Authors: Ran Tao*, University of South Florida, Jean-Claude Thill, University of North Carolina at Charlotte
Topics: Spatial Analysis & Modeling, Transportation Geography, Geographic Information Science and Systems
Keywords: bivariate, flow, exploratory, spatial statistics
Session Type: Paper
Presentation File: No File Uploaded
Coordinating multimodal and intermodal transport is a major goal in achieving smarter and greener transportation. With the increasingly available large-volume and fine-spatiotemporal resolution transport origin-destination (OD) flow data, such as taxi pick-up and drop-off data; bus smart card on-board and off-board data; passenger flight inbound and outbound data, it becomes critical to develop methods that can effectively evaluate the spatial patterns and relationships of more than one mode of transportation. However, most existing methods are limited to univariate flow data (Berglund and Karlström, 1999; Liu et al. 2015). It is the main purpose of this study to develop a new method to measure the spatial association between two types of flow data, in other words, how the value of type I flows associate with the value of nearby type II flows. In particular, we extend the bivariate LISA (Anselin et al. 2002) to bivariate flow LISA by solving several key issues: measuring spatial weights of flow data with a move-based flow distance; treating the commonly spared OD matrix; designing conditional permutation to guarantee the correct interpretation of the result statistics. We conduct experiments with intracity travel flows in different modes: bus, taxi, and ride-hailing service. We prove the usefulness of bivariate flow LISA in urban transportation, especially how one transport mode competes and/or coexists in the same space with another.