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In recent decades, a strong focus has emerged on exploring the scale-independent nature of our daily spatial motion. Similarly, heavy-tailed distributions have been observed for human locomotor activity, measured by actigraphs in medical fields. We recently proved that the raw acceleration data and also the activity signals calculated from them in diverse ways exhibit a general spectral characteristic; 1/f noise is observable above a certain cutoff frequency, while white noise and peaks corresponding to daily rhythms are visible at lower frequencies. We show that this pattern is strikingly similar to what we found earlier for GPS data and this similarity raises fundamental questions like what is the relation between human mobility and physical activity, what are the benefits of the analysis from this perspective, and how it helps us to better understand and model the scale-independent nature of human dynamics. [ABSTRACT FROM AUTHOR]

This study conducts a detailed analysis of population mobility during the COVID-19 pandemic, utilizing a unique approach that contrasts two types of mobile phone data: GPS-based and cell-based. The primary objective is to evaluate the effects of governmental restrictions on a variety of activities including school attendance, work, shopping, and leisure. We compare both data sets by using a set of defined criteria, including anticipated activity reductions during full and partial closures, as well as the timing of activity changes in response to policy implementations. Our research reveals that while cell-based data lacks the precision to differentiate between various out-of-home activities effectively, GPS-based data, especially when integrated with OpenStreetMap, proves significantly more adept at identifying and categorizing specific activity types. The GPS-based data shows, for example, that school activities fell by more than 80% while work activities were only reduced by around 50%. [ABSTRACT FROM AUTHOR]

Based on our interest in properties of human movement, we investigated Japanese GPS data, and arrived at the following three observations: (1) there is a strong correlation between the area of polygons created by human movement trajectories and their perimeters; (2) short-distance movement trajectories less than 5 km tend to enclose a large area like a circle; and (3) long-distance movement trajectories over 5 km tend to be straight. We also clarified the following four observations on individual attributes and external factors related to long-distance movements: (1) women tend to travel more linearly than men; (2) linearity is stronger on weekends and national holidays in areas with a large theme park; (3) linearity is weaker on weekends and holidays in areas with many historical tourist attractions; and (4) these variations are created by people visiting such areas. These properties should be incorporated when modeling the movement trajectories of humans. [ABSTRACT FROM AUTHOR]

Abstract This study conducts a detailed analysis of population mobility during the COVID-19 pandemic, utilizing a unique approach that contrasts two types of mobile phone data: GPS-based and cell-based. The primary objective is to evaluate the effects of governmental restrictions on a variety of activities including school attendance, work, shopping, and leisure. We compare both data sets by using a set of defined criteria, including anticipated activity reductions during full and partial closures, as well as the timing of activity changes in response to policy implementations. Our research reveals that while cell-based data lacks the precision to differentiate between various out-of-home activities effectively, GPS-based data, especially when integrated with OpenStreetMap, proves significantly more adept at identifying and categorizing specific activity types. The GPS-based data shows, for example, that school activities fell by more than 80% while work activities were only reduced by around 50%.

Two key goals for sustainable spatial planning are to promote low-carbon travel in daily life and to enhance human wellbeing through diverse human-environment interactions. Yet, the integration of these goals has been underexplored. This study investigates the potential for experiential diversity via active travel in different residential contexts within the Gävle city-region, Sweden. Over 15 months, we collected spatiotemporal data from 165 participants, analyzing 4,362 reported experiences and 13,192 GPS-derived travel trajectories. Our analysis uncovered a significant spatial discrepancy: while the travelled distances to locations of positive experiences typically ranged from 1.5 km to 5 km, active travel predominated only within 1.5 km. This discrepancy persisted across urban, suburban, and peripheral contexts. Although residents in different contexts reported the same types of experiences, urban dwellers travelled about 50 % farther for nature experiences compared with other positive experiences, whereas peripheral dwellers travelled twice the distance for urbanicity experiences compared with other positive experiences. Consequently, urban residents mostly relied on active travel for urbanicity experiences and motorised travel for nature experiences, with the reverse trend observed among peripheral dwellers. These results illustrate the importance of spatial scale for promoting diverse positive experiences via active travel, regardless of residential context. Effective planning strategies may include enhancing environmental diversity near homes and developing infrastructure that favours active over motorised travel for short to moderate distances.

Abstract This study introduces an algorithmic strategy for measuring dimensions of police presence at microgeographic units using GPS data from police patrol units. The proposed strategy builds upon the integrated theory of hot spots patrol strategy from Sherman et al. (Journal of Contemporary Criminal Justice 30:95–122, 2014), focusing on three key dimensions: the frequency, duration, and intermittency of police presence. This study provides pseudocodes for the algorithm, facilitating the pre-processing of GPS-derived data sequences to generate measures of these three central concepts. The measures presented in this article offer a framework for investigating the impact of police presence on crime and other relevant crime-related outcomes at microgeographic units, using GPS data. This algorithmic strategy may further contribute to the development of evidence-based strategies in place-based policing initiatives.

This article investigates the relationship between income and the diversity of sociospatial networks as described by high-density mobile phone application (MPA) Global Positioning System data. Looking at the counties that contain the Atlanta, Boston, Chicago, and Los Angeles metropolitan regions in August and September 2022, this study asks the following questions: How does the racial-ethnic diversity and spatial extent of network of activity space-times—the place and time of daily activities—vary across different income levels? Given the existing literature, are more diverse networks composed of higher income classes? Are there key types of activity space-times that are more likely to be in these networks? Given that the overlap of activity spaces might lead to the formation of stronger social ties, this study aims to provide new evidence of the role of activity spaces in determining the diversity of social exposures with high-resolution spatiotemporal MPA activity. Results suggest that income is an important determinant of diversity in networks, with the highest and lowest income groups both exhibiting the least diversity in networks, whereas institutional spaces like church or school and other surprising places such as the dentist's office are the most likely activity space-times in these networks. [ABSTRACT FROM AUTHOR]

Hiking plays a significant role in experiencing linear heritage, and gaining insights into the spatiotemporal distribution of hikers' behavior is imperative for effectively utilizing heritage resources. This study employed GPS trajectory data to examine the spatiotemporal patterns of hiking behavior on the Great Wall in Beijing and investigate the utilization patterns of heritage resources in hiking tourism. The results revealed that: there were notable variations in trajectory quantity among different months with extended activity durations, early finishing times, and an absence of nighttime activities. Spatially, the trajectories exhibited dispersion and uneven distribution, leading to the underutilization of numerous heritage resources. The distribution of starting and ending points of trajectories demonstrated a substantial correlation with neighboring natural villages. Consequently, this study offers valuable insights for informing decision‐making processes related to the development, construction, and optimization of the scenic area along the Great Wall and the planning and design of tourist routes. [ABSTRACT FROM AUTHOR]

This study introduces an algorithmic strategy for measuring dimensions of police presence at microgeographic units using GPS data from police patrol units. The proposed strategy builds upon the integrated theory of hot spots patrol strategy from Sherman et al. (Journal of Contemporary Criminal Justice 30:95–122, 2014), focusing on three key dimensions: the frequency, duration, and intermittency of police presence. This study provides pseudocodes for the algorithm, facilitating the pre-processing of GPS-derived data sequences to generate measures of these three central concepts. The measures presented in this article offer a framework for investigating the impact of police presence on crime and other relevant crime-related outcomes at microgeographic units, using GPS data. This algorithmic strategy may further contribute to the development of evidence-based strategies in place-based policing initiatives. [ABSTRACT FROM AUTHOR]

Despite its crucial role in students' daily lives, commuting time remains an underexplored dimension in higher education research. To address this gap, this study focuses on challenges that students face in urban environments and investigates the impact of commuting time on the Grade Point Average (GPA) of first-year bachelor students of Politecnico di Milano, Italy. This research employs an innovative two-step methodology. In the initial phase, machine learning algorithms trained on privacy-preserving GPS data from anonymous users are used to construct accessibility maps to the university and to obtain an estimate of students' commuting times. In the subsequent phase, authors utilize polynomial linear mixed-effects models and investigate the factors influencing students' GPA, with a particular emphasis on commuting time. Notably, this investigation incorporates causal inference analyses from the observational studies domain, which enable to establish the effect of commuting time on academic outcome. The findings underscore the significant impact of travel time on students' performance and may support policies and implications aiming at improving students' educational experience in metropolitan areas. The study's innovation lies both in its exploration of a relatively uncharted factor and the novel methodologies applied in both phases. [ABSTRACT FROM AUTHOR]

Safety is a major concern in bicycle traffic, and understanding safety-related factors that influence bicycle route choice is crucial for improving safety and promoting sustainable transportation. However, there is a notable lack of research on the impact of accident risk and perceived safety on cyclists' route choices. This study addresses this gap by investigating whether accident-prone areas or perceived insecure locations affect actual route choice decisions. The contribution explores this relationship by leveraging an extensive dataset comprising approximately 4000 trips from around 170 participants, alongside additional data on infrastructure, operations, exposure, accidents, and mobility diaries reporting critical incidents. The findings broadly confirm results from other studies regarding the influence of route characteristics (e.g., existence of cycling infrastructure, volumes of motorized traffic, or presence of signal-controlled intersections). Moreover, the study reveals that a high accident risk along a route does have a slight negative influence on route choice. Surprisingly, perceived safety does not significantly influence cyclists' route choice.

The challenge of “cruising for parking” in urban areas has long been a subject of study, but existing research often relies on biased surveys or arbitrary assumptions in the absence of ground truth data. This paper addresses these gaps by introducing the first-ever collection of ground truth data on parking search durations gathered through a self-developed app. The dataset encompasses more than 3500 journeys collected in Germany, with approximately two-thirds of them ending in Frankfurt am Main. Utilizing this unique dataset, we developed a deep learning neural network model that accurately identifies parking search routes in GPS data and predicts search duration. Our model outperforms existing parking search identification models proposed in previous studies. The model’s efficacy is further evaluated on an independent park-and-visit dataset and then applied to a large-scale dataset from Frankfurt/Germany. This generates the first reliable statistics on parking search durations and reveals key insights about parking search patterns in this city. Notably, the predicted mean parking search duration from this extensive dataset, comprising over 860,000 journeys, is approximately 1.5 min. This work not only advances the field by providing a new data collection methodology and a superior predictive model but also offers a reusable framework that can be applied to other cities and datasets for broader urban mobility insights.

This study proposes a method for measuring travel delays caused by traffic crashes based on taxi GPS data and other open-source spatial data. Travel delays caused by traffic crashes are quantified according to the difference between the post-crash and typical travel speeds on affected road segments. Based on multiple sources of data in Hong Kong, we also develop a generalized linear model with explanatory variables including crash characteristics, temporal attributes, road network features, traffic indicators, and built environment features, to unveil the relationship between travel delays and these factors. The findings show that crash characteristics alone inadequately explain variations in delays. The model performance improves after factors about the built environment and the dynamic road conditions are incorporated. This underscores the importance of urban factors in traffic delay analysis. Furthermore, we estimate the total travel delays caused by traffic crashes in the city. It is estimated that Hong Kong has suffered from a total delay of 713,877 vehicle-hours in 2021. The associated economic loss amounts to US$11.02 million. This study contributes to methodological advances in estimating crash-induced travel delays. The explanatory model considers factors which help policy makers and planners to identify risky factors and hot spots for devising more targeted and effective strategies of shortening crash-induced traffic congestion in the future. In addition, the findings highlight the significance and magnitude of another negative externality of traffic crashes – traffic delays – in a complex urban road network.

The Carpathians and other mountainous regions around the world are renowned for their specific landscapes shaped by pastoralism, a millennia-old traditional and sustainable economic system. In Romania, this traditional occupation has an established place within the Romanian culture. In an environment where large predators are present, the livestock owners and shepherds have traditionally relied, and still do, on livestock guardian dogs (LGDs) to protect the flock against carnivores or theft, therefore, the dogs are perceived as an integral component of the traditional pastoral system. However, from late April until the end of September, many outdoor recreational activities like hiking, mountain trail running, or biking overlap with the pastoral calendar, creating a potential for conflict between two, very different categories of landscape users, with recurring incidents happening over the years. In this study, a winter GPS monitoring campaign was proposed, between November 2023 and January 2024 that used GPS professional collars to track the movements of two livestock guardian dogs stationed at two sheepfolds located at their winter bases in the hills at the foot of Ignis Mountains (part of the Romanian northern Carpathians) from northwestern Maramureș Land, Romania. The campaign generated point-based spatiotemporal data processed and analyzed in M. Excel and QGIS using Kernel density estimation as the main method to generate metrics and identify potential clusters of LGD activity in their usual environment. The results highlight high observational clusters near the winter folds but also lower observational clusters in areas situated hundreds of meters distance around the main compound, in certain locations. Although temporally limited, the results have the potential to help the understanding of the animal's preferred zone of habitation and substantiate future win-win cohabitation solutions that minimize conflictual encounters between the shepherds and their guardian dogs on one side, as primary land users, and outdoor recreationists as other landscape users.

Abstract Integrated step-selection analyses (iSSAs) are versatile and powerful frameworks for studying habitat and movement preferences of tracked animals. iSSAs utilize integrated step-selection functions (iSSFs) to model movements in discrete time, and thus, require animal location data that are regularly spaced in time. However, many real-world datasets are incomplete due to tracking devices failing to locate an individual at one or more scheduled times, leading to slight irregularities in the duration between consecutive animal locations. To address this issue, researchers typically only consider bursts of regular data (i.e., sequences of locations that are equally spaced in time), thereby reducing the number of observations used to model movement and habitat selection. We reassess this practice and explore four alternative approaches that account for temporal irregularity resulting from missing data. Using a simulation study, we compare these alternatives to a baseline approach where temporal irregularity is ignored and demonstrate the potential improvements in model performance that can be gained by leveraging these additional data. We also showcase these benefits using a case study on a spotted hyena (Crocuta crocuta).

Vacant cruising is an inevitable part of taxi services caused by spontaneous demand, and the efficiency of cruising strategies has purported impact on the profit of individual drivers. Extensive studies have been conducted to analyze taxi cruising patterns and propose effective cruising strategies. However, existing studies mainly focused on the collective behavior of certain driver groups and failed to capture cruising behavior patterns at the individual driver or trip level. Also, prior studies considered different types of factors affecting taxi cruising, but we still lack an integrated model to compare their relative importance. In this study, we analyze trip-level cruising time and the associated external and internal factors using a taxi trajectory dataset in Shanghai, China. A trajectory annotation technique is introduced to segment taxi trajectories into different phases. Various external (supply and demand, traffic condition and built environment) and internal (cruising strategies and historical driver performance) factors are derived from taxi trajectories and other data sources. A spatiotemporal embedding method is devised to capture unobserved effects over time and space. The impacts of external and internal factors on taxi cruising time are examined using regression and XGBoost—a machine learning model. The results show external and internal factors are both important in determining taxi cruising time. Cruising strategies contribute 49.0% in taxi cruising time, which implies effective cruising strategies can greatly reduce vacant cruising time. Additionally, nonlinear associations of some variables (e.g., supply–demand patterns, traffic speed) with taxi cruising time are discussed. [ABSTRACT FROM AUTHOR]

Integrated step-selection analyses (iSSAs) are versatile and powerful frameworks for studying habitat and movement preferences of tracked animals. iSSAs utilize integrated step-selection functions (iSSFs) to model movements in discrete time, and thus, require animal location data that are regularly spaced in time. However, many real-world datasets are incomplete due to tracking devices failing to locate an individual at one or more scheduled times, leading to slight irregularities in the duration between consecutive animal locations. To address this issue, researchers typically only consider bursts of regular data (i.e., sequences of locations that are equally spaced in time), thereby reducing the number of observations used to model movement and habitat selection. We reassess this practice and explore four alternative approaches that account for temporal irregularity resulting from missing data. Using a simulation study, we compare these alternatives to a baseline approach where temporal irregularity is ignored and demonstrate the potential improvements in model performance that can be gained by leveraging these additional data. We also showcase these benefits using a case study on a spotted hyena (Crocuta crocuta). [ABSTRACT FROM AUTHOR]

Fire incidences are classed as catastrophic events, which mean that persons may experience mental distress and trauma. The development of a robotic vehicle specifically designed for fire extinguishing purposes has significant implications, as it not only addresses the issue of fire but also aims to safeguard human lives and minimize the extent of damage caused by indoor fire occurrences. The primary goal of the AFRC is to undergo a metamorphosis, allowing it to operate autonomously as a specialized support vehicle designed exclusively for the task of identifying and extinguishing fires. Researchers have undertaken the tasks of constructing an autonomous vehicle with robotic capabilities, devising a universal algorithm to be employed in the robotic firefighting process, and designing a fuzzy controller algorithm that can be used in all expected scenarios. The use of a fuzzy logic algorithm in this design demonstrates the usefulness of this system, all factors are involved in which cases are previously identified and taught, as well as the overall map of the premises have been uploaded so that the system can identify the exact place of the fire source, and two types of fire have also been examined. When the performance of the foam pump, water pump, and robotic car motors is compared to the data from the flam sensor, temperature sensor and GPS data, it demonstrates a high responsiveness in terms of applying the appropriate approach based on the type of fire due to the probable action for which the system has been trained. This will have the benefit of shortening the required process for fire extinguishment and using the appropriate fire extinguishing tools. This technology may be used to put out flames, deploy in different areas, and handle a variety of fire scenarios inside buildings. [ABSTRACT FROM AUTHOR]

Congestion free mobility has become nearly impossible in most of the metropolitan cities of India especially during peak hours. The understanding of factors inducing congestion demands huge amount of data pertaining to urban traffic. The developed countries have adopted different kinds of automatic data collection systems such as loop detectors, surveillance cameras and radars for the data collection of road traffic condition. In developing countries like India, the collection and monitoring of data related to movement of traffic stream are mostly manual, very time consuming and expensive. In India, Intelligent Transport System (ITS) has been implemented to Mysore City public transport in the year 2012. This study makes use of Global Positioning System (GPS) data of Mysore ITS. The major objective of the present study is to evaluate the congestion on urban roads using public transit GPS data with the help of visualization techniques. Spatio-temporal visualization-based analysis has been carried out to evaluate the traffic congestion patterns of urban roads. Initially, the comparison of traffic states on urban street and arterial road has been carried out. Later, the difference in congestion patterns before and after the operation of grade separator and the impact of route diversion on the congestion patterns have been evaluated. This study shows that public transit GPS data can be a potential data source to evaluate the traffic state or congestion, especially when there are limited sources of traffic data. [ABSTRACT FROM AUTHOR]

In recent years, GPS data on truck movements have become much more available, leading researchers and practitioners to leverage these sources to generate a wealth of data and information related to truck OD flows. In this study large amount of truck GPS data used to investigate characteristics of freight transportation in particularly OD land uses and freight vehicle classes. Aiming to realize the freight flows characteristics of the Chicago Metropolitan Agency Planning (CMAP) region. The data are used primarily to gain insight into the nature of urban goods movement and the distribution of retail goods. The main objective in this respect is analyzing urban freight flows and retail goods movement. The data-driven analysis generates a number of interesting findings regarding the nature and volume of trips by light-duty, medium-duty, and heavy-duty truck that serve various types of retail and industrial functions are generated. The design of this study especially its land use data fusion aspect is expected to benefit both practitioners and researchers as they plan future data collection, model calibration and urban freight studies. [ABSTRACT FROM AUTHOR]

The Carpathians and other mountainous regions around the world are renowned for their specific landscapes shaped by pastoralism, a millennia-old traditional and sustainable economic system. In Romania, this traditional occupation has an established place within the Romanian culture. In an environment where large predators are present, the livestock owners and shepherds have traditionally relied, and still do, on livestock guardian dogs (LGDs) to protect the flock against carnivores or theft, therefore, the dogs are perceived as an integral component of the traditional pastoral system. However, from late April until the end of September, many outdoor recreational activities like hiking, mountain trail running, or biking overlap with the pastoral calendar, creating a potential for conflict between two, very different categories of landscape users, with recurring incidents happening over the years. In this study, a winter GPS monitoring campaign was proposed, between November 2023 and January 2024 that used GPS professional collars to track the movements of two livestock guardian dogs stationed at two sheepfolds located at their winter bases in the hills at the foot of Ignis Mountains (part of the Romanian northern Carpathians) from northwestern Maramureș Land, Romania. The campaign generated point-based spatiotemporal data processed and analyzed in M. Excel and QGIS using Kernel density estimation as the main method to generate metrics and identify potential clusters of LGD activity in their usual environment. The results highlight high observational clusters near the winter folds but also lower observational clusters in areas situated hundreds of meters distance around the main compound, in certain locations. Although temporally limited, the results have the potential to help the understanding of the animal's preferred zone of habitation and substantiate future win-win cohabitation solutions that minimize conflictual encounters between the shepherds and their guardian dogs on one side, as primary land users, and outdoor recreationists as other landscape users. [ABSTRACT FROM AUTHOR]

Hinterland transport is a critical segment in maritime cross-border logistics, which links the end-users of global supply chains to the maritime segment. Truck-based hinterland transport is known to cause congestion in and around ports. This study aimed to quantify the congestion caused by trucks at the Port of Colombo, which has not been a subject of a systematic study. To this end, the study makes use of GPS data. In addition to revealing heavy congestion within the port, the study also reveals significant variations in congestion during different times of the day with the duration of journeys peaking from 1200hrs to 1800hrs. Furthermore, the most congested segment of the truck journey is found to be the port exit gate. The findings provide a foundation for appraising infrastructure investments and other related improvement initiatives for easing congestion within and in the vicinity of the port. The study illustrates the potential for data to reveal insights that transcend its original purpose. From a theoretical point of view, the study proposes a novel way of conceptualising the truck turnaround time at ports which goes beyond the confines of the port and is more meaningful for users in the hinterland. The analysis presented in the study is limited to data obtained from a single haulage company.

The attraction and generation of a trip play a crucial role in transport planning and forecasting of trips. Multiple linear regression (MLR) is the most popular method of calculating trip attractions (TA) and trip generations (TG) to produce a distribution that can be used to forecast trips with updated values of independent variables such as electricity consumption, no of households, area of the land use etc. Literature shows that the surveys require independent variables to be updated; making them expensive and time consuming. This study aims to develop an MLR model for TA and TG based on available survey data from 2013 for Western Province, Sri Lanka and to update those independent variables using High Frequency (HF) proxy data for the predicted year (2019). Data from HF proxy sources such as electricity consumption, GPS customer points, and Landsat satellite imagery data have been used to update the independent variables of TG and TA for 2019. In the initial stage of this research, data from a home visit survey conducted in 2013 and land use data from the Western Province of Sri Lanka were used to develop the MLR model for TG and TA. A correlation and a regression analysis were performed using these surveyed data. According to the study, the MLR model for TG for home-based work trips has an r2 value of 0.79 and TA for general purposes (including shops and businesses) and industrial has an r2 value of 0.74 and 0.79, respectively, indicates the strong relationship between the considered variables to predict TG and TA. The model is validated with 2013 survey data and would be helpful for real-time estimation of the TG and TA of each zone.

Understanding animal movement and behaviour can aid spatial planning and inform conservation management. However, it is difficult to directly observe behaviours in remote and hostile terrain such as the marine environment. Different underlying states can be identified from telemetry data using hidden Markov models (HMMs). The inferred states are subsequently associated with different behaviours, using ecological knowledge of the species. However, the inferred behaviours are not typically validated due to difficulty obtaining 'ground truth' behavioural information. We investigate the accuracy of inferred behaviours by considering a unique data set provided by Joint Nature Conservation Committee. The data consist of simultaneous proxy movement tracks of the boat (defined as visual tracks as birds are followed by eye) and seabird behaviour obtained by observers on the boat. We demonstrate that visual tracking data is suitable for our study. Accuracy of HMMs ranging from 71% to 87% during chick‐rearing and 54% to 70% during incubation was generally insensitive to model choice, even when AIC values varied substantially across different models. Finally, we show that for foraging, a state of primary interest for conservation purposes, identified missed foraging bouts lasted for only a few seconds. We conclude that HMMs fitted to tracking data have the potential to accurately identify important conservation‐relevant behaviours, demonstrated by a comparison in which visual tracking data provide a 'gold standard' of manually classified behaviours to validate against. Confidence in using HMMs for behavioural inference should increase as a result of these findings, but future work is needed to assess the generalisability of the results, and we recommend that, wherever feasible, validation data be collected alongside GPS tracking data to validate model performance. This work has important implications for animal conservation, where the size and location of protected areas are often informed by behaviours identified using HMMs fitted to movement data. [ABSTRACT FROM AUTHOR]

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 B 1 MAE of 429.04 ± 84.87 and RMSE of 581.34 ± 185.84 , and B 2 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. [ABSTRACT FROM AUTHOR]

How evacuations are managed can substantially impact the risks faced by affected communities. Having a better understanding of the mobility patterns of evacuees can improve the planning and management of these evacuations. Although mobility patterns during evacuations have traditionally been studied through surveys, mobile phone location data can be used to capture these movements for a greater number of evacuees over a larger geographic area. Several approaches have been used to identify hurricane evacuation patterns from location data; however, each approach relies on researcher judgment to first determine the areas from which evacuations occurred and then identify evacuations by determining when an individual spends a specified number of nights away from home. This approach runs the risk of detecting non‐evacuation behaviors (e.g., work trips, vacations, etc.) and incorrectly labeling them as evacuations where none occurred. In this article, we developed a data‐driven method to determine which areas experienced evacuations. With this approach, we inferred home locations of mobile phone users, calculated their departure times, and determined if an evacuation may have occurred by comparing the number of departures around the time of the hurricane against historical trends. As a case study, we applied this method to location data from Hurricanes Matthew and Irma to identify areas that experienced evacuations and illustrate how this method can be used to detect changes in departure behavior leading up to and following a hurricane. We validated and examined the inferred homes for representativeness and validated observed evacuation trends against past studies. [ABSTRACT FROM AUTHOR]

With the spread of smartphones and mobile internet, Global Positioning System (GPS) data from vehicles has become widely available. This data represents a unique opportunity to automatically extract road network features and generate detailed maps that can be used in the creation of transport network models, while minimising the quantity of resources usually invested in that task. Accurate transport network models can be used in a variety of applications either in transport simulation models or autonomous vehicles navigation. Although two relevant literature reviews were performed during the last decade, they were not systematic and did not explore the road network inference methods from a transport network modelling point of view. The objective of this research is to perform a systematic and reproducible literature review on the use GPS data in transport network modelling and provide limitations and future work to extract a road network representation for transport models and autonomous vehicles navigation. This was done by systematically examining the studies' different approaches with respect to relevant criteria. Most studies produced a simple representation of the road network, not detailed enough for transport models. Other limitations were the bias introduced by the GPS sample and the reproducibility of the different methods. [ABSTRACT FROM AUTHOR]

Abstract The labor market has transformed with the advent of the gig economy, characterized by short-term and flexible work arrangements facilitated by online platforms. As this trend becomes increasingly prevalent, it presents unique opportunities and challenges. In this manuscript, we comprehensively characterize the social networks of gig economy workers in each of the 15 cities studied. Our analysis reveals a scaling relationship between networks and the city population. In particular, we note the high level of modularity of the networks, and we argue that it results from the natural specialization of couriers along different areas of the cities. Furthermore, we show that degree and betweenness centrality is positively correlated with income but not with tenure. Our findings shed new light on the social organization of the gig economy workers and provide valuable insights for the management and design of gig economy platforms.

Quality data is critically important for research and policy-making. The availability of device location data carrying rich, detailed information on travel patterns has increased significantly in recent years with the proliferation of personal GPSenabled mobile devices and fleet transponders. However, in its raw form, location data can be inaccurate and contain embedded biases that can skew analyses. This report describes the development of a method to process, clean, and enrich location data. Researchers developed a computational framework for processing large scale location datasets. Using this framework several hundred days of location data from the San Francisco Bay Area was (a) cleaned, to identify and discard inaccurate or problematic data, (b) enriched, by filtering and annotating the data, and (c) matched to links on the road network. This framework provides researchers with the capability to build link-level metrics across large scale geographic regions. Various applications for this enriched data are also discussed in this report (including applications related to corridor planning, freight planning, and disaster and emergency management) along with suggestions for further work.

Abstract Understanding animal movement and behaviour can aid spatial planning and inform conservation management. However, it is difficult to directly observe behaviours in remote and hostile terrain such as the marine environment. Different underlying states can be identified from telemetry data using hidden Markov models (HMMs). The inferred states are subsequently associated with different behaviours, using ecological knowledge of the species. However, the inferred behaviours are not typically validated due to difficulty obtaining ‘ground truth’ behavioural information. We investigate the accuracy of inferred behaviours by considering a unique data set provided by Joint Nature Conservation Committee. The data consist of simultaneous proxy movement tracks of the boat (defined as visual tracks as birds are followed by eye) and seabird behaviour obtained by observers on the boat. We demonstrate that visual tracking data is suitable for our study. Accuracy of HMMs ranging from 71% to 87% during chick‐rearing and 54% to 70% during incubation was generally insensitive to model choice, even when AIC values varied substantially across different models. Finally, we show that for foraging, a state of primary interest for conservation purposes, identified missed foraging bouts lasted for only a few seconds. We conclude that HMMs fitted to tracking data have the potential to accurately identify important conservation‐relevant behaviours, demonstrated by a comparison in which visual tracking data provide a ‘gold standard’ of manually classified behaviours to validate against. Confidence in using HMMs for behavioural inference should increase as a result of these findings, but future work is needed to assess the generalisability of the results, and we recommend that, wherever feasible, validation data be collected alongside GPS tracking data to validate model performance. This work has important implications for animal conservation, where the size and location of protected areas are often informed by behaviours identified using HMMs fitted to movement data.

AbstractGPS-derived strain rates can provide a tight constraint in understanding tectonic evolution of the Tibetan Plateau and are helpful in seismic hazard assessment. For this purpose, we at first verify the method, proposed by Zhu et al. in 2005 and 2006, for the computation of the strain rates, and found that the approach is reasonable and accurate. Then, based on the updated GPS data, we compute strain rates in southeastern Tibetan Plateau (SETP). The computed results showed that the strain rates in the Sichuan Basin and South China Block are very small, and the high values are largely concentrated along the Sagaing Fault and Xianshuihe-Anninghe-Xiaojiang fault system. In addition, the highest principal strain rates are located around the eastern Himalayan syntaxis (EHS), with the compressive orientations perpendicular to the Main Thrust Belt. In particular, the calculated extensive deformation basically matches the normal faulting earthquakes. In general, the characteristic of the strain rate distribution is in good agreement with the tectonic structures from the geological and geophysical investigations. At last, according to the strain rates, seismicity, and tectonic structures, we estimate five most likely zones for future major earthquakes in SETP. These areas include the Zemuhe Fault, northwestern segment of the Red River Fault, and at the intersection between the Ganzi-Yushu Fault and the Xianshuihe Fault, and at the one between the Zhongdian Fault and Jinshajiang Fault. Consequently, this study is significant in deep understanding of the geodynamics in the Tibetan Plateau and earthquake hazard assessment.

The use of geodetic observation data for seismic fault parameters inversion is the research hotspot of geodetic inversion, and it is also the focus of studying the mechanism of earthquake occurrence. Seismic fault parameters inversion has nonlinear characteristics, and the gradient-based optimizer (GBO) has the characteristics of fast convergence speed and falling into local optimum hardly. This paper applies GBO algorithm to simulated earthquakes and real LuShan earthquakes in the nonlinear inversion of the Okada model to obtain the source parameters. The simulated earthquake experiment results show that the algorithm is stable, and the seismic source parameters obtained by GBO are slightly closer to the true value than the multi peak particle swarm optimization (MPSO). In the 2013 LuShan earthquake experiment, the root mean square error between the deformation after forwarding of fault parameters obtained by the introduced GBO algorithm and the surface observation deformation was 3.703 mm, slightly better than 3.708 mm calculated by the MPSO. Moreover, the inversion result of GBO algorithm is better than MPSO algorithm in stability. The above results show that the introduced GBO algorithm has a certain practical application value in seismic fault source parameters inversion.

Whether the computer is driving your car or you are, advanced driver assistance systems (ADAS) come into play on all levels, from weather monitoring to safety. These modern-day ADASs use various assisting tools for drivers to keep the journey safe; these sophisticated tools provide early signals of numerous events, such as road conditions, emerging traffic scenarios, and weather warnings. Many urban applications, such as car-sharing and logistics, rely on accurate and up-to-date road map data. Map generation methods use a variety of data sources, including but not limited to global positioning systems (GPS). In this research we propose a GPS-only data trajectory analysis and a novel scheme to convert GPS trajectory data to image-based data to train a custom Convolutional Neural Network (CNN) model. The empirical results with an extensive 5-fold cross-validation show that the proposed scheme identifies turn and not turn with more than 94% recall. It outperforms the existing turn detection schemes on two major frontiers, the required data and the accuracy achieved in detecting different driving behaviors.