Trip analyzer through smartphone apps.
ABSTRACT Broad usage of Smartphones and mobile apps enables both individual trip summary and regional travel demand analysis. In this paper, we describe a trip analysis system, as part of smarter transit service. This trip analysis system consists of mobile apps and a centralized analyzer. It identifies the travel mode and purpose of the trips sensed by mobile devices, provides trip summaries and insights to mobile subscribers, and generates meaningful patterns to support traffic operation planning and transit system design. It is developed and deployed to the Smartphones of the volunteers in Dubuque, IA, to serve both the volunteers and the transit agencies. Preliminary evaluation has demonstrated the applicability of the design.
SourceAvailable from: Roberto Arroyo[Show abstract] [Hide abstract]
ABSTRACT: This paper presents DriveSafe, a new driver safety app for iPhones that detects inattentive driving behaviors and gives corresponding feedback to drivers, scoring their driving and alerting them in case their behaviors are unsafe. It uses computer vision and pattern recognition techniques on the iPhone to assess whether the driver is drowsy or distracted using the rear-camera, the microphone, the inertial sensors and the GPS. We present the general architecture of DriveSafe and evaluate its performance using data from 12 drivers in two different studies. The first one evaluates the detection of some inattentive driving behaviors obtaining an overall precision of 82% at 92% of recall. The second one compares the scores between DriveSafe vs the commercial AXA Drive app obtaining a better valuation to its operation. DriveSafe is the first app for smartphones based on inbuilt sensors able to detect inattentive behaviors evaluating the quality of the driving at the same time. It represents a new disruptive technology because, on the one hand, it provides similar ADAS features that found in luxury cars, and on the other hand, it presents a viable alternative for the “black-boxes” installed in vehicles by the insurance companies.IEEE Intelligent Vehicles Symposium (IV), Dearborn, Michigan, USA; 06/2014
GeoInformatica 01/2014; DOI:10.1007/s10707-014-0218-2 · 1.29 Impact Factor
Conference Paper: Mining the semantics of origin-destination flows using taxi traces[Show abstract] [Hide abstract]
ABSTRACT: Origin-destination(OD) flows reflect both human activity and urban dynamic in a city. However, our understanding about their patterns remains limited. In this paper, we study the GPS traces of taxis in a city with several millions people, China and find that there are significant patterns under the OD flows constructed from taxis' random motion. Our spatiotemporal analysis shows that those patterns have close relationship with the semantics of OD flows, hence we can mine the semantics of OD flows from raw GPS trace data. The approach we proposed offers a novel way to explore the human mobility and location characteristic.Proceedings of the 2012 ACM Conference on Ubiquitous Computing; 09/2012