A Web-Based Diary and Companion Smartphone app for Travel/Activity Surveys

Greaves, Stephen; Ellison, Adrian B.; Ellison, Richard; Rance, Dean; Standen, Christopher; Rissel, Chris; Crane, Melanie

doi:10.1016/j.trpro.2015.12.026

Cited by 52 publications

(25 citation statements)

References 12 publications

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“…Several different incentive structures have been attempted in the pilot studies. Similarly burdensome research studies provided monetary incentives to each participant (Greaves et al, 2014;Kowald et al, 2010;Montini et al, 2014). In particular, Kowald and Axhausen (2014) argue that a monetary reward is the best way to incentivise participants due to its universally understandable nature.…”

Section: Incentivesmentioning

confidence: 99%

We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning

2018

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Recent work in transport research has increasingly tried to broaden out beyond traditional areas such as mode choice or car ownership and has tried to position travel decisions within the broader life context. However, while important progress has been made in terms of how to capture these additional dimensions, both in terms of detailed tracking of movements and in-depth data collection of long term decisions or social network influences, surveys have tended to look at only a handful (or often one) of these issues in isolation, especially at the data collection end. Making these links is the key aim of the data collection described in this paper. We conducted a comprehensive survey capturing respondents' travel, energy and residential choices, their social environment, life history and short-term travel patterns. The survey is composed of a detailed background questionnaire, a life-course calendar and a name generator and name interpreter. Participants were also required to use a smartphone tracking app for two-weeks. We believe that this is an unprecedented effort that joins complexity of the survey design, amount of information collected and sample size. The present paper gives a detailed overview of the different survey components and provides initial insights into the resulting data. We share lessons that we have learned and explain how our decisions in terms of specification were shaped by experiences from other data collections.

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Section: Incentivesmentioning

confidence: 99%

We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning

2018

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“…As we have not been able to control for selection bias in our survey sample in relation to the population under study, caution is recommended when generalising our results to other contexts. For instance, and as other authors have found (Gadziński, 2018;Greaves et al 2015), participant attrition due to phone battery drainage or perceived survey fatigue (Assemi et al 2018) is a common reason for leaving this kind of survey. In our case, this resulted in 36 persons (out of 172 registered) not recording any data (for a further discussion, see Berggren et al 2019).…”

Section: Discussion On Methodology and Resultsmentioning

confidence: 95%

Effects from usage of pre-trip information and passenger scheduling strategies on waiting times in public transport: an empirical survey based on a dedicated smartphone application

et al. 2019

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Waiting times are important indicators of the degree of travel time optimisation and other behavioural traits among public transport (PT) passengers. As previous studies have shown, the level and usage of pre-trip information regarding schedule or realtime departures are important factors that influence the potential to realise travel time savings by enabling PT passengers to optimise waiting times. Most empirical evidence regarding the revealed PT travel behaviour concerning information levels is based on manual interviews or traditional travel surveys, in which there is a risk that the actual context of where and when the choice of departure time was made is not taken into account. This paper reports the results of a travel survey based on a dedicated smartphone application applied in a field study in a Swedish mid-size urban and regional context. Context-aware notification prompting was used to allow respondents to state their use of pre-trip information as well as whether they had pre-planned their trip and how contingent planning aids were used for time optimisation. The implications on passenger waiting times of the use of information regarding departure times by passengers were emphasised during analyses of the resulting data, along with personal characteristics, in which auxiliary sources such as timetable data and Automatic Vehicle Location were utilised to determine ground truth trip trajectories and trip-contextual factors. The results indicate the significance of having access to pre-trip information, especially for long trips above one hour's duration, in order to pre-plan and thereby optimise waiting times. In addition, the use and source of pre-trip information differ among age and gender groups. Trip purpose and time of day to some extent determine waiting times and choice of trip optimisation strategy (arrival or departure time).

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“…Participants were recruited from two inner-city areas shown in Fig. 1, an intervention area in which the bicycle infrastructure was being built and the control area in which there was no change in the provision of bicycle infrastructure (Greaves et al 2014). The original aim was to recruit 800 participants between the ages of 18 and 55, who had ridden a bicycle at least once in their lifetime and had no disabilities that would prevent them from riding a bicycle.…”

Section: Data Collectionmentioning

confidence: 99%

Spatiotemporal Identification of Trip Stops from Smartphone Data

Ellison

Ahmed

et al. 2016

Appl. Spatial Analysis

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As part of a 3-year study on cycling infrastructure, a smartphone app was developed to passively collect location information from about 500 participants resulting in 96 million observations. By using WiFi and network location rather than the Global Positioning System (GPS) receiver in the smartphones, the app is able to collect frequent and good quality location data for long periods of time without limiting the use of the smartphones for other purposes. Tests of the data collected using the smartphone app compared to a standalone GPS devices carried by the same participants are presented to provide evidence of the relative differences between the methods presented here with those traditionally used with GPS travel surveys. The data are then used to identify trip stops using a new method that employs a moving average position and incorporates mechanisms that are used to detect relatively short stops including stops involving a transfer of transport mode. In total 86,407 stops are identified with a median time of 1 h and a spatial distribution consistent with the travel diary data collected as part of the same study. The duration of the detected stops are also reasonably consistent with those from the travel diary for stops detected at activity locations such as workplaces. This paper provides evidence of the feasibility of using smartphone WiFi and network location sensors to collect and process data that can be used for analysis of travel behaviour.

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A Web-Based Diary and Companion Smartphone app for Travel/Activity Surveys

Cited by 52 publications

References 12 publications

We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning

We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning

Effects from usage of pre-trip information and passenger scheduling strategies on waiting times in public transport: an empirical survey based on a dedicated smartphone application

Spatiotemporal Identification of Trip Stops from Smartphone Data

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