A discounted recursive logit model for dynamic gridlock network analysis
A discounted recursive logit model for dynamic gridlock network analysis
复制标题
动态拥堵网络分析的折扣递归logit模型
DOI：
10.1016/j.trc.2017.10.001
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Hato Eiji
中科院分区：
文献类型：
--
作者：
Oyama Yuki;Hato Eiji
新兴的传感技术，如配备全球定位系统（GPS）设备的探测车辆，为我们提供了实时的车辆轨迹。它们有助于理解那些重要但由于不常见而难以观察的情况，例如交通堵塞网络。在这类新兴技术的前提下，本文提出了一种顺序路径选择模型，该模型既描述了驾驶员通过自身经验获取网络空间知识的普通网络中的路径选择行为，也描述了驾驶员很少经历且适用于实时交通仿真的特殊网络中的路径选择行为。在特殊的网络中，司机没有任何经验或适当的信息。在这种情况下，驾驶员对网络的空间知识很少，选择路线是基于动态决策的，这种决策是顺序的，具有一定的前瞻性。为了对这些决策动态进行建模，我们提出了一个折现递归logit模型，这是一个具有预期未来效用折现因子的顺序路径选择模型。通过举例说明，贴现因子反映了驾驶员的决策动态，而短视决策会混淆网络拥塞水平。我们还使用2011年3月4日和2011年3月11日东京都地区发生东日本大地震时收集的探测出租车轨迹数据来估计所提出模型的参数。结果表明，在交通阻塞网络中，折现因子的值比普通网络中要低。
Emerging sensing technologies such as probe vehicles equipped with Global Positioning System (GPS) devices on board provide us real-time vehicle trajectories. They are helpful for the understanding of the cases that are significant but difficult to observe because of the infrequency, such as gridlock networks. On the premise of this type of emerging technology, this paper propose a sequential route choice model that describes route choice behavior, both in ordinary networks, where drivers acquire spatial knowledge of networks through their experiences, and in extraordinary networks, which are situations that drivers rarely experience, and applicable to real-time traffic simulations. In extraordinary networks, drivers do not have any experience or appropriate information. In such a context, drivers have little spatial knowledge of networks and choose routes based on dynamic decision making, which is sequential and somewhat forward-looking. In order to model these decision-making dynamics, we propose a discounted recursive logit model, which is a sequential route choice model with the discount factor of expected future utility. Through illustrative examples, we show that the discount factor reflects drivers’ decision-making dynamics, and myopic decisions can confound the network congestion level. We also estimate the parameters of the proposed model using a probe taxis’ trajectory data collected on March 4, 2011 and on March 11, 2011, when the Great East Japan Earthquake occurred in the Tokyo Metropolitan area. The results show that the discount factor has a lower value in gridlock networks than in ordinary networks.