Risk-Aware Travel Path Planning Algorithm Based on Reinforcement Learning during COVID-19

Wang, Zhijian; Yang, Jianpeng; Zhang, Qiang; Wang, Li

doi:10.3390/su142013364

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…The unique features of the proposed architecture compared to the existing simulation models are presented in Table 1. Most models in the literature focus on analyzing specific aspects of a pandemic, such as disease propagation [32], intervention effectiveness [5], and transportation [13,33,34]. However, the PDSIM takes a multi-dimensional approach, accounting for numerous key variables associated with pan-demic spread.…”

Section: A Brief Review On Epidemic Modelsmentioning

confidence: 99%

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Pandemic Simulator: An Agent-Based Framework with Human Behavior Modeling for Pandemic-Impact Assessment to Build Sustainable Communities

et al. 2023

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It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent-based simulator will enable policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM), which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of the PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of COVID-19 are used to validate the proposed model. However, they are highly extendable to all pandemics in general, enabling robust planning for more sustainable communities.

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Section: A Brief Review On Epidemic Modelsmentioning

confidence: 99%

“…Covasim [14] OpenABM-Covid19 [41] A particle-based COVID-19 simulator [42] Simulator of interventions for COVID-19 [43] People meet people [15] COVID-town [44] Agent-based modeling of COVID-19 [45] Social bubble vanpooling (SBV) [33] A road network impedance matrix based on SUMO simulation [34] PDSIM(Ours)…”

Section: Compartment Modelsmentioning

confidence: 99%

Pandemic Simulator: An Agent-Based Framework with Human Behavior Modeling for Pandemic-Impact Assessment to Build Sustainable Communities

et al. 2023

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“…The path planning navigation based on avoiding virus infection is mainly studied for people's safe travel. Wang et al [31] designed a search mechanism to avoid areas related to the risk of new epidemics, and proposed a restricted reinforcement learning-artificial potential field (RRL-APF) algorithm to solve the problem of residents' travel path planning under new epidemics.…”

Section: Related Workmentioning

confidence: 99%

Path Planning of Ant Colony Algorithm Based on Decision Tree in the Context of COVID-19

Shao,

Deng,

Feng

2023

Wireless Communications and Mobile Computing

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Reasonable planning of travel routes can keep people away from crowded areas and reduce the probability of contracting the COVID-19. In view of the characteristics related to virus infection and human flow density, it can overcome the shortcomings of using the same pheromone initial value and slow initial convergence in route planning of ant colony optimization (ACO) algorithm. In this paper, the decision tree algorithm is used to divide the human flow density into three levels: high risk, medium risk, and low risk; and different pheromone volatility coefficients are set to change the distribution of pheromone concentration. The experimental results show that the improved ACO algorithm could help to reduce the likehood of passing through the medium-risk areas and the high-risk areas, which is reduced to less than 1%. This scheme provides an efficient route planning method for epidemic prevention and control that can be applied in the daily prevention of COVID-19 in universities.

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“…In addition to improving the potential field function, the APF algorithm can also be integrated with other algorithms. This includes the Rapid Exploration Random Tree (RRT) algorithm [11], A* algorithm [12], complex resistivity method [13], ant colony algorithm [14], and cyclic reinforcement learning algorithm [15]. The fusion algorithm can enable autonomous vehicles to avoid obstacles more safely and achieve the purpose of complementary advantages.…”

Section: Introductionmentioning

confidence: 99%

The Dynamic Path Planning of Autonomous Vehicles on Icy and Snowy Roads Based on an Improved Artificial Potential Field

Zhai,

Pei

2023

Sustainability

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The crucial dynamic path planning of autonomous vehicles is achieved via obstacle avoidance path planning technology. The reduction of the tire adhesion coefficient on icy and snowy roads (ISRs) increases the difficulty of autonomous vehicles’ control. In this paper, the driving characteristics of vehicles on ISRs are established, and the artificial potential field function is introduced to avoid collision risk when planning a path. A dynamic path planning algorithm for autonomous vehicles based on the artificial potential field (APF) is established. The adjustment factor is added to the gravitational potential field, and a judgment coefficient is added to the repulsive potential field to improve the artificial potential field function, based on the low adhesion of vehicles on ISRs. Moreover, a path with a continuous curvature is generated to achieve the driving comfort and driving safety of the planned path via trajectory smoothing. By establishing the Carsim/Simulink co-simulation platform, the effectiveness of dynamic path planning for autonomous vehicles under different algorithms and different obstacle models is compared. The results show that the improved APF algorithm has an obvious effect on the smoothness of the path and the reduction of the curvature mutation and can generate a safe and efficient path on icy and snowy roads. The dynamic obstacle avoidance of the improved APF algorithm improves the pre-judgment accuracy of the collision risk assessment of autonomous vehicles and shows the superiority of the improved algorithm.

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Risk-Aware Travel Path Planning Algorithm Based on Reinforcement Learning during COVID-19

Cited by 6 publications

References 21 publications

Pandemic Simulator: An Agent-Based Framework with Human Behavior Modeling for Pandemic-Impact Assessment to Build Sustainable Communities

Pandemic Simulator: An Agent-Based Framework with Human Behavior Modeling for Pandemic-Impact Assessment to Build Sustainable Communities

Path Planning of Ant Colony Algorithm Based on Decision Tree in the Context of COVID-19

The Dynamic Path Planning of Autonomous Vehicles on Icy and Snowy Roads Based on an Improved Artificial Potential Field

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