Routing technologies have long been available in many automobiles and smart phones, but the nearly random nature of traffic on road networks has always encouraged further efforts to improve the reliability of navigation systems. Given the networks' uncertainty, an adaptive dynamic route selection model based on reinforcement learning is proposed. In the proposed method, the Markov decision process (MDP) is used to train simulated agents in a network so that they are able to make independent decisions under random conditions and, accordingly, determine the set of routes with the shortest travel time. The aim of the research was to integrate the MDP with a multi-nomial logit model (a widely used stochastic discrete-choice model) to improve finding the stochastic shortest path by computing the probability of selecting an arc from several interconnected arcs based on observations made at the arc location. The proposed model, tested with real data from part of the road network in Isfahan, Iran, and the results obtained demonstrated its good performance under 100 randomly applied stochastic scenarios.

中文翻译：

---

将马尔可夫决策过程应用于自适应动态路由选择模型

路由技术早已在许多汽车和智能手机中可用，但道路网络上几乎随机的交通性质一直鼓励人们进一步努力提高导航系统的可靠性。鉴于网络的不确定性，提出了一种基于强化学习的自适应动态路由选择模型。在所提出的方法中，马尔可夫决策过程（MDP）用于训练网络中的模拟代理，以便它们能够在随机条件下做出独立决策，从而确定旅行时间最短的路线集。该研究的目的是将 MDP 与多项式 logit 模型（一种广泛使用的随机离散选择模型）相结合，以通过计算从多个互连弧中选择弧的概率来改进随机最短路径的发现在圆弧位置。所提出的模型使用来自伊朗伊斯法罕部分道路网络的真实数据进行了测试，获得的结果证明了其在 100 个随机应用的随机场景下的良好性能。