Workshop Synthesis: Passive and sensor data - potential and application

Abstract The workshop on technology, tools and applications around passive and sensor travel data is summarized in this paper. Such data requires protocols for collection, storing/retrieving, sharing and processing; as well as the need for validation methods and multi-disciplinary work. Traditional surveys can complement passive and sensor data to aid a deeper understanding of travel behaviour. Passive data is particularly beneficial for planning long-distance travel and freight transport. While access to massive data collected by licensed operators should be guaranteed, maintaining data privacy and cultural sensitivity is a priority.

[1]  Stephen Greaves,et al.  Household travel surveys: Where are we going? , 2007 .

[2]  Kouros Mohammadian,et al.  Workshop Synthesis: Conducting Travel Surveys using Portable Devices - Role of Technology in Travel Surveys , 2015 .

[3]  Pavlos S. Kanaroglou,et al.  Activity–Travel Behaviour Research: Conceptual Issues, State of the Art, and Emerging Perspectives on Behavioural Analysis and Simulation Modelling , 2007 .

[4]  Susan L Handy,et al.  The science and art of intersectoral collaboration on transport and health , 2016 .

[5]  Nadia Nedjah,et al.  Soft computing in big data intelligent transportation systems , 2016, Appl. Soft Comput..

[6]  Alex Pentland,et al.  Big Data and Management , 2014 .

[7]  Michael Batty,et al.  Big data, smart cities and city planning , 2013, Dialogues in human geography.

[8]  Norbert Brändle,et al.  Supporting large-scale travel surveys with smartphones – A practical approach , 2014 .

[9]  Sergio R. Jara-Díaz,et al.  Understanding time use: Daily or weekly data? , 2015 .

[10]  F. Witlox,et al.  When Transport Geography Meets Social Psychology: Toward a Conceptual Model of Travel Behaviour , 2010 .

[11]  Ying Wah Teh,et al.  Mining Personal Data Using Smartphones and Wearable Devices: A Survey , 2015, Sensors.

[12]  Henry Leung,et al.  Data fusion in intelligent transportation systems: Progress and challenges - A survey , 2011, Inf. Fusion.

[13]  Flora Cornish,et al.  Too Much Information: Visual Research Ethics in the Age of Wearable Cameras , 2014, Integrative Psychological and Behavioral Science.

[14]  Chandra R. Bhat Workshop Synthesis: Conducting Travel Surveys using Portable Devices- Challenges and Research Needs , 2015 .

[15]  Upkar Varshney,et al.  Mobile health: Four emerging themes of research , 2014, Decis. Support Syst..

[16]  Paul Kelly,et al.  Developing a Method to Test the Validity of 24 Hour Time Use Diaries Using Wearable Cameras: A Feasibility Pilot , 2015, PloS one.

[17]  C. Bhat,et al.  Comparative Analysis of Global Positioning System–Based and Travel Survey–Based Data: , 2006 .

[18]  E. Leeuw,et al.  To mix or not to mix data collection modes in surveys. , 2005 .

[19]  Catherine T. Lawson,et al.  Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study , 2010 .

[20]  Catherine Morency,et al.  Survey Mode Integration and Data Fusion: Methods and Challenges , 2009 .

[21]  Steve E Hodges,et al.  Wearable cameras in health: the state of the art and future possibilities. , 2013, American journal of preventive medicine.

[22]  Peter R. Stopher,et al.  Review of GPS Travel Survey and GPS Data-Processing Methods , 2014 .

[23]  Bernard C. K. Choi,et al.  Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness. , 2006, Clinical and investigative medicine. Medecine clinique et experimentale.

[24]  Peter R. Stopher,et al.  The Challenges and Opportunities of In-depth Analysis of Multi-day and Multi-year Data , 2015 .