Progress in Location-Based Services 2016

Indoor positioning which uses signal strength values of Wi-Fi networks have become popular as these wireless networks often already exist and many mobile devices, such as smartphones or tablets, have built-in Wi-Fi cards. Usually fingerprinting is employed for positioning which achieves relatively low positioning accuracies on the several meter level. In the scope of this work two methods are presented which have the potential to improve the fingerprinting performance using long-time RSS observations at reference stations. Both methods employ the usage of at least three reference stations surrounding the area of interest on which signal strength observations are continuously performed during the whole measurement process. Thereby the first method uses a 2-D linear plane-interpolation for the deduction of real-time corrections. For that purpose, the measured signal strengths are reduced by the long-time measurements which are interpolated at the approximate position of the measuring point. In the second method the daily average of the long-time measurements is applied and the improvements of the measurements are calculated by the deviation from the daily average. For this method it is conceivable that a single reference station may be sufficient if it is located in the middle of the area of interest. Field tests were performed in an office building and are analyzed. The fingerprinting algorithms reached an averaged positioning accuracy of around 5 m in dependence on the used smartphone. The daily average improvements (DAI) method provided a better performance than the interpolation method which is highly influenced by the required approximate position of the user.

Knowing the location of Wi-Fi antennas may be critical for indoor localization. However, in a real environment, their positions may be unknown since they can be managed by external entities. This paper introduces a new method for evaluating the suitability of using the weighted centroid method for the 2D localization of a Wi-Fi AP. The method is based on the idea that the weighted centroid method provides its best results when there are fingerprints taken around the AP. In order to find the probability of being in the presence of such situations, a natural neighbor interpolation method is used to find the regions with the highest signal strengths. A geometrical method is then used to characterize that probability based on the distribution of those regions in relation to the AP position estimation given by the weighted centroid method. The paper describes the testing location and the used Wi-Fi fingerprints database. That database is used to create new databases that recreate different sampling possibilities through a samples deletion strategy. The original database and the newly created ones are then used to evaluate the localization results of several AP localization methods and the new method proposed in this paper. The evaluation results have shown that the proposed method is able to provide a proper probability for the suitability of using the weighted centroid method for localizing a Wi-Fi AP.

Many methods of indoor navigation for smartphones are augmented with Pedestrian Dead Reckoning (PDR) to improve accuracy and to reduce latency. PDR requires an accurate estimate of the device orientation. From the pitch and roll angles the sensor readings can be rotated to the horizontal plane, and with the yaw angle the direction of movement can be determined. While a simple implementation using only accelerometer and magnetometer is possible, more accurate results may be obtained by also including the gyroscope measurements. The approach in this paper uses a Kalman filter to fuse gyroscope with accelerometer and magnetometer readings. The system equation uses random walk on straight trajectories and additional gyroscope readings on turns. Turns are detected using a statistical test on the innovation of the Kalman filter as well as a condition on the estimated yaw-rate from the gyroscope. A second Kalman filter separates gravity from specific force by processing acceleration measurements. The estimated gravity is used in the orientation filter to determine pitch and roll. The filter has been tested using trajectories with known ground truth taken with off the shelf mobile devices in corridor and office environments. The outer heading accuracy approaches 10°, dominated by systematic effects, largely due to magnetic disturbances. The achieved inner accuracy for the heading is 4°.

Location information that is crucial for all location-based services is almost always available due to a number of different positioning techniques and technologies such as GPS and WiFi positioning. However, positioning technologies cannot provide sufficient position information when a user is underground, e.g. travelling with a car through a tunnel or on subways on an underground public transportation network. While there have been a number of attempts to utilize expensive infrastructure and smartphone sensors to address this situation, all of these techniques are either limited in scope, very expensive, or somewhat limited in accuracy. In this paper, we present a novel smartphone-based approach called SubwayAPPS (Subway Air Pressure Positioning System) that for the first time utilizes relative air pressure changes as detected by smartphone barometers to position a user. We first demonstrate the feasibility of this approach by comparing the depth characteristics of five major underground transportation networks across the globe and show that our novel approach is feasible for positioning users while they are underground in these networks. Second, we show with two user tests in Brussels and London that our lightweight approach works well as other more complex techniques, e.g. techniques that rely on pattern matching using the build-in accelerometers or gyroscopes, under realistic conditions.

Maps on mobile devices provide the convenience of accessing spatial information of the surroundings. They also raise the concern that the small screen size impacts user’s acquisition of spatial knowledge as the small size causes the fragmentation of spatial knowledge. This fragmentation leads to the expense of more cognitive efforts and degradation of spatial knowledge. Following some effective approaches of representing distance to off-screen locations as contextual cues, this paper reports a design that incorporates not only the direction but also the distance using symbols. In addition, this study uses three different visual variables including size, color value, and crispness at the ordinal level of measurement and then compares their effectiveness on the perception of distance. Results show that color value contributes the least to the perception of distance. Size leads to slightly higher accuracy than crispness in comparing distances of off-screen landmarks. This study provides valuable information to further explore the impacts of different visual variables that could facilitate the acquisition of spatial knowledge on mobile devices. This study also points out the necessity of follow-up studies to clarify some issues in the current design as well as its impact on actual wayfinding performance.

Eye movements provide information on the mental processing of landmark objects while navigating. The present study investigates landmark-based navigation by pedestrians in real world environments using mobile eye tracking technology. The goal of the study is to identify whether landmarks on maps optimize the navigation procedure and the usage of a map, and imprint the cognitive map sustainably. Two independent test groups navigated through unfamiliar urban environment and were subsequently interviewed. One group had landmark visualized on a map as an additional aid, the control group did not. The results show that objects that are focused longer and more frequently transfer onto the mental map. Upon recalling objects present in the surroundings, on average 8.3 landmarks were named per interview by the landmark group, compared to 7.0 for the control group. For the control group, the usage and duration of observation of the map was thereby approximately 1.7 times greater than for the landmark group. Following the memory test, the participants in the landmark group remembered significantly more objects and located these correctly as compared to the control group. In summary, the results show that the visualization of landmarks on maps optimizes the use of maps for navigation, whereby more landmark objects transfer to long-term memory and the mental map.

Multiple approaches to support non-visual navigation have been proposed, of which traditional auditory turn-by-turn navigational systems achieved high popularity. Despite being modified according to the needs of visually impaired users, the underlying dataset communicated to the wayfinder is sourced primarily from traditional POI databases which are of limited use to blind navigators. This work proposes the use of environmental features spontaneously detected by blind navigators during their everyday locomotion as ‘local landmarks’ for enriching auditory navigational instructions. We report results of a survey which served to identify such environmental features. Consequently, we propose a list of potential local landmarks for the blind. Next, in a usability study, we demonstrate that enriching traditional turn-by-turn auditory instructions with local landmarks can improve the subjective satisfaction and confidence in navigation. Results indicate that the improvements seem to be achieved even without increasing the subjective complexity of the instructions. Finally we discuss how using local landmarks to enrich auditory navigational instructions can benefit visually impaired users.

Nowadays indoor navigation and the understanding of indoor maps and floor plans are becoming increasingly important fields of research and application. This paper introduces clustering of floor plan areas of buildings according to different characteristics. These characteristics consist of computed human perception of space, namely isovist features. Based on the calculated isovist features of floorplans we can show the possible existence of greatly varying alternative routes inside and around buildings. These routes are archetypes, since they are products of archetypal analysis, a fuzzy clustering method that allows the identification of observations with extreme values. Besides archetypal routes in a building we derive floor plan area archetypes. This has the intention of gaining more knowledge on how parts of selected indoor environments are perceived by humans. Finally, our approach helps to find a connection between subjective human perceptions and defined functional spaces in indoor environments.

Location referencing systems (LRS) are a crucial requisite for referencing traffic information to a road network. In the past, several methods and standards for static or dynamic location referencing have been proposed. All of them support machine-interpretable location references but only some of them include human-interpretable concepts. If included, these references are based on pre-defined locations (e.g. as location catalogue) and often miss meaningful interlinking with road network models (e.g. locations being simply mapped to geographic coordinates). In a parallel research strand, ontological concepts for structuring road networks based on human conceptualizations of space have been proposed. So far, both methods have not been integrated. The current work closes this gap and proposes a generation process for meaningful location references on top of road networks based on qualitative spatial concepts. A prototypical implementation using OWL, Neo4J graph database and a standardized nationwide road network graph shows the practical applicability of the approach. Results indicate that the proposed approach is able to bridge the gap between existing road network models and human conceptualizations on multiple levels of abstraction.

Current popular multi-modal routing systems often do not move beyond combining regularly scheduled public transportation with walking, cycling or car driving. Seldom included are other travel options such as carpooling, carsharing, or bikesharing, as well as the possibility to compute personalized results tailored to the specific needs and preferences of the individual user. Partially, this is due to the fact that the inclusion of various modes of transportation and user requirements quickly leads to complex, semantically enriched graph structures, which to a certain degree impede downstream procedures such as dynamic graph updates or route queries. In this paper, we aim to reduce the computational effort and specification complexity of personalized multi-modal routing by use of a preceding heuristic, which, based on information stored in a user profile, derives a set of feasible candidate travel options, which can then be evaluated by a traditional routing algorithm. We demonstrate the applicability of the proposed system with two practical examples.

Traditional survey methods have long been used in the field of highway engineering to measure the cross-slope, longitudinal grade, rut depth, and ride quality of existing roadways. However, these methods are slow, tedious, labor intensive, and almost always require partial or full lane closure resulting in traffic delays, increase in costs, and inconvenience to the traveling public. Advances in inertial sensor and inertial navigation technologies have allowed their implementation as state-of-the-art mobile data collection systems. The Florida Department of Transportation (FDOT) operates two mobile data collection systems referred to as Multi-Purpose Survey Vehicles (MPSVs). They collect pavement data including but not limited to cross-slope, longitudinal grade, and wheel-paths’ rut depth at typical highway speeds. The MPSVs are equipped with a position and orientation system (POS) coupled with an inertial profiler unit. The core of the POS consists of a tightly-coupled Inertial Measurement Unit (IMU) and a Differential Global Positioning System (DGPS). This paper presents a methodology for the development of an Automated Roadway Deficiency Information System using Geographical Information System (GIS) software to map areas prone to hydroplaning. The functionality of the developed information system was tested on a pilot project using MPSV collected data. Highway agencies can successfully implement this methodology to complement and enhance their existing safety and pavement management programs.

In this paper, we present the results of developing a geo-visual analytics application to support urban services. The goal is to allow non-GIS users to explore the taxi traveler’s hot spots in Shanghai extracted from one week taxi floating car data (FCD). To achieve this, we proposed a workflow based on the visualization pipeline. Firstly, we preprocess the data to extract the origins (o) and destinations (d) from the FCD and apply data mining methods to detect taxi traveler’s hot spots, to which semantics are further tagged using point of interest (POI) data extracted from OpenStreetMap (OSM) project. The detected hot spots are selected to show in the application for the user to conduct further visual analysis. Furthermore, we implement a web-based interactive visual explorative system, in which the graphic user interface contains multiple views (spatial, temporal and thematic) and interactive components are built up using the current web technologies. Finally, a possible use case of the application is introduced. Our results show that the developed geo-visual analytics application enables studying traveler’s activity patterns. The visual analysis can be conducted with this tool for several aspects. The visual queries help to detect when and where hot spots occur and to compare the temporal distributions for nearby hot spots.

Location recommendation (LR) or rather location-based recommender systems (LBRS) are an integral part of modern location-based services (LBS). Most LR algorithms only focus on location-specific attributes when calculating recommendations, while completely ignoring the urban structure surrounding the locations. (In this paper we refer to a geographic coordinate (latitude and longitude) as position. Locations and places in contrast refer to physical entities e.g. a restaurant, a bus stop or a lake). This paper demonstrates how the urban structure can be modelled in LR calculations by using data from OpenStreetMap (OSM) and the location data itself. Based on these datasets, we present two approaches to extend the LR process by (1) including the urban structure in direct proximity of the location (Proximity Indicators and Areal Descriptors) and by (2) not only looking for individual locations but location clusters (Similarity Clusters). Thereby we acknowledge the complexity of a location, which can not be perceived as a detached entity. A location is part of a given urban structure and we need to include the parameters of this structure in our algorithms. A prototypical implementation compares locations from four major German cities: Berlin, Hamburg, Munich and Cologne and thereby highlights the applicability of the underlying data structures derived from OSM and the location data itself. We conclude by outlining the potential of the presented approaches in the context of LR as well as their relevancy for urban planning and neighboring disciplines.

Today, a large set of location-based services exists and millions of people use these applications on a daily base. These services are capable of fulfilling and assisting with a variety of tasks, such as fetching points of interest or indicating friends nearby, performing web search requests, which respect the context of the user, or navigating to a destination point. Even though these are very helpful tools, most LBS perform simplistic ‘point-to-point’ queries. In this paper, we propose to extend the query types of current LBS and show how these also extend their potentials. We explain, why the absence of additional query types is an issue in certain cases, and summarize the work accomplished in this area. In addition, we concentrate on one usage domain which is missing in the majority of current LBS—information about space usage rules, e.g. ‘no smoking in the restaurant’ or ‘no dogs allowed in this park’ and illustrate these with various examples.

Sharing hyperlocal geospatial knowledge with one’s community may foster many positive outcomes, such as increasing a sense of community. However, much of this information travels by word of mouth and, therefore, does not reach everyone in time or even at all. We present the human-centred concept design process of #hylo, a geosocial network in which users can easily share personal, hyperlocal geospatial knowledge about their surroundings. Users may gain hyperlocal information of their own places, filtered with their own interests. We expect #hylo to increase (1) attachment to location, (2) interest in the area, and (3) social participation in local community. We explain the design process and show results from initial user studies that were encouraging, as the participants were interested in this kind of hyperlocal information and found it useful. Even those who are usually hesitant to post in social media, found this kind of community-focused, hyperlocal platform to be a pleasant place to share information.

The visualization of cross-domain spatial data sets has become an important task within the analysis of energy models. The representation of these models is especially important in urban areas, in which the under-standing of patterns of energy production and demand is key for an efficient city planning. Location Based Services (LBS) provide a valuable addition towards the analysis and visualization of those data sets as the user can explore the output of different models and simulations in the real environment at the location of interest. Towards this aim, the present research explores mobile alternatives to the visual analysis of temporal data series and 3D building models. Based on the fields of numerical simulation, GIS and computer graphics, this work presents a novel mobile service that allows exploring urban models at different Level of Details (LoDs) using well-known standards such as CityGML. Ultimately, the project enables researchers, city planners and technicians to explore urban energy datasets in an interactive and immersive manner as Virtual Globes, Virtual Reality and Augmented Reality. Using models of the city of Karlsruhe, the final service has been implemented and tested on the iOS platform providing an empirical insight on the performance of the system. In addition, this research provides a holistic approach by developing one application that is capable of seamlessly change the visualization mode.

Location information is essential to location-based services (LBS), but also has the potential to reveal sensitive information about the users of LBS to malicious agents. Therefore, location privacy is an important issue to address for both users and providers of LBS. In this paper, we investigate how location privacy can be realized in the context of a location-based service. Based on a review of architectures for LBS and key issues related to location privacy, we discuss several measures to integrate location privacy into LBS. In order to address privacy threats associated with the storage of location information, we propose an approach based on privacy-by-design principles and introduce a conceptual model to facilitate the implementation of those principles. In addition, we investigate the role of location data management in the context of privacy preservation, and propose the concept of temporal and spatial ephemerality to improve location privacy in the context of a location-based service.

A course named Seminar of Culture Studies is given to undergraduate students in the School of Cultural and Creative Studies of Aoyama Gakuin University in Tokyo, Japan, in which a new mobile application named Manpo was applied for the students to create location-based audio tour content in 2013, 2014 and 2015. With little background knowledge on geography and cartography, the students tried to draw maps and create georeferences for positioning on the maps using Manpo’s functions. They also focused on recording audio guides for the POIs and walking routes, and bundled them to the maps together with photos and texts. The procedures of the classes and the students’ performances were observed, in which the problems encountered by the students showed the difficulties, such as achieving appropriate georeferencing for accurate positioning, for non-professional users. However, the classes and students provided hints to the further development of Manpo, which also improved the classes and the students’ works. As the course was proved to be a success, it inspired future research plans of a platform that can involve researchers, developers, mapmakers, local communities, and ordinary users to provide user-generated content including diverse maps for more attractive location-based services.

In the last decade there have been various attempts to foster location-based public participation. Researchers as well as municipalities have explored several methods, among them the implementation of digital participation tools. However, the actual level of participation has remained low. This gives cause to both analyze the reasons for the ineffectiveness of the explored methods as well as to “think outside the box” and try novel approaches. One of such approaches is gamification. This research investigates the effects adding game-inspired elements can have on participation. In particular, we focus on how motivational factors differ for gamified participation and whether motivations influence citizens’ level of engagement. To do so, we conducted an experiment with a location-based mobile participation prototype. Our results suggest that participation in a gamified application was higher than in one without, but also decreased intrinsic motivation, which was found to influence activity in location-based public participation. The strongest reported motivation was reporting issues regarding urban topics and stating one’s opinion.