Field measurement analysis to validate lane-changing behavior in a cellular automaton model

Fukuda, Eriko; Tanimoto, Jun; Iwamura, Yoshiro; Nakamura, Kosuke; Akimoto, Mitsuhiro

doi:10.1103/physreve.94.052209

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…The lth layer is recorded as L l , and the input layer is L 1 and the output layer is L n l . There is (W, b) = (W (1) , b (1) , W (2) , b (2) ) as parameters in the above neural network, W (l) i,j is the connection parameter between unit j in layer l and unit i in layer…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…TSV data can be used to extract massive amounts of meaningful traffic information including traffic density, average velocity of vehicles, traffic flux, lane-changing rate and other various statistical properties for traffic flow. In fact, there are some pioneer studies [2,3] that obtain those properties from traffic videos, which can be applied to validate mathematical models of traffic flow.…”

Section: Introductionmentioning

confidence: 99%

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Grassmann Manifold Based State Analysis Method of Traffic Surveillance Video

et al. 2019

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Featured Application: Based on Grassmann Manifold, a method for identifying congestion state of Traffic Surveillance Video has been proposed in this paper. Meanwhile, the effectiveness of the proposed method is verified via UCSD dataset and the Capital Airport Expressway dataset.Abstract: For a contemporary intelligent transport system, congestion state analysis of traffic surveillance video (TSV) is one of the most crucial and intricate research topics because of the rapid development of transportation systems, the sustained growth of surveillance facilities on road, which lead to massive traffic flow data, and the inherent characteristics of our analysis target. Traditional methods on feature extractions are usually operated on Euclidean space in general, which are not accurate for high-dimensional TSV data analysis. This paper proposes a Grassmann manifold based neural network model to analysis TSV data , by mapping the video data from high dimensional Euclidean space to Grassmann manifold space, and considering the inner relation among adjacent cameras. The accuracy of the traffic congestion is improved, compared with several traditional methods. Experimental results are conducted to validate the accuracy of our method and to investigate the effects of different factors on performance.

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Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grassmann Manifold Based State Analysis Method of Traffic Surveillance Video

et al. 2019

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“…STCA model can well describe lane-changing process with simple evolution rules. Using cellular automata model, the asymmetric lanechanging behavior between the fast lane and the slow lane and effects of lane-changing rules on multilane highway traffic were discussed, respectively [8,9].…”

Section: Lane-changing Behaviormentioning

confidence: 99%

Research on Decision-Making Behavior of Discretionary Lane-Changing Based on Cumulative Prospect Theory

Long

Zhang

Liu

et al. 2020

Journal of Advanced Transportation

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In this paper, the decision-making model of discretionary lane-changing is established using cumulative prospect theory (CPT). Through analyzing the vehicles’ dynamic running states, safety spacing calculating approaches for discretionary lane-changing and lane-keeping have been put forward firstly. Then, based on CPT, a lane-changing decision model with accelerating space as its utility is proposed by estimating the difference between actual spacings and the safety spacings for discretionary lane-changing as well as lane-keeping. In order to calculate the utility of discretionary lane-changing, dynamic reference points and a parameter representing driver’s risk preference are introduced into the model. With the real data collected from an urban expressway, the distribution of discretionary lane-changing duration is analyzed, and the model parameters are also calibrated. Furthermore, the applicability of the model is evaluated by comparing with the actual observation and random unity model. Finally, the sensitivity analysis of the model is carried out, that is, assessing the influence degree of each variable on the decision result. The study reveals that the CPT-based model can describe discretionary lane-changing behavior more accurately, which consider drivers’ risk-aversion during decision-making.

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“…The car-following model assumes a lead vehicle followed by a subsequent one within a designated single lane, maintaining a minimum space and time gap between them [ [14] , [15] , [16] , [17] , [18] , [19] , [20] ]. To better mimic actual traffic flow, several improved car-following model variants have been proposed, including gas kinetic, hydrodynamic lattice, optimum velocity (OV), generalized force (GF), and full velocity difference (FVD) models [ [21] , [22] , [23] , [24] , [25] , [26] , [27] ].…”

Section: Introductionmentioning

confidence: 99%