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Purpose — This paper describes what the authors believe to be the first GPS-only full-scale household travel survey.

Design/methodology — The survey commenced in early 2009 with the conduct of a pilot survey to help establish various parameters and procedures for the main survey. The main survey commenced in August 2009 and was completed in August 2010. It was designed as a household travel survey to be collected steadily over a 12 month period. The target sample size was originally set at over 3500 households, although this target was reduced downwards during the course of the survey. Each household member over the age of 12 was asked to carry a GPS device with them everywhere they went for a period of 3 days. After the 3-day collection period was completed, GPS devices were retrieved from households, the data were downloaded and processing of the data commenced. The study also involved a PR survey performed on the Internet.

Findings — The paper concludes with lessons learnt from this GPS-only survey and suggestions for how future GPS-only surveys might be conducted.

Originality/value of the paper — The paper describes the first GPS-only household travel survey and concludes that it is now feasible to conduct household travel surveys by GPS.

Over the past decade, transportation researchers have leveraged global positioning system (GPS) technology to improve the accuracy and increase the depth of spatial and temporal details obtained through household travel surveys. While earlier studies used GPS as a supplement to traditional household travel survey methods, measuring the accuracy of trips reported (Wolf et al., 2006), studies are now underway to identify the methods and tools that will allow us to do away with paper diaries entirely and simply rely on GPS to obtain trip details. This paper finds that while GPS clearly helps to improve participation among some groups, it decreases participation among others. Thus, it should be considered a tool in the household travel survey toolbox and not “the” solution to non-response issues in household travel surveys.

In the recent past, mobile technologies that track the movement of people, freight and vehicles have evolved rapidly. The major categories of such technologies are reviewed and a number of attributes for classification are proposed. The willingness of people to engage in such technologically based surveys and the reported biases in the make-up of the sample obtained are reviewed. Lessons are drawn about the nature of the samples that can be achieved and the representativeness of such samples is discussed. Data processing is addressed, particularly in terms of the processing requirements for logged data, where additional travel characteristics required for travel analysis may need to be imputed. Another issue explored is the reliability of data entered by respondents in interactive devices and concerns that may arise in processing data collected in real time for prompting or interrogating respondents. Differences, in relation to the data user, between data from mobile devices and data from conventional self-report surveys are discussed. Potentials that may exist for changes in modelling from using such data are explored. Conclusions are drawn about the usefulness and limitations of mobile technologies to collect and process data. The extent to which such mobile technologies may be used in future, either to supplement or replace conventional methods of data collection, is discussed along with the readiness of the technology for today and the advances that may be expected in the short and medium term from this form of technology.

The purpose of this paper is to introduce a new multiple imputation method that can effectively manage missing values in online review data, thereby allowing the online review analysis to yield valid results by using all available data.

This study develops a missing data method based on the multivariate imputation chained equation to generate imputed values for online reviews. Sentiment analysis is used to incorporate customers’ textual opinions as the auxiliary information in the imputation procedures. To check the validity of the proposed imputation method, the authors apply this method to missing values of sub-ratings on hotel attributes in both the simulated and real Honolulu hotel review data sets. The estimation results are compared to those of different missing data techniques, namely, listwise deletion and conventional multiple imputation which does not consider text reviews.

The findings from the simulation analysis show that the imputation method of the authors produces more efficient and less biased estimates compared to the other two missing data techniques when text reviews are possibly associated with the rating scores and response mechanism. When applying the imputation method to the real hotel review data, the findings show that the text sentiment-based propensity score can effectively explain the missingness of sub-ratings on hotel attributes, and the imputation method considering those propensity scores has better estimation results than the other techniques as in the simulation analysis.

This study extends multiple imputation to online data considering its spontaneous and unstructured nature. This new method helps make the fuller use of the observed online data while avoiding potential missing problems.