Enriching Activity-Based Models using Smartphone-Based Travel Surveys

Abstract

Smartphone-based travel surveys have attracted much attention recently, for their potential to improve data quality and response rate. One of the first such survey systems, Future Mobility Sensing (FMS), leverages sensors on smartphones, and machine learning techniques to collect detailed personal travel data. The main purpose of this research is to compare data collected by FMS and traditional methods, and study the implications of using FMS data for travel behavior modeling. Since its initial field test in Singapore, FMS has been used in several large-scale household travel surveys, including one in Tel Aviv, Israel. We present comparative analyses that make use of the rich datasets from Singapore and Tel Aviv, focusing on three main aspects: (1) richness in activity behaviors observed, (2) completeness of travel and activity data, and (3) data accuracy. Results show that FMS has clear advantages over traditional travel surveys: it has higher resolution and better accuracy of times, locations, and paths; FMS represents out-of-work and leisure activities well; and reveals large variability in day-to-day activity pattern, which is inadequately captured in a one-day snapshot in typical traditional surveys. FMS also captures travel and activities that tend to be under-reported in traditional surveys such as multiple stops in a tour and work-based sub-tours. These richer and more complete and accurate data can improve future activity-based modeling.

Smartphone-baseret dataindsamling i Transportvaneundersøgelsen - Resultater og erfaringer fra pilotprojekt

“…MMM har gennem en årraekke udviklet og forbedret en smartphone applikation til indsamling af transportvane data, og denne er blevet benyttet i en raekke projekter rundt om i verden, eksempelvis Singapore og Tel Aviv (se Nahmias-Biran et al, 2018) og Arizona (se Hong et al, 2021). I projektet benyttes dataindsamlingsmetoderne fra denne applikation i en customiseret version, hvor Transportvaneundersøgelsens spørgeskema og logikker laegges oven på den eksisterende app, og tilpasses således at det kan forsøges at indsamle TU data på denne nye platform.…”

Laboratories for Research on Freight Systems and Planning

“…Second, as it is challenging to recruit participants for freight surveys, there is a need to design incentive methods that can effectively increase response rate and encourage long-term participation. Some of these efforts have been piloted in household travel surveys (Nahmias-Biran et al 2018) and are related to informational incentives, which can complement or be alternatives to monetary incentives. Third, new and alternative data sources have to be explored.…”

Use of a Smartphone GPS Application for Recurrent Travel Behavior Data Collection

“…A typical HTS collects both demographic data and detailed travel diary data from all household members ( 1 , 2 ). As technological advances have been made, HTS data collection modes have evolved from paper surveys to phone, online, and smartphone surveys to achieve desired response rates and obtain representative data ( 1 – 5 ). As smartphone ownership increases and technologies advance, providing a mobile data collection option for survey respondents has become a necessity to achieve a representative sample ( 2 , 6 – 10 ).…”

“…HTS using online survey instruments as the primary means of data collection have reduced the bias incurred by telephone recruitment, but still face challenges from trip underreporting because of recall bias and survey burden ( 1 , 2 , 11 , 12 ). As a result, HTS studies have adopted smartphone-based applications for data collection to obtain a more representative sample and collect more accurate and robust trip data through the passive data collection capabilities that smartphones provide ( 2 , 4 , 5 , 13 ).…”

“…By using a smartphone-based global positioning system (GPS) application as the primary means of data collection for the majority of households (those that own smartphones), more accurate spatial and temporal data can be obtained in comparison with traditional data collection modes ( 2 , 4 , 5 ). The Future of Mobility Study (FMS), a smartphone-based travel survey conducted in Singapore and Tel-Aviv, showed clear advantages for smartphone data collection when compared with traditional HTS methods, including more accurate trip arrival and departure times, more variability in respondents’ daily activities, a higher frequency of non-work activities, and the collection of trip path data ( 4 , 14 ). Similarly, the Ohio Statewide HTS, a multi-day smartphone-based study, has found multi-day smartphone data valuable for travel modeling because of the precision of passively collected trip data and the wealth of information on schedule variability in a travel diary spanning multiple days ( 2 ).…”