Context-aware stated preferences with smartphone-based travel surveys

Abstract

Stated preferences surveys are most commonly used to provide behavioral insights on hypothetical travel scenarios such as new transportation services or attribute ranges beyond those observed in existing conditions. When designing SP surveys, considerable care is needed to balance the statistical objectives with the realism of the experiment. This paper presents an innovative method for smartphone-based stated preferences (SP) surveys leveraging state-of-theart smartphone-based survey platforms and their revealed preferences sensing capabilities. A random experimental design generates context-aware SP profiles using user specific socioeconomic characteristics and past travel data along with relevant web data for scenario generation. The generated choice tasks are automatically validated to reduce the number of dominant or inferior alternatives in real-time, then validated using Monte-Carlo simulations offline. In this paper we focus our attention on mode choice and design an experiment that considers a wide range of possible existing mode alternatives along with a new alternative ondemand mobility service that does not exist in real life. This experiment is then used to collect SP data or a sample of 224 respondents in the Greater Boston Area. A discrete mode choice model is estimated to illustrate the benefit of the proposed method in capturing current contextspecific preferences in response to the new scenario.

Ridesourcing and Travel Demand: Potential Effects of Transportation Network Companies in Bogotá

“…Stated-preference (SP) surveys have traditionally been the go-to method for mode choice model applications in the transportation field for over three decades [34]. Stated-preference data stems from hypothetical choice situations where the researcher defines the trade-offs to which respondents are confronted [29,30].…”

“…When considering new alternatives for transportation choices, it is important to present information in a similar way as their market framing. We tried to apply context-specific considerations for the application of the SP survey in Bogotá, which are consistent with the recommendations by Danaf et al [34], building on observable travel behavior in the local context and previously applied instruments for a similar target population in the city.…”

SMART mobility via prediction, optimization and personalization

“…The machine learning techniques are continuously being improved in order to reduce the burden on the user, i.e., minimize the need for the user to correct trip and activity information on their diaries. Note that, FMS is extended with stated preferences capability for understanding the behavior toward new mobility services (Atasoy et al, 2018;Danaf et al, 2019b) and it can be used for obtaining reasonable parameter values in the early phases of the Smart Mobility solution (in a simulation environment or real-life) before any estimation is possible based on observed choices.…”

Public transport path choice estimation based on trip data from dedicated smartphone app survey

“…Many smartphone survey apps used in transport research involve the respondent to some degree in the reporting of trips or for validation of the automatically recorded trips. This principle of semi-automatic guided trip diary reporting, sometimes termed prompted recall ( Stopher et al 2015;Danaf et al 2019), has been used to increase validity and accuracy by optimising the trade-off between automation and user involvement ( Chang et al 2017). As suggested by Danaf et al (2019), putting the survey respondent into a realistic choice setting, derived from his or her revealed past behaviour, has a significant positive impact on the validity of stated survey responses regarding tentative actions.…”

“…This principle of semi-automatic guided trip diary reporting, sometimes termed prompted recall ( Stopher et al 2015;Danaf et al 2019), has been used to increase validity and accuracy by optimising the trade-off between automation and user involvement ( Chang et al 2017). As suggested by Danaf et al (2019), putting the survey respondent into a realistic choice setting, derived from his or her revealed past behaviour, has a significant positive impact on the validity of stated survey responses regarding tentative actions. We argue that, by prompting for survey responses in relevant travel situations 'in situ', it is possible to improve the validity of survey responses even further.…”