Crowdsourcing mobility insights: reflection of attitude based segments on high resolution mobility behaviour data

Abstract Recently, the use of market segmentation techniques to promote sustainable transport has significantly increased. Populations are segmented into meaningful groups that share similar attitudes and preferences. This segmentation provides valuable information about how policy options, such as pricing measures or advertising campaigns, should be designed and promoted in order to successfully target different user groups. In this paper, we aim to bridge between psychological, social marketing and ICT research in the field of transportation. We explore how attitude based segments are reflected in high resolution mobility behaviour data, crowdsourced via mobile phones. We use support vector machines to map eight attitudinal segments, as defined under the European project SEGMENT, to the n dimensional space defined by crowdsourced data. The success rate of the proposed approach is 98.9%. This demonstrates the applicability of the method as a way to automatically map attitudinal segments to a wider population based on observed mobility data instead of using explicit attitudinal surveys. In addition, the proposed approach can facilitate the delivery of personalised target messages to individuals (e.g. via smartphones) or at target locations where users, belonging to specific segment, are located at specific time windows since the data includes the time-space indications.

[1]  Xinkai Wu,et al.  Using high-resolution event-based data for traffic modeling and control: An overview , 2014 .

[2]  Antonella Ferrara,et al.  Integrated Mobility: A Research in Progress , 2013 .

[3]  I. Ajzen.  The theory of planned behavior , 1991 .

[4]  Yusak O. Susilo,et al.  The influence of individuals' environmental attitudes and urban design features on their travel patterns in sustainable neighborhoods in the UK , 2012 .

[5]  Jin Wang,et al.  Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory , 2013 .

[6]  D. McKenzie‐Mohr,et al.  Promoting Sustainable Behavior : An Introduction to Community-Based Social Marketing , 2000 .

[7]  Kees Maat,et al.  Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands , 2009 .

[8]  Satish V. Ukkusuri,et al.  Urban activity pattern classification using topic models from online geo-location data , 2014 .

[9]  Cristina Pronello,et al.  Traveler segmentation strategy with nominal variables through correspondence analysis , 2010 .

[10]  Shinhye Joo,et al.  Categorizing bicycling environments using GPS-based public bicycle speed data , 2015 .

[11]  Eleni I. Vlahogianni,et al.  Optimization of traffic forecasting: Intelligent surrogate modeling , 2015 .

[12]  Mark R. McCord,et al.  Transit passenger origin–destination flow estimation: Efficiently combining onboard survey and large automatic passenger count datasets , 2015 .

[13]  Akshay Vij,et al.  When is big data big enough? Implications of using GPS-based surveys for travel demand analysis , 2015 .

[14]  Glenn Lyons,et al.  Public attitudes to transport : Knowledge review of existing evidence , 2008 .

[15]  S. Bamberg,et al.  Social context, personal norms and the use of public transportation: Two field studies , 2007 .

[16]  Bin Yu,et al.  Bus arrival time prediction at bus stop with multiple routes , 2011 .

[17]  E. Murakami,et al.  Can using global positioning system (GPS) improve trip reporting , 1999 .

[18]  Michael F. Goodchild,et al.  Assuring the quality of volunteered geographic information , 2012 .

[19]  Yu Liu,et al.  The promises of big data and small data for travel behavior (aka human mobility) analysis , 2016, Transportation research. Part C, Emerging technologies.

[20]  Catherine T. Lawson,et al.  A GPS/GIS method for travel mode detection in New York City , 2012, Comput. Environ. Urban Syst..

[21]  Xuesong Zhou,et al.  Traffic zone division based on big data from mobile phone base stations , 2015 .

[22]  Davide Anguita,et al.  In-sample model selection for Support Vector Machines , 2011, The 2011 International Joint Conference on Neural Networks.

[23]  Marlon G. Boarnet,et al.  Illuminating the unseen in transit use: A framework for examining the effect of attitudes and perceptions on travel behavior , 2013 .

[24]  Alexandre M. Bayen,et al.  How Much GPS Data Do We Need , 2015 .

[25]  Pu Wang,et al.  Development of origin–destination matrices using mobile phone call data , 2014 .

[26]  undefined Manoël Rekinger,et al.  Global Market Outlook for Solar Power 2015-2019 , 2014 .

[27]  Marta C. González,et al.  The path most traveled: Travel demand estimation using big data resources , 2015, Transportation Research Part C: Emerging Technologies.

[28]  Xiaowei Hu,et al.  Understanding the Travel Behavior of Elderly People in the Developing Country: A Case Study of Changchun, China , 2013 .

[29]  Marta C. González,et al.  Origin-destination trips by purpose and time of day inferred from mobile phone data , 2015 .

[30]  Zvonko Kavran,et al.  Hybrid approach for urban roads classification based on GPS tracks and road subsegments data , 2011 .

[31]  J. Anable,et al.  Smarter Choices: Assessing the Potential to Achieve Traffic Reduction Using ‘Soft Measures’ , 2008 .

[32]  Louise Eriksson,et al.  Is the intention to travel in a pro-environmental manner and the intention to use the car determined by different factors? , 2011 .

[33]  Claudio O. Delang,et al.  Consumers' attitudes towards electric cars: A case study of Hong Kong , 2012 .

[34]  Moshe Ben-Akiva,et al.  A meta-model for passenger and freight transport in Europe , 2004 .

[35]  Haiyong Luo,et al.  Device-clustering algorithm in crowdsourcing-based localization , 2012 .

[36]  Lothlorien S. Redmond.  Identifying and Analyzing Travel-Related Attitudinal, Personality, and Lifestyle Clusters in the San Francisco Bay Area , 2000 .

[37]  Eric Molin,et al.  Multimodal travel groups and attitudes: A latent class cluster analysis of Dutch travelers , 2016 .

[38]  Sonja Haustein,et al.  Attitude-Based Target Groups to Reduce the Ecological Impact of Daily Mobility Behavior , 2010 .

[39]  Chih-Jen Lin,et al.  Training v-Support Vector Classifiers: Theory and Algorithms , 2001, Neural Computation.

[40]  Chandra R. Bhat,et al.  An analysis of the factors influencing differences in survey-reported and GPS-recorded trips , 2012 .

[41]  Jillian Anable,et al.  'Complacent Car Addicts' or 'Aspiring Environmentalists'? Identifying travel behaviour segments using attitude theory , 2005 .

[42]  Carlo Ratti,et al.  Understanding individual mobility patterns from urban sensing data: A mobile phone trace example , 2013 .

[43]  J. Prillwitz,et al.  Moving towards sustainability? Mobility styles, attitudes and individual travel behaviour , 2011 .

[44]  T. Gärling,et al.  Behaviour Theory and Soft Transport Policy Measures , 2011 .

[45]  Sidharta Gautama,et al.  Smart City Mobility Application—Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data , 2015, Sensors.

[46]  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 .