This paper introduces a smartphone-based system for performing personal travel data collection. The Smartphone-Based Individual Travel Survey System (SITSS), is designed to deal with the challenges associated with applying smartphones in real-world travel surveys, including battery depletion, participant involvement, and privacy concerns. A prompted-recall smartphone-based data collection approach was used in the development of SITSS to facilitate the procedure of data collection while improving the accuracy of collected data. The system was used as part of the national household travel survey of New Zealand to investigate the various aspects of performing a national-level travel survey with smartphones. The study confirmed the successful performance of SITSS in a multiday travel survey. Participants found the data collection procedure an interesting experience, and 71% continued their participation beyond the requested data collection interval. Participants found that the procedure of data collection was straightforward—94% could complete the task without any support request. The smartphone application did not interrupt the normal use of the smartphone. The battery-saving algorithms used in the application was satisfactorily extended the battery life. The study addressed other challenges associated with implementing SITSS, including the procedure for labeling trips, respondents' experience with the data collection method, and concerns related to the performance of the smartphone application.

Smartphone travel surveys are able to capture accurate details about individuals' travel behavior. However, extracting the required information (e.g., travel mode and purpose) from the data captured by smartphone applications is relatively complex, particularly when relying on the computational power of smartphones and limiting the communications between these applications and third parties [e.g., geographic information systems (GIS)]. These limitations are mainly enforced to enable passive data collection through smartphones by automatically recognizing the mode and purpose of trips. Furthermore, limited data transfer between the application and third parties ensures the privacy protection of survey participants and facilitates real-world travel surveys with large sample sizes. Accordingly, the objective of this paper is to develop a model of travel mode identification, which can be integrated with smartphone travel surveys without using GIS data or interacting with participants. Most existing models and algorithms are either inaccurate or computationally complex, and require extensive processing power. A smartphone travel survey, namely, the Advanced Travel Logging Application for Smartphones II (ATLAS II), has been used to collect individuals' travel data across New Zealand and Queensland, Australia. A detailed algorithm is put forward to clean the captured data, segment trips into single modal trips, and develop multiple statistical models for comparison, using the data collected from New Zealand. The preferred approach, which is adapted for the integration with smartphone travel survey applications, is validated using the two separate data sets from New Zealand and Australia. The resulting mode identification model (i.e., a nested logit model with eight variables) could detect travel modes with the accuracy of 97% for New Zealand after preprocessing (i.e., data cleaning and trip segmentation) and 79.3% for Australia without any preprocessing.

Smartphone travel surveys are becoming of central importance in collecting detailed, accurate data of people’s travel activities. As with their conventional survey counterparts, the quality of data collected through these surveys is adversely affected by participants’ non-response and resulting biases. However, little is known about the factors affecting people’s perceptions and intentions to continue participating in such surveys. Although literature has investigated the associations between individuals’ socio-demographic attributes and their likelihood of survey participation, the impact of their subjective perceptions and attitudes on their survey participation intentions and behaviour is underexplored. Hence, through a model of participants’ perceptions of smartphone travel survey applications (survey apps), this study aims to reveal such impacts and how they affect the participants’ intentions to continue participating in these surveys. In this study, a survey is designed and used to collect data in a smartphone travel survey before the participants are asked about their personal perceptions and attitudes. Partial least squares path modelling (PLS-PM) is applied to analyse the data, as it allows simultaneous estimation of the relationships between multiple latent constructs as well as the indicators of each construct. The results showed a significant, positive impact of the perceived “ease of use” and “usefulness” of the survey app as the technological medium of data collection on the participants’ “satisfaction” and “intention” to continue participating in the corresponding survey. The study also found that participants’ perceived “risk” associated with privacy concerns did not have any significant impact on their intention to continue participating in the survey.