Abstract
Recently, new traffic data sources have emerged raising new challenges and opportunities when applying novel methodologies. The purpose of this research is to analyse car travel time data collected from smartphones by Google Company. Geographic information system (GIS) tools and Python programming language were employed in this study to establish the initial framework as well as to automatically extract, analyse, and visualize data. The analysis resulted in the calculation of travel time fluctuation during the day, calculation of travel time variability and estimation of origin-destination (OD) skim matrices. Furthermore, we accomplished the accessibility analysis and provided recommendations for further research.
Highlights
Most traffic engineering or transport planning tasks begin with a collection of traffic data
Results of Google Distance Matrix API were provided as JavaScript Object Notation (JSON) objects, an open-standard format that uses human-readable text to transmit data objects consisting of attribute–value pairs
Google company provides rich set of data on car travel times which can be accessed via Google Distance Matrix API
Summary
Most traffic engineering or transport planning tasks begin with a collection of traffic data. The major disadvantage of traditional traffic monitoring technologies (sensor-based, video-based, radio-frequency-based) is high deployment and maintenance cost. These are limited in coverage [1]. Mobile-phone carriers automatically collect Call Detail Records (CDR) which contain time-stamped coordinates of anonymized customers. This information can provide detailed spatial-temporal information regarding user’s mobility pattern [2], which can be used for origin-destination (OD) matrix development [2,3,4,5,6,7], estimation of travel times [8, 9], composition of traffic analysis zones [10]. According to researchers [11], GPS technology can provide 10-15 metres’ accuracy (up to 5 metres in open areas), whereas CDR only 100 metres
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