Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft

The explosive growth of the location-enabled devices coupled with the increasing use of Internet services has led to an increasing awareness of the importance and usage of geospatial information in many applications. The mobile navigation apps (often called “Maps”), use a variety of available data sources to calculate and predict the travel time for different modes. This paper evaluates the pedestrian mode of Maps apps in three major smartphone operating systems (Android, iOS and Windows Phone). We will demonstrate that the Maps apps on iOS, Android and Windows Phone in pedestrian mode, predict travel time without learning from the individual’s movement profile. Then, we will exemplify that those apps suffer from a specific data quality issue (the absence of information about location and type of pedestrian crossings). Finally, we will illustrate learning from movement profile of individuals using predictive analytics models to improve the accuracy of travel time estimation for each user (personalization)

The usability analysis for the use of augmented reality and visual instructions in navigation services

The use of Augmented Reality and visual cues as a part of navigational instructions, in addition to conventional audio and textual instructions, may improve the experience of the users of the navigation services. This approach can be also more compatible with the way people give instructions in everyday life; People usually associate directions with visual cues (e.g. “turn right at the square”) when giving navigational instructions in their daily conversations. In this regard, landmarks as the unique and easy-to-recognise features can play an important role. Such easy to remember features, which are available both indoors and outdoors, can be helpful when exploring an unfamiliar environment. A Landmark-based navigation service can make users sure that they are on the correct route, as the user is reassured by seeing the landmark whose information/picture has just been provided as a part of navigational instruction. Such advantages of use of landmarks visual information as a part of the instructions can decrease the time of travel and improve the experiences of the users. This paper assesses how landmarks can improve the performance of pedestrian movements following landmark-based navigational instructions

NoSQL storage and management of geospatial data with emphasis on serving geospatial data using standard geospatial web services

Today a huge amount of geospatial data is being created, collected and used more than ever before. The ever increasing observations and measurements of geo-sensor networks, satellite imageries, point clouds from laser scanning, geospatial data of Location Based Services (LBS) and location-based social networks has become a serious challenge for data management and analysis systems. Traditionally, Relational Database Management Systems (RDBMS) were used to manage and to some extent analyse the geospatial data. Nowadays these systems can be used in many scenarios but there are some situations when using these systems may not provide the required efficiency and effectiveness. In these situations, NoSQL solutions can provide the efficiency necessary for applications using geospatial data. It is important to differentiate between the physical way a NoSQL product is implemented, and the interfaces, coding and access methods they use for the abstraction of data. This paper provides an overview of the major types of NoSQL solutions, their advantages and disadvantages and the challenges they present in managing geospatial data. Then the paper elaborates on serving geospatial data using standard geospatial web services with a NoSQL database as a backend

Using crowdsourced trajectories for automated OSM data entry approach

The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and the generation of information by large groups of users/contributors. OpenStreetMap (OSM) is a very successful example of a crowd-sourced geospatial data project. Unfortunately, it is often the case that OSM contributor inputs (including geometry and attribute data inserts, deletions and updates) have been found to be inaccurate, incomplete, inconsistent or vague. This is due to several reasons which include: (1) many contributors with little experience or training in mapping and Geographic Information Systems (GIS); (2) not enough contributors familiar with the areas being mapped; (3) contributors having different interpretations of the attributes (tags) for specific features; (4) different levels of enthusiasm between mappers resulting in different number of tags for similar features and (5) the user-friendliness of the online user-interface where the underlying map can be viewed and edited. This paper suggests an automatic mechanism, which uses raw spatial data (trajectories of movements contributed by contributors to OSM) to minimise the uncertainty and impact of the above-mentioned issues. This approach takes the raw trajectory datasets as input and analyses them using data mining techniques. In addition, we extract some patterns and rules about the geometry and attributes of the recognised features for the purpose of insertion or editing of features in the OSM database. The underlying idea is that certain characteristics of user trajectories are directly linked to the geometry and the attributes of geographic features. Using these rules successfully results in the generation of new features with higher spatial quality which are subsequently automatically inserted into the OSM database

Spatial uncertainty management in pedestrian navigation

Location-based services use location as contextual data to exclude irrelevant services from users. However almost all positioning technologies can only provide a location with a certain degree of accuracy. It is necessary to have a framework which can handle this inaccuracy and other uncertainties in order to provide a better and more adaptive service. In addition to positioning inaccuracy, location-based services can suffer from other aspects of uncertainty, such as data incompleteness and inconsistency. There is no universal positioning technique which can provide the position of the user seamlessly indoors and outdoors with an acceptable degree of accuracy. Consequently, it is possible to lose the position of the user for a period of time. To avoid this, some systems use more than one positioning technology, each having incomplete datasets; however they still may produce mutually inconsistent data. If an uncertain spatial dataset is stored and analysed in a framework which cannot handle uncertainty, some aspects of the input data may be missed and the outcome may not be fully applicable in real world applications. This chapter aims at developing a rough set-theory-based navigation application which can provide navigational instructions to users by taking spatial uncertainty into account

Seamless pedestrian positioning and navigation using landmarks

Many navigation services, such as car navigation services, provide users with praxic navigational instructions (such as “turn left after 200 metres, then turn right after 150 metres”), however people usually associate directions with visual cues (e.g. “turn right at the square”) when giving navigational instructions in their daily conversations. Landmarks can play an equally important role in navigation and routing services. Landmarks are unique and easy-to-recognise and remember features; therefore, in order to remember when exploring an unfamiliar environment, they would be assets. In addition, Landmarks can be found both indoors and outdoors and their locations are usually fixed. Any positioning techniques which use landmarks as reference points can potentially provide seamless (indoor and outdoor) positioning solutions. For example, users can be localised with respect to landmarks if they can take a photograph of a registered landmark and use an application for image processing and feature extraction to identify the landmark and its location. Landmarks can also be used in pedestrian-specific path finding services. Landmarks can be considered as an important parameter in a path finding algorithm to calculate a route passing more landmarks (to make the user visit a more tourist-focussed area, pass along an easier-to-follow route, etc.). Landmarks can also be used as a part of the navigational instructions provided to users; a landmark-based navigation service makes users sure that they are on the correct route, as the user is reassured by seeing the landmark whose information/picture has just been provided as a part of navigational instruction. This paper shows how landmarks can help improve positioning and praxic navigational instructions in all these ways

Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data

Ambient intelligence (AmI) provides adaptive, personalized, intelligent, ubiquitous and interactive services to wide range of users. AmI can have a variety of applications, including smart shops, health care, smart home, assisted living, and location-based services. Tourist guidance is one of the applications where AmI can have a great contribution to the quality of the service, as the tourists, who may not be very familiar with the visiting site, need a location-aware, ubiquitous, personalised and informative service. Such services should be able to understand the preferences of the users without requiring the users to specify them, predict their interests, and provide relevant and tailored services in the most appropriate way, including audio, visual, and haptic. This paper shows the use of crowd sourced trajectory data in the detection of points of interests and providing ambient tourist guidance based on the patterns recognised over such data