Wi-Fi Location Determination for Semantic Locations

In Wi-Fi location determination literature, little attention is paid to locations that do not have numeric, geometric coordinates, though many users prefer the convenience of non-coordinate locations (consider the ease of giving a street address as opposed to giving latitude and longitude). It is not often easy to tell from the title or abstract of a Wi-Fi location determination article whether or not it has applicability to semantic locations such as room-level names. This article surveys the literature through 2011 on Wi-Fi localization for symbolic locations

Efficient traffic congestion estimation using multiple spatio-temporal properties

Traffic estimation is an important issue to analyze the traffic congestion in large-scale urban traffic situations. Recently, many researchers have used GPS data to estimate traffic congestion. However, how to fuse the multiple data reasonably and guarantee the accuracy and efficiency of these methods are still challenging problems. In this paper, we propose a novel method Multiple Data Estimation (MDE) to estimate the congestion status in urban environment with GPS trajectory data efficiently, where we estimate the congestion status of the area through utilizing multiple properties, including density, velocity, inflow and previous status. Among them, traffic inflow and previous status (combination of time and space factors) are not both used in other existing methods. In order to ensure the accuracy and efficiency, we apply dynamic weights of data and parameters in MDE method. To evaluate our methods, we apply it on large-scale taxi GPS data of Beijing and Shanghai. Extensive experiments on these two real-world datasets demonstrate the significant improvements of our method over several state-of-the-art methods

Positioning Commuters And Shoppers Through Sensing And Correlation

Positioning is a basic and important need in many scenarios of human daily activities. With position information, multifarious services could be vitalized to benefit all kinds of users, from individuals to organizations. Through positioning, people are able to obtain not only geo-location but also time related information. By aggregating position information from individuals, organizations could derive statistical knowledge about group behaviors, such as traffic, business, event, etc. Although enormous effort has been invested in positioning related academic and industrial work, there are still many holes to be filled. This dissertation proposes solutions to address the need of positioning in people’s daily life from two aspects: transportation and shopping. All the solutions are smart-device-based (e.g. smartphone, smartwatch), which could potentially benefit most users considering the prevalence of smart devices. In positioning relevant activities, the components and their movement information could be sensed by different entities from diverse perspectives. The mechanisms presented in this dissertation treat the information collected from one perspective as reference and match it against the data collected from other perspectives to acquire absolute or relative position, in spatial as well as temporal dimension. For transportation, both driver and passenger oriented solutions are proposed. To help drivers improve safety and ease the tension from driving, two correlated systems, OmniView [1] and DriverTalk [2], are provided. These systems infer the relative positions of the vehicles moving together by matching the appearance images of the vehicles seen by each other, which help drivers maintain safe distance from surrounding vehicles and also give them opportunities to precisely convey driving related messages to targeted peer drivers. To improve bus-riding experience for passengers of public transit systems, a system named RideSense [3] is developed. This system correlates the sensor traces collected by both passengers’ smart devices and reference devices in buses to position passengers’ bus-riding, spatially and temporally. With this system, passengers could be billed without any explicit interaction with conventional ticketing facilities in bus system, which makes the transportation system more efficient. For shopping activities, AutoLabel [4, 5] comes into play, which could position customers with regard to stores. AutoLabel constructs a mapping between WiFi vectors and semantic names of stores through correlating the text decorated inside stores with those on stores’ websites. Later, through WiFi scanning and a lookup in the mapping, customers’ smart devices could automatically recognize the semantic names of the stores they are in or nearby. Therefore, AutoLabel-enabled smart device serves as a bridge for the information flow between business owners and customers, which could benefit both sides

Symbolic trajectories

Due to the proliferation of GPS enabled devices in vehicles or with people, large amounts of position data are recorded every day and the management of such mobility data, also called trajectories, is a very active research eld. A lot of e ort has gone into discovering "semantics" from the raw geometric trajectories by relating them to the spatial environment or fi nding patterns, e.g., by data mining techniques. A question is how the resulting "meaningful" trajectories can be represented or further queried. In this paper, we propose a very simple generic model called symbolic trajectory to capture a wide range of of meanings derived from a geometric trajectory. Essentially a symbolic trajectory is just a time dependent label; variants have sets of labels, places, or sets of places. They are modeled as abstract data types and integrated into a well established framework of data types and operations for moving objects. Symbolic trajectories can represent, for exam- ple, the names of roads traversed obtained by map matching, transportation modes, speed pro le, cells of a cellular network, behaviours of animals, cinemas within 2 kms distance, etc. Besides the model, the core technical contribution of the paper is a language for pattern matching and rewriting of symbolic trajectories. A symbolic trajectory can be represented as a sequence of pairs (called units) consisting of a time interval and a label. A pattern consists of unit patterns (speci cations for time interval and/or label) and wildcards, matching units and sequences of units, respectively, as well as regular expressions over such elements. It may further contain variables that can be used in conditions and in rewriting. Conditions and expressions in rewriting may use arbitrary operations available for querying in the host DBMS environment which makes the language extensible and quite powerful. We formally de ne the data model and syntax and semantics of the pattern language. Query operations are off ered to integrate pattern matching, rewriting, and classi cation of symbolic trajectories into a DBMS querying environment. Implementation of the model using finite state machines is described in detail. An experimental evaluation demonstrates the effi ciency of the implementation. In particular, it shows dramatic improvements in storage space and response time in a comparison of symbolic and geometric trajectories for some simple queries that can be executed on both symbolic and raw trajectories

Extracting Semantic Location from Outdoor Positioning Systems

With help of context, computer systems and applications could be more user-friendly, flexible and adaptable. With semantic locations, applications can understand users better or provide helpful services. We propose a method that automatically derives semantic locations from user’s trace. Our experimental results show that the proposed method identities up to 96 % correct semantic locations. 1