Some features of car‐following behaviour in the vicinity of signalised intersection and how to model them

Zhang, Jian; Tang, Tie-Qiao; Wang, Tao

doi:10.1049/iet-its.2018.5510

Cited by 23 publications

(7 citation statements)

References 39 publications

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“…Two established engineering models were used to evaluate the performance of the developed methodology against existing models. For this purpose, the parameters of IDM as well as an extension of the full velocity difference (FVD) model ( 30 ), which is explicitly designed for approaches to traffic signals, were calibrated with a genetic algorithm on each training dataset for every cluster. We adapted both models for traffic lights by inserting a virtual leading vehicle at the stop line when the light turned yellow.…”

Section: Methodsmentioning

confidence: 99%

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Incorporation of Human Factors to a Data-Driven Car-Following Model

Harth

Amjad

Kates

et al. 2022

Transportation Research Record: Journal of the Transportation R

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In recent years, there have been intensive efforts to consider human factors (HFs) in the modeling of human driver behavior. In particular, “engineering” car-following models widely used in traffic simulation have been extended to include HFs. This extension is needed to generate critical situations, which are often attributable to human error. However, incorporation of reaction processes requires advanced models that take driver predictions and delayed responses into account. In this paper, a methodology for integrating HFs into driver behavior modeling is developed based on a long short-term memory architecture. The proposed methodology employed a three-layer psychological concept: perception, information processing, and action. The perception layer modeled (imperfect) estimation of visually received stimuli. Information processing included short-term memory and the projection of perceived stimuli into the near future. The executed action, based on the sensed as well as anticipated dynamic driving state, was delayed by the perception–reaction time. To represent individual differences among driver types, the available training dataset was classified in four clusters according to observable driver characteristics. The methodology was demonstrated with data recorded at an urban signalized intersection. Model performance was compared with that of two established engineering models, the intelligent driver model and the (extended) full velocity difference model. The results indicated that the human driver model developed here showed superior performance in replicating real-world trajectories compared with existing models and was able to represent the varying driving strategies of different groups.

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Section: Methodsmentioning

confidence: 99%

“…The extended FVD model of Zhang et al considered the phase of the preceding traffic light in the model equations ( 30 ). The parameter theta represents the influence of the traffic light,…”

Section: Methodsmentioning

confidence: 99%

Incorporation of Human Factors to a Data-Driven Car-Following Model

Harth

Amjad

Kates

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…In another study [31], the complementary usage of traffic counter and probe car data is taken into account in estimating the path travel time. Besides, for instance in the car following models, trajectory dataset is considered, e.g., when investigating the impact of signal status on headway in [32], and generating Markov chain and training neural network in [33]. Regarding the purpose of this study, only vehicle speed data measured at a fixed point on the road is needed.…”

Section: Data Processing and Analysesmentioning

confidence: 99%

Examining vehicular speed characteristics through divergences from prior distributions

Koşun

2021

SN Appl. Sci.

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A variety of approaches, within literature, has been conducted to interpret vehicular speed characteristics. This study turns the attention to the entropy-based approaches, and thus focuses on the maximum entropy method of statistical mechanics and the Kullback–Leibler (KL) divergence approach to examining the vehicular speeds. The vehicle speeds at the selected highway are analyzed in order to find out the disparities among them. However, it is turned out that the speed dynamics could not be distinguished over the speed distributions; hence the maximization of Shannon entropy seems insufficient to compare the speed distributions of each data set. For this reason, the KL divergence approach was performed. This approach displays the comparison, among the speed distributions, based on two prior distribution models, i.e., uniform and Gauss. The examination of the trends of KL divergences obtained from both distributions was made. It was concluded that the KL divergence values for the highway speed data sets ranged between about 0.53 and 0.70 for the uniform case, while for the Gaussian case the obtained values are between 0.16 and 0.33. The KL divergence trends for the real speeds were obtained analogous for both cases, but they differed significantly when the synthetic data sets were employed. As a result, the KL divergence approach proves suitable as an appropriate indicator to compare the speed distributions.

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“…Some other studies focus on modelling the twodimensional movement at intersections [34]. Previous microscopic traffic flow models emphasized in numerous influencing factors [35][36][37][38]. From these studies, the microscopic traffic flow models have been used to simulate single and multiple vehicle-driver units and describe traffic flow in signalized intersections.…”

Section: Introductionmentioning

confidence: 99%

Modelling the operation of vehicles at signalised intersections with special width approach lane based on field data

Zhao

Kigen

Wang

2020

IET intell. transp. syst

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The intersection with special width approach lane (SWAL) is a newly proposed unconventional intersection design. A microscopic traffic flow model was proposed for describing the operation of vehicles at signalized intersections with SWAL. The operation process of driving on the SWAL was divided into four segments, including entering segment, transition segment, special width lane segment, and exiting segment. The car-following and lane selection behaviours of vehicles in these segments are analysed. The parameters used in the model were calibrated using the field data collected in Germany. The proposed model was realized in a time-discretized simulation. The sensitivity analysis of geometric, traffic, and signal factors were conducted. The results show that for the car-following behaviour, the passenger cars on the narrowed lanes cannot drive as efficient as on the normal width lane. For lane selection behaviour, it mainly depends on the distance between the two nearest vehicles in front on the two narrow lanes. The effectiveness of the SWAL depends on whether it is long enough to accommodate the queuing vehicles, which is a combined result of the layout design and the signal timing.

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Some features of car‐following behaviour in the vicinity of signalised intersection and how to model them

Cited by 23 publications

References 39 publications

Incorporation of Human Factors to a Data-Driven Car-Following Model

Incorporation of Human Factors to a Data-Driven Car-Following Model

Examining vehicular speed characteristics through divergences from prior distributions

Modelling the operation of vehicles at signalised intersections with special width approach lane based on field data

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