This paper addresses the challenge of safe path planning for mobile robots operating in human-shared environments, where human movements are inherently stochastic. To this end, we propose a reinforcement-learning-based path planning algorithm that accounts for human-related uncertainties at the planning level. The algorithm first employs a Markov decision process learner to explore the environment and generate multiple candidate paths. Second, to reduce computational redundancy, a path eliminator module filters out similar paths based on a proposed diversity metric, ensuring path diversity with minimal overhead. Simultaneously, a Monte Carlo-simulated human risk predictor is integrated into the decision-making unit to select the safest path among the candidates. This integrated algorithm enables robots to generate safe and efficient trajectories without the need for frequent re-planning, even in environments with stochastic human behavior. Simulation results demonstrate the effectiveness of the proposed method. In high-density settings, a $40\times 40$ grid map with 10 humans, the proposed method reduces the average number of conflicts by −69.8%, −54.8%, and −73.4% compared with A*, MDP, and RRT methods, respectively. Meanwhile, it improves task success rates by 94.4%, 70.7%, and 118.75% relative to the same baseline methods.

Public transit has been continuously developing because of advocacy for low-carbon living, and concerns about its safety have gained prominence. The various factors that constitute the bus operating environment are extremely complex. Although existing research on operational security is crucial, previous studies often fail to fully represent this complexity. In this study, a novel method was proposed to identify the risk factors for bus operations based on a Bayesian network. Our research was based on monitoring data from the public transit system. First, the Tabu Search algorithm was applied to identify the optimal structure of the Bayesian network with the Bayesian Information Criterion. Second, the network parameters were calculated using bus monitoring data based on Bayesian Parameter Estimation. Finally, reasoning was conducted through prediction and diagnosis in the network. Additionally, the most probable explanation of bus operation spatial risk was identified. The results indicated that factors such as speed, traffic volume, isolation measures, intersections, bus stops, and lanes had a significant effect on the spatial risk of bus operation. In conclusion, the study findings can help avert dangers and support decision-making for the operation and management of public transit in metropolitan areas to enhance daily public transit safety.

Mobile robot path planning in static environments has been extensively studied. However, ensuring a safe path in the presence of stochastically moving humans remains a significant challenge. This work focuses on solving the pathfinding problem of a mobile robot operating in human-shared environments with unknown human motions. To prevent conflicts at the planning level, we propose a multi-policy rapidly exploring random tree (MP-RRT)-based safe pathfinding algorithm. A MP-RRT diverse path generator is developed within this framework to produce multiple diverse candidate paths, which are considered as the initial solution set. Additionally, a dynamic quadrant-based stochastic exploration mechanism is introduced for efficient environment exploration. To obtain an optimally safe path, we design a path optimization mechanism based on stochastic risk evaluation, which explicitly models human motion uncertainties. Finally, an optimal safe path is generated by considering human risks at the planning level to ensure the safety for a robot collaborating with humans. We evaluate the proposed algorithm under different configurations ideal warehouse grid environment from conflict numbers, task success rate, and path reward. The proposed method outperforms A*, MDP, and RRT in terms of conflict number (−70.2%, −72.8%, and −73.8%), task success rate (+66.0%, +95.0%, and +85.7%). Simulation results prove the efficiency of our proposals in safe path planning in human-shared environments.

Various approaches have been explored to address the path planning problem for mobile robots. However, it remains a significant challenge, particularly in environments where a multi-tasking mobile robot operates alongside stochastically moving humans. This paper focuses on path planning for a mobile robot executing multiple pickup and delivery tasks in an environment shared with humans. To plan a safe path and achieve high task success rate, a Reinforcement Learning (RL)-based double layer controller is proposed in which a double-layer learning algorithm is developed. The high-level layer integrates a Finite-State Automaton (FSA) with RL to perform global strategy learning and task-level decision-making. The low-level layer handles local path planning by incorporating a Markov Decision Process (MDP) that accounts for environmental uncertainties. We verify the proposed double layer algorithm under different configurations and evaluate its performance based on several metrics, including task success rate, reward, etc. The proposed method outperforms conventional RL in terms of reward (+63.1%) and task success rate (+113.0%). The simulation results demonstrate the effectiveness of the proposed algorithm in solving path planning problem with stochastic human uncertainties.

The growing tension between heritage protection and transportation demands in historic urban districts poses significant planning challenges. This study addresses this issue by developing a multi-modal transportation coordination optimization model from a dual protection–renewal perspective. The model integrates three objectives—minimizing comprehensive impacts, maximizing traffic utility, and minimizing travel costs—encompassing both time and monetary expenditure. A linear weighting method is used to consolidate these into a single objective function, which is then solved with a particle swarm optimization algorithm. Applied to the historic urban districts of Yangzhou, China, the model evaluates and compares three typical policy-oriented transport development strategies: the protection strategy, the balanced strategy, and the renewal strategy. The protection strategy minimizes the comprehensive impact on the historic environment and total social costs, making it suitable for core areas with high conservation pressure. The balanced strategy achieves the optimal trade-off among comprehensive impact, traffic utility, and total cost, serving as a baseline for most historic districts. The renewal strategy, while improving traffic utility, significantly undermines historical integrity and increases total costs, and is therefore applicable only to peripheral areas with low protection pressure. The study concludes that strategy selection is inherently context-dependent, considering local heritage values, spatial constraints, and travel demand patterns. This method provides a transferable decision-support framework for sustainable transportation planning in historic urban districts.