An extended car-following model with consideration of the reliability of inter-vehicle communication

Tang, Tie-Qiao; Shi, Weifang; Shang, Hongyan; Wang, Yunpeng

doi:10.1016/j.measurement.2014.08.051

Cited by 126 publications

(36 citation statements)

References 35 publications

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“…, as the OV function describing the honk effect of the following vehicle, is similar to the backward looking effect of the current vehicle proposed by Nakayama [41] and can increase the velocity of the current vehicle when the headway of the following vehicle becomes small, here x n−1 (t) can be obtained by ITS (e.g., inter-vehicle communication [25,26]). Note that the parameter p represents the weight of the honk effect, i.e., as x n−1 (t) < h c , the possible probability for following vehicle to honk the horn, which also indirectly reflects the relative roles of the two OV functions.…”

Section: Modelmentioning

confidence: 93%

“…Subsequently, many new car-following models were developed to describe the nature of traffic more realistically. Some of them were extended by considering the information of vehicle or road obtained by intelligent transportation system (for short, ITS) (e.g., inter-vehicle communication, multiple headway and relative velocity information [25][26][27][28][29]), and the others were improved by considering driver's behaviors (e.g., anticipation, reactiontime delay and sensory memory effects [30][31][32][33][34]) and attributions (e.g., the driver's bounded rationality, aggressive and conservative characteristics [35][36][37]). …”

Section: Introductionmentioning

confidence: 99%

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An extended car-following model accounting for the honk effect and numerical tests

Kuang

et al. 2016

Nonlinear Dyn

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In this paper, an extended car-following model is proposed to simulate traffic flow by considering the honk effect. The stability condition of this model is obtained by using the linear stability analysis. The phase diagram shows that the honk effect plays an important role in improving the stabilization of traffic system. The mKdV equation near the critical point is derived to describe the evolution properties of traffic density waves by applying the reductive perturbation method. Furthermore, the numerical simulation is carried out to validate the analytical results and indicates that the traffic jam can be suppressed efficiently via taking into account the honk effect.

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Section: Modelmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

An extended car-following model accounting for the honk effect and numerical tests

Kuang

et al. 2016

Nonlinear Dyn

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show abstract

“…Recently driving behaviors have been seen to offer considerable potential method for reducing fuel consumptions and exhaust emissions [46]. The existing studies indicate that driving behaviors can affect drivers' fuel consumptions and exhaust emissions [47][48][49][50][51][52][53][54][55][56]. Wu et al [46] developed and validated a new fuel-economy optimization system to help drivers, especially new drivers learn how to manipulate pedals according to traffic and environmental situations and eventually form an eco-driving style.…”

Section: Simulation For the Traffic Flow Evolution With A Small Distumentioning

confidence: 99%

Dynamics of connected cruise control systems considering velocity changes with memory feedback

Shi

2015

Measurement

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“…The speed is faster of the following vehicle, the more sensitive the acceleration/deceleration is. On this basis, Sheu [7], Yu [8], Liu [9], Tang [10,11], Yu [12], Zhu [13], Zheng [14], Saifuzzaman [15], Davoodi [16] calibrated parameters in the basic stimulation-reaction model from different perspectives to optimize the model. The study of these researchers helps the model greatly reflect the characteristic of car-following in actual traffic stream.…”

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 ...

show abstract

An extended car-following model with consideration of the reliability of inter-vehicle communication

Cited by 126 publications

References 35 publications

An extended car-following model accounting for the honk effect and numerical tests

An extended car-following model accounting for the honk effect and numerical tests

Dynamics of connected cruise control systems considering velocity changes with memory feedback

An extended car-following model considering the influence of bus

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