Traffic control in a smart intersection by an algorithm with social priorities

Barzilai, Orly; Voloch, Nadav; Hasgall, Alon; Steiner, Orna Lavi; Ahituv, Niv

doi:10.12988/ces.2018.83126

Cited by 13 publications

(8 citation statements)

References 9 publications

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“…Several studies employed a smart social junction (SSJ) algorithm where traffic light timings are adjusted based on both traffic load and societal preferences [19,20,39,53]. To facilitate the implementation of this algorithm, the concept of conflict side (CS) was introduced.…”

Section: Smart Social Junction With Social Preferencesmentioning

confidence: 99%

“…Barzilai et al [20] suggested incorporating an innovative preference system based on the social characteristics of the vehicle driver (e.g., a handicapped person) or on its travel purpose (e.g., A doctor traveling to an emergency surgery at a hospital). The concept involves adjusting traffic signals according to both traffic load and social priority.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning for Traffic Control using social preferences

Barzilai

2024

Civil Engineering

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Traffic congestion arises from all directions, particularly during peak hours, and requires the implementation of a preference mechanism—designated lanes are set up as fast lanes for prioritizing public transportation and ride sharing. Defining a rigid criterion for using the fast lanes can be ineffective if the criterion for using these lanes is unrelated to traffic volume. In situations where fast lanes become overloaded, the rigid criteria do not ensure efficient travel. A social preference criterion, similar to those utilized in priority queues found in various service sectors such as government, travel, and cultural events, could be adapted for use in managing traffic flow and lane prioritization. The social preference criteria will be based on the driver’s characteristics (e.g., a handicraft driver) or not its travel purpose (e.g., a doctor traveling for emergency surgery). To facilitate efficient travel for vehicles utilizing the fast lanes, the implementation of a reinforcement learning (RL) algorithm, specifically the Q-learning algorithm, is proposed. The results indicated that individuals exhibit social preference for various categories of vehicle passenger characteristics. The Q-learning algorithm regulated traffic flow in a junction simulation, distinguishing between fast lanes and regular lanes based on both social preference and traffic volume. This approach ensured efficient prioritization and allocation of resources.

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Section: Smart Social Junction With Social Preferencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Traffic Control using social preferences

Barzilai

2024

Civil Engineering

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“…A system agent at the same time bids benevolently at a level which guarantees a minimum service quality for those not willing to pay. A smart traffic light in [82] decides on the time intervals of red and green based on various factors, including the social characteristics of passengers. They introduce a social preference parameter to make traffic handling more just, and to reduce driver stress (e.g.…”

Section: G Intelligent Intersectionsmentioning

confidence: 99%

Privacy and Trust in the Internet of Vehicles

Zavvos

Gerding

Yazdanpanah

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The Internet of Vehicles aims to fundamentally improve transportation by connecting vehicles, drivers, passengers, and service providers together. Several new services such as parking space identification, platooning and intersection control-to name just a few-are expected to improve traffic congestion, reduce pollution, and improve the efficiency, safety and logistics of transportation. Proposed end-user services, however, make extensive use of private information with little consideration for the impact on users and third parties (those individuals whose information is indirectly involved). This article provides the first comprehensive overview of privacy and trust issues in the Internet of Vehicles at the service level. Various concerns over privacy are formalised into four basic categories: privacy of personal information, trust, consent to provide information, and multi-party privacy. To help analyse services and to facilitate future research, the main relevant end-user services are taxonomised according to voluntary and involuntary information they require and produce. Finally, this work identifies several open research problems and highlights general approaches to address them. These especially relate to measuring the tradeoff between privacy and service functionality, automated consent negotiation, trust towards the IoV and its individual services, and identifying and resolving multi-party privacy conflicts.

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“…Previous studies (Barzilay et al, 2018a;Barzilay et al, 2018b;Fine et al, 2019;Barzilay et al, 2020) have expanded the concept of a smart junction by introducing a social preference and a benefit factor for junction traffic management, combined with traffic load. Social preference is based on the social characteristics of the vehicle passengers (e.g.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane

Barzilai

Rika

Voloch

et al. 2023

Transport and Telecommunication Journal

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Traffic lights monitoring that considers only traffic volumes is not necessarily the optimal way to time the green/red allocation in a junction. A “smart” allocation should also consider the necessities of the vehicle’s passengers and the needs of the people those passengers ought to serve. This paper deals with a “smart” junction, where several cars approach the intersection from different directions and a traffic light is set to comply to a sequence of time intervals of red and green lights in each direction. The novel approach presented here is based not only on traffic congestion parameters, but also on the social and economic characteristics of the passengers (e.g. a handicapped person, a medical doctor, an employee who is extremely required in a certain organization due to an emergency situation). This paper proposes to enhance the smart junction with a fast lane, which has a flexible entry permit based on social and economic criteria. Machine learning (specifically, Reinforcement Learning (RL)) is added to the junction’s algorithm with the aim of optimizing the social utility of the junction. For the purposes of this study, the utility of the junction is defined by the total social and economic potential benefits given a certain red/green time allocation is set. This is defined as the measure of the reward function which contains positive factors for vehicles which crossed the junction or advanced their position and a negative factor for vehicles which remains in their positions. In addition, a weight value for the vehicles with high priority is also part of the equation. A simplified version of the smart junction has been used, serving as a model for incorporating RL into the “smart’ junction with Fast Lane (FL). Specifically, the Q-Learning algorithm is used to maximize the reward function. Simulation results show that prioritizing high priority vehicles via FL is influenced by the weights and factors given to the reward components. Farther research should enhance the “Smart” junction with FL to a more complex and realistic one using a varying amount of vehicles crossing the junction.

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Traffic control in a smart intersection by an algorithm with social priorities

Cited by 13 publications

References 9 publications

Reinforcement Learning for Traffic Control using social preferences

Reinforcement Learning for Traffic Control using social preferences

Privacy and Trust in the Internet of Vehicles

Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane

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