Attribute selection for modelling driver's car-following behaviour in heterogeneous congested traffic conditions

Aghabayk, Kayvan; Sarvi, Majid; Young, William

doi:10.1080/23249935.2013.787558

Cited by 28 publications

(13 citation statements)

References 11 publications

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“…Studies have shown that although the number of trucks on freeway is relatively smaller than the number of cars, it has a significant influence on traffic flow characteristics [1][2][3][4][5][6][7][8]. Regarding the car-truck traffic flow, early researchers mainly focused on investigating the difference between following a car and following a truck.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

et al. 2015

Physica A: Statistical Mechanics and its Applications

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Section: Introductionmentioning

confidence: 99%

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

Yang

et al. 2015

Physica A: Statistical Mechanics and its Applications

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“…Two more recent studies used the real world trajectory data sets to analyse the CF behaviours of drivers in more details amongst four possible car‐truck following combinations. The CF combinations that were studied included heavy vehicle following passenger car (H‐C), passenger car following heavy vehicle (C‐H), passenger car following passenger car (C‐C) and heavy vehicle following another heavy vehicle (H‐H). …”

Section: Introductionmentioning

confidence: 99%

“…Two more recent studies [18,19] used the real world trajectory data sets to analyse the CF behaviours of drivers in more details amongst four possible car-truck following combinations. The CF combinations that were studied included • heavy vehicle following passenger car (H-C),…”

Section: Introductionmentioning

confidence: 99%

Including heavy vehicles in a car‐following model: modelling, calibrating and validating

Aghabayk

Sarvi

Young

2016

J of Advced Transportation

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Summary Heavy vehicles influence general traffic in many different ways compared with passenger vehicles, and this may result in different levels of traffic instability. Increases in the number and proportion of heavy vehicles in the traffic stream will therefore result in different traffic flow conditions. This research initially outlines the different car‐following behaviour of drivers in congested heterogeneous traffic conditions indicating the necessity for developing a car‐following model, which includes these differences. A psychophysical car‐following model, similar in form to Weideman's car‐following model, was developed. Due to the complexity of the developed model, the calibration of the model was undertaken using a particle swarm optimisation algorithm with the data recorded under congested traffic conditions. This was then incorporated into a traffic microsimulation model. The results showed that the car‐following perceptual thresholds and thus action points of drivers differ based on their vehicle and the lead vehicle types. The inclusion of the heavy vehicles in the model showed significant impacts on the traffic dynamic and interactions amongst different vehicles. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Compared to passenger cars, the effects of heavy vehicles on traffic flow are more adverse with respect to safety and delay because of their larger size and low maneuverability. Their interaction with smaller vehicles and the resulting headways are also different from the mutual interaction of smaller vehicles (Aghabayk et al, 2014). …”

Section: Literature Reviewmentioning

confidence: 99%

Decreasing of Traffic Delay With Intellegent Transportation System ITS Relationship Between Speed Mangement and Traffic Delay Along Arterials

Ghssan¹

2017

Seventh International Conference on Advances in Civil and Structural Engineering - CSE 2017

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This study investigated the effect of speed management and ramp metering on traffic delay along arterials in Istanbul-Turkey. Urban arterial congestion is a recurring problem that affects urban traffic flow. Data were collected along 5·7 km long westbound approach arterial from the Anatolian side of Istanbul to the bridge initiated a study to investigate methods to mitigate congestion through traffic management and improved traffic flow. A micro simulation model based on traffic data using VISSIM software package suggested the applicability of traffic speed management along the existing infrastructure to relieve congestion and provide flow stability. Simulation was conducted along five hour which is the period of recorded data from 6:30 to 11:30 Am. The results indicated that the speed management could decreased traffic delay by an approximate 8% of delay from the existing value of 242 s/vehicle at 36 km/h to 202 s/vehicle at 65 km/h speed limits. Also, there were about of 17% increase in the total number of served vehicles from 23 844 vehicles/5 h to 28 244 vehicle/5 h. On the other hand, the optimum cycle time for ramp metering signal was 18 second. Applying of ramp metering strategy for 18 second cycle time led to 13,3% reduction in delay and 6.3 % increasing in flow speed. Finally, if ramp metering and speed management are applied together, the average delay is reduced to 188 seconds / vehicle from 242 s/vehicle ( reduction in average delay by 25% ). Also, average speed was increased by a 13% increase (from 36 to 41 km / h and).

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Attribute selection for modelling driver's car-following behaviour in heterogeneous congested traffic conditions

Cited by 28 publications

References 11 publications

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

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

Including heavy vehicles in a car‐following model: modelling, calibrating and validating

Decreasing of Traffic Delay With Intellegent Transportation System ITS Relationship Between Speed Mangement and Traffic Delay Along Arterials

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