Measuring Travel Behavior Changes to Variably Priced Carsharing Using Mobile Applications

Carsharing programs help reduce car use and increase reliance on congestion-reducing modes, including transit, bicycling, and walking. The basic pricing model, a flat hourly rate, creates an opportunity to study variable pricing as a strategy to increase demand for carsharing, while influencing travel behavior modally, spatially, and temporally. This chatper discusses the use of a GPS-enabled mobile phone application to collect travel behavior data while changes to the hourly rental rates were administered to an experimental group of carsharing users. To assess shifts in peak-hour travel in response to variable pricing, nonparametric methods are used to estimate rental start-time probability density functions. Findings show that using pricing to influence when carsharing members take trips can serve to redirect demand to capacity travel times on the road system.

Obtaining Public Transport Level-of-Service Measures Using In-Vehicle GPS Data and Freely Available GIS Web-Based Tools

In this chapter, the authors present a procedure to obtain some Level-Of-Service (LOS) measures, such as waiting times, travel times, and their variability, at any spatial and temporal aggregation level for dense bus networks using freely available map and geographic software. The proposed methodology is highly flexible, as it can accommodate either fixed or variable space-time aggregations. It can handle vast amounts of GPS data yielding LOS results relatively quickly. Furthermore, it can be implemented at relatively low cost in terms of software requirements using freely available software. An illustration of the proposed procedure and its results to obtain LOS measures such as travel times and their variability among bus stops and waiting times for every bus stop are reported using the geographic location of bus stops and offline GPS data available (every 30 seconds) for all operating buses in Santiago´s public transport system.

A Real Time Topological Map Matching Methodology for GPS/GIS-Based Travel Behavior Studies

The map matching problem arises when GPS measurements are incorrectly assigned to the roadway network in a GIS environment. This chapter presents a real-time topological decision rule-based methodology that detects and solves spatial mismatches as GPS measurements are collected. A real-time map matching methodology is required in several applications, such as fleet management, transit control and management, and travel behavior studies, in which decision-making must be performed simultaneously with the movement of vehicles, individuals, or objects. A computational implementation in a real case scenario in Chile indicates that the algorithm successfully resolves over 96% of the spatial mismatches encountered in real time. Various algorithmic parameter values were employed to test the performance of the algorithm for data collected every 5 and 10 seconds. Overall, the algorithm requires larger buffer sizes and speed ranges to obtain better results with lower spatial data qualities.

The Annotation of Global Positioning System (GPS) Data with Activity Purposes Using Multiple Machine Learning Algorithms

The aim of this chapter is to evaluate whether GPS data can be annotated or semantically enriched with different activity categories, allowing GPS data to be used in the future in simulation systems. The data in the study stems from a paper-and-pencil activity-travel diary survey and a corresponding survey in which GPS-enabled Personal Digital Assistants (PDAs) were used. A set of new approaches, which are all independent of additional sensor data and map information, thus significantly reducing additional costs and making the set of techniques relatively easily transferable to other regions, are proposed. Furthermore, this chapter makes a detailed comparison of different machine learning algorithms to semantically enrich GPS data with activity type information.

Multi-Week Travel Surveys Using GPS Devices

Previous research has demonstrated that the value of GPS technology in collecting activity-travel data as an alternative to traditional travel surveys depends largely on the accuracy of data imputation and good survey management. In this chapter, the authors discuss experiences in the use of GPS-devices in a large-scale study aimed at collecting multi-week activity-travel diaries in two regions in The Netherlands. GPS devices were used to collect basic movement information, which was processed using Bayesian Belief Network to derive daily activity-travel diaries, and validated using a Web-based prompted recall instrument. The large-scale travel survey was administered across one year. The chapter addresses several issues regarding the design and management of GPS data collection. Reported experiences are expected to provide a useful source of reference for future multi-week travel surveys using GPS technology.

Behavioural Data Collection Using Mobile Phones

This chapter reviews the development of mobile phone-based travel survey instruments and systems over the last 15 years and discusses the issues and challenges that they will likely face in the future. The essential ideas were proposed in earlier mobile phone surveys in the 1990s but have since become more sophisticated. Probe Person (PP) survey systems were developed in the 2000s using GPS-assisted mobile phones connected to Internet Web diaries, and were implemented in several cities in Japan. This chapter presents the characteristics of PP systems and survey examples. Smartphone-based travel survey systems have recently been developed and implemented all over the world. This chapter includes a case study of a smartphone-based PP survey system in Kumamoto, Japan. Advantages and remaining issues are discussed with the goal of improving information use and enhancing communication technologies in the field of travel data collection and analysis.

Using Smartphones to Capture Personal Travel Behavior

Embedded with a wide variety of sensors, such as GPSs, accelerometers, gyroscopes, and microphones, smartphones have become a very useful tool in the context of travel surveys. In this chapter, the authors present an innovative tool to estimate individuals' mobility patterns using an application for smartphones that records GPS and accelerometer data from trips annotated by the user. The authors also present a neural network model for the classification of trips into four transportation modes, based on features extracted from the accelerometer signal. A small sample was collected in Valencia (Spain) to train and evaluate the model. The best classification results were achieved for detecting walking trips (98.2%) and bike rides (99.3%).

Understanding Trip Misreporting Behavior Using Global Positioning System-Assisted Household Travel Survey

Trip misreporting has been a persistent and well-known problem with household travel surveys. Global Positioning System (GPS)-based prompted-recall method provides the opportunity to capture reliable and accurate travel information from the respondents. By comparing the GPS sample with the diary sample, this chapter investigates the pattern and magnitude of trip misreporting behavior, with a focus on shopping and discretionary tours within 15-minutes trip distance. Econometric models are developed to account for trip misreporting in tour frequency models by introducing a sample-indicator variable. The interaction effects of the sample-indicator variable with various personal and household variables are tested, which reflect the influences of these personal and household attributes on trip misreporting behavior. A number of personal and household characteristics showed significant impacts on misreporting, including driver license status, race, person type, household type, household income, and number of household vehicles.

Characterizing Urban Structure Using Taxi GPS Data

Scholars have explored urban structure from many perspectives. Developments in ICT have made it possible to discover spatial patterns in activities using big data. The identified patterns allow us to better understand urban structure. This chapter reports the collection of taxi GPS records for a single day in the inner city of Guangzhou, China. Taxi trips are connected to urban space by defining travel intensities. The spatial-temporal distribution of trips shows differences between three time periods (daytime, evening, before dawn). Different types of spatial facilities provide different activity places, the importance of which depends on their location and time of day. The study illustrates how descriptive analyses of taxi GPS data can enhance our understanding of urban space from the perspective of activities.

Using a GPS Active Logger to Implement Travel Behaviour Change Programs

Implementing behavioural strategies aimed at reducing car use represents one of the most topical challenges for current transport research. Most of the current Voluntary Travel Behaviour Change (VTBC) programs are moving towards ICT devices for data collection. The advantages of using ICT have been recognized for implementing behavioural strategies and VTBC in order to improve observation of pre- and post-implementation behaviour. This chapter describes the implications of a personal Active Logger (AL) implemented by CRiMM (University of Cagliari, Italy) for the collection of individual activity-travel patterns before and after a VTBC implementation. In particular, VTBC data collected through an active tracking system (GPS tracking + real time activity diary completion) are compared with data collected using a hybrid tracking system (GPS-only system + deferred activity-travel patterns). The results show that, despite the greater effort involved in real time compilation, the information collected by the active logger is more in line with VTBC requirements and expectations.