Hamid Safi

Use of smart card fare data to estimate public transport origin-destination matrix

Over the past few years, several techniques have been developed for using smart card fare data to estimate origin–destination (O-D) matrices for public transport. In the past, different walking distance and allowable transfer time assumptions had been applied because of a lack of information about the alighting stop for a trip. Such assumptions can significantly affect the accuracy of the estimated O-D matrices. Little evidence demonstrates the accuracy of O-D pairs estimated with smart card fare data. Unique smart card fare data from Brisbane, Queensland, Australia, offered an opportunity to assess previous methods and their assumptions. South East Queensland data were used to study the effects of different assumptions on estimated O-D matrices and to conduct a sensitivity analysis for different parameters. In addition, an algorithm was proposed for generating an O-D matrix from individual user transactions (trip legs). About 85% of the transfer time was non-walking time (wait and short activity time). More than 90% of passengers walked less than 10 min to transfer between alighting and the next boarding stop; this time represented about 10% of the allowable transfer time. A change in the assumed allowable transfer time from 15 to 90 min had a minor effect on the estimated O-D matrices. Most passengers returned to within 800 m of their first origin on the same day.

Trip Detection with Smartphone-Assisted Collection of Travel Data

This paper puts forward a method that automatically detects trips and trip segments with data on the instantaneous movement attributes of individuals that can be collected automatically by smartphone sensors. The goal is to enhance the accuracy of the data collected through the better identification of single-mode trips and trip segments while minimizing the participant’s involvement and preserving battery life. The proposed method works independently of data from external sources and can be implemented in smartphone applications to enhance the accuracy of the data that are collected and minimize the amount of data that need to be transferred. The method consists of a combination of real-time processing and postprocessing of the data and incorporates a series of rules to clean, split, and merge trips and trip segments, if required. The performance of the model was evaluated in a real-world experiment, in which it achieved an overall accuracy of 97% for the detection of trips from records of daily tracks. The analysis of the results shows that the implementation of the trip detection model increased the proportion of nonmotorized trips detected by 6%. In addition, the implementation of the model increased the accuracy of the data on the duration and the length of the recorded trips.

Crowdsourcing as a Method for the Collection of Revealed Preferences Data

Crowdsourcing has been used widely for the collection of stated preference data (e.g., responses in a survey) by researchers. However, the use of crowdsourcing for collection of revealed preference data (e.g., real-life data collected in natural experiments) is much less common. The study reported in this short (research-in-progress) paper shows how crowdsourcing can be used as a method for the collection of revealed preference data in the context of transport studies. In transport studies, data is traditionally collected through surveys, diaries or simulations. Here, crowdsourcing could provide an alternative method that provides real-life data very fast and very cheap to researchers. To generate insights on crowdsourcing as an alternative data collection method, we use an open call on a crowdsourcing platform (Amazon Mechanical Turk - AMT), a mobile application (Advanced Travel Logging Application for Smartphones II - ATLAS II) and a participant survey to practically perform such a crowdsourced data collection and evaluate the effectiveness of the method. While the full study is still in progress, the initial results reported in this paper are promising and support the idea that crowdsourcing can indeed be used as an effective method for the collection of revealed preference data.