Estimating O-D travel time matrix by Google Maps API: implementation, advantages, and implications

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Many spatial analysis tasks call for the use of travel time between multiple origins and destinations, that is, O–D travel time matrix. Commercial geographical information systems (GIS) software requires the input of a well-defined road network dataset and significant efforts in implementing the task. However, road network data are often outdated, miss critical road condition details, or are expensive to acquire; and skillful usage of related software is a major obstacle for researchers without advanced training in GIS. This research develops a desktop tool for implementing the task by calling the Google Maps Application Programming Interface (API). By doing so, we are able to tap into the dynamically updated transportation network data and the routing rules maintained by Google and obtain a reliable estimate of O–D travel time matrix. The results are compared with those computed by the ArcGIS Network Analyst module to demonstrate its advantages. A case study in accessibility analysis is presented to illustrate the implications. 1. Introduction Estimation of travel time between a set of origins and a set of destinations (i.e., O–D travel time matrix) through a transportation network is a common task in spatial analysis. To list a few, spatial interaction modeling uses travel time between any pair of interacting places (Fotheringham and O'Kelly 1989); traffic demand forecasting relies on an accurate estimation of travel time among locations in various land uses (Black 2003); trade area analysis needs the travel time between each store and each residential area to define a store's customer base (Huff 2003); and accessibility measurement requires the travel time data between supply and demand locations (Luo and Wang 2003). In the absence of data of a transportation network or the computational power of geographical information systems (GIS), one has …