A traffic congestion analysis by user equilibrium and system optimum with incomplete information

Zhang, Qiang; Liu, Shi Qiang; Masoud, Mahmoud

doi:10.1007/s10878-020-00663-4

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…Wie et al [17] established a dynamic trafc allocation model with scheduling delay under the principles of system optimization and vehicle equilibrium and compared the total travel time and planned delay under diferent trafc congestion levels. Zhang et al [18] studied a trafc congestion analysis model based on game theory by using the concepts of user equilibrium with incomplete information (UEII) and system optimization with incomplete information (SOII). Lujak et al [19] proposed an optimization model to bridge the gap between user-optimal and systemoptimal, and a new mathematical planning formulation based on Nash welfare optimization to achieve good averages for all origin-destination (OD) pairs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Repeat (7) Divide the solutions x i into q subsolutions; (8) For each q (9) while No change in route detected do (10) Execute AMSBC subalgorithm; (11) end while (12) Detecting change strength CS; (13) ifCS < CT then (14) q � q + 1; (15) q � q − 1; (16) Iteration � Iterantion + 1; (17) UntilIteration � Max It; (18) Output optimal route of subroute populations; (19) Compare the optimal route fit i of the subroutes; (20) Output equilibrium routes of vehicles with optimal fit i ; ALGORITHM 2: AMSBC algorithm.…”

Section: Cs � Fit Bestmentioning

confidence: 99%

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Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

Yan,

Tang,

Peng

et al. 2023

Journal of Advanced Transportation

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Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Cs � Fit Bestmentioning

confidence: 99%

Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

Yan,

Tang,

Peng

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Regarding future research directions, we will extend this work to consider the iron ore supply chain, especially using the public data of the Australian mining industry [ 57 ]. Besides, we will implement operations research (i.e., Mixed Integer Programming), machine learning and game theory techniques as analytical tools to deal with quantitative energy security management problems [ 58 – 61 ].…”

Section: Conclusion and Suggestionmentioning

confidence: 99%

How is China’s energy security affected by exogenous shocks? Evidence of China–US trade dispute and COVID-19 pandemic

Liu

Huang

et al. 2021

Discov Energy

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The trade dispute between China and the United States (US) since 2018 and the global COVID-19 pandemic since 2020 has significantly impacted China’s economic development. As China’s energy sources heavily depend on imports, its economic viability is becoming more and more risky. This study proposes a novel conceptual framework, involving macroeconomic, industrial and geopolitical factors, to evaluate China’s energy security as a major player in the trade dispute. This study also provides a comprehensive strategy for policymakers to make better decisions on reforming renewable energy patterns to guarantee energy security and achieve geopolitical advantages. The PESTEL (political, economic, social, technical, environmental and legislative) and SWOT (strengths, weaknesses, opportunities and threats) analytical methods are applied to evaluate the factors and attributes of China’s energy development and energy security in the current background. The China-US bipartite game reciprocity model and the QSPM (Quantitative Strategic Planning Matrix) analysis are conducted to assess which energy security strategy and policy are more suitable to deal with China-US trade dispute. To enhance energy security, China should diversify its energy supply chain, develop new sources of energy supply, advance the shale gas technology, popularise cleaner power-generation plants, increase nuclear-energy safety, introduce energy-conservation measures, promote alternative-energy vehicles, engage in international energy diplomacy, and rebuild international energy transaction and settlement systems.

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Deep reinforcement learning for the dynamic and uncertain vehicle routing problem

Pan

Liu

2022

Appl Intell

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A traffic congestion analysis by user equilibrium and system optimum with incomplete information

Cited by 9 publications

References 29 publications

Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

How is China’s energy security affected by exogenous shocks? Evidence of China–US trade dispute and COVID-19 pandemic

Deep reinforcement learning for the dynamic and uncertain vehicle routing problem

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