e-mission: An Open-Source, Smartphone Platform for Collecting Human Travel Data (original) (raw)

Using Smartphones and Sensor Technologies to Automate Collection of Travel Data

Transportation Research Record: Journal of the Transportation Research Board, 2013

This paper presents a data collection framework and its prototype application for personal activity–travel surveys through the use of smartphone sensors. The core components of the framework run on smartphones backed by cloud-based (online) services for data storage, information dissemination, and decision support. The framework employs machine-learning techniques to infer automatically activity types and travel modes with minimum interruption for the respondents. The three main components of the framework are (a) 24-h location data collection, (b) a dynamic land use database, and (c) a transportation mode identification component. The location logger is based on the smartphone network and can run for 24 h with minimal impact on smartphone battery life. The location logger is applicable equally in places where Global Positioning System signals are and are not available. The land use information is continuously updated from Internet location services such as Foursquare. The transport...

Examining the use of smartphones for travel behavior data collection

The practices and standards of travel behavior data collection have changed significantly over the past decade with the introduction of mobile, location based technology. The use of GPS devices such as loggers has been a great enhancement to the field. However, with the increased ubiquity of smartphones, which come equipped with a variety of sensors useful to behavioral data collection, the possibilities and methods used in data collection are again shifting. These mobile devices offer several exciting opportunities to either collect more data from respondents with a similar amount of burden, or collect previously burdensome data (such as detailing time use by paper and pen survey) with little or no interaction from the respondent. In this paper, an overview of possible sensors is presented, as well as current research efforts in the field. A forthcoming analysis of sensor frequency, battery expenditure and accuracy of detection will also be discussed in the final version of this paper.

Supporting large-scale travel surveys with smartphones – A practical approach

Transportation Research Part C: Emerging Technologies, 2014

Collection of travel data is a key task of transportation modeling. Data collection is currently based on costly and time-intensive questionnaires, and can thus only provide limited cross-sectional coverage and inadequate updates. There is an urgent need for technologically supported travel data acquisition tools. We present a novel approach for supporting travel surveys using data collected with smartphones. Individual trips of the person carrying the phone are automatically reconstructed and trip legs are classified into one of eight different modes of transport. This task is performed by an ensemble of probabilistic classifiers combined with a Discrete Hidden Markov Model (DHMM). Classification is based on features extracted from the motion trajectory recorded by the smartphone's positioning system and signals of the embedded accelerometer. Our approach can cope with GPS signal losses by including positioning data obtained from the mobile phone cell network, and relies solely on accelerometer features when the trajectory cannot be reconstructed with sufficient accuracy. To train and evaluate the models, 355 h of probe travel data were collected in the metropolitan area of Vienna, Austria by 15 volunteers over a period of 2 months. Distinguishing eight different transportation modes, the classification results range from 65% (train, subway) to 95% (bicycle). The increasing popularity of smartphones gives the proposed method the potential to be used on a widespread basis and can complement existing travel survey methods.

An App-Assisted Travel Survey in Official Statistics: Possibilities and Challenges

Journal of Official Statistics, 2021

Advances in smartphone technology have allowed for individuals to have access to near-continuous location tracking at a very precise level. As the backbone of mobility research, the Travel Diary Study, has continued to offer decreasing response rates over the years, researchers are looking to these mobile devices to bridge the gap between self-report recall studies and a person’s underlying travel behavior. This article details an open-source application that collects real-time location data which respondents may then annotate to provide a detailed travel diary. Results of the field test involving 674 participants are discussed, including technical performance, data quality and response rate.

Using smart phone and sensor technology to automate travel data collection

Transportation Research Record Journal of the Transportation Research Board

This paper presents a data collection framework and its prototype application for personal activity-travel survey through the exploitations of smartphone sensors. The core components of the framework run on smartphones, backed by cloud-based services for data storage, information dissemination and online decision support. The framework employs machine learning techniques to automatically infer activity types and travel modes with minimum interruptions for the respondents. There are three main components of the framework: 1) a 24 hours location data collection, 2) a dynamic land use database, and 3) a transportation mode identification component. The location logger is based on the smartphone network and it can run for 24 hours with minimum impact on smartphone battery and equally applicable in places where GPS is available or not. The land use information is continuously updated from internet location services such as Foursquare. Transportation mode identification module is able to distinguish six different modes with 98.85% accuracy. Prototype application is conducted in the city of Toronto and results clearly indicate the viability of this framework.

Gathering individual travel data with GPS-enabled smartphones: A proof of concept study

Policies aimed at Traffic Demand Management (TDM) rely heavily on the gathering of accurate individual level activity and travel data to understand and unpack the demand for transport. Moreover, self-report based data collection methods face problems such as a high-respondent burden and inaccuracies in the number and duration of the reported trips. On that account, this paper presents ongoing research to assess the reliability and feasibility of passively collecting high resolution spatiotemporal data on activity and travel behaviour using GPS-enabled smartphones. A small-scale pilot study was conducted in which respondents from the University of Stellenbosch, South Africa, were passively tracked for the course of two days by means of a purposefully designed smartphone application, termed TrackLog. The results of the small experiment indicate that while GPS technology in smartphones potentially holds a number of benefits for collecting activity and travel data, the technology is not without problems. This project has highlighted that these problems can be classified as: (1) user, (2) technology, and (3) methodology related problems. Notwithstanding these problems, the results indicate that gathering high resolution space-time data by means of GPS-enabled smartphones is feasible and that it opens doors to a range of possible applications that are unattainable by traditional survey methods.

Future Mobility Survey: Experience in Developing a Smartphone-Based Travel Survey in Singapore

Transportation Research Record Journal of the Transportation Research Board, 2013

The Future Mobility Survey (FMS) is a smartphone-based prompted-recall travel survey that aims to support data collection initiatives for transport modelling purposes. This paper details the considerations that have gone into its development, including the smartphone apps for iPhone and Android platforms, the online activity diary and user interface, and the background intelligence for processing collected data into activity locations and travel traces. We discuss the various trade-offs regarding user comprehension, resource use, and participant burden, including findings from usability tests and a pilot study. We find that close attention should be paid to the simplicity of the user interaction, determinations of activity locations (such as the positive/false negative trade-off in their automatic classification), and the clarity of interactions in the activity diary. The FMS system design and implementation provides pragmatic, useful insights into the development of similar platforms and approaches for travel/activity surveys.

The Use of Smartphone Applications in the Collection of Travel Behaviour Data

International Journal of Intelligent Transportation Systems Research, 2014

The MOVE project deals with the collection and analysis of crowd behaviour data. The main goals of the project are to collect data through the use of mobile phones and to develop new technologies to process and mine the collected data for crowd behaviour analysis. This paper describes the different steps in the development of tracking applications for smartphones that make use of advanced data mining. The results on data collection, analysis, and reporting have led to the development and operation of an advanced urban data monitoring system.

An Evaluation of Smartphone Tracking for Travel Behavior Studies

ISPRS International Journal of Geo-Information

The use of smartphone tracking is seen as the way forward in data collection for travel behavior studies. It overcomes some of the weaknesses of the classical approach (which uses paper trip diaries) in terms of accuracy and user annoyance. This article evaluates if these benefits hold in the practical application of smartphone tracking and compares the findings of a travel behavior survey using smartphone tracking to the findings of a previous paper survey. We compare three phases of the travel behavior study. In the recruitment phase, we expect smartphone tracking to make people more willing to participate in surveys, given the innovative nature and reduced burden to participants. However, we found the recruitment of participants equally challenging as for classical methods. In the data collection phase, however, we observe that participants entering the smartphone tracking survey are much more likely to complete the data collection period than when using paper trip diaries. Becau...

A Strategy on How to Utilize Smartphones for Automatically Reconstructing Trips in Travel Surveys

Procedia - Social and Behavioral Sciences, 2012

The acquisition of travel data is currently based on cost-and time-intensive questionnaires and yields mostly an incomplete picture due to limited coverage and inadequate updates. There is an urgent need for technologically supported data acquisition tools. This paper introduces a novel approach to developing a large-scale travel survey by intelligently employing data from smartphones. Based on signals of the embedded accelerometers and GPS reveivers, an ensemble of probabilistic classifiers is trained for automatically reconstructing the individual trips composing a tour, including the mode choice. In the region of Vienna, Austria, 266 hours of travel data were collected to train and evaluate the models. Using a set of 72 features, the best classification results are achieved for detecting walks (92%) and bike rides (98%). Railway modes were correctly identified in 80% of all cases, which is subject to further research. In case of GPS losses only accelerometer data are used, which still shows promising results. This allows the method to incorporate places where there is normally only a weak or no GPS signal. Future smartphone applications are intended to spread the tool among traffic users, while the effort for them should be kept to a minimum i.e. no manual entries or questionnaires are necessary. Due to the increasing popularity of smartphones, the tool has the potential to be used on a widespread basis.