Sustainable deployment of autonomous vehicles dedicated lanes in urban traffic networks

Pourgholamali, Mohammadhosein; Miralinaghi, Mohammad; Ha, Paul (Young Joun); E． Seilabi, Sania; Labi, Samuel

doi:10.1016/j.scs.2023.104969

Sustainable Cities and Society

2023

DOI: 10.1016/j.scs.2023.104969

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Sustainable deployment of autonomous vehicles dedicated lanes in urban traffic networks

Mohammadhosein Pourgholamali,

Mohammad Miralinaghi,

Paul (Young Joun) Ha

et al.

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Cited by 6 publications

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“…With the development of EV technology, the advent of connected and automated vehicles (CAVs) has introduced the possibility of creating dedicated lanes. These lanes could potentially enhance the efficiency of medical waste transportation and alleviate congestion, leading to further reductions in transport cost and carbon emissions (9)(10)(11). However, the use of EVs is hampered by challenges such as restricted capacity, limited driving range, and insufficient charging infrastructure (8).…”

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Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

Lin,

Musa,

Yap

2023

Transportation Research Record: Journal of the Transportation R

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This paper addresses the complex issue of managing medical waste transportation using electric vehicles, with the goal of minimizing both energy consumption and the risks associated with hazardous waste. A multi-objective mixed-integer linear programming model is introduced, incorporating practical factors such as time windows, partial recharge policy, load-dependent discharge, infection risk, and trips to waste disposal facilities. Our proposed method, a combination of the multi-objective evolutionary algorithm using decomposition (MOEA/D) with adaptive large neighborhood search (ALNS) and local search (LS) techniques, is referred to as MOEA/D-ALNS. This method demonstrates superior performance compared with the non-dominated sorting genetic algorithm, NSGA-II, modified MOEA/D and MOEA/D-LNS in benchmark instances with realistic assumptions. Our experimental results revealed an inverse correlation between energy consumption and risk objectives. Sensitivity analyses showed that eliminating time-window constraints results in more energy-efficient and safer routes while maintaining a slightly lower battery energy level can strike an ideal balance between energy consumption, risk, and battery health. This research contributes to the understanding of infectious medical waste management with its consideration of electric vehicles and waste disposal. It lays a solid foundation for future studies aiming to improve the sustainability and efficiency of medical waste routing practices.

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Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

Lin,

Musa,

Yap

2023

Transportation Research Record: Journal of the Transportation R

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Efficiency and fuel consumption of mixed traffic flow with lane management of CAVs

Wang,

Li,

et al. 2024

Physica A: Statistical Mechanics and its Applications

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Traffic assignment optimization to improve urban air quality with the unified finite-volume physics-informed neural network

Mei,

Mo,

Zhou

et al. 2024

Sustainable Cities and Society

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Sustainable deployment of autonomous vehicles dedicated lanes in urban traffic networks

Cited by 6 publications

References 59 publications

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

Efficiency and fuel consumption of mixed traffic flow with lane management of CAVs

Traffic assignment optimization to improve urban air quality with the unified finite-volume physics-informed neural network

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