Quantifying benefits of a dynamic gap-out feature at an actuated traffic signalized intersection under cooperative vehicle infrastructure system

Agbolosu-Amison, Seli; Yun, Ilsoo; Park, Byungkyu Brian

doi:10.1007/s12205-012-1495-7

Cited by 17 publications

(10 citation statements)

References 4 publications

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“…In [4], a dynamic gap-out algorithm has been presented. Total vehicular delays are minimized and the optimization determines "phase sequence, phase green times, and gap-out times (both dynamic and regular gap-outs)."…”

Section: B State Of the Artmentioning

confidence: 99%

A vehicle-to-infrastructure communication based algorithm for urban traffic control

Phu¹,

Farhi²,

Haj-Salem

et al. 2017

2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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Abstract-We present in this paper a new algorithm for urban traffic light control with mixed traffic (communicating and non communicating vehicles) and mixed infrastructure (equipped and unequipped junctions). We call equipped junction here a junction with a traffic light signal (TLS) controlled by a road side unit (RSU). On such a junction, the RSU manifests its connectedness to equipped vehicles by broadcasting its communication address and geographical coordinates. The RSU builds a map of connected vehicles approaching and leaving the junction. The algorithm allows the RSU to select a traffic phase, based on the built map. The selected traffic phase is applied by the TLS; and both equipped and unequipped vehicles must respect it. The traffic management is in feedback on the traffic demand of communicating vehicles. We simulated the vehicular traffic as well as the communications. The two simulations are combined in a closed loop with visualization and monitoring interfaces. Several indicators on vehicular traffic (mean travel time, ended vehicles) and IEEE 802.11p communication performances (endto-end delay, throughput) are derived and illustrated in three dimension maps. We then extended the traffic control to a urban road network where we also varied the number of equipped junctions. Other indicators are shown for road traffic performances in the road network case, where high gains are experienced in the simulation results.

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Section: B State Of the Artmentioning

confidence: 99%

A vehicle-to-infrastructure communication based algorithm for urban traffic control

Phu¹,

Farhi²,

Haj-Salem

et al. 2017

2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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“…As in Agbolosu‐Amison et al. (), we assume cars to respect a safe traveling distance of about 2 seconds. A slot length of 2 seconds is also in line with Sun et al.…”

Section: A Discrete Time Markov Modelmentioning

confidence: 99%

“…For an overview see Papageorgiou et al (2003) and Zhao et al (2012), and references there in. Recently, Agbolosu-Amison et al (2012) studied the effect of a dynamic gap-out feature to anticipate a gap-out, rather than to wait and see. They show that the capacity of intersections is better used and delays are reduced.…”

Section: Dynamic or Traffic Responsive Controlmentioning

confidence: 99%

Traffic Responsive Control of Intersections with Predicted Arrival Times: A Markovian Approach

Haijema

Hendrix

2013

Computer aided Civil Eng

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The dynamic adaptive control of traffic lights can be formulated as a Markov decision problem (MDP). This framework is hardly used, as solving an MDP can be very time‐consuming and is only possible for simple infrastructures with a small number of traffic flows. Nevertheless, we show that the MDP framework can be used to construct control policies that approximately minimize the long‐run average waiting time at intersections. The MDP‐based approach is fast and thus allows real‐time use of actual information on traffic conditions, like queue lengths and the arrival times of near‐future arrivals. Simulation of an isolated intersection as well as a small network shows that the new policies with arrival information improve pretimed as well as exhaustive control. The new control policies and underlying algorithms scale up well to control networks.

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“…Recent development in Vehicle Infrastructure Integration (VII) [9][10][11] technology based on wireless communication between vehicle and infrastructure offers a new way for traffic detection. This new type of traffic detection is a floating environment, which uses two wireless communication channels to collect continuously (actually in small time interval) both the traffic data and the positioning data between floating vehicles and fixed-location communication center.…”

Section: Introductionmentioning

confidence: 99%

Development of Dynamic Platoon Dispersion Models for Predictive Traffic Signal Control

Shen

Liu

Yao

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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As the development of traffic detection technology, recent research is directed to a new generation of signal control system supported by new traffic data. One of these directions is dynamic predictive control by incorporating short-term prediction capability. This paper focuses on investigating of dynamic platoon dispersion models which could capture the variability of traffic flow in a cross-sectional traffic detection environment. The dynamic models are applied to predict the evolution of traffic flow, and further used to produce signal timing plans that account not only for the current state of the system but also for the expected short-term changes in traffic flows. We investigate factors affecting model accuracy including time-zone length, position of upstream traffic detection equipment, road section length, traffic volume, turning percentages, and computation time. The impact of these factors on the model performance is illustrated through a simulation analysis, and the computation performance of models is discussed. The results show that both the dynamic speed-truncated normal distribution model (DNDM) and dynamic Robertson model (DRM) with dynamics outperform their respective static versions, and that they can be further applied for dynamic control.

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Quantifying benefits of a dynamic gap-out feature at an actuated traffic signalized intersection under cooperative vehicle infrastructure system

Cited by 17 publications

References 4 publications

A vehicle-to-infrastructure communication based algorithm for urban traffic control

A vehicle-to-infrastructure communication based algorithm for urban traffic control

Traffic Responsive Control of Intersections with Predicted Arrival Times: A Markovian Approach

Development of Dynamic Platoon Dispersion Models for Predictive Traffic Signal Control

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