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.
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ABSTRACT: We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations.Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia; 01/2012
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
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ABSTRACT: Existing works on moving objects mainly focus on a single environment such as free space and road network, and do not investigate the complete trip for humans who can pass several environments, e.g., road network, pavement areas, indoor. In this paper, we consider multiple environments and study moving objects with different transportation modes, also called generic moving objects. We aim to answer a new class of queries supporting three kinds of conditions: temporal, spatial, and transportation modes. To efficiently provide the result, we propose an index structure called TM-RTree, which takes into account the feature of moving objects in different environments and has the capability of managing objects on not only temporal and spatial data but also transportation modes. This property is not maintained by existing indices for moving objects. Different cases on transportation modes are supported. Correspondingly, several algorithms are developed. The TM-RTree and related algorithms are developed in a real DBMS to have a practical and solid result for applications. In the experiment, we conduct the performance evaluation using extensive datasets and compare the proposed technique with the other two competitors, demonstrating the efficiency and significant superiority of our solution in various settings.GeoInformatica 07/2014; 19(3). DOI:10.1007/s10707-014-0218-2 · 1.29 Impact Factor