Individual Mobility Analysis Using Smartphone Data

Introduction

Nowadays, efforts to ameliorate the operations of transport systems have shifted from a supply oriented to a demand oriented approach. The space and budget constraints for extending the current infrastructure has led to an attempt to better understand and predict the demand in order to subsequently enable optimizing the operations and management of the existing network. Understanding individual choices pertaining to the various dimensions of travel behavior, such as mode, destination and route choice, is important in achieving this goal. In this context, an increasing interest in human behavior at all scales arises. This fact has triggered the exploration of new types of models of decision-making, and new sources of data.

The availability of data about users' behavior in the transportation network is the driving force of mobility analysis. Recent advances in data collection based on GPS devices and GPS-enabled smartphones have revolutionized the acquisition of information, modeling and eventually the understanding of behavior. These methods become very popular as data collection tools in studying mobility patterns (González et al., 2008), and transportation network performance (e.g., Jenelius & Koutsopoulos, 2013). Choice data is traditionally collected by means of surveys where people are asked to describe their choices, such as the route they followed for a specific trip, or their mode of transport. Traditional surveys are conducted via mail, telephone or computer assisted tools and hence they entail complex logistics. In addition, these methods are costly, limited by accuracy of recall, reliability, and respondent compliance. Since the GPS technologies became available, the choice observations have been extracted by tracking users as they traverse the network (passive monitoring).