Pregled bibliografske jedinice broj: 876228
Crowdsourcing mobility insights : reflection of attitude based segments on high resolution mobility behaviour data
Crowdsourcing mobility insights : reflection of attitude based segments on high resolution mobility behaviour data // Transportation research part c-emerging technologies, 71 (2016), 434-446 doi:10.1016/j.trc.2016.08.016 (međunarodna recenzija, članak, znanstveni)
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Naslov
Crowdsourcing mobility insights : reflection of attitude based segments on high resolution mobility behaviour data
Autori
Šemanjski, Ivana ; Gautama, Sidharta
Izvornik
Transportation research part c-emerging technologies (0968-090X) 71
(2016);
434-446
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
BIG DATA ; travel behavior ; patterns ; Crowdsourcing mobility behaviour ; Attitude based segmentation techniques ; Smart city mobility ; Support vector machines ; Smartphone based mobility data collection ; Data-driven mobility management
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Tehnologija prometa i transport
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus