A novel regional traffic control strategy for mixed traffic system with the construction of congestion warning communities

Gu, Xiaoning; Chen, Chao; Feng, Tao; Yao, Baozhen

doi:10.1016/j.physa.2024.129666

Physica A: Statistical Mechanics and its Applications

2024

DOI: 10.1016/j.physa.2024.129666

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A novel regional traffic control strategy for mixed traffic system with the construction of congestion warning communities

Xiaoning Gu,

Chao Chen,

Tao Feng

et al.

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Cited by 3 publications

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An optimal multi-objective dynamic traffic guidance approach based on dynamic traffic assignment

Zhao,

Ma,

Zhao

et al. 2025

Physica A: Statistical Mechanics and its Applications

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An optimal multi-objective dynamic traffic guidance approach based on dynamic traffic assignment

Zhao,

Ma,

Zhao

et al. 2025

Physica A: Statistical Mechanics and its Applications

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Advanced self-sensing road stud: integrating deep learning-based speed detection and energy harvesting

Aslam,

Azam,

Ahmed

et al. 2024

Smart Mater. Struct.

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This study introduces a self-powered and self-sensing vehicle speed detection sensor, representing a significant advancement in transportation. The system employs mechanical components like a slider crank, bevel gears, and one-way bearings for unidirectional rotation, converting translational motion into electrical energy upon the impact of vehicle tyres on road studs. The electrical power generation module, including a DC generator, rectifier, and battery circuit, captures and stores this energy. In addition to energy harvesting, the system integrates a deep learning model using Long Short-Term Memory (LSTM) networks to precisely calculate vehicle speed from the displacement signals of the road studs. Displacement data from an SR-04 sensor is processed and fed into the LSTM network, achieving a classification accuracy of 99.45% for vehicle speeds of 5, 10, 15, and 20 km/h. A mathematical model and MATLAB Simscape simulations were developed, followed by experimental validation using a Mechanical Testing and Sensing System (MTS) under laboratory conditions. Lab-scale testing, a maximum output power of 3.72W and an efficiency of 62.7% were recorded at 8 Hz. Field tests were performed at various vehicle speeds. A peak voltage output of 10V was recorded for a single phase of a three-phase DC generator at 15 km/h. The displacement sensor beneath the road stud was used to record the relative time signal between adjacent peaks to calculate vehicle speed. The sensor is sustainable in energy and easily installable without infrastructure changes, enhances transportation efficiency, and is useful for traffic management, road safety, and smart transportation networks.

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Strategic Traffic Management in Mixed Traffic Road Networks: A Methodological Approach Integrating Game Theory, Bilevel Optimization, and C-ITS

Kotsi,

Politis,

Mitsakis

2024

Future Transportation

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The integration of Connected Vehicles into conventional traffic systems presents significant challenges due to the diverse behaviors and objectives of different drivers. Conventional vehicle drivers typically follow User Equilibrium principles, aiming to minimize their individual travel times without considering the overall network impact. In contrast, Connected Vehicle drivers, guided by real-time information from central authorities or private service providers, can adopt System Optimum strategies or Cournot-Nash oligopoly behaviors, respectively. The coexistence of these distinct player classes in mixed-traffic environments complicates the task of achieving optimal traffic flow and network performance. This paper presents a comprehensive framework for optimizing mixed-traffic road networks through a multiclass traffic assignment model. The framework integrates three distinct types of players: conventional vehicle drivers adhering to User Equilibrium principles, Connected Vehicle drivers following System Optimum principles under a central governing authority, and Connected Vehicle drivers operating under Cournot-Nash oligopoly conditions with access to services from private companies. The methodology includes defining a model to achieve optimal mixed equilibria, designing an algorithm for multiclass traffic assignment, formulating strategic games to analyze player interactions, and establishing key performance indicators to evaluate network efficiency and effectiveness. The framework is applied to a real-world road network, validating its practicality and effectiveness through computational results. The extraction and analysis of computational results are used to propose optimal traffic management policies for mixed-traffic environments. The findings provide significant insights into the dynamics of mixed traffic networks and offer practical recommendations for improving traffic management in increasingly complex urban transportation systems.

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A novel regional traffic control strategy for mixed traffic system with the construction of congestion warning communities

Cited by 3 publications

References 50 publications

An optimal multi-objective dynamic traffic guidance approach based on dynamic traffic assignment

An optimal multi-objective dynamic traffic guidance approach based on dynamic traffic assignment

Advanced self-sensing road stud: integrating deep learning-based speed detection and energy harvesting

Strategic Traffic Management in Mixed Traffic Road Networks: A Methodological Approach Integrating Game Theory, Bilevel Optimization, and C-ITS

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