A bimodal accessibility analysis of Australia through application programming interfaces

Abstract

In this paper we discuss some of the major strengths and limitations of an application programming interfaces (API)-based approach to studying accessibility in transport networks. To this end, we present a framework for assessing the bimodal accessibility of locations by using publicly available webbased resources that can be accessed via APIs. By way of empirical example, we query the Google Maps Distance Matrix and Google QPX Express APIs to collect travel time for both land-and airside travel, and process the extracted data using Python. We then apply our analytical strategy to the case of Australian statistical units to illustrate our data collection and processing framework, and in the process try to improve our understanding of the Australian urbangeographical landscape in terms of its bimodal accessibility. In addition to a largely-expected accessibility pattern centred on major agglomerations, we also reveal a number of idiosyncratic and perhaps less-expected geographical patterns.

Measuring Disruptions in Last-Mile Delivery Operations

“…The authors show that these studies shed light on transport behaviour. Similar studies show the relevance of these studies for policymaking, urban transport [8,18,23,24], emergency management [25], and environment protection [25,26]. Some applications of Google Maps API include vehicle routing problems [25].…”

“…Ref. [18] presents the strengths and limitations of an API-based approach to studying urban transport.…”

Dynamic Accessibility Analysis of Urban Road-to-Freeway Interchanges Based on Navigation Map Paths

“…Rong et al [15] used the Internet map navigation service and GIS spatial analysis technology platform to study the spatial accessibility and equity of the medical treatment of residential buildings in the main urban area of Zhengzhou. Meire and Derudder [22] discussed some of the major strengths and limitations of a Google Maps (API)-based approach to studying accessibility in transport networks. With the development of big data technology, dynamic accessibility that captures the temporal variations of accessibility has also become an important research focus [19].…”