Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections

Yu, Chunhui; Feng, Yiheng; Liu, Henry; Ma, Wanjing; Yang, Xiaoguang

doi:10.1016/j.trb.2018.04.007

Cited by 296 publications

(125 citation statements)

References 20 publications

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“…Müller, Carlson, and Junior (2016); Yang, Guler, and Menendez (2016); Xu et al (2017) and Yu et al (2018) formulated the problem as a two-stages problem: solve intersection optimisation first, and then optimise the trajectory. Müller, Carlson, and Junior (2016) and Yu et al (2018) assumed that every vehicle can achieve the maximum speed or desire speed when entering the intersection which may not be possible for the vehicle close to the intersection, or in high demand situation. Yang, Guler, and Menendez (2016) assumed a piece-wise linear trajectory in the trajectory optimisation model which may not be realistic, but unconnected vehicles are also considered in their model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yang, Guler, and Menendez (2016) assumed a piece-wise linear trajectory in the trajectory optimisation model which may not be realistic, but unconnected vehicles are also considered in their model. Xu et al (2017) and Yu et al (2018) combined traffic signal optimisation with phases and an optimal control model for vehicle trajectory optimisation. However, they still use the conventional duel ring signal structure for the case of autonomous vehicles and there is no feedback between signal optimisation and vehicle trajectory optimisation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, they still use the conventional duel ring signal structure for the case of autonomous vehicles and there is no feedback between signal optimisation and vehicle trajectory optimisation. Besides, Yu et al (2018) also optimised the lane changing behaviour for autonomous vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where the safety headway h is set to 1.5 s. Various safety headways for autonomous vehicles are used in the literature around 1 ∼ 2 s (Yang, Guler, and Menendez 2016;Ghiasi et al 2017;Jia and Ngoduy 2016b). In many studies, the process time for every vehicle is usually set to be a constant value by assuming that the vehicle passes the intersection with the maximum velocity or desired velocity (Müller, Carlson, and Junior 2016;Yu et al 2018), but this may not be possible if the vehicle is too close to the intersection or the traffic volume is high. So we define the process time as a function of the entering velocity.…”

Section: Upper Level Optimisationmentioning

confidence: 99%

See 3 more Smart Citations

A bilevel programming model for autonomous intersection control and trajectory planning

Zhao

Liu

Ngoduy

2019

Transportmetrica A: Transport Science

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Advances in autonomous and connected vehicles bring new opportunities for intelligent intersection control strategies. In this paper, we propose a centralised way to jointly integrate an intersection control problem with vehicle trajectory planning. It is formulated as a bilevel optimisation problem in which the upper level is designed to minimise the total travel time by a mixed integer linear programming (MILP) model. In contrast, the lower level is a linear programming (LP) model with an objective function to maximise the total speed entering the intersection. The two levels are coupled by the arrival time and terminal speed. By using the relationship between the safe time headway and the process time, a novel platoon based method is developed to reduce the computational burden. Finally, simulation tests are carried out to investigate the control performance under different demands, intersection lengths, communication ranges and traffic compositions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Upper Level Optimisationmentioning

confidence: 99%

See 2 more Smart Citations

A bilevel programming model for autonomous intersection control and trajectory planning

Zhao

Liu

Ngoduy

2019

Transportmetrica A: Transport Science

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“…Gartner and Stamatiadis proposed a variable green wave band method for reasonable signal timing in urban road network. Yu et al proposed a mixed integer linear programming model (MILP) for optimizing vehicle trajectories and traffic signals within a unified framework of isolated signalized intersections in a “Constant” environment. Cui et al and Cai et al proposed a real‐time stochastic optimal control method for traffic signals at asymmetric intersections.…”

Section: Introductionmentioning

confidence: 99%

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Jiang

Chen

et al. 2019

Concurrency and Computation

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Summary The ant colony labor division model can be used to solve the dynamic and variable traffic signal timing problem because of its adaptive and self‐adjusting characteristics. Based on the basic model, this paper proposes a new extended ant colony labor division model for traffic signal timing. This model is combined with the vehicle characteristics to modify the calculation method of environmental stimulus values. Using the vehicle delay in the unit period, the original fixed response threshold is modified to the dynamic response threshold, and the state transition equation is reconstructed. Based on the mixed traffic flow model of two‐phase single‐point intersection cellular automata, through comparative experiments and discussion and analysis, it is found that the extended ant colony labor division model can effectively improve road traffic capacity according to road conditions and reasonable traffic signal timing.

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Efficient Traffic Control System Using Fuzzy Logic with Priority

Ayuba

Zachariah

Damuut

et al. 2021

Communications in Computer and Information Science

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The increase in the number of vehicles on the road is evident by the rate of traffic congestions on daily basis. Problems of traffic congestions are difficult to be measured. Emission of dangerous substances are some of the worrisome effects on weather, theft and delays to motorist are other effects. More and better road network connections have been found to be effective. However, road networks often have intersection(s) which introduces conflicts to right-of-way. These are solved using road traffic light control systems. In this work, an attempt to improve upon an existing programmed stationary road traffic light control system of the Kaduna Refinery Junction (KRJ) is considered. The KRJ is the major road connection to Kaduna main town from the southern part of the state. During working days, motorist from other parts of the country, public and private servants, students, business men, etc. traveling to other parts of the country through the southern part of the state, meet at the KRJ. Trucks conveying petroleum products from the Kaduna Refinery, and vehicles transporting workers, and business men affect the flow of traffic at the junction. An efficient model of fuzzy logic (FL) technique is developed for the optimal scheduling of traffic light control system using TraCI4MATLAB and Simulation of Urban Mobility (SUMO). An average improvement of 2.74% over an earlier result was obtained. Considering priority for emergency vehicles, an improvement of 66.79% over the static phase scheduling was recorded. This shows that FL can be effective on traffic control system.

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Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections

Cited by 296 publications

References 20 publications

A bilevel programming model for autonomous intersection control and trajectory planning

A bilevel programming model for autonomous intersection control and trajectory planning

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Efficient Traffic Control System Using Fuzzy Logic with Priority

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