Online Route Choice Modeling for Mobility-as-a-Service Networks With Non-Separable, Congestible Link Capacity Effects
Online Route Choice Modeling for Mobility-as-a-Service Networks With Non-Separable, Congestible Link Capacity Effects
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DOI：
10.1109/tits.2021.3105230
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发表时间：
2019-12
影响因子：
8.5
通讯作者：
S. Xu;Joseph Y. J. Chow
中科院分区：
文献类型：
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作者：
S. Xu;Joseph Y. J. Chow
随着MaaS系统的普及，路由选择模型需要考虑其独有的特征。MaaS系统往往涉及具有车队的服务系统;因此，可用的服务能力取决于系统不同部分的其他旅行者的选择。我们的模型，这与一个新的概念“可消化能力”，即链路容量是一个函数的流量，而不是链接成本。这种依赖性也是不可分离的;一个链路中的容量可能取决于来自多个链路的流量。一个离线在线估计方法被引入到捕获的结构性影响，流量上的能力和由此产生的影响，对路线选择的效用。该方法首先应用于获得独特的可消化的能力影子价格在一个多模态网络，以验证能力，以捕捉拥塞对容量的影响。示出的能力，以改变和影响的效用的路线。使用纽约市Citi Bike的真实的系统数据对该方法进行了验证。结果表明，该模型可以很好地拟合数据，并表现出更好的基线建模方法，忽略了可消化的容量的影响。通过使用随机效用模型将路径选择与可消化容量相关联，建模者可以真实的实时监测和量化对旅行者消费者剩余的影响。该模型和在线方法的应用包括监测容量对消费者剩余的影响，使用该模型来指导再平衡和其他收入管理策略的激励计划，并指导资源分配以减轻由于事故中断而造成的消费者剩余影响。
With the prevalence of MaaS systems, route choice models need to consider characteristics unique to them. MaaS systems tend to involve service systems with fleets of vehicles; as a result, the available service capacity depends on the choices of other travelers in different parts of the system. We model this with a new concept of “congestible capacity”; that is, link capacities are a function of flow instead of link costs. This dependency is also non-separable; the capacity in one link can depend on flows from multiple links. An offline-online estimation method is introduced to capture the structural effects that flows have on capacities and the resulting impacts on route choice utilities. The method is first applied to obtain unique congestible capacity shadow prices in a multimodal network to verify the capability to capture congestion effects on capacities. The capacities are shown to vary and impact the utility of a route. The method is validated using real system data from Citi Bike in New York City. The results show that the model can fit to the data quite well and performs better than a baseline modeling approach that ignores congestible capacity effects. By relating the route choice to congestible capacities using a random utility model, modelers can monitor and quantify the impacts to traveler consumer surplus in real time. Applications of the model and online method include monitoring capacity effects on consumer surplus, using the model to direct incentives programs for rebalancing and other revenue management strategies, and to guide resource allocation to mitigate consumer surplus impacts due to disruptions from incidents.