuracy of the proposed model. Moreover, the two popular Gipps and Helly models were implemented. Finally, it was shown that the accuracy of the proposed model was much higher -and its computational costs were lower -than those of other models when calibration operations were not performed on these models. Therefore, the proposed model can be used for displaying and predicting trajectories of moving objects being followed.

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“…Traditional car-following type models have been widely used, [14][15][16][17][18][19][20][21][22][23][24] but they had difficulties in reproducing the jamming transition. Recently more sophisticated models appeared from the physics community: the optimal velocity model based on second-order differential equations, 25,26 cellular automaton (CA) models, 3,27-35 and a coupled-map model which we have proposed in previous papers.…”

Section: The Modelmentioning

confidence: 99%

Complexity in a Critical Traffic Flow

Yukawa

Kikuchi

2000

Int. J. Mod. Phys. C

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The traffic flow near the jamming transition is numerically studied by computer simulations of a coupled-map model. Just above the transition, a variety of velocities with which the traffic jams propagate are observed. The entropy of the traffic flow becomes maximum at the same time. Moreover, the current-concentration relation obtained by local measurements shows clear double-valued behavior near the critical concentration. From these properties, the traffic flow can be said as maximally complex near the critical concentration.

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A Safety Index for Traffic with Linear Spacing

Cited by 81 publications

References 2 publications

A Review Analysis of Optimal Velocity Models

A Review Analysis of Optimal Velocity Models

Moving Objects Trajectoty Prediction Based on Artificial Neural Network Approximator by Considering Instantaneous Reaction Time, Case Study: Car Following

Complexity in a Critical Traffic Flow

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