Abstract
Abstract. Due to the advances in location-acquisition techniques, smartphone GPS location data has emerged with opportunity for research, development, innovation, and business. A variety of research has been developed to study human behaviour through exploring patterns from these data. In this paper, we use smartphone GPS location data to investigate municipal mobility. Kernel density estimation and emerging hot spot analysis are used to the GPS dataset to demonstrate GPS user (point) distribution across space and time. Flow maps are capable of tracking clustering behaviours and direction maps drawn upon the orientation of vectors can precisely identify location of the events. Case study with Indianapolis metropolitan area traffic rush hour verifies the effectiveness of these methods. Furthermore, we identify smartphone GPS data of vehicles and develop a concise and effective method for traffic volume estimation for county highway network. It is shown that the developed smartphone GPS data analytics is powerful for predicting reliable annual average daily traffic estimation. The study showcases the capability of GPS location data in identifying municipal mobility patterns for both citizens and vehicles.
Highlights
Understanding mobility of human beings at the municipality scale is a key factor for understanding the mechanism of urban expansion and smart cities
Human mobility study has already showed that people moved to and leave from downtown during morning rush hour and evening rush hour respectively
Our flow maps generated by density difference-based method depict that people converge in the downtown area from all directions during the morning rush hour and left from the downtown area in the evening rush hour
Summary
Understanding mobility of human beings at the municipality scale is a key factor for understanding the mechanism of urban expansion and smart cities. The connection between two locations is usually represented by a straight or curved flow line and magnitude of flow could be visualized by varying colours or width of the flow line (Guo et al, 2009) Another aspect of investigating municipal mobility is to evaluate the traffic volume around the city. Municipal traffic volume is a primary factor to reflect spatial pattern of human mobility It is a key element of transportation system design.
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