A cellular automata model for car–truck heterogeneous traffic flow considering the car–truck following combination effect

Yang, Da; Li, Xiaoping; Yu, Dan; Sun, Ruoxiao; Yun, Pu

doi:10.1016/j.physa.2014.12.020

Cited by 50 publications

(17 citation statements)

References 28 publications

(40 reference statements)

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“…At the initial moment, the initial velocity of the vehicle is subject to a uniform distribution between 0 and 4. Considering that the speed of the lane depends on the long car, it is reasonable for the long cars and short cars to appear randomly in the lane according to the mixing ratio of 3:7 . The relevant parameters related to the calculation of Gipps safety distance in the model are listed in Table .…”

Section: Experimental Simulationmentioning

confidence: 99%

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Jiang

Chen

et al. 2019

Concurrency and Computation

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Summary The ant colony labor division model can be used to solve the dynamic and variable traffic signal timing problem because of its adaptive and self‐adjusting characteristics. Based on the basic model, this paper proposes a new extended ant colony labor division model for traffic signal timing. This model is combined with the vehicle characteristics to modify the calculation method of environmental stimulus values. Using the vehicle delay in the unit period, the original fixed response threshold is modified to the dynamic response threshold, and the state transition equation is reconstructed. Based on the mixed traffic flow model of two‐phase single‐point intersection cellular automata, through comparative experiments and discussion and analysis, it is found that the extended ant colony labor division model can effectively improve road traffic capacity according to road conditions and reasonable traffic signal timing.

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Section: Experimental Simulationmentioning

confidence: 99%

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Jiang

Chen

et al. 2019

Concurrency and Computation

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“…Besides, heavy freight vehicles intend to keep a stable speed when following and the reaction time of different drivers' changes less obviously [21]. Yang et al [22] used cellular automation to simulate the car-following behaviors of heavy freight vehicles when following passenger cars, which indicates that car-following behaviors between heavy freight vehicles and passenger cars will exert different impact on traffic capacity if traffic flow state changes. In the congested conditions, highway capacity will be improved efficiently if the random combination of car-following behaviors between heavy vehicles and passenger cars is decreased [23].…”

Section: Introductionmentioning

confidence: 99%

An extended car-following model considering the influence of bus

Shen

Qiu

et al. 2017

Teh. vjesn.

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Original scientific paper In order to describe car-following behaviour of traffic flow which is composed of buses and passenger cars on freeway, an extended car-following model is proposed for single lane traffic in this paper. The proposed model discriminates four types of car-bus following combination, car-following-bus, busfollowing-bus, bus-following-car and car-following-car. The four combinations are considered in terms of following distance, following speed and following acceleration/deceleration. The proposed methodology is demonstrated using data collected from the combination of microwave radar detector and roadside laser detector on Xuanwu Avenue in the main urban area of Nanjing. Besides, the field data is divided into two data sets, one used for the training of the model, and the other for evaluation purpose. Gazis model and Edie model, the two most extensively used car-following models, are calibrated against the same training data sets and used as a reference benchmark. Finally, the performance of the model, proposed by this paper, was compared with the two classic models based on the evaluation data sets. The results show that buses have different characteristics and manoeuvrability compared with passenger cars. With the influence of buses in the car-following process, it could lead to uneven distribution of traffic flow on the lanes and become the main reason for traffic highway capacity decline. The model, proposed in this paper, is more accurate and stable when predicting acceleration/deceleration of different vehicles during car-following. It has better superiority to describe the car-following behaviours under the influence of bus on freeway. Keywords: bus; car-following model; model calibration; stimulus-response process; traffic flow Prošireni model kolone vozila koji uzima u obzir utjecaj autobusaIzvorni znanstveni članak Kako bi se opisalo ponašanje prometa kolone vozila, koja se sastoji od autobusa i osobnih automobila na autocesti, u ovom članku je predložen prošireni model kolone vozila za promet u jednoj traci. Predloženi model razlikuje četiri vrste kombinacija kolona automobil-autobus, automobil-nakon-autobusa, autobus-nakon-autobusa, autobus-nakon-automobila i automobil-nakon-automobila. Četiri kombinacije uzimaju u obzir udaljenost, brzinu i ubrzavanje/usporavanje kolone. Predložena metodologija je prikazana uporabom podataka prikupljenih kombinacijom mikrovalnog radarskog detektora i cestovnog laserskog detektora na aveniji Xuanwu u glavnom urbanom području Nanjinga. Osim toga, podaci o terenu podijeljeni su u dva seta podataka, jedan se koristi za uvježbavanje modela, a drugi je za ocjenu. Gazis model i Edie model, dva najčešće korištena modela kolone vozila, kalibrirani su prema istim skupovima podataka za vježbu, a koriste se kao referentna vrijednost. Konačno, izvedba modela, predložena ovim radom, uspoređena je s dva klasična modela na temelju evaluacijskih skupova podataka. Rezultati pokazuju da autobusi imaju različite karakteristike i manevriranost u usporedbi s osobnim ...

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“…Various dynamical models [1][2][3][4] have been proposed to explain the phenomena generated by traffic flow, such as fluid dynamical models, gas kinetic models and vehiclefollowing models [5][6][7][8]. From the microscopic point of view, vehicles can be represented by particles.…”

Section: Introductionmentioning

confidence: 99%

The effects of overtaking strategy in the Nagel-Schreckenberg model

Deng

Zhao

et al. 2016

Eur. Phys. J. B

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Abstract. Based on the Nagel-Schreckenberg (NS) model with periodic boundary conditions, we proposed the NSOS model by adding the overtaking strategy (OS). In our model, overtaking vehicles are randomly selected with probability q at each time step, and the successful overtaking is determined by their velocities. We observed that (i) traffic jams still occur in the NSOS model; (ii) OS increases the traffic flow in the regime where the densities exceed the maximum flow density. We also studied the phase transition (from free flow phase to jammed phase) of the NSOS model by analyzing the overtaking success rate, order parameter, relaxation time and correlation function, respectively. It was shown that the NSOS model differs from the NS model mainly in the jammed regime, and the influence of OS on the transition density is dominated by the braking probability p.PACS. 89.40.-a Transportation -45.70.Vn Granular models of complex systems; traffic flow

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A cellular automata model for car–truck heterogeneous traffic flow considering the car–truck following combination effect

Cited by 50 publications

References 28 publications

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

An extended car-following model considering the influence of bus

The effects of overtaking strategy in the Nagel-Schreckenberg model

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