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A sustainable alternative transportation mode to address growing transportation and environmental stress is cycling, which is eco-friendly and healthy for humans. Improving the quality of the bicycling experience is crucial for increasing bicycle use. Good bicycling experience is more critical for child bicyclists because they are less experienced and need more space for error. Therefore, a scientific assessment of child bicyclist perception of selected route safety, comfort, and environment is of great interest. Finding an effective way to learn child bicyclist behavior and help them reduce cycling risk is necessary. In response to this need, we utilize a data mining model to develop a methodology for measuring children's bicycling route safety conditions by evaluating multiple road safety-related features. The proposed method uses a set of route features representing the situation of street environments extracted from state data and first-hand children’s bicycle trajectory data collected using Global Positioning Systems (GPS) from volunteer children bicyclists. A random forest (RF), a well-known classifier, is adopted to predict child bicyclists' behavior. We extract the different route segments between children’s selected routes and the shortest path to learn the child bicyclists' behavior and use the selected best features to interpret their changing cycling behavior. The result shows that children bicyclists' behavior could be analyzed by giving trajectory and nearby road safety situation data. Our model achieves a promising accuracy with an average rate of 92% over multiple scenarios, demonstrating the proposed method's feasibility and the effectiveness of selected features. In addition, we compare our feature effectiveness with the state's generated road safety score data to evaluate the feature robustness. Our features outperform the safety score feature with an average of 10% improvement in prediction accuracy. Furthermore, our method proposes a model framework that can be applied to different study regions and adult bicycling behavior learning.

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players’ energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder–Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87±61.42 and root mean squared error (RMSE) of 520.69±88.66 achieved by our model, as opposed to the B1 MAE of 429.04±84.87 and RMSE of 581.34±185.84, and B2 MAE of 421.57±95.96 and RMSE of 613.47±300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players’ responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics.

The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC.

This paper presents a novel approach to distinguishing driving styles with respect to their energy efficiency. A distinct property of our method is that it relies exclusively on the global positioning system (GPS) logs of drivers. This setting is highly relevant in practice as these data can easily be acquired. Relying on positional data alone means that all features derived from them will be correlated, so we strive to find a single quantity that allows us to perform the driving style analysis. To this end we consider a robust variation of the so-called "jerk" of a movement. We give a detailed analysis that shows how the feature relates to a useful model of energy consumption when driving cars. We show that our feature of choice outperforms other more commonly used jerk-based formulations for automated processing. Furthermore, we discuss the handling of noisy, inconsistent, and incomplete data, as this is a notorious problem when dealing with real-world GPS logs. Our solving strategy relies on an agglomerative hierarchical clustering combined with an L-term heuristic to determine the relevant number of clusters. It can easily be implemented and delivers a quick performance, even on very large, real-world datasets. We analyse the clustering procedure, making use of established quality criteria. Experiments show that our approach is robust against noise and able to discern different driving styles.

Abstract Background Migration is a widespread strategy among ungulates to cope with seasonality. Phenology, especially in seasonally snow-covered landscapes featuring “white waves” of snow accumulation and “green waves” of plant green-up, is a phenomenon that many migratory ungulates navigate. Guanacos (Lama guanicoe) are native camelids to South America and might be the last ungulate in South America that migrates. However, a detailed description of guanacos´ migratory attributes, including whether they surf or jump phenological waves is lacking. Methods We quantified the migratory movements of 21 adult guanacos over three years in Patagonia, Argentina. We analyzed annual movement patterns using net squared displacement (NSD) and home range overlap and quantified snow and vegetation phenology via remotely sensed products. Results We found that 74% of the individual guanacos exhibited altitudinal migrations. For migratory guanacos, we observed fidelity of migratory ranges and residence time, but flexibility around migration propensity, timing, and duration of migration. The scarce vegetation and arid conditions within our study area seemed to prevent guanacos from surfing green waves; instead, guanacos appeared to avoid white waves. Conclusion Our study shows that guanaco elevational migration is driven by a combination of vegetation availability and snow cover, reveals behavioral plasticity of their migration, and highlights the importance of snow phenology as a driver of ungulate migrations.

Bike facility features in urban transport systems are one of the most important elements for encouraging user choices regarding sustainable transport modes. The process of designing the bikeway does involve biker perception but the act of designing does not often rely on this perception. In order to identify whether gender differences exist for bike route choices, the actual choices made by bikers - both male and female - have been detected by means of GPS data, with the pathways characteristics being known. Detected route choices have been analyzed using the Oaxaca-Blinder decomposition method (Blinder, 1973 ; Oaxaca, 1973), which provides a possible explanation for differences in gender-specific route attributes that male and female cyclists experience under similar conditions. The results show that differences between female and male cyclists exist in terms of the ease of use of the pathways and related choices. Some analyses regarding age classes have also shown that gender differences tend to be less relevant with increasing age, thus suggesting that more-experienced female cyclists make choices similar to those of their male counterparts. • Identify gender differences for bike route choices based on the actual choices made by bikers under similar conditions. • Use the Oaxaca-Blinder decomposition method to explain the possible differences of gender-specific route attributes. • Assess if differences between female and male cyclists exist in terms of easiness of the pathways. • Understand whether gender differences tend to be less relevant with increasing age. [ABSTRACT FROM AUTHOR]

Bus bunching refers to the phenomenon that several buses arrive at a station within a short period. It dramatically increases passengers’ waiting time and reduces the quality of transit service. Evaluating the features of bus bunching and identifying the causes are important to developing countermeasures. The primary of this study was to analyze the temporal-spatial features of bus bunching by conducting an in-depth analysis of empirical bus GPS trajectory data obtained in Nanjing, China. The GPS data were inputted into the ArcGIS to track the spatial map's bus trajectories. A data processing procedure was proposed to analyze the data, including data cleaning, trip cutting, each station's arrival and departure time estimation, and time headway calculation. Then the spatiotemporal trajectory picture was drawn for the bus route where the bus bunching was identified. The study also analyzed the headway features of consecutive buses at the different stations and evaluated the variation of time headway, indicating the severity of bus bunching. The results showed that there are significant differences in the spatiotemporal features of bus bunching between bus stations. When the bus bunching occurred, it persisted on downstream stations for a long time. The bunching severity dramatically increased at downstream stations, reducing bus arrival reliability on the whole bus line. We also identified that the bus bunching was primarily caused by the overlong bus dwelling time at a station and the different travel times of buses between stations. The study fills the gap by developing the methodology to investigate the bus bunching features and causes with point-by-point empirical GPS trajectory data. Findings of the study can also support the real-time prediction and warning of bus bunching in practical applications. [ABSTRACT FROM AUTHOR]

Modeling the destination choice has been of great interest for travel behavior community as well as policymakers in understanding the demand for land use and transportation infrastructures at aggregate and disaggregate levels and possibly devising policies to balance the demand and supplies. One of the challenges underlying predictions of location choice is the large choice set. While traditionally many methods had been devised to limit the choice set size either on a rather ad hoc basis or based on space–time prism by removing the locations out of reach of the subjects, the current study takes a substantially different approach and proposes a data-driven method to customize the generation of the choice set. The proposition is that observing the mobility patterns of citizens for multiple weeks would enable us to limit the choice set, depending on how far the subjects travel (beyond or within the distance they travel for their most frequent activities) to conduct their various activities. More precisely, using longitudinal trajectory data, we first classify people into two subgroups: returners and explorers, based on the size of the area (around their k most visited locations: k -radius of gyration) they move during the observation period. The destination choice set for four types of activities is then customized for returners (and explorers) and is used in a sequence of decisions represented by decision trees for the prediction of their destinations. The models for the whole sample and each subgroup separately are compared. The results suggest that the accuracy of destination prediction improves substantially for all four selected activity types, especially for the returners whose choice sets are formed based on their radius of gyration. [ABSTRACT FROM AUTHOR]

Natural hazards, such as wildfires, pose a significant threat to communities worldwide. Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. To tackle this research gap, the study develops a new methodological framework for modeling highly granular spatiotemporal trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i.e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations. The proposed methodological framework is tested using a real-world case study: the 2019 Kincade Fire in Sonoma County, CA. The results show that SA-MGCRN significantly outperforms all the selected state-of-the-art benchmarks in terms of prediction performance. Our finding suggests that the most important model components of SA-MGCRN are weekend indicator, population change, evacuation order/warning information, and proximity to fire, which are consistent with behavioral theories and empirical findings. SA-MGCRN can be directly used in future wildfire events to assist real-time decision-making and emergency management. [ABSTRACT FROM AUTHOR]

• Uncover intercity freight demand dynamics under the context of COVID-19 pandemic outbreak. • Develop an interpretable machine learning framework for intercity freight trips generation (FTG). • Reveal pandemic-induced variations in the factors influencing truck movements across local and broader regions. Intercity truck transport emerged as a crucial lifeline for maintaining city operations during COVID-19 pandemic. Understanding pandemic-imposed impacts on intercity truck transport can inform policymakers in crafting more effective strategies for future crises and disruptions. However, to our best knowledge, previous research predominantly focused on freight movements under normal circumstances. Due to the data limitation, the pandemic-related studies commonly relied on freight survey and focused on specific industries, which cannot capture the full spectrum of factors influencing freight trip generation (FTG) during the pandemic. Here, a novel dataset capturing large-scale individual truck movements during the COVID-19 pandemic is provided. By leveraging the mobility dataset, pandemic-induced changes in truck transport demand structure are quantified using spatial statistical methods. Furthermore, an interpretable machine learning framework for intercity freight demand estimation is developed, revealing the complex interplay of factors that influence and shape the behavior shifts of intercity truck transport systems due to the pandemic outbreak. The findings suggest significant changes in various factors influencing intercity truck movements across local and broader regions, emphasizing city-specific challenges amidst pandemic. The developed FTG model could serve as a tool to predict freight demand between cities for future crises and to support policymaking in the practice of freight management. [ABSTRACT FROM AUTHOR]

• For the first time, the starting point of the parking search is accurately measured. • A theoretical model for starting the parking search is tested using empirical data. • Factors that influence the start of parking search are identified. • Search starts earlier when driving speed is low and finding a space is difficult. • The analysis reveals cases of parking search being skipped with immediate parking. This study investigates the starting point of parking search, presenting new findings through empirical and theoretical approaches. It introduces a probabilistic model that describes the transition from normal driving to actively searching for parking, aiming to minimize journey costs. The model is tested using real-world data collected via a smartphone app that tracks the start of parking searches. Results validate the model, showing that drivers are more likely to begin searching for parking earlier when parking spaces are scarce and driving speeds are reduced (e.g., by congestion). Additionally, various factors influence the start of the parking search, including driver age, vehicle class, and familiarity with the destination. Specific conditions such as proximity to amenities, rush hour timing, and destination familiarity prompt earlier search initiation. The study also identifies scenarios where drivers skip the search process and park immediately, influenced by factors like driving home, short parking durations, and destination familiarity. [ABSTRACT FROM AUTHOR]

The advancement of data collection technologies has brought an upsurge in GPS applications. For example, travel behaviour research has benefited from the integration of multiple sources of Global Positioning System (GPS) data. However, the effective use of such data is still impeded by the challenge in data processing. For instance, GPS data, despite providing detailed spatial movement information, do not label the starting and finishing points of a trip, especially for commercial trucks. Hence, there is a critical need to develop a trip identification method to effectively use the trajectory data provided by GPS without additional information. This paper focused on identifying trips from the raw GPS data. Specifically, a systematic method is proposed to extract trips on the basis of origin-destination (OD) pairs by using a 5-step procedure. An application was provided on estimating the performance of travel time reliability using three metrics based on the OD trips for each dedicated truck. The application showed that, in general, trucks on long-distance routes have less reliable travel times compared to trucks on short-distance routes. This paper provides an example of using GPS data, without further information, to study travel time for freight performance and similar needs of punctuality in logistics.

Monitoring players’ fatigue is essential to maintaining the best performance of players during sports games. The level of fatigue can be measured by the external workload, the aggregated amount of physical activity or internal workload, which is an individual’s psycho-physiological response to that activity. There have been a growing number of studies focusing on the relationship between external and internal workloads for efficient fatigue monitoring. However, they utilize aggregated features to represent the external workload, losing raw data details such as sequential information. This study proposes a deep learning algorithm to predict Rate of Perceived Exertion (RPE) from players’ movement data instead of aggregated features. Electronic Performance and Tracking Systems (EPTS) powered by GPS sensors collected players’ movement data and the RPE from training and match sessions during a Korean professional soccer team season. We preprocessed the raw GPS data to obtain linear and angular components of velocity, acceleration, and jerk. Our proposed model, named FatigueNet, effectively predicted the RPE with mean absolute error (MAE) = 0.8494 ± 0.0557 and root mean square error (RMSE) = 1.2166 ± 0.0737 using the preprocessed movement features. To interpret the predictions of the FatigueNet, we also performed regression activation mapping to localize the discriminative time intervals that contributed more to the prediction results. Our experimental results imply the possibility of automated and objective fatigue monitoring systems based on deep learning instead of arduous manual data collection from players.

The Natural Heritage Site of the Coast of Yellow Sea-Bohai Gulf of China plays a prominent role in the conservation of global biodiversity. However, with the increase in the number of species inhabiting here, the problem of competition in the habitat space of species within the heritage site has gradually emerged, which has become an important bottleneck restricting the sustainable development of the heritage site. Therefore, this study selected the typical wetland wildlife in this area, red crowned crane (Grus japonensis) and Chinese water deer (Hydropotes inermis), as the study objects. This study used their continuous GPS tracking data to reveal the seasonal laws of habitat selection and suitability of two typical wetland species, and analyze their spatial competition and coexistence relationship. The study results showed that the distribution of home range of the crane and the deer in spring and summer was significantly larger than that in autumn and winter. The area of the sub and most suitable area of the deer in spring was larger than that of the crane. In autumn and winter, the area of the sub and most suitable areas for the deer was small, while the area of the most suitable area for the crane was more than 50 hm2. Except in spring, the two species kept a certain distance from each other in other seasons, and their habitat selection was stable. The optimal threshold range of the crane for D_ree variable was 0–202 m in spring and 0–1200 m in summer and autumn. The deer was affected by vegetation factors in the four seasons. The threshold range of D_ree variable in spring, autumn and winter was 0–80 m, the suitable vegetation height of the deer was 2.31–2.92 m. Finally, this study proposed a refined management pattern of habitat with multiple species coexist.

In order to properly planning transportation systems and providing adequate services, it is essential to characterize the passenger demands, understand the behavior patterns and associate them to the routes considering space and time. Currently, promising alternatives based on the Internet of Things concepts allow adequately estimate the demand and provide very useful information. However, these devices are not widespread and usually, single GPS systems are more frequently available to monitor transportation systems worldwide. Conventional data analysis infrastructures commonly require great computational efforts to run the huge amount of data usually provided by GPS systems to get statistical and visual reports. In this quest for continuous improvement, new analysis or visualization infrastructures may require a highly repetitive processing algorithm applied in all data. This way of acting hinders the cognitive process, the capacity for analysis, and the inference of relevant information. In this context, this paper proposes an approach based on visual analytics for inferring passenger demand from GPS data. The proposed approach combines two stages. In the first stage, a space-time algorithm extracts indicators from the GPS data and makes them available. The second stage, an iterative visualization interface containing configurable filters and statistical functions, helps the user to explore intuitively the relationship between indicators and passenger demand. The case studies are based on actual data collected in the Living Lab for Electrical Mobility at University of Campinas. Results show that the proposed approach is promising and the inferred demand can be used to propose new routes and schedules for the buses at the University. [ABSTRACT FROM AUTHOR]

Trajectory outlier detection is one of the fundamental data mining techniques used to analyze the trajectory data of the Global Positioning System. A comprehensive literature review of trajectory outlier detectors published between 2000 and 2022 led to a conclusion that conventional trajectory outlier detectors suffered from drawbacks, either due to the detectors themselves or the pre-processing methods for the variable-length trajectory inputs utilized by detectors. To address these issues, we proposed a feature extraction method called middle polar coordinates (MiPo). MiPo extracted tabular features from trajectory data prior to the application of conventional outlier detectors to detect trajectory outliers. By representing variable-length trajectory data as fixed-length tabular data, MiPo granted tabular outlier detectors the ability to detect trajectory outliers, which was previously impossible. Experiments with real-world datasets showed that MiPo outperformed all baseline methods with 0.99 AUC on average; however, it only required approximately 10% of the computing time of the existing industrial best. MiPo exhibited linear time and space complexity. The features extracted by MiPo may aid other trajectory data mining tasks. We believe that MiPo has the potential to revolutionize the field of trajectory outlier detection. [ABSTRACT FROM AUTHOR]

Vehicle air pollution is a significant problem for health and climate change that can be solved by several approaches. The route is one of the many components to be considered. In this work, we propose a statistical analysis of a large FCD database in November 2017 in Lyon (France) in order to find alternative sustainable trips and evaluate potential emission reductions (CO2, NOx, PM10). To this end, an innovative framework was built. First, we assessed vehicle speeds for each network section and the fifteen-minute period, when this information was reachable. Then, we used a regression random forest (RF) algorithm to fill in the missing data. This dynamical speed map allowed us to search for fewer pollutant trips, for the first ten days of November. By using COPERT emission factors (EFs) and the time-dependent Dijkstra algorithm, we successfully identified between 51% and 72% of alternative sustainable paths, depending on the engine technology and the pollutant. We investigated the influence of vehicle technology. In all cases, the number of alternative trips found tends to be the same as soon as the emission savings exceed 5%. Moreover, about 400 trips out of 11,000 have the potential to mitigate about 20% of emissions. [ABSTRACT FROM AUTHOR]

Congestion is one of the main problems prevalent in surface public transport systems. Congestion affects travel time, service regularity, and system costs. The accuracy of identification of speed-related problems depends on the quality and precision of the tools used to analyze the available operational data, that is, data on the impact on users and operational costs. Furthermore, traffic signals contribute significantly to operational delay, and programming traffic signals to prioritize transit can substantially increase the operating speeds. For example, green split redistributions are simple and low-cost adjustments to traffic signals that can significantly improve operating speeds. However, it might be expensive to collect the necessary traffic flow information. This study had a twofold contribution. First, it presents an extension of a tool that uses buses GPS data to identify and rank bottlenecks, in which queue lengths and bus load profiles are now considered for estimating user delay. Second, a methodology to identify which of these bottlenecks can be easily removed (Quick wins) by reallocating the traffic signal's green times among phases. The modified methodology was applied in the city of Santiago de Chile, where the inclusion of both the queue length and bus load profiles is shown to modify the ranking. Additionally, Quick wins opportunities were detected and addressed resulting in an average 85% reduction in bus delays. [ABSTRACT FROM AUTHOR]

Abstract To prevent the spread of the COVID-19 pandemic, governments in various countries have severely restricted the movement of people. The large amount of detailed human location data obtained from mobile phone users is useful for understanding the change of flow patterns of people under the effect of pandemic. In this paper, we observe the synchronized human flow during the COVID-19 pandemic using Global Positioning System data of about 1 million people obtained from mobile phone users. We apply the drainage basin analysis method which we introduced earlier for characterization of macroscopic human flow patterns to observe the effect of the spreading pandemic. Before the pandemic the afternoon basin size distribution has been approximated by an exponential distribution, however, the distribution of Tokyo and Sapporo, which were most affected by the first wave of COVID-19, deviated significantly from the exponential distribution. On the other hand, during the morning rush hour, the scaling law holds universally, i.e., in all cities, even though the number of moving people in the basin has decreased significantly. The fact that these scaling laws, which are closely related to the three-dimensionality structure of the city and the fractal structure of the transportation network, have not changed indicates that the macroscopic human flow features are determined mainly by the means of transport and the basic structure of cities which are invariant of the pandemic.

Much research shows that urban parks can benefit human health. Research has shown that perceptions of park environments are an important determinant of park usage. Most perception-based research collects data through costly and time-consuming survey approaches, which limits data collection on a large scale. Google Street View (GSV) imagery presents a cost-effective source for deriving perceptions of the park environment: for large parks, GSV images are available on both peripheral roads and internal roads; for small parks, majorly covered by grassland, GSV images on peripheral roads can capture their general built environment. Additionally, the available Global Positioning System (GPS) incorporated into cellular phones enables researchers to measure how long people stay in parks conveniently by checking the time of the first and last GPS points in a park. Taking Chicago as the case study, this research introduces GSV images and the SafeGraph phone-based GPS dataset to study the association between perceptions of park environments and time spent in parks, which is rarely explored by previous studies. We derive both perceived safety and beauty of park environments using GSV images in 2018 and machine-learning Support Vector Machine models trained by a crowdsourcing dataset on human perception of environments. Time spent in each park is obtained from SafeGraph data in 2018. We build negative binomial regression models to explore the relationship between perception variables and time spent in parks. Results show that higher levels of both perceived safety and beauty are positively associated with increased time, and adding perception variables can improve the model performance. It benefits urban planners in designing a better park environment in support of park usage.

The jobs-housing imbalance and long commuting distances for residents in many megacities in China are promoting the increase in mode share with rail transit. The emergence of free-floating bike sharing (FFBS) provides an attractive and cost-effective multi-modal solution to the first/last mile problem. This study identifies the mobility patterns of free-floating bikes as a feeder mode to 277 rail transit stations in Beijing using detailed GPS data, and the relationships between these patterns, culture and spatial layout of the city are examined. The results show that the distribution of free-floating bikes, as a feeder mode to rail transit, exhibits an aggregating feature in the spatial-temporal pattern on weekdays. According to the results of the Clusters method and ANOVA analysis, the operation characteristics of free-floating bikes are related to the location of the transit station and the job-to-housing ratio around that area, and imbalanced usage of shared bikes across the city may result from the extreme values of job-to-housing ratios. Based on the fitted distance decay curve, accessing distance is greatly influenced by urban morphology and location. Based on these findings, recommendations for planning, management, and rebalancing of the FFBS system as a feeder mode to rail transit are proposed to promote the integration of FFBS and the rail transit system. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to distinguishing driving styles with respect to their energy efficiency. A distinct property of our method is that it relies exclusively on the global positioning system (GPS) logs of drivers. This setting is highly relevant in practice as these data can easily be acquired. Relying on positional data alone means that all features derived from them will be correlated, so we strive to find a single quantity that allows us to perform the driving style analysis. To this end we consider a robust variation of the so-called "jerk" of a movement. We give a detailed analysis that shows how the feature relates to a useful model of energy consumption when driving cars. We show that our feature of choice outperforms other more commonly used jerk-based formulations for automated processing. Furthermore, we discuss the handling of noisy, inconsistent, and incomplete data, as this is a notorious problem when dealing with real-world GPS logs. Our solving strategy relies on an agglomerative hierarchical clustering combined with an L-term heuristic to determine the relevant number of clusters. It can easily be implemented and delivers a quick performance, even on very large, real-world datasets. We analyse the clustering procedure, making use of established quality criteria. Experiments show that our approach is robust against noise and able to discern different driving styles. [ABSTRACT FROM AUTHOR]

We used the 250‐day postseismic displacements derived from Global Positioning System data to explore various postseismic deformation processes of the 14 April 2010 Mw 6.9 Yushu earthquake, including the afterslip of the fault, viscoelastic relaxation in the lower crust and upper mantle, and the poroelastic rebound. The preferred model shows that the afterslip of the fault decays rapidly with time. Viscoelastic relaxation in the lower crust and upper mantle decays slower with time but affects a broader area. Our results show that the range of the steady‐state viscosity in the lower crust is 0.3–2 × $\times $ 1019 Pa s. The optimal steady‐state viscosity in the lower crust is ∼5 × $\times $ 1018 Pa s. We simulate the deformation due to the poroelastic rebound in the top 10 km upper crust. Model results indicate that the poroelastic rebound only produces a few millimeters surface deformation and may be a secondary‐order postseismic process. Plain Language Summary: In 2010, the Mw 6.9 Yushu earthquake occurred on the Yushu fault in the eastern Tibetan Plateau. Global Positioning System (GPS) measurements in the first 250 days after the 2010 event show that short‐term postseismic displacements featured mainly a left‐lateral motion across the fault. Model results show that the afterslip of the fault controls mainly the deformation in the near‐field area. The rheological structure in the lower crust in this region has no obvious heterogeneity. Our model provides further constraints on the rheological structures in the lower crust and upper mantle beneath the eastern Tibetan Plateau. Key Points: Afterslip of the fault and viscoelastic relaxation in the lower crust together control the short‐term postseismic deformationAfterslip takes place mostly within 10 years after the earthquake and decays rapidly with timeThe steady‐state viscosity in the lower crust is 0.3–2 × 1019 Pa s with an optimal value ∼5 × 1018 Pa s [ABSTRACT FROM AUTHOR]

Background: Migration is a widespread strategy among ungulates to cope with seasonality. Phenology, especially in seasonally snow-covered landscapes featuring "white waves" of snow accumulation and "green waves" of plant green-up, is a phenomenon that many migratory ungulates navigate. Guanacos (Lama guanicoe) are native camelids to South America and might be the last ungulate in South America that migrates. However, a detailed description of guanacos´ migratory attributes, including whether they surf or jump phenological waves is lacking. Methods: We quantified the migratory movements of 21 adult guanacos over three years in Patagonia, Argentina. We analyzed annual movement patterns using net squared displacement (NSD) and home range overlap and quantified snow and vegetation phenology via remotely sensed products. Results: We found that 74% of the individual guanacos exhibited altitudinal migrations. For migratory guanacos, we observed fidelity of migratory ranges and residence time, but flexibility around migration propensity, timing, and duration of migration. The scarce vegetation and arid conditions within our study area seemed to prevent guanacos from surfing green waves; instead, guanacos appeared to avoid white waves. Conclusion: Our study shows that guanaco elevational migration is driven by a combination of vegetation availability and snow cover, reveals behavioral plasticity of their migration, and highlights the importance of snow phenology as a driver of ungulate migrations. [ABSTRACT FROM AUTHOR]

Clustering taxi data is commonly used to understand spatial patterns of urban mobility. In this paper, we propose a new clustering model called Origin-Destination-means (OD-means). OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster. The algorithm is tested on a large data set of taxi GPS data from Santiago, Chile, and compared to other clustering algorithms. In contrast to them, our proposed model is capable of detecting general and local travel patterns in the city due to its hierarchical structure. [ABSTRACT FROM AUTHOR]

The traffic speed information of an urban road network is generally estimated using the widely available taxi GPS data. However, taxi usages are preponderantly restricted to areas with high population density, which results in limited spatial coverage of collected taxi GPS data. Moreover, the traffic speeds of taxies are not guaranteed to well represent the traffic speeds of other types of vehicles. In this study, we address these issues by introducing an infinite Gaussian mixture model to estimate traffic speed distribution. The variational inference method is employed to deal with the complicated parameter estimation problem. The proposed mixture model simultaneously combines taxi GPS data, bus GPS data, and mobile phone GPS data, which not only generates the mixed traffic-speed distribution of different types of vehicles but also improves the spatial coverage and the quality of traffic speed estimation. Surprisingly, we find that the incorporation of mobile phone GPS data can considerably improve the model’s ability to sense anomalous traffic conditions. Finally, the mixed traffic-speed distribution is validated using the license plate recognition data. [ABSTRACT FROM AUTHOR]

The movement of trucks represents a significant portion of travel. Surveys have traditionally been used to measure truck movement, but this costly and limited data collection method typically involves in-person interviews and requiring high workload. This study explores different ways in which passive truck GPS data can be used to complement traditional data collection methods, for obtaining detailed information about the travel behaviors of freight trucks. First, we develop a heuristic-based model to identify truck stops. A new methodology is proposed to classify truck stops into a primary or secondary sto, which make identifying trip purposes possible. Primary stops are defined as the locations where the loading or unloading of the goods takes place. Secondary stops are those associated with all other purposes, including refueling, and driver breaks. Finally, we develop a destination choice model for modeling truck movements. This model applies a discrete choice modeling technique to distribute truck trips within the Calgary region, Canada. We test the utility function in the destination choice model to include of business establishment data, travel impedances, and other dummy variables that are likely to influence truck demand. The results show that a combination of trucks’ dwelling times and their entropy can be used to classify truck stops by purpose. This study also shows the potential of using passive GPS data to gain additional insights into truck movements characterization and truck trip distribution modeling. [ABSTRACT FROM AUTHOR]

The 2019 MW6.9 Banten Intraslab earthquake occurred at ∼100 km to the northeast of the Sunda Trench with two nodal plane models estimated by the Global Centroid-Moment-Tensor (GCMT) Project with a strike of 200° and a dip of 65°.Continuous GPS data from 11 GPS sites were used to model the source of the earthquake in three-components. The coseismic displacements and its uncertainties are obtained from the coordinates of these GPS sites from 7 days before to 7 days after the earthquake. The coseismic slip is the inversion result of those displacements based on the best fit in an elastic half-space. The maximum displacement is ∼5 cm with a large uncertainty that is comparable to the amplitude of displacement. A seismic moment of the best model (strike of 65° and dip of 54°) is 2.79 × 1019 Nm or equivalent to MW6.89. The fault model of the earthquake is highly presumed as a continuation of Sumatran Fault Zone.

BackgroundBecause empirical studies of animal movement are most-often site- and species-specific, we lack understanding of the level of consistency in movement patterns across diverse taxa, as well as a framework for quantitatively classifying movement patterns. We aim to address this gap by determining the extent to which statistical signatures of animal movement patterns recur across ecological systems. We assessed a suite of movement metrics derived from GPS trajectories of thirteen marine and terrestrial vertebrate species spanning three taxonomic classes, orders of magnitude in body size, and modes of movement (swimming, flying, walking). Using these metrics, we performed a principal components analysis and cluster analysis to determine if individuals organized into statistically distinct clusters. Finally, to identify and interpret commonalities within clusters, we compared them to computer-simulated idealized movement syndromes representing suites of correlated movement traits observed across taxa (migration, nomadism, territoriality, and central place foraging).ResultsTwo principal components explained 70% of the variance among the movement metrics we evaluated across the thirteen species, and were used for the cluster analysis. The resulting analysis revealed four statistically distinct clusters. All simulated individuals of each idealized movement syndrome organized into separate clusters, suggesting that the four clusters are explained by common movement syndrome.ConclusionsOur results offer early indication of widespread recurrent patterns in movement ecology that have consistent statistical signatures, regardless of taxon, body size, mode of movement, or environment. We further show that a simple set of metrics can be used to classify broad-scale movement patterns in disparate vertebrate taxa. Our comparative approach provides a general framework for quantifying and classifying animal movements, and facilitates new inquiries into relationships between movement syndromes and other ecological processes.

Van ili ve çevresi, bölgesinde var olan güçlü tektonik hareketler ve deformasyonların etkisi altındadır. Bu deformasyonların esas kaynağını Arabistan levhasının Anadolu levhasına göre göreceli kuzey ve kuzey batı yönlü hareketi oluşturur. Bu bağıl hareket sonucunda Bitlis Zagros Kenet Kuşağı, Doğu Anadolu ve Kuzey Anadolu Fay sistemleri bölgenin depremselliğinde önemli rol oynamaktadır. Bölgede var olan gerilme analizlerinin belirlenmesi, Van ili ve çevresinde oluşacak deprem tehlikesini daha net ortaya çıkaracaktır. Bu bağlamda bölgede var olan GPS verileri ve Global CMT kataloğundan elde edilen depremsellik verileri kullanılarak çalışma alanında deformasyon oranı tespit edilmiştir. Buna göre çalışma alanında 40 n gerinim/yıl deformasyon alanı elde edilmiş, bölgesel gerilim içerisindeki bu küçük KB-GD sıkışma bileşeninin, 2011 Van depreminde olduğu gibi deprem mekanizmasında beklenenden daha büyük bir rol oynayabileceği saptanmıştır. Bölgede hâkim olan dilatasyon mekanizması incelendiğinde ise; Van ili ve çevresinde 25 n gerinim/yıl olan kesme bileşeni Karlıova bölgesine yaklaşıldıkça 170 n gerinim/yıl mertebesine erişmektedir. 2003 yılında bu bölgede meydana gelen Mw 6.4 Bingöl depreminin sağ yanal bir yapıda olması, Kuzey Anadolu Fay mekanizmasının Van ili civarına kadar uzanabileceğini işaret etmektedir. Bu alanda meydana gelebilecek büyük ölçekte deprem, Van Ovası’nda yerel zemin koşullarına bağlı olarak, yerleşim yerlerinde potansiyel tehlike oluşturabileceği ön görülmektedir.

Recent interplate coupling results from the inversion of GPS data in northwestern Colombia have shed light on the seismogenic and tsunami potential along the Caribbean coast. The identified locked region on the subduction interface could generate an M w 8.0 earthquake every 600 years, followed by a tsunami. While observed horizontal velocities have been reproduced successfully by the elastic coupling model, vertical velocities remain unexplained and differ, both in their signs and magnitudes, from the model prediction. To explain 3-dimensional velocities, particularly rapid coastal subsidence, we evaluate the viscoelastic response of the lithosphere-asthenosphere system to an earthquake cycle at the Caribbean subduction zone. We confirm that the role of viscous relaxation on interseismic deformation is critical when the recurrence interval is longer than the asthenosphere relaxation time. Moreover, fitting observed crustal motions requires a strong lithosphere (60–100 km) consistent with the depth of the lithosphere-asthenosphere boundary. Our results support the hypothesis of the Caribbean as a locus of seismic and tsunami hazards but do not resolve the vertical motion paradox at different time scales since the interseismic crustal motions recover completely the coseismic and postseismic displacements caused by the potential mainshock. Supplementary geological investigation is essential to validate our current interpretation and resolve the remaining gaps. • Possibility of an M w 8 earthquake in the Caribbean of Colombia validated by a viscoelastic earthquake cycle model. • The viscoelastic response of the Earth-system is crucial for geodetic data interpretation. • Large co- and inter-seismic vertical motions are independent of the subduction rate and the geometry of the subduction zone. • No marine terrace creation due to earthquake activity as co- and post-seismic motions are recovered in one earthquake cycle. • A strong "thick" lithosphere is required to support tectonic stresses over long earthquake cycles. [ABSTRACT FROM AUTHOR]

• We conduct an in-depth analysis of the impacts of social vulnerabilities on wildfire evacuation. • We used large-scale GPS data generated by mobile devices. • We focused on evacuation rates, delay in departure time, and evacuation destination distance. • The 2019 Kincade Fire in Sonoma County, California, was used as the case study. Vulnerable populations (e.g., populations with lower income or disabilities) are disproportionately impacted by natural hazards like wildfires. It is crucial to develop equitable and effective evacuation strategies to meet their unique needs. While existing studies offer valuable insights, we need to improve our understanding of how vulnerabilities affect wildfire evacuation decision-making, as well as how this varies spatially. The goal of this study is to conduct an in-depth analysis of the impacts of social vulnerabilities on aggregated evacuation decisions, including evacuation rates, delay in departure time, and evacuation destination distance by leveraging large-scale GPS data generated by mobile devices. Specifically, we inferred evacuation decisions at the level of the census block group, a geographic unit defined by the U.S. Census, utilizing GPS data. We then employed ordinary least squares and geographically weighted regression models to investigate the impacts of social vulnerabilities on evacuation decisions. We also used Moran's I to test if these impacts were consistent across different block groups. The 2019 Kincade Fire in Sonoma County, California, was used as the case study. The impacts of social vulnerabilities on evacuation rates show significant spatial variations across block groups, whereas their effects on the other two decision types do not. Additionally, unemployment, a factor under-explored in previous studies, was identified as contributing to both an increased delay in departure time and a reduction in destination distance of evacuees at the aggregate level. Furthermore, upon comparing the significant factors across different models, we observed that some of the vulnerabilities contributing to evacuation rates for all residents differed from those affecting the delay in departure time and destination distance, which only applied to evacuees. These new insights can guide emergency managers and transportation planners to enhance equitable wildfire evacuation planning and operations. [ABSTRACT FROM AUTHOR]

This article presents the development of a estimation model using an artificial neural network to estimate the traffic factor parameter, which reflects changes in travel time for a single transport connection. The modeling of traffic is highly challenging due to the complex and non-linear nature of road systems, the interactions between diverse road users, and the ripple effects of congestion. The research problem addressed is the need to consider a wide range of factors that contribute to congestion, as identified in the literature. To address this challenge, a universal Feedforward Neural Network (FNN) was designed, specifically the Multilayer Perceptron (MLP) 38–10-1 model. The input layer neurons of the MLP receive a vector of input values representing various factors such as road types, technical properties, time of day, days of the week, incidents, weather conditions, and population density. The model was trained using a dataset consisting of 41,945 records of fully completed input variables, extracted from a larger dataset of 300,000 travel time measurements collected from a real transport network mapped on a macro scale (Mazovia, Poland). During the research, the model achieved a satisfactory level of Mean Absolute Percentage Error (MAPE) for the test set (9.12%) and a Normalized Root Mean Squared Error (NRMSE) below 0.02, indicating good estimation performance. Furthermore, this work proposes specialized models with the structure of MLP 34–10-1 for individual types of roads, which also demonstrated satisfactory estimation abilities. These models have practical applications in traffic management and planning. Overall, this research addresses the research problem of modeling factors for predicting travel time changes using an artificial neural network approach. The developed models provide accurate estimations and offer potential applications in transportation systems. [ABSTRACT FROM AUTHOR]

Over the past few years, real-time visualisation of pedestrian dynamics has become more crucial to successfully organise and monitor open-crowded events. However, the process of collecting, efficiently handling and visualising a large volume of pedestrians' dynamic data in real time is challenging. This challenge becomes even more pronounced when pedestrians move in large-size, high-density, open and complex environments. In this article, we propose an efficient and accurate approach to acquire, process and visualise pedestrians' dynamic behaviour in real time. Our goal in this context is to produce GPS-based heat maps that assist event organisers as well as visitors in dynamically finding crowded spots using their smartphone devices. To validate our proposal, we have developed a prototype system for experimentally evaluating the quality of the proposed solution using real-world and simulation-based experimental datasets. The first phase of experiments was conducted in an open area with 37,000 square meters in Palestine. In the second phase, we have carried out a simulation for 5000 pedestrians to quantify the level of efficiency of the proposed system. We have utilised PHP scripting language to generate a larger-scale sample of randomly moving pedestrians across the same open area. A comparison with two well-known Web-based spatial data visualisation systems was conducted in the third phase. Findings indicate that the proposed approach can collect pedestrian's GPS-based trajectory information within 4 m horizontal accuracy in real time. The system demonstrated high efficiency in processing, storing, retrieving and visualising pedestrians' motion data (in the form of heat maps) in real time. [ABSTRACT FROM AUTHOR]

In order to identify geolocation of defaulter and extract travel information from trajectory data, spatial–temporal grid clustering method are adopted to analysis massive trajectory data. Firstly, the trajectory data are preprocessed, and the spacetime cluster method is applied to detect the travelers' geolocation information based on the information the travel segments are extracted. Secondly, for the recognition of frequent stay point, we proposed the spatial–temporal grid clustering model with smooth trajectory division algorithm and which improve the efficiency of processing a large amount of trajectory data. Thirdly, we proposed the spatial–temporal grid clustering method based on frequent stay point recognition. The experiment results of stationary trajectory division indicate that the frequent stay point and frequent paths can be effectively excavated under the condition of small information loss. These results demonstrate convincingly the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Online Positioning User Service (OPUS)-Projects was made available to public by the National Geodetic Survey (NGS) in 2010 so that users can process static Global Positioning System (GPS) data involving multiple occupations of multiple points. Being fully online adjustment software, OPUS-Projects offers simple management and processing tools for GPS data processing and analysis. In this study, three research questions are investigated using OPUS-Projects: (1) How many Continuously Operating Reference Stations (CORS) stations should be used?, (2) How does baseline length impact 3D coordinates of the stations involved?, and (3) What are the impacts of network size? In order to answer these questions, 24 h GPS data are downloaded for the CORSs stations in Missouri and neighboring states from the NOAA CORS Network (NCN) web site. Utilizing these data, effect of varying the number of stations in the network, distances to constrained CORS stations, and in terms size how big or small the network should be is investigated. At the end of the investigations, it is found out that adding more CORS stations into the adjustment did not affect the results significantly. Size of the network did not matter either. Regarding the distances to constraint CORSs, some variations are experienced as the baselines get longer. However, these variations are within the precision that CORS network maintains. [ABSTRACT FROM AUTHOR]