论文标题
运输网络中自动驾驶和常规车辆的随机多级交通分配
Stochastic Multi-class Traffic Assignment for Autonomous and Regular Vehicles in a Transportation Network
论文作者
论文摘要
必须进行从普通车辆（RV）到自动驾驶汽车（AV）的过渡期。本文使用路由选择模型探索两种类型的车辆，该模型被公开为随机多级交通分配（SMTA）问题。在RV中，使用了跨嵌段的logit（CNL）模型，因为驱动程序没有完整的信息和CNL的独特特性。但是，由于有关网络的完整信息，AV被认为在用户平衡（UE）中行为。本文的主要创新包括引入SMTA的三种解决方案方法。根据网络的大小，可以使用每种方法。这些方法包括使用GAMS软件，分解 - 分配算法和修改后的Wang的算法解决非线性互补问题（NCP）。通过修改Wang的算法，我们提高了Wang算法的收敛速度，并显示了Nguyen和Sioux Falls网络的数值结果。由于不可能考虑流量分配中的所有路径，我们提出了一种创意的路径分配（PGA）算法。该算法为每个原点目的地（OD）生成了几个有吸引力的路径，而修改后的王算法分配了流量流。关键字：自动驾驶汽车，随机多级交通分配，跨嵌段的logit模型
A transition period from regular vehicles (RVs) to autonomous vehicles (AVs) is imperative. This article explores both types of vehicles using a route choice model, formulated as a stochastic multi-class traffic assignment (SMTA) problem. In RVs, cross-nested logit (CNL) models are used since drivers do not have complete information and the unique characteristics of CNL. AVs, however, are considered to behave in a user equilibrium (UE) due to complete information about the network. The main innovation of this article includes the introduction of three solution methods for SMTA. Depending on the size of the network, each method can be used. These methods include solving the nonlinear complementary problem (NCP) with GAMS software, the decomposition-assignment algorithm, and the modified Wang's algorithm. Through the modification of Wang's algorithm, we have increased the convergence speed of Wang's algorithm and shown its numerical results for the Nguyen and Sioux Falls networks. As it is not possible to consider all paths in the traffic assignment, we proposed a creative path generation-assignment (PGA) algorithm. This algorithm generates several attractive paths for each origin-destination (OD), and the modified Wang's algorithm assigns traffic flow. Keywords: Autonomous Vehicles, Stochastic Multi-class Traffic Assignment, Cross-Nested Logit Model
