The Effects of Connectivity on Intersection Operations with “White Phase”

Niroumand, Ramin; Hajibabai, Leila; Hajbabaie, Ali

doi:10.1109/itsc48978.2021.9564622

Cited by 9 publications

(9 citation statements)

References 28 publications

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“…The distribution of a central optimization problem into EV user level optimizations may introduce infeasible solutions due to the relaxation of the user connection constraints. Given charging location and duration decision pair ( y z , n z ) is bounded, Proposition 2 shows that the solution obtained by GNE with an embedded consensus-based coordination scheme [e.g., [40], [44], [45], and [46], [47]] converges to the solution of (2a)-(2f) for sufficiently large z.…”

Section: B Monte Carlo Tree Searchmentioning

confidence: 99%

A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities

Hajibabai

Mirheli

2022

IEEE Trans. Intell. Transport. Syst.

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Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging stations near highway ramps, or on parking lots next to office buildings, shops, airports, among other locations. Charging time and duration can be fixed and continuous or flexible and intermittent. EV user preferences of charging services depend on many factors (e.g., charging prices, choice of destinations), causing EV charging patterns to shift in real-time. Hence, there is a need for a highly flexible EV charging network to support the rapid adoption of the technology. This study presents a dynamic scheduling scheme for EV charging facilities considering uncertainties in charging demand, charger availability, and charging rate. The problem is formulated as a dynamic programming model that minimizes the travel and waiting costs and charging expenses while penalizing overcharging attempts. An integrated generalized Nash equilibrium technique is introduced to solve the problem that incorporates a Monte Carlo tree search algorithm to efficiently capture the uncertainties and approximate the value function of the dynamic program. Numerical experiments on hypothetical and real-world networks confirm the solution quality and computational efficiency of the proposed methodology. This study will promote EV adoption and support environmental sustainability by helping users lower the charging spot search burden via a real-time, user-adaptive optimizer. Stakeholders can retrieve charger utilization and pricing data and get feedback on their charging network policies.

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Section: B Monte Carlo Tree Searchmentioning

confidence: 99%

A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities

Hajibabai

Mirheli

2022

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…The distribution of a central optimization problem into EV user level optimizations may introduce infeasible solutions due to the relaxation of the user connection constraints. Given charging location and duration decision pair (y z , n z ) is bounded, Proposition 2 shows that the solution obtained by GNE with an embedded consensus-based coordination scheme [e.g., 39,43,44,45] converges to the solution of (2a)-(2f) for sufficiently large z.…”

Section: B Monte Carlo Tree Searchmentioning

confidence: 99%

A Game-theoretic Approach for Dynamic Service Scheduling at Charging Facilities

Hajibabai¹,

Mirheli²

2022

Preprint

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Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging stations near highway ramps, or on parking lots next to office buildings, shops, airports, among other locations. Charging time and duration can be fixed and continuous or flexible and intermittent. EV user preferences of charging services depend on many factors (e.g., charging prices, choice of destinations), causing EV charging patterns to shift in real-time. Hence, there is a need for a highly flexible EV charging network to support the rapid adoption of the technology. This study presents a dynamic scheduling scheme for EV charging facilities considering uncertainties in charging demand, charger availability, and charging rate. The problem is formulated as a dynamic programming model that minimizes the travel and waiting costs and charging expenses while penalizing overcharging attempts. An integrated generalized Nash equilibrium technique is introduced to solve the problem that incorporates a Monte Carlo tree search algorithm to efficiently capture the uncertainties and approximate the value function of the dynamic program. Numerical experiments on hypothetical and real-world networks confirm the solution quality and computational efficiency of the proposed methodology. This study will promote EV adoption and support environmental sustainability by helping users lower the charging spot search burden via a realtime, user-adaptive optimizer. Stakeholders can retrieve charger utilization and pricing data and get feedback on their charging network policies.

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“…These studies use the traditional green and red signals to control the traffic stream. However, research shows that sufficiently penetrated traffic streams can be controlled by controlling the movements of CAVs (Niroumand et al, 2020a(Niroumand et al, , 2020b(Niroumand et al, , 2021(Niroumand et al, , 2022Niroumand, Hajibabai, & Hajbabaie, 2023;Qian et al, 2014). Hence, Niroumand et al (2020a) have proposed a "white" phase that enforces groups of CHVs to follow CAVs to go through the intersection from opposing lane groups simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Advancing the white phase mobile traffic control paradigm to consider pedestrians

Niroumand,

Hajibabai,

Hajbabaie

2024

Computer aided Civil Eng

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Current literature on joint optimization of intersection signal timing and connected automated vehicle (CAV) trajectory mostly focuses on vehicular movements paying no or little attention to pedestrians. This paper presents a methodology to safely incorporate pedestrians into signalized intersections with CAVs and connected human‐driven vehicles (CHVs). The movements of vehicles are controlled using both traffic lights and mobile CAV controllers during our newly introduced “white phase.” CAVs navigate platoons of CHVs through the intersection when the white phases are active. In addition to optimizing CAV trajectories, the model optimally selects the status of the traffic light signal among white and green indications for vehicular and walk and do‐not‐walk intervals for pedestrian movements. A receding horizon‐based methodology is used to capture the stochastic nature of the problem and to reduce computational complexity. The case study results show the successful operation of fleets consisting of pedestrians, CAVs, and CHVs with various demand levels through isolated intersections. The results also show that increasing the CAV market penetration rate (MPR) can decrease average intersection delay by up to 27%. Moreover, the average pedestrian, CHV, and CAV delays decrease as the CAV MPR increases and reach their minimum values with a fully CAV fleet. In addition, the presence of the white phase can decrease the intersection average delay by up to 14.7%.

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The Effects of Connectivity on Intersection Operations with “White Phase”

Cited by 9 publications

References 28 publications

A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities

A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities

A Game-theoretic Approach for Dynamic Service Scheduling at Charging Facilities

Advancing the white phase mobile traffic control paradigm to consider pedestrians

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