论文标题
与实时信息进行建模路线选择：比较递归和非恢复模型
Modeling Route Choice with Real-Time Information: Comparing the Recursive and Non-Recursive Models
论文作者
论文摘要
我们研究随机时间依赖性（STD）网络中的路由策略选择问题。路由策略定义为在每个链接末尾应用的决策规则，该规则将已实现的流量条件映射到链接上的决策中，以进行下一步。通过完美的在线信息（POI）制定了两种类型的路由策略选择模型：递归logit模型和非收回logit模型。在非恢复模型中，生成了原点 - 死亡（OD）对之间的一组路由策略，并在原点上建模了概率选择，而每个链接处的下一个链接的选择是所选路由策略的确定性执行。在递归模型中，按照动态离散选择模型的框架在每个链接上对下一个链接的概率选择进行建模。从估计和预测中的计算效率方面进一步比较了这两个模型，以及系统效用规范和建模相关性的灵活性。
We study the routing policy choice problems in a stochastic time-dependent (STD) network. A routing policy is defined as a decision rule applied at the end of each link that maps the realized traffic condition to the decision on the link to take next. Two types of routing policy choice models are formulated with perfect online information (POI): recursive logit model and non-recursive logit model. In the non-recursive model, a choice set of routing policies between an origin-destination (OD) pair is generated, and a probabilistic choice is modeled at the origin, while the choice of the next link at each link is a deterministic execution of the chosen routing policy. In the recursive model, the probabilistic choice of the next link is modeled at each link, following the framework of dynamic discrete choice models. The two models are further compared in terms of computational efficiency in estimation and prediction, and flexibility in systematic utility specification and modeling correlation.
