Transportation mode detection using mobile phones and GIS information

Abstract

The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

“…A large number of studies has focused on the recording and analysis of mobility and activity data using smartphones, usually by utilizing location (GPS) and accelerometer data ( [18], [19], [20], [21], [22], [23], [24], [25], [26]). Issues such as suitability of sensors for activity recognition [27], accuracy of transport mode classification [28], [29] and energy consumption of the app [30] are well researched areas.…”

Comparative framework for activity-travel diary collection systems

“…Travel diary collection methods and systems 1) Mode Inference: Inferring the transportation mode of a user is a task that is of interest for two research areas: transportation science [2,7,10,13] and Location Based Services (e.g., GeoLife [21][22][23], and others [14,20]). Scientists collect different data sets -such as GPS-only data [21][22][23], accelerometer-only data [7,20], GPS traces fused with accelerometer data [10,13,14] or GPS traces complemented by GIS information [2,15] -that are annotated by users and afterward use machine learning or rule based systems to train classifiers that automatically determine the transportation modes of future data sets. High classification accuracies have been achieved, which are suitable for travel diary generation: e.g., Prelipcean et al [13] 90.8% (seven classes), Reddy et al [14] 93.6% (five classes), Stenneth et al [15] 93.5% (five classes), and Yu et al [20] 90.6% (five classes).…”

The four key challenges of advanced multisensor navigation and positioning

“…Other types of radio signal; environmental features detected using cameras, laser scanners, radar, or sonar; ambient light; sounds; odors; magnetic anomalies, and air pressure could all be used. Context may also be inferred simply by comparing the position solution with a map, provided both are sufficiently accurate [99].…”