Behrang Assemi

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.

A cost–effective ubiquitous method for motor vehicle speed estimation using smartphones

Estimating longitudinal vehicle speed is required in a wide range of applications from road safety to vehicular emissions modelling. Each vehicle speed estimation method has specific challenges including the high cost of measurement equipment, the small range of vehicle models accessible for performing tests and the low resolution of data in time and space. Thus, the goals of this study are (a) to investigate the use of smartphones’ integrated sensors as a convenient, reliable and powerful means of vehicle speed data collection which would mitigate the issues observed in previous methods, and (b) to propose a post-processing pipeline for generating vehicles’ speed profiles based on the raw data captured by the proposed data collection method. Accordingly, a smartphone application is developed to facilitate the collection of vehicles’ movement data, particularly inside tunnels and high-density urban areas, where state-of-the-art global positioning system (GPS) applications fail to record the desired data. Moreover, a post-processing pipeline is proposed for calculating vehicles’ speed profiles and emissions from the raw smartphone acceleration data, and the full system is evaluated through a series of controlled experiments as well as simulations.

Retraction notice to: Provider Feedback Information and Customer Choice Decisions on Crowdsourcing Marketplaces: Evidence from Two Discrete Choice Experiments [Decision Support Systems 82 (2016) 1-11]

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief following a dispute over authorship that the original listed authors were not able to resolve. The authorship of the paper was changed during the latter stages of review without notification to either the Editor-in-Chief or Elsevier.

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.

Search Engine Advertising Adoption and Utilization: An Empirical Investigation of Inflectional Factors

Search engine advertising (SEA) is a prominent source of revenue for search engine companies, and also a solution for businesses to promote their visibility on the web. However, there is little academic research available about the factors and the extent to which they may influence businesses’ decision to adopt SEA. Building on Theory of Planned Behavior, Technology Acceptance Model, and Unified Theory of Acceptance and Use of Technology, this study develops a context-specific model for understanding the factors that influence the decision of businesses to use SEA. Using structural equation modeling and survey data collected from 142 businesses, this research finds that the intention of businesses to use SEA is directly influenced by four factors: (i) attitude toward SEA, (ii) subjective norms, (iii) perceived control over SEA, and (iv) perceived benefits of SEA in terms of increasing web traffic, increasing sales and creating awareness. Furthermore, the research we discover six additional factors that have an indirect influence: (i) trust in search engines, (ii) perceived risk of SEA, (iii) ability to manage keywords and bids, (iv) ability to analyze and monitor outcomes, (v) advertising expertise, and (vi) using external experts.