Traffic signal timing optimization for isolated urban intersections considering environmental problems and non-motorized vehicles by using constrained optimization solutions

Tran, Quang Hoc; Manh, Van; Dinh, Tuan Hai

doi:10.1007/s41062-022-00895-9

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Although there are many city government efforts to determine solutions to limit congestion, few synchronous methods meet the current reality. The optimal solution is to optimize the current traffic signal timings of traffic networks and provide a predicted road map for commuters [6][7][8]. Therefore, Traffic Speed Forecasting (TSF) plays a vital role in improving efficiency and reducing traffic congestion, and there is more and more quality scientific research on this issue.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Do,

Tran,

et al. 2024

Sustainability

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Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts.

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Section: Introductionmentioning

confidence: 99%

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Do,

Tran,

et al. 2024

Sustainability

Self Cite

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“…However, a singleobjective traffic signal optimization approach may have negative impacts on other objectives. Therefore, a suggested formulation model should emphasize the application of multi-objective optimization in the field of traffic control and traffic management [5,6].…”

Section: Introductionmentioning

confidence: 99%

Enhanced-GA supports traffic signal optimizations and protects the urban environment

Do Van,

Tran Quang,

Le Khanh

et al. 2024

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Much current research on traffic signal optimization often neglects the impact of environmental factors in urban areas. This can result in suboptimal solutions that do not consider the effects of traffic on air pollution and the overall urban environment. To address this issue, this article proposes a solution that combines Enhanced GA with a comprehensive framework for considering environmental factors in traffic signal optimization. By optimizing traffic signal timings and minimizing emissions, the proposed solution aims to reduce congestion and improve urban transportation networks' efficiency while protecting the environment. The proposed approach uses a set of optimization algorithms and assumptions to generate a comprehensive framework for traffic signal optimization. These algorithms and assumptions consider environmental factors such as air quality and the impact of traffic on the local ecosystem. Moreover, this article provided the enhanced genetic algorithm operators and suggested model formulation that could be applied in other research on traffic signal optimization directly to reduce calculation times and increase the efficiency of the novel suggested models

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“…Many scholars have studied the allocation of spatial and temporal resources at single-point intersections in terms of efciency [3][4][5][6], safety [7,8], and environmental friendliness [3,[9][10][11]. Te benefts (e.g., confict severity, delays, emissions, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Coordinated Allocation Method for Right-Turn Strategy at Signalized Intersections with Optimal Pedestrian and Vehicle Delays

Bai,

Luo

2023

Journal of Advanced Transportation

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With strict enforcement of pedestrian right of way at all intersections, the inappropriate right-turn resource allocation from a spatial and temporal perspective will lead to a reduction in the operational efficiency of the intersection. In this paper, three spatiotemporal resource allocation schemes for right-turning vehicles are proposed, considering the vehicle and pedestrian traffic efficiency in all directions of the intersection. To minimize vehicle and pedestrian delay at the intersection individually, an optimization model is established with the effective green time of each phase and three schemes as decision variables. A right-turn vehicle and pedestrian conflict delay model is developed based on the pedestrian-vehicle interaction behavior as the constraints of the optimization model. The NSGA-II algorithm is used to solve the model, and the quantitative criteria for the exclusive right-turn lane and phase are obtained by sensitivity analysis. The results of this paper can be used as a guide for traffic design and for planning and controlling the operation of right-turning vehicles at intersections.

show abstract

Traffic signal timing optimization for isolated urban intersections considering environmental problems and non-motorized vehicles by using constrained optimization solutions

Cited by 4 publications

References 27 publications

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Enhanced-GA supports traffic signal optimizations and protects the urban environment

A Coordinated Allocation Method for Right-Turn Strategy at Signalized Intersections with Optimal Pedestrian and Vehicle Delays

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