ABSTRACT

This paper presents a novel approach for establishing vehicle usage patterns by Floating Car Data based on their daily mobility making. Vehicle trajectories were firstly sequenced into meaningful trips and then clustered into different trip types. Trips pertaining to each vehicle were aggregated as a vector of counts per type to obtain the mobility profile of the vehicle. Based on these profiles, a topic modeling approach using Latent Dirichlet Allocation was developed to discover the patterns of vehicle daily usage, thereby constituting a typology. An application was conducted for the Paris Region, which identified 3 vehicle usage types associated with local usage within specific areas and the other two holding hybrid patterns between different areas. The prevailing pattern of vehicle usage was found on short-medium trips around pericenter and near suburban areas. Overall, this study offered a data-driven framework to help understand vehicle daily usage patterns and their differentiation

摘要

本文提出了一种新颖的方法，该方法可通过根据汽车日常行驶的浮动数据来建立汽车使用模式。车辆轨迹首先被排序为有意义的行程，然后聚类为不同的行程类型。将与每种车辆有关的行程汇总为每种类型计数的向量，以获得车辆的机动性。基于这些配置文件，开发了使用潜在狄利克雷分配的主题建模方法，以发现车辆日常使用的模式，从而构成类型学。巴黎地区进行了一项申请，确定了3种与特定区域内的本地使用相关的车辆使用类型，另外两种则在不同区域之间保持混合模式。在中枢周围和郊区附近的中短途旅行中发现了普遍的车辆使用模式。总的来说，这项研究提供了一个数据驱动的框架，以帮助了解车辆的日常使用模式及其差异。