Acceleration-Deceleration Behaviour of Various Vehicle Types

Bokare, P.S.; Maurya, Akhilesh Kumar

doi:10.1016/j.trpro.2017.05.486

Cited by 196 publications

(109 citation statements)

References 13 publications

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“…The maximum acceleration and deceleration are m a =2 m/s 2 and m d =-4 m/s 2 , respectively (around the typical maximum values [18]). The distance ds [19] is the one in which a vehicle will stop if it applies a constant deceleration (d m ) at the current speed (v); d is the Euclidian distance (from the vehicles centre) between the leader's position (x l , y l ) and the follower's position (x f , y f ) minus the expected distance between the vehicles (d e ); for example, setting d e =10 m, the centre-to-centre distance between two vehicles will be 10 m when d=0 (with a vehicle length of 4 m, there will be a distance of 6 m between the back of the leading vehicle and the front of the following vehicle).…”

Section: Acceleration (Deceleration) Modelmentioning

confidence: 85%

“…(1) the deceleration of the following vehicles (as a reaction if the leader decelerates) is smooth, not abrupt, and (2) the time cycle to refresh the data and maintain the desired inter-vehicle distance is 1 s (this time is a common GPS logging capacity [18]). The procedure in priority, Equation 4 regulates its acceleration (deceleration) behaviour again.…”

Section: Intersection Simulationsmentioning

confidence: 99%

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Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data

Carrillo-González

Lizárraga²,

Barbosa-Santillán³

2018

PROMET

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This paper proposes a mathematical model to regulate the acceleration (deceleration) applied by self-driving vehicles in car-following situations. A virtual environment is designed to test the model in different circumstances: (1) the followers decelerate in time if the leader decelerates, considering a time delay of up to 5 s to refresh data (vehicles position coordinates) required by the model, (2) with the intention of optimizing space, the vehicles are grouped in platoons, where 3 s of time delay (to update data) is supported if the vehicles have a centre-to-centre spacing of 20 m and a time delay of 1 s is supported at a spacing of 6 m (considering a maximum speed of 20 m/s in both cases), and (3) an algorithm is presented to manage the vehicles’ priority at a traffic intersection, where the model regulates the vehicles’ acceleration (deceleration) and a balance in the number of vehicles passing from each side is achieved.

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Section: Acceleration (Deceleration) Modelmentioning

confidence: 85%

Section: Intersection Simulationsmentioning

confidence: 99%

Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data

Carrillo-González

Lizárraga²,

Barbosa-Santillán³

2018

PROMET

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show abstract

“…In our experiment, there are two types of vehicles (truck and car), and each type has different configurations in terms of length, max speed, acceleration, brake deceleration and driver reaction time. All these configurations are set according to the real-world statistics from the literature [59]. Table 2 describes the details of the configurations in simulator:…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach to Coordinating Green Wave System With Adaptation Evolutionary Strategy

Zheng

Guo

et al. 2020

IEEE Access

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“…While one can use this model in the development of the robust extension of the SPRP, as we will show later in the computational results section of the paper, very realistic cycles could be generated from the same model only by simply using empirical 'mean' acceleration and deceleration rates (ᵉ and ᵊ ) in the model instead of ᵉ and ᵊ . In this paper, for these parameters we use the reported results by Bokare and Maurya (2017) from their study on the A/D behaviour of various vehicle types including trucks. Based on their results while ᵉ ≅ 1 m/s 2 , and ᵊ ≅ 0.88 m/s 2 , the mean acceleration rate of a truck is around 0.3 m/s 2 and the mean deceleration rate is around 0.5 m/s 2 .…”

Section: Construction Of Realistic Spatiotemporal Driving Cyclesmentioning

confidence: 99%

The multi-objective Steiner pollution-routing problem on congested urban road networks

Raeesi

Zografos

2019

Transportation Research Part B: Methodological

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This paper introduces the Steiner Pollution-Routing Problem (SPRP) as a realistic variant of the PRP that can take into account the real operating conditions of urban freight distribution. The SPRP is a multi-objective, time and load dependent, fleet size and mix PRP, with time windows, flexible departure times, and multi-trips on congested urban road networks, that aims at minimising three objective functions pertaining to (i) vehicle hiring cost, (ii) total amount of fuel consumed, and (iii) total makespan (duration) of the routes. The paper focuses on a key complication arising from emissions minimisation in a time and load dependent setting, corresponding to the identification of the full set of the eligible road-paths between consecutive truck visits a priori, and to tackle the issue proposes new combinatorial results leading to the development of an exact Path Elimination Procedure (PEP). A PEP-based mixed integer programming model is further developed for the SPRP and embedded within an efficient mathematical programming technique to generate the full set of the non-dominated points on the Pareto frontier of the SPRP. The proposed model considers truck instantaneous Acceleration/Deceleration (A/D) rates in the fuel consumption estimation, and to address the possible lack of such data at the planning stage, a new model for the construction of reliable synthetic spatiotemporal driving cycles from available macroscopic traffic speed data is introduced. Several analyses are conducted to demonstrate the added value of the proposed approach, exhibit the trade-off between the business and environmental objectives on the Pareto front of the SPRP, show the benefits of using multiple trips, and verify the reliability of the proposed model for the generation of driving cycles. A real road network based on the Chicago's arterial streets is also used for further experimentation with the proposed PEP algorithm.

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Acceleration-Deceleration Behaviour of Various Vehicle Types

Cited by 196 publications

References 13 publications

Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data

Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data

A Novel Approach to Coordinating Green Wave System With Adaptation Evolutionary Strategy

The multi-objective Steiner pollution-routing problem on congested urban road networks

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