Real life applicative timing algorithm for a smart junction with social priorities and multiple parameters

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

doi:10.1109/icsee.2018.8646018

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…This study elaborates the smart junction with social priority presented in previous research (Barzilay et al, 2018a; Barzilay et al, 2018b;Fine et al, 2019;Barzilay et al, 2020), by adding a fast lane dedicated to high priority vehicles. The traffic allocation is managed by RL algorithm, specifically the Q-Learning algorithm.…”

Section: Discussionmentioning

confidence: 90%

“…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%

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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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Section: Discussionmentioning

confidence: 90%

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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“…Barzilai et al [39] explored public sentiments related to the notion of prioritizing intersections using a rating scale from 1 (indicating strong opposition) to 5 (indicating strong agreement). Furthermore, insights into social preferences regarding various compositions of vehicle passengers were obtained through participants' ratings on a scale ranging from 1 (indicating no preference) to 5 (indicating a very strong preference).…”

Section: Social Preference As a Criterion For Traffic Controlmentioning

confidence: 99%

“…In recent years, there has been a growing public awareness of the need to reduce road congestion through public and cooperative transportation. Barzilai et al [39] suggested that people are affected by this public propaganda when selecting social preferences. The second category and most surprising finding was that the value of good virtues was associated with the highest preferences.…”

Section: Moral Principles Contained the Following Categoriesmentioning

confidence: 99%

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Reinforcement Learning for Traffic Control using social preferences

Barzilai

2024

Recent Topics in Highway Engineering - Up-to-Date Overview of Practical Knowledge [Working Title]

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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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