User Movement Patterns Research Articles

Travel-mode inference based on GPS-trajectory data through multi-scale mixed attention mechanism

Identifying travel modes is essential for modern urban transportation planning and management. Recent advancements in data collection, especially those involving Global Positioning System (GPS) technology, offer promising opportunities for rapidly and accurately inferring users' travel modes. This study presents an innovative method for inferring travel modes from GPS trajectory data. The method utilizes multi-scale convolutional techniques to capture and analyze both temporal and spatial information of the data, thereby revealing the underlying spatiotemporal relationships inherent in user movement and behavior patterns. In addition, an attention mechanism is integrated into the model to enable autonomous learning. This mechanism enhances the model's capacity to identify and emphasize key information across different time periods and spatial locations, thus improving the accuracy of travel mode inference. Evaluation on the open-source GPS trajectory dataset, GeoLife, demonstrates that the proposed method attained an accuracy of 83.3%. This result highlights the effectiveness of the method, demonstrating that the model can more accurately understand and predict user travel modes through the integration of multi-scale convolutional technologies and attention mechanisms.

Modeling multi-factor user preferences based on Transformer for next point of interest recommendation

With the rapid development of the mobile network and the gradual popularization of mobile devices, more and more users try to find attractive places to visit through WeChat, Twitter applications. In this trend, personalized next point of interest(POI) recommendation in the Location-based Social Network (LBSN) has become the focus of research and practice. Most existing studies capture user interest changes between different days (i.e. weekend and weekday), however, they ignore seasonal factors in time transition and category factors and thus fail to capture seasonal-level and category-level movement patterns in users’ mobile trajectories. Besides, they neglect the relevance between POIs from all users’ trajectory and fail to generate expressive POI embedding representation without constructing trajectory graph, which will reduce the accuracy of the next POI recommendation. To address the issues above, a next POI recommendation method for modeling Multi-factor User Preferences based on Transformer (MUPT) is developed, which consists of a global POI relationship modeling, a local multi-factor user preference modeling and a prediction module. It first learns the collaborative information of users with similar behavior to generate expressive POI embedding representation. Then it captures the personalized movement patterns of users at the POI, category and time levels based on Transformer mechanism in the local module. Especially the seasonal and other fine-grained information on the time series are learned in the time preference modeling part. The prediction module designed tracks the relationship between multi-level motion pattern representation of user check-in behavior and the next POI accessed by the user, and it finally obtains user’s preference probability for next POIs. An extensive experiment has been conducted on four datasets, and the experimental results analysis demonstrates that our proposed MUPT method is superior to other methods in terms of accuracy(ACC), mean reciprocal rank(MRR) and normalized discounted cumulative gain(NDCG).

Utilization of Public Open Space: Movement Pattern and Compromised Territory

Understanding open space management based on local culture is an important aspect in creating an inclusive quality of community life. Serang City Square is a public space that has high vitality in its utilization for the local community. The government of Serang City (the capital of Banten Province) currently manages the area including the square. Its spatial layout is optimally organized for directed development, so that it suits the needs of users. The research objectives are 1) to study the movement patterns of space users through user considerations in choosing space, time and place for activities - 2) to reveal the territoriality of space utilization and informal agreements underlying its management. The research method used a quasi-qualitative approach, with place-centered mapping techniques and in-depth interviews. The results show that 1) the pattern of public open space utilization and its changes are influenced by the choice of time, type of activity, weather conditions and distance, 2) spatial territory is built by attachment to space due to habits and agreements of space users based on informal rules and compromises. This research contributes to local governments in considering the planning and arrangement of open spaces.

A Trajectory-Based User Movement Pattern Similarity Measure for User Identification

Recently, matching the cross-site user accounts based on user trajectory similarity has been attracting much attention, which benefits many applications. Most of existing works measure the user trajectory similarity based on the proximity of stay points. However, since the user trajectory is noisy, sparse and biased, the trajectory similarity measure is not a trivial task. In theory, the user movement pattern inherent in user trajectory is unique to the user and site-independent, which may be the key to address the above question. Thus, we propose a trajectory-based user movement pattern similarity measure for user identification. Specifically, we first find the frequent user stay points at each time slot to represent the user daily activities, and then present a similarity measure method for user daily activities, where we assign a global popularity and local popularity to stay point with the aim of accurately characterizing the contribution of stay point to the similarity. To represent the user-specific and site-independent movement pattern, we propose a trajectory-oriented embedding method, called T-LINE, which preserves the similarity of user daily activities. Finally, based on the similarity of movement patterns between a user and his/her candidates, we select top <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> similar users as the matching users. The experiments conducted on three ground-truth datasets show a significant improvement compared to the representative works.

User Re-Identification via Confusion of the Contrastive Distillation Network and Attention Mechanism.

With the rise of social networks, more and more users share their location on social networks. This gives us a new perspective on the study of user movement patterns. In this paper, we solve the trajectory re-identification task by identifying human movement patterns and then linking unknown trajectories to the user who generated them. Existing solutions generally focus on the location point and the location point information, or a single trajectory, and few studies pay attention to the information between the trajectory and the trajectory. For this reason, in this paper, we propose a new model based on a contrastive distillation network, which uses a contrastive distillation model and attention mechanisms to capture latent semantic information for trajectory sequences and focuses on common key information between pairs of trajectories. Combined with the trajectory library composed of historical trajectories, it not only reduces the number of candidate trajectories but also improves the accuracy of trajectory re-identification. Our extensive experiments on three real-world location-based social network (LBSN) datasets show that our method outperforms existing methods.

A method proposal to detect user movement patterns in urban space: The case of Edirne-Kaleiçi

A method proposal to detect user movement patterns in urban space: The case of Edirne-Kaleiçi

Design Factors and Criteria for Building a Non-Level Pedestrian Crossing at an Urban Campus

Bandung Islamic University (UNISBA) is in a densely populated urban area of Bandung. UNISBA lecture and administrative buildings are scattered in several locations. In architecture typo-morphology terms, a campus building with this environmental setting is often referred to as an urban campus. The high traffic flow on these roads, together with the intense conflict between road crossers, often results in congestion due to vehicle deceleration needed to allow pedestrian crossing. To facilitate the need for the movement for the users of the UNISBA campus buildings and minimize conflicts with road users, infrastructure that connects the UNISBA campus buildings is needed to facilitate pedestrian crossing. The pedestrian crossing could be in the form of a level crossing in the same elevation as the road or a non-level crossing situated above or under the road. The type of pedestrian crossing studied in this paper is a non-level pedestrian crossing in the form of a pedestrian bridge. In designing the infrastructure for non-level crossings, it is necessary to pay attention to the movement patterns that occur and the factors that influence willingness to use. This study aims to determine the movement pattern of UNISBA campus facility users consisting of students, lecturers, educational staff, and visitors by using quantitative origin-destination spatial analysis. Descriptive exploratory factor analysis was also used to determine the preferences of the users of the UNISBA campus buildings to use non-level crossings. The results of this analysis will be the basis for designing non-level accessibility between UNISBA campus buildings.&#x0D; Keywords: pedestrian crossing, pedestrian bridge, urban campus, urban design, urban mobiity

Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks

In this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to VR users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for them using VLC. Here, VR users move in real time and their movement patterns change over time according to their applications, where both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and establish THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the average number of successfully served VR users by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm that adopts a meta-learning approach is proposed. The proposed meta policy gradient (MPG) algorithm enables the trained policy to quickly adapt to new user movement patterns. In order to solve the problem of maximizing the average number of successfully served users for VR scenarios with large numbers of users, a low-complexity dual method based MPG algorithm (D-MPG) with a low complexity is proposed. Simulation results demonstrate that, compared to a baseline trust region policy optimization algorithm (TRPO), the proposed MPG and D-MPG algorithms yield up to 26.8% and 21.9% improvement in the average number of successfully served users as well as 81.2% and 87.5% gains in the convergence speed, respectively.

Single- and Multiagent Actor–Critic for Initial UAV’s Deployment and 3-D Trajectory Design

This article considers a wireless network consisting of unmanned aerial vehicles (UAVs), deployed as aerial base stations, and a large number of terrestrial users randomly distributed in a dense urban area. The main objective of this work is to maximize the downlink rate of users along with clustering of users and 2-D initial placement of UAVs, which effectively minimizes the clustering error. To achieve this goal, we estimate the next users’ locations with deep echo-state network (ESN) to find the movement pattern of users with high accuracy. Then, we propose the single- and multiagent actor–critic (AC) algorithms for UAVs’ initial deployment and trajectory design, where the multiagent scheme employs an efficient bandwidth allocation. Simulation results supported by a real data set of the terrestrial users’ coordinates indicate that, when the deep ESN algorithm is used, the accuracy is 93.75% for longitude and 88.36% for latitude compared to the simple ESN performance. Moreover, the use of single- and multiagent AC algorithms display better performance in terms of downlink rate and convergence speed than value-based algorithms such as deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network schemes.

Integration Of Mass Public Transport Fare In The Jakarta Area

This study specifically discusses the concept of integrating mass public transport fares in Jakarta. Based on the identification of problems and also based on several studies of mass transportation in various cities in the world, the integration of fares can increase the number of mass public transportation ridership. This causes a change in the movement pattern of mass public transport users. An interim analysis has been carried out on a simple public transport network with the number of movements in the origin and destination matrix between stations/stops was 19,611 passengers/hour and variations in the amount of fare between IDR 2,500,-/passenger up to IDR 7,500,-/passenger (Existing average fare is IDR 4,188,-/passenger). In the optimization process, an existing operator’s revenue is an objective function. The optimization process for determining mass public transport fares is carried out recursively because it is influenced by changes in the integrated fare amount to the number of mass public transport users. The results of the optimization process show that the integrated fare of IDR 3,850/passenger (9% lowest than the average existing fare) that increasing public transport users by 3% compared to the number of passengers in the existing condition for 1 operating hour.

Assessing the quality of pedestrian paths related to street vendor activities at Johar Market, Semarang

Abstract This study examines pedestrian and street vendor activity in the vicinity of Johar Market, one of Semarang City’s busiest markets. Observations were made to get a complete picture of this study based on four targets: user characteristics, user movement patterns, physical environment characteristics around pedestrian paths, and the availability of street furniture. Data were obtained based on the results of seven days of observations, namely, over a thousand pedestrians and street vendor activities that occurred along the pedestrian path. Spatial analysis techniques with GIS are used to analyze the data from the observations. In addition, the pedestrian path’s quality is calculated using the PEQI (Pedestrian Environmental Quality Index) standard. The results of the analysis show that the quality of the pedestrian path around Johar Market is still relatively low, with a value of 19.84, placing it in category 4, indicating that the pedestrian path is insufficient because it does not meet the standard. The pedestrian path design category and the availability of street furniture received the lowest ratings. The results of this study can be used as reference material in designing pedestrian paths that meet the needs and meet service standards.

The effect of public park boundary order on the environmental control of local government central offices

The existence of park facilities as public open spaces is a phenomenon of development activities that are much done, referring to the need for interaction space for the community. The function of the park in its development cannot be separated from the historical value since the colonial era, where its existence is a form of providing green open space oriented to parks in Europe. Seeing the reality of its development, the park facilities now are not only built in the city enclave, but also penetrate into formal environments such as government offices. Bandung, Surabaya and Semarang are the 3 (three) examples of capitals in Indonesia that carried out that development, which are the City Hall complex. The two facilities located in one area can encourage issues regarding the borders of public parks which accommodate informal activities over the control of the city government center office environment structure as an existing building that accommodates formal activities. The method used in this study is descriptive qualitative through an analytical approach based on theory. The analysis is carried out by observing the landscape layout and functions since its inception in order to find out what aspects are maintained or transformed. After that, regarding the ownership of the elements of its present spatial borders. Based on research, Bandung, Surabaya, and Semarang City Hall shows that the existence of public parks in an office environment results in an arrangement that can influence the overall arrangement and user movement pattern of the environment.

Movement Patterns of Commuterline Users in the Medan City Train Station Area Based on Transit

&#x0D; &#x0D; &#x0D; &#x0D; At the time of this research, Medan City was threatened with the gridlock, a situation where the number of vehicles exceeds the available road capacity. To prevent the gridlock happens, Medan Train Station (Medan ts.) area as the center activity of Medan City, will be developed into an area based on the Transit-Oriented Development (TOD) concept by adding Light Rail Transit (LRT) and Bus Rapid Transit (BRT). The addition is hoped to encourage walking and public transportation usage for visitors in Medan ts. area. This study aims to determine and mapping the movement patterns of commuterline users, in this case Medan – Binjai line which is the only line available, as the basis for the application of the concept of TOD in the Medan ts. area. The research uses observation and interview as the methods. The results of the study showed that the majority of commuterline users of the Medan ts. relied on paratransit when heading or leaving the station than walking. This can be seen from 70% of users (weekday) and 83.3% of users (weekends) using paratransit when heading to the station and 86.6% of users (weekday) and 66.6% of users (weekends) using paratransit when leaving the station.&#x0D; &#x0D; &#x0D; &#x0D;

Empowering Self-Organized Feature Maps for AI-Enabled Modeling of Fake Task Submissions to Mobile Crowdsensing Platforms

Mobile crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of smart devices to improve the sensing capacity and reduce sensing costs in large regions. Due to the ubiquitous nature of MCS services, smart devices require awareness of against misbehaving users that are becoming smarter to clog the resources in such a nondedicated sensing environment. In an MCS setting, the primary goal of a fake sensing task submission is to keep participant devices occupied, such as the battery, sensing, storage, and computing. Since the development of robust sensing campaigns highly depends on the existence of a realistic model of misbehaving users, this article leverages artificial intelligence and introduces a region-based self-organizing feature map (SOFM)-based model on user movement patterns so as to place the fake sensing tasks with the objective of maximum impacted participants and recruits. Uniformly and randomly initialized neurons are designed with fixed and adaptive quantities that are determined based upon the affected area on the covered terrain. Through numerical studies, we show that the impact of the SOFM structures can affect up to 46% of the participants and up to 37% of the recruits under various SOFM topologies. Furthermore, SOFM-based task submission models can increase the energy consumption in recruited devices by up to 39% due to the illegitimate task submission.

Estimation of the user's movement pattern in a standing movement measurement device

Estimation of the user's movement pattern in a standing movement measurement device

Adding Haptic Feedback to Virtual Environments With a Cable-Driven Robot Improves Upper Limb Spatio-Temporal Parameters During a Manual Handling Task.

Physical interactions within virtual environments are often limited to visual information within a restricted workspace. A new system exploiting a cable-driven parallel robot to combine visual and haptic information related to environmental physical constraints (e.g. shelving, object weight) was developed. The aim of this study was to evaluate the impact on user movement patterns of adding haptic feedback in a virtual environment with this robot. Twelve healthy participants executed a manual handling task under three conditions: 1) in a virtual environment with haptic feedback; 2) in a virtual environment without haptic feedback; 3) in a real physical environment. Temporal parameters (movement time, peak velocity, movement smoothness, time to maximum flexion, time to peak wrist velocity) and spatial parameters of movement (maximum trunk flexion, range of motion of the trunk, length of the trajectory, index of curvature and maximum clearance from the shelf) were analysed during the reaching, lowering and lifting phases. Our results suggest that adding haptic feedback improves spatial parameters of movement to better respect the environmental constraints. However, the visual information presented in the virtual environment through the head mounted display appears to have an impact on temporal parameters of movement leading to greater movement time. Taken together, our results suggest that a cable-driven robot can be a promising device to provide a more ecological context during complex tasks in virtual reality.

Minimizing the Signaling Overhead and Latency Based on Users’ Mobility Patterns

We demonstrate a distributed and a centralized 4G/5G compliant approach to minimize signaling and latency related to user mobility in cellular networks. This is crucial due to the densification of networks and the additional signaling introduced by the new 5G service-based architecture. By exploiting standardized protocols, our solutions dynamically reorganize the association between nodes in radio access network (RAN) and the core. We validated the proposed approaches using real user mobility datasets. Our results show that both our distributed and centralized solutions significantly reduce the signaling between core and RAN compared to the traditional approach based on geographical proximity. As a result, both approaches significantly reduce the average handover procedure processing time. Moreover, by relying on locally available information, the distributed approach can quickly adapt to changes in the user movement patterns as they happen.

Spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth in Internet of Things

The temporal and spatial characteristics of users are involved in most Internet of Things (IoT) applications. The spatial and temporal movement patterns of users are the most direct manifestation of the temporal and spatial characteristics. The user’s interests, activities, experience and other characteristics are reflected by mobile mode. In view of the low clustering efficiency of moving objects in convergent pattern mining in the IoT, a spatiotemporal feature mining algorithm based on multiple minimum supports of pattern growth is proposed. Based on the temporal characteristics of user trajectories, frequent and asynchronous periodic spatiotemporal movement patterns are mined. Firstly, the location sequence is modeled, and the time information is added to the model. Then, a mining algorithm of asynchronous periodic sequential pattern is adopted. The algorithm is based on multiple minimum supports of pattern growth. According to multiple minimum supports, the sequential pattern of asynchronous period is mined deeply and recursively. Finally, the proposed method is validated and evaluated by Gowalla dataset, in which the user characteristics are truly reflected. It is shown by the experimental results that the average pointwise mutual information (PWI) of the proposed algorithm reaches 0.93. And the algorithm is proved to be effective and accurate.

ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

The recognition of physical activities using sensors on mobile devices has been mainly addressed with supervised and semi-supervised learning. The state-of-the-art methods are mainly based on the analysis of the user's movement patterns that emerge from inertial sensors data. While the literature on this topic is quite mature, existing approaches are still not adequate to discriminate activities characterized by similar physical movements. The context that surrounds the user (e.g., semantic location) could be used as additional information to significantly extend the set of recognizable activities. Since collecting a comprehensive training set with activities performed in every possible context condition is too costly, if possible at all, existing works proposed knowledge-based reasoning over ontological representation of context data to refine the predictions obtained from machine learning. A problem with this approach is the rigidity of the underlying logic formalism that cannot capture the intrinsic uncertainty of the relationships between activities and context. In this work, we propose a novel activity recognition method that combines semi-supervised learning and probabilistic ontological reasoning. We model the relationships between activities and context as a combination of soft and hard ontological axioms. For each activity, we use a probabilistic ontology to compute its compatibility with the current context conditions. The output of probabilistic semantic reasoning is combined with the output of a machine learning classifier based on inertial sensor data to obtain the most likely activity performed by the user. The evaluation of our system on a dataset with 13 types of activities performed by 26 subjects shows that our probabilistic framework outperforms both a pure machine learning approach and previous hybrid approaches based on classic ontological reasoning.

Exploring the influence of road network structure on the spatial behaviour of cyclists using crowdsourced data

This study explores the effect of the spatial configuration of street networks on movement patterns of users of a cycling monitoring app, employing crowdsourced information from OpenStreetMap and S...