ABSTRACT The proliferation of the Smartphone industry and GPS technologies have made it easier and more precise to track accurate location of customers, allowing marketers to target them in real-time at their real-time locations. This topic has gained scholarly attention over the years but it lacks a clear and coherent roadmap for future research. Therefore, the objective of this study is to conduct a systematic review and comprehensive analysis of the extant literature on LBA using two databases- Scopus and Web of Science. Accordingly, a systematic literature review of 128 articles is structured using Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) to gather, analyse, and synthesise the literature on Location-based Advertising (LBA). The findings indicate that the technology acceptance model followed by the privacy calculus are the most prominent theories. In addition, the study highlights the focus of articles by grouping them into five major themes. Further, an integrative framework using antecedents, moderators, mediators, and outcomes is developed. Additionally, the antecedents of the customers’ LBA usage intention are categorised into five categories. This study contributes substantially to the current reservoir of knowledge and builds the foundation for future research using the theories, constructs, characteristics, and methods (TCCM) framework.

ABSTRACT Understanding human mobility undoubtedly enriches common goods. Among available location-aware technologies, Wi-Fi provides a more sustainable and high-resolution means to study human mobility patterns as it has become conspicuous and affordable recently. Whilst existing studies have shed light on facets of personal mobility, the intricate dynamics of group mobility have garnered comparatively scant empirical scrutiny in real-world settings, especially in the juxtaposition with individual mobility. Moreover, they have often overlooked the multifaceted nature of personal attributes influencing daily routines. This study introduces a comprehensive framework that takes advantage of the readily available Wi-Fi connection data and cloud-assisted computing for juxtaposing individual and group mobility. The framework was tested on auniversity campus that provides representative human mobility patterns with diverse attributes. Two tests that aim to demonstrate the framework’s capability to (1) capture individual mobility patterns by using data processing amenable to the spatiotemporal sparseness and to formulate group mobility and (2) differentiate quantitatively the spatiotemporal signatures of distributions, night activities, transitions, and network topologies were conducted. This study uncovers distinct disparities between individuals and groups, and heterogeneities among different attributes in both an empirical and real-world scenario with a reduction in computation time to approximately 6% of the baseline.

ABSTRACT For a public transport operator, estimating passenger flows is vital to plan inclusive and efficient service. Understanding where and when to optimally allocate resources results in better customer experiences, further shaping and optimizing public transport services. To this end, the project Mobile Data Fusion aims at developing a technique to collect, process, and merge data from different sources. The data are used to inform public transport operators about passenger demand, by combining Wi-Fi and Bluetooth signals from customer devices with time-of-flight counting sensors. The project had access to a large study area in Kassel, Germany, with a fleet of 50 vehicles servicing eight passenger lines, all equipped with automatic passenger counting sensors. In the course of this study, we found out that Bluetooth signals did not offer any significant insight , while Wi-Fi signals could be used to reconstruct a trend, albeit not the absolute occupancy level. In this paper, we present our hardware setup and a subset of our data set to show how the Wi-Fi and Bluetooth signals correlate with the measured occupancy. The ongoing data collection will provide additional material for future studies, and answer some of the pending questions discussed in this work.

ABSTRACT Accurate prediction of ship trajectories can be an important capability for various maritime transport applications, such as vessel traffic services (VTS), traffic flow assessment, and collision avoidance systems. The widespread availability of AIS (Automatic Identification System) data and the progress made in the deep learning methods in the last decade motivate us to attack this problem from a data-driven perspective. This paper presents the results of a study where various encoder–decoder architectures were applied to the ship trajectory prediction problem using AIS data that were collected from the Rotterdam port approach area. The models were trained with the AIS data along four routes belonging to different ship types/lengths without any clustering or filtering. An average position RMSE of 1.6 km was obtained when the best-performing model predicts ship positions 30 min into the future using 60 min of historical data.

ABSTRACT The objective of this paper is to investigate and propose a method for Indoor Localisation based on Isovists, with the aim of extending the fields of Location-based Services and Geomatics. Various methods and combinations incorporating Isovist concepts, Space Syntax, and visibility graphs are examined and assessed. By investigating these approaches, this study aims to create a comprehensive methodology to achieve localisation using Isovists. The main conclusion drawn from this research is that an Indoor Localisation method based on Isovists is not only feasible but can also effectively support Location-based Services. The analysis and evaluation of all the components have been thoroughly conducted, indicating that when properly integrated, they can provide substantial value for LBS applications. As this is a new method for Indoor Localisation, there is significant scope for future work, particularly in terms of connecting it with existing techniques and integrating them into user applications.

ABSTRACT To ensure good usability, Location Based Services (LBS) should be context-aware, i.e. adapting the information and services according to the context of their user, such as his/her location, tasks, preferences, and the underlying geo-social environment. This article reviews the main challenges related to the context modelling and processing in LBS, and proposes a list of essential research opportunities that can be pursued to overcome the challenges. These research challenges and opportunities are classified into four groups: ‘modelling of the geo-social environment’, ‘modelling of the mobile user’, ‘context-aware adaptation’, and ‘ethical data modelling and processing’. Sufficiently addressing these issues will enable LBS to provide ‘5R’, i.e. the ‘right’ information, in the ‘right’ way, at the ‘right’ time, in the ‘right’ place, to the ‘right’ person.

ABSTRACT This work investigates Point Cloud Semantic Segmentation (PCSS) to detect bicycle paths from 3D point cloud data of bicycle-mounted LiDARs. For the task of PCSS, an existing Convolutional Neural Network architecture (CNN) is first pre-trained on the Semantic KITTI data set, an important data set for PCSS in LiDAR scans of driving situations. Furthermore, a new, semantically-labelled 3D LiDAR data set, the Salzburg Bicycle LiDAR Data Set (SBLD), is presented, which consists of 16,008 point clouds recorded by a ROS2-enabled sensor bicycle with five 3D LiDARs covering every direction. After fine-tuning the CNN on the SBLD train set, the segmentation performance is evaluated on the SBLD test set. The CNN shows promising results in recognising bike paths, vegetation, terrain and buildings in the SBLD. To further push the segmentation performance, the existing CNN is enhanced with self-attention blocks. The evaluation of this enhanced architecture shows a performance gain of 2.79% points compared to the original architecture. The proposed segmentation approach shows practical potential for detecting bike paths. The SBLD is provided to the scientific community to further drive research in the field of sensor bicycles.

ABSTRACT Locating and removing landmines and other ERW (Explosive Remnants of War) is dangerous, hazardous, and time-consuming. It requires implementing multilevel on-site surveys: general non-technical surveys to mark the areas affected and technical surveys to determine the perimeter of related minefields. This paper introduces a landmine location-based prediction model, combining military experience with machine-learning techniques and spatiotemporal data, by introducing a new approach for area selection and adding military-based features for context modelling and model training. Besides predicting landmine’s location areas, this model classifies the affected regions by priority and difficulty of clearance, in such a way as to minimise the long time needed by surveys and reduce the danger related to that task, thus providing the clearance organisations with a good resource allocation for their operations. We applied several machine learning techniques that combine Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBOOST), taking into consideration the imbalanced data problem and tweaking for the best performance and accuracy. The experimental results show that the model has the potential to provide reliable predictions and valuable services for demining operations on the field.

ABSTRACT Background This study assesses the impact of COVID-19 on U.S. domestic tourist travel to selected Florida state parks and two New York City (NYC) attractions, inferred from user-generated content (UGC), i.e. TripAdvisor reviews, Yelp reviews, and tweets, as well as SafeGraph Point of Interest (POI) visitation patterns. More specifically, it determines the effect of the pandemic on the localness of POI reviewers and visitors, measured by the distance between home and POI, as well as the proportion of reviewer/visitor home regions that fall within the same state as the POI. Results Results show a shift towards more local reviewers and visitors of POIs in both study areas during the pandemic, which is statistically significant for all four data sources for POIs in NYC, but only for SafeGraph and a subset of UGC platforms for Florida state parks. Conclusions This shows that the power of UGC-based analysis of travel pattern change in rural regions is limited by data scarcity compared to more populated areas and that SafeGraph visitation patterns provide a reliable data source for such a purpose even for POIs in rural settings.

ABSTRACT From oral histories to mnemonic devices, humans have an excellent ability to remember object sequences and their relationships inside of narratives. In pedestrian wayfinding, remembering landmarks and their relationships is considered key to learning routes. This research explores whether augmenting verbal route instructions with a narrative increases the memorability of a route. Narrative theory is applied as a framework to develop narrative-based navigation instructions, which were tested in a field study (N = 18). After learning a route, participants recalled the route verbally, completed a photo-based landmark sequencing task and discussed their answers. One week later, a route recognition task and a second photo-based landmark sequencing task was completed online. Results show few significant differences between the two groups when compared quantitatively. However, during interviews, the narrative group repeatedly cited the narrative when remembering the route. The results suggest that incorporating narratives into route directions can be further explored, and that some novel direction types may not be well-measured using quantitative methods. This research confirms the prowess of landmark-based instructions to facilitate route memory, contributes to the growing body of work augmenting landmark-based route directions with detailed information, and further encourages designers to consider alternate route communication methods.