Assessing the representativeness of a smartphone-based household travel survey in Dar es Salaam, Tanzania

Abstract

The household travel survey (HTS) finds itself in the midst of rapid technological change. Traditional methods are increasingly being sidelined by digital devices and computational powerfor tracking movements, automatically detecting modes and activities, facilitating data collection, etc. Smartphones have recently emerged as the latest technological enhancement. FMS is a smartphone-based prompted-recall HTS platform, consisting of an app for sensor data collection, a backend for data processing and inference, and a user interface for verification of inferences (e.g., modes, activities, times, etc.). FMS, has been deployed in several cities of the global north, including Singapore. This paper assesses the first use of FMS in a city of the global south, Dar es Salaam. FMS in Dar was implemented over a one-month period, among 581 adults chosen from 300 randomly selected households. Individuals were provided phones with data plans and the FMS app preloaded. Verification of the collected data occurred every three days, via a phone interview. The experiment reveals various social and technical challenges. Models of individual likelihood to participate suggest little bias. Several socioeconomic and demographic characteristics apparently do influence, however, the number of days fully verified per individual. Similar apparent biases emerge when predicting the likelihood of a given day being verified. Some risk of non-random, non-response is, thus, evident.

Positioning household surveys for the next decade

“…Research has demonstrated the extent and econometric implications of non-classical measurement errors in self-reported survey data on a range of topics, while also documenting the accuracy, feasibility, and cost implications of adopting direct measurement tools, such as GPS technology for plot area measurement and outline capture [79][80][81][82], crop cutting for crop yield estimation [83][84][85][86], high-frequency phone survey data collection for measuring household agricultural labor inputs [87,88], DNA fingerprinting for crop variety identification [89,90], physical activity trackers (i.e. accelerometers) for informing the measurement and analysis of labor productivity, effort, and poverty [91][92][93][94], smartphone applications for time use measurement, recording social interactions between respondents and interviewers, or real-time travel patterns [95][96][97][98][99], lowcost testing kits for the rapid measurement of water quality [100], and "web scraping" for automating the collection of prices for selected internet retailers, as opposed to relying exclusively on survey operations for the Consumer Price Index (CPI) [101].…”

New and emerging data forms in transportation planning and policy: Opportunities and challenges for “Track and Trace” data

“…Two out of twenty seven network studies (7%), two of the twelve social media studies (17%) and eleven out of twenty seven GPS studies (41%). The objective and location of these studies varies widely, but we note that the majority rely on GPS and are related to assessment for use in travel surveys (Donaire-Gonzalez et al, 2016, Geurs et al, 2015, Montini et al, 2015, Safi et al, 2017, Thomas et al, 2018, Zegras et al, 2018. Other objectives for papers considering full T&T datasets are the development of methodologies (Calabrese et al, 2013, Thomas et al, 2018, Toole et al, 2015, Zegras et al, 2018, analysis of travel patterns (Gong et al, 2018, Huang and Li, 2019, Toole et al, 2015 and prediction of preferences (Bantis and Haworth, 2017, Gong et al, 2018, Huang and Li, 2019, Semanjski and Gautama, 2016, Xiao et al, 2016, In general, studies using Network data focus on travel demand (mobility content) whereas those analysing Social Media data concentrate on socio-demographics (individual context).…”

Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility

“…Travel surveys have been a key tool, relying on a small, but carefully orchestrated, sample, access to census data and statistical craftsmanship. They are expensive and time consuming to produce [2] and they show heavy under-reporting [3]. This traditional source is contrasted with the information that can be extracted from available and emerging pervasive data sources, such as mobile phone [4], Wi-Fi [5], and social network data [6].…”