Search

The mobility of scientists, manifested by movements to new academic institutions, grows with globalization and plays a crucial role in individual careers, institutional productivity, and knowledge dissemination. Current research on scientists' mobility focuses on aggregated levels such as inter-country mobility, with little attention paid to fine-grained institutional level, leading to a simplified spatial portrayal of the mobility. To fill the gap, we take scientists in geography as examples, and reconstructed their dynamic mobility network among institutions from 1970 to 2020 based on massive literature metadata. Our findings reveal the spatial mobility pattern that is now dominated by North America, Western and Northern Europe, East Asia, and Oceania, with the trend of intensification, multipolarity, and inequality over time. Specifically, the mobility network exhibits clear community structure largely constrained by spatial proximity and national borders. We also uncovered a universal downward mobility pattern embedded in the hierarchical structure. Our quantitative analysis further suggest that mobility is facilitated by multiple realities, including spatial, cultural, and scientific proximity, institutional rankings and national economic levels, cooperation, and visa-free policies, with varying dynamics. These results contribute to spatiotemporal insights into the mechanisms of scientific development in theory, and the basis for talent policymaking in practice. KEY POLICY HIGHLIGHTS: • The mobility of scientists has long been dominated by institutions in developed countries, with China emerging in recent decades. • Spatial proximity and national borders largely constrain the mobility of scientists. • The hierarchy in mobility network determines the decrease trend in institutional prestige from original to new academic positions. • The mobility of scientists between institutions is positively associated with stronger cooperation, same language, and visa-free policies. [ABSTRACT FROM AUTHOR]

The significance of unimpeded mobility in everyday activities, including commuting, shopping, and social interactions, cannot be overstated. However, many people, particularly women, face challenges that restrict their ability to move freely because of various circumstances, such as gender-related concerns and socio-economic inequalities. The absence of a well-defined framework and comprehensive indicators for addressing gender-related mobility concerns is a notable challenge in the Kingdom of Saudi Arabia (KSA) and other similar regions. This article comprehensively analyses several mobility indicators and dimensions from diverse literature, specifically emphasizing their applicability in promoting gender equality in urban mobility within the Kingdom of Saudi Arabia (KSA). A comprehensive framework is established through rigorous analysis and comparison, taking inspiration from the "Four A's" concept introduced by the World Bank in 2020. The approach offers valuable help in navigating the complex interplay between gender dynamics and urban mobility. The comparison underscores the significance of including privacy, security, availability, and diversity of public transportation indicators in the toolkit. This inclusion is necessary to address the unique requirements, safety considerations, and cultural contexts of women when designing public transportation systems. The primary objective of these initiatives is to advance gender equality in the context of urban transportation in Saudi Arabia. [ABSTRACT FROM AUTHOR]

In the opportunistic network, communication between two communities is challenging and time-consuming. A community is made up of several nodes that are grouped together according to distinct behaviors. This attribute is known as the mobility pattern of the nodes. Mobility-casting is the process of gathering nodes according to a common mobility pattern. A community is made up of many casts. It is extremely hard to locate both a local and global forwarder for the message transfer inside the community. Due to the absence of information on the path between the source and the destination, particularly in diverse landscapes with both sparse and dense populations, searching for a destination might take a long time. When a user U, of a particular community sends a message, it is received by users in community B who have the same cast and mobility pattern as U (referred to as social friends collectively). In order to regulate relay, drop, and needless abort of the message, a robust protocol architecture is presented in this article. Additionally, it carries out the synchronization of message dumping and relaying. The network's average latency and overhead ratio are decreased by the proposed method. [ABSTRACT FROM AUTHOR]

Research in the field of Wireless Sensor Networks aims to develop protocols ensuring minimal energy dissipation. In this work, we propose a lightweight congestion control based sink mobility solution to improve data gathering that considers jointly energy and memory constraints. Thus, we propose a mechanism to avoid and resolve situations when nodes may not receive new messages because of buffer saturation. This can causes data loss and energy waste while retransmitting non-received data by the source node. Indeed, our model moves the sink close to the congested nodes to alleviate their queues once a congestion risk was detected. The performances of our approach were approved by intensive simulations. Obtained results proved that the network lifetime achieved by our strategy is two to five times better than that reached by comparative approaches. We note also a considerable improvement in packet delivery ratio and a high adaptability to increased data traffic. [ABSTRACT FROM AUTHOR]

Summary: Mobility pattern recognition is a complex task in vehicle ad hoc networks (VANET) because the driving state of each vehicle is different. An intelligent transportation system on VANET is used for traffic control and accident prevention. For this reason, human driver behaviour is first analysed to identify mobility patterns. A novel driver behaviour prediction model using a Siamese deep learning architecture is proposed to achieve the goal. Here, an image‐based behaviour prediction model is performed to achieve the highly accurate driving state of the driver. A warning message is forwarded to the neighbouring vehicles based on the driver's behaviour. Due to the dynamic properties of real‐time vehicle mobility, a faster data transmission model is achieved using the ad hoc on‐demand distance vector routing protocol. To achieve faster data transmission and nullified retransmission, here a weighted location‐based routing model is framed. The optimization problem in the location‐aided routing protocol is solved using the vector algorithm's weighted mean. As a result, the proposed method improved the throughput of ASHLOSR to 8.1% and AODV to 7.6%. [ABSTRACT FROM AUTHOR]

In recent years, slow travel modes (walking, cycling) have gained much interest in the context of urban air quality management. This article presents the findings from a novel air quality measurement experiment in the Netherlands, by regarding cyclists as carriers and transmitters of real-world information on fine-grained air quality conditions. Using individual sensors on bicycles—connected to a GPS positioning system—online local pollution information originating from cyclists' detailed spatial mobility patterns is obtained. Such air quality surface maps and cyclists' mobility maps are then used to identify whether there are significant differences between the actual route choice and the cyclists' shortest route choice, so as to identify the implications of poor air quality conditions for their mobility choices. Thus, the article seeks to present both a detailed pollution surface map and the complex space-time mobility patterns of cyclists in a region, on the basis of online quantitative data—at any point in time and space—from bicycle users in a given locality. In addition, the article estimates their response—in terms of route choice—to detailed air-quality information through the use of a novel geoscience-inspired analysis of space-time "big data." The empirical test of our quantitative modeling approach was carried out for the Greater Utrecht area in the Netherlands. Our findings confirm that spatial concentration of air pollutants have great consequences for bike users' route choice patterns, especially in the case of non-commuting trips. We also find that cyclists make longer trips on weekends and in the evenings, especially towards parks and natural amenities. [ABSTRACT FROM AUTHOR]

Summary: With the widespread adoption of electric vehicles (EVs), it is crucial to plan for charging in a way that considers both EV driver behavior and the electricity grid’s demand. Here, we integrate detailed mobility data with empirical charging preferences to estimate charging demand and demonstrate the power of personalized shifting recommendations to move individual EV drivers’ demand on the grid out of peak hours. We find an unbalanced geographical distribution of charging demand in the San Francisco Bay Area, with temporal peaks in both grid off-peak hours in the morning and on-peak hours in the evening. Aligning with mobility patterns, our strategy effectively shifts demand to off-peak times. With the 2050 target of 90% EVs, this shifting reduces total on-peak charging demand by 61%, which could require over ∼18,000 additional level 3 chargers. We recommend building more charging stations and implementing shifting recommendations for EV grid integration. Science for society: The electrification of transportation stands as a promising avenue for emissions reduction. To promote the widespread adoption of electric vehicles (EVs), we need to plan strategically their future charging infrastructure. To that end, we must account for diverse charging habits, mobility patterns, and effective grid management. Our research presents an approach to assess personalized recommendations that adapt charging schedules to alleviate the strain on the grid during its busiest evening hours. Based on data-informed activity patterns, we deploy public charging stations to support this shifted demand. The proposed framework offers strategies to cater to the needs of urban areas facing escalating demands of charging while taking into account the varying levels of EV adoption. Our findings reveal the potential for flexible EV charging scheduling without compromising driver mobility while highlighting the pivotal role of public charging in harnessing this newfound flexibility.

The literature on internal migration in China has paid little attention to rapidly changing migration circumstances and has failed to capture the heterogeneity of population mobility patterns in less economically developed regions. Utilizing data from the 2017 China Migrants Dynamic Survey and the China Statistical Yearbook (County-Level), we investigate the destination choices of migrants registered in impoverished counties and underdeveloped areas. Our findings reveal that these migrants are more inclined to move to counties than to municipal districts and to move to economically underdeveloped counties than to developed counties. We uncover a paradoxical phenomenon of a choice consisting of "abandoning the high for the low" in low-income migrants of less economically developed regions, which can be explained by social relations, proximity migration, and health status. These findings contribute to a better understanding of population mobility in less developed regions. [ABSTRACT FROM AUTHOR]

As a prestigious international accolade in science and technology, the Nobel Prize laureates gain significant attention from researchers. Scholars strive to distill patterns and insights from various aspects of this award. This article specifically examined the institutions generating Nobel Prize-winning work, discussed the distribution and mobility patterns of laureates among different types of institutions to roughly map the scientific and technological landscape. Our research findings indicate that universities are the primary generators of Nobel Prize-winning work, accounting for approximately 70% of the institutions, and demonstrate particular adeptness in "attracting" and "retaining" laureates in mobility analysis. The remaining 30% of institutions include governmental research institutions, enterprises, and non-profit organizations. Notably, North America and Europe lead the count with over 300 counts of prize-producing institutions each, establishing themselves as major centers for Nobel Prize production. However, over the past decade, there has been a slight decline in the aggregation effect of the distribution of these institutions. [ABSTRACT FROM AUTHOR]

Despite growing interest in various groups of tourism-led migrants, limited research has concerned whether and how social inclusion contributes to tourism entrepreneurial migrants' lives. This study explores the dimension of social inclusion and its effect on tourism entrepreneurial migrants' subjective well-being and further tests the moderating effects of mobility patterns. The main effects were analyzed with structural equation models and the moderation effect was tested via multigroup analysis, respectively using 298 and 157 samples from China's Lijiang and Dali heritage cities. Results confirmed the significant role of migrants' motivation in their social inclusion and revealed several significant impacts of such inclusion on their subjective well-being. Moreover, mobility patterns moderated linkages between the above constructs in certain model relationships. These findings enrich the knowledge of tourism entrepreneurial migrants and offer practical implications for destinations. [ABSTRACT FROM AUTHOR]

Opportunistic sensing has become an appealing mobile crowd sensing (MCS) paradigm due to the fact that it can reduce the energy consumption and cost of cellular network connections. However, its success rate and transmission speed depend on the social interaction and mobility patterns of nodes. In this paper, we provide a spatiotemporal opportunistic transmission method for MCS networks. Firstly, to characterize the mobility patterns and social attributes of nodes more precisely and combine their advantages, this method defines spatiotemporal encountering and visiting parameters related to specific space-time units for nodes in a MCS network. Further, to realize reliable opportunistic transmission across regions and time intervals, this method searches publishers or participants of sensing tasks in a space-time unit according to the spatiotemporal encountering parameters of nodes in the unit and tracks the publishers or participants across the space-time units according to the spatiotemporal visiting parameters of nodes. The simulation results verify that the proposed method can achieve higher success rate with less transmission delay than existing typical methods. [ABSTRACT FROM AUTHOR]

Science and technology innovation talents are at the core of technology innovation, and their mobility has become a source of competition between regions and institutes. This paper analyzes the curriculum vitaes (CVs) of young and middle-aged leading talents in technology innovation, and explores their mobility patterns during their schooling and employment, leading to the following observations: A high-quality learning environment is an essential for the development of innovation talents; the mobility intensity has increased, and the phenomenon of "inbreeding" has been alleviated; the total number of institution or location changes is positively correlated with age, and the overall trend of mobility is stable; the regional imbalance of talent mobility has intensified, and the Matthew Effect, whereby initial advantage amplifies success and initial disadvantage amplifies disadvantage, has become prominent. This paper proposes measures such as refining cross-border mobility policy, expanding the academic vision of innovation talents, encouraging talent employment mobility, optimizing the allocation of resources, improving the human resource environment, establishing a talent development ecology, enriching educational resources, and coordinating the relationship between talent cultivation and talent attraction. These measures are expected to provide insights into the development and mobility of technology innovation talents and to be beneficial for the improvement of science and technology innovation talents policies. [ABSTRACT FROM AUTHOR]

Heavy metal movement pattern, hazards of consuming vegetables and risk assessment of soil-dust at auto-body workshops were studied. Soil-dusts collected from eight different auto-body workshops (A–H), at different depths and distances from workshops in Eastern Nigeria, were pulverized, sieved to obtain finest dust, digested by standard method. Vegetables were ashed, digested using standard methods. Filtrates were assayed for cadmium, chromium, lead, nickel, manganese and copper using atomic absorption spectrophotometry (AAS 205). Results of soil-dust of workshop F has highest Cd (3.93 ± 0.01 mg kg−1). Cr and Pb in workshop F and B were 3.53 ± 0.02 mg kg−1 and 5.19 ± 0.05 mg kg−1. Copper in workshop D (7.31 ± 0.01 mg kg−1) were higher than other metals in all workshops, metals from graduated distances and depths reduced. Highest HQ (adult, children) in workshop A ranged 7.575E−7–1.006E−2, 8.385E−14–2.716E−2 (ingestion); 3.714 E−10–4.933E−6, 1.646E−10–2.530E−6 (inhalation); 7.408E−17 –9.842E−13, 4.244E−18–6.525E−14 (dermal contact). ILCR in workshop A ranged 2.396 E−9–9.745E−8 (ingestion); 1.175E−12–4.778E−11 (inhalation): 2.343E−19–9.530E−18 (dermal contact) for adult, that of children ranged from 5.590E−9–2.27E−7, 5.207E−13–2.118E−11, 1.343 E−20–5.460 E−19 (same routes). Hazard quotient and cancer risk via all routes ranged 10–18 –10–2 and 10–20–10–7 below EPA's 1 and 1 × 10–6–1 × 10–4 standard. Vegetable metals were minimal than soil-dust. True intake (Cd, Cr, Pb, Mn, Ni, Cu) ranged 14–718 (mg kg−1). Exposure via soil-dust/vegetables can add to human and environmental burden of heavy metals. [ABSTRACT FROM AUTHOR]

Female passengers dominate the mobility by employing Bus Rapid Transit (BRT) in Indonesia and they possess a unique travel pattern compared to men. Women require more genuine investment efforts in comfortable and safer infrastructure for accessing and utilizing public transportation. The implementation of a smart city is one of the infrastructure investments. Smart mobility is one of the service cluster divisions, and smart mobility indicators are taken into account in the implementation. The objective of this study is to convert female mobility patterns and attitudes toward BRT service into smart mobility indicators. The method used is quantitative, and the questionnaire is designed to poll 209 people. Chi-Square and Likert scale are employed to analyze those criteria. The results demonstrate that the BRT has provided the female passenger services that they require at the current service level. The passenger pattern demonstrates that women depend on the BRT service to meet their transportation needs. Furthermore, the ICT criteria as a core for smart mobility has been challenging for both parties (female passengers and BRT operator). The limitation of this study is that it cannot demonstrate empirically that smart mobility is free gender biased and gaps until public transportation is fully inclusive. [ABSTRACT FROM AUTHOR]

Map matching is a fundamental research topic with the objective of aligning GPS trajectories to paths on the road network. However, existing models fail to achieve satisfactory performance for low-quality (i.e., noisy, low-frequency, and non-uniform) trajectory data. To this end, we propose a general and robust deep learning-based model, L2MM, to tackle these issues at all. First, high-quality representations of low-quality trajectories are learned by two representation enhancement methods, i.e., enhancement with high-frequency trajectories and enhancement with the data distribution. The former employs high-frequency trajectories to enhance the expressive capability of representations, while the latter regularizes the representation distribution over the latent space to improve the generalization ability of representations. Secondly, to embrace more heuristic clues, typical mobility patterns are recognized in the latent space and further incorporated into the map matching task. Finally, based on the available representations and patterns, a mapping from trajectories to corresponding paths is constructed through a joint optimization method. Extensive experiments are conducted based on a range of datasets, which demonstrate the superiority of L2MM and validate the significance of high-quality representations as well as mobility patterns. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has already had significant impact on tourist flows worldwide. The requirements of safe models of tourism in the time of COVID-19, avoiding crowded localities and providing individual types of accommodation, can largely be met in second homes. This study aims to examine whether and how the COVID-19 pandemic and related restrictions impacted the usage patterns of second homes in terms of: (1) the number of visits and length of stay, (2) the purpose of the second-home utilisation. An integral part of the study was to recognise how these new and existing im/mobilities were determined by a range of personal, social, contextual, and relational factors. The data collected from direct interviews and online surveys was tested using sign and Wilcoxon tests, while the interactive classification tree (C&RT) model was used to explain the reasons for changing or maintaining an existing second-home usage pattern. The research results showed that for most second-home owners their home-usage pattern remained the same as in 2019. If it changed, it was more common to extend the stay by moving in, working at a distance, or commuting to work, rather than to shorten the stay at the second home. [ABSTRACT FROM AUTHOR]

It is imperative to understand human movement and behavior, from epidemic monitoring to complex communications. So far, most research and studies on investigating and interpreting human movements have traditionally depended on private and accumulated data such as mobile records. In this work, social network data is suggested as a proxy for human mobility, as it relies on a large amount of publicly accessible data. A mechanism for urban mobility mining and extraction scheme is proposed in this research to shed light on the importance and benefits of the publicly available social network data. Given the potential value of the Big Data obtained from social network platforms, we sought to demonstrate the process of analyzing and understanding human mobility patterns and activity behavior in urban areas through the social network data. Human mobility is far from spontaneous, follows well-defined statistical patterns. This research provides evidence of spatial and temporal regularity in human mobility patterns by examining daily individual trajectories of users covering an average time span of three years (2018 to 2020). Despite the diversity of individual movements history, we concluded that humans follow simple, reproducible patterns. Additionally, we studied and evaluated the effect of COVID-19 on human mobility and activity behavior in urban areas and established a strong association between human mobility and COVID-19 spread. Numerous years of mobility data analysis can reveal well-established trends, such as social or cultural activities, which serve as a baseline for detecting anomalies and changes in human mobility and activity behavior.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study aimed to provide an in-depth assessment of the commuting patterns of scholar communities of southwestern European countries and to identify measures to improve their sustainable performance regarding mobility. The adopted methodology characterised the mobility pattern of students as a sustainability indicator and the availability of related infrastructures and local public transport network. Data were gathered by qualitative (behavioural questionnaires) and quantitative (technical audits) approaches, based on measurable indicators (key performance indicators and scores (ranging between 0–5)). Overall, French schools showed the best sustainable performance regarding mobility (2.0) and Gibraltar had the lowest (1.2). The existence of bike parking and electric car charging points were the main weaknesses founds (with their related mean scores being 0.6 and 0.2, respectively). The score associated with annual CO2 emissions due to students' mobility had the best performance, where all countries managed to obtain an average of 3.1. The global score, which assessed the sustainable performance of scholar communities regarding mobility, had a mean value of 1.5 for all studied countries, which highlights the potential for improvement of the studied schools, mainly targeting the public transport network optimisation and the enhancement of scholar infrastructures concerning bicycle parking and electric cars. [ABSTRACT FROM AUTHOR]

Seeking spatiotemporal patterns about how citizens interact with the urban space is critical for understanding how cities function. Such interactions were studied in various forms focusing on patterns of people's presence, action, and transition in the urban environment, which are defined as human-urban interactions in this paper. Using human activity datasets that utilize mobile positioning technology for tracking the locations and movements of individuals, researchers developed stochastic models to uncover preferential return behaviors and recurrent transitional activity structures in human-urban interactions. Ad-hoc heuristics and spatial clustering methods were applied to derive meaningful activity places in those studies. However, the lack of semantic meaning in the recorded locations makes it difficult to examine the details about how people interact with different activity places. In this study, we utilized geographic context-aware Twitter data to investigate the spatiotemporal patterns of people's interactions with their activity places in different urban settings. To test consistency of our findings, we used geo-located tweets to derive the activity places in Twitter users' location histories over three major U.S. metropolitan areas: Greater Boston Area, Chicago, and San Diego, where the geographic context of each location was inferred from its closest land use parcel. The results showed striking spatial and temporal similarities in Twitter users' interactions with their activity places among the three cities. By using entropy-based predictability measures, this study not only confirmed the preferential return behaviors as people tend to revisit a few highly frequented places but also revealed detailed characteristics of those activity places. [ABSTRACT FROM AUTHOR]

As an important branch of the Internet of Things (IoT), the Internet of Vehicles (IoV) has attracted extensive attention in the research field. To deeply study the IoV and build a vehicle spatiotemporal interaction network, it is necessary to use the trajectory data of private cars. However, due to privacy and security protection policies and other reasons, the data set of private cars cannot be obtained, which hinders the research on the social attributes of vehicles in the IoV. Most of the previous work generated the same type of data, and how to generate private car data sets from various existing data sets is a huge challenge. In this paper, we propose a tri-layer framework to solve this problem. First, we propose a novel region division scheme that considers detailed inter-region relations connected by traffic flux. Second, a new spatial-temporal interaction model is developed to estimate the traffic flow between two regions. Third, we devise an evaluation pipeline to validate generation results from microscopic and macroscopic perspectives. Qualitative and quantitative results demonstrate that the data generated in heavy density scenarios can provide strong data support for downstream IoV and mobility research tasks. [ABSTRACT FROM AUTHOR]

Private cars’ travel has been one of the main factors causing urban traffic congestion. Especially during morning and evening rush hours, private car commuters bring a significant burden to traffic. However, there is very little literature on them due to the lack of access to relevant data. A real-world dataset containing vehicle passing records of Electronic Registration Identification (ERI) of vehicles provides us with an opportunity to research private car commuters. We propose a regular behavior-based model to recognize private car commuters. In the model, a regular behavior-based definition of private car commuters is firstly proposed. Then, TDSP(Time dependent shortest path)-based distance measurement and a hierarchical clustering method are designed to extract regular behaviors. Furthermore, we utilize a regular threshold $p $ to help determine regular behaviors. The experiment, which is conducted on a real-world dataset containing one-week vehicle passing records in Chongqing of China, validates the effectiveness and accuracy of the proposed model. Moreover, we analyze the mobility pattern of private car commuters, and some typical mobility patterns of them are successfully found. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has resulted in countries reacting differently to an ongoing crisis situation. Latent to this reaction mechanism is the inherent cultural characteristics of each society resulting in differential responses to epidemic spread. Epidemiological studies have confirmed the positive effect of population mobility on the growth of infection. However, the effect of culture on indigenous mobility patterns during pandemics needs further investigation. This study aims to bridge this gap by exploring the moderating role of country culture on the relationship between population mobility and growth of CoVID-19. Hofstede's cultural factors; power distance, individualism/collectivism, masculinity/femininity, uncertainty avoidance, long-term and short-term orientation are hypothesised to moderate the effect of mobility on the reproduction number (R) of COVID-19. Panel regression model, using mobility data and number of confirmed cases across 95 countries for a period of 170 days has been preferred to test the hypotheses. The results are further substantiated using slope analysis and Johnson-Neyman technique. The findings suggest that as power distance, individualism and long-term orientation scores increase, the impact of mobility on epidemic growth decreases. However, masculinity scores in a society have an opposite moderating impact on epidemic growth rate. These Hofstede factors act as quasi moderators affecting mobility and epidemic growth. Similar conclusions could be not be confirmed for uncertainty avoidance. Cross-cultural impact, as elucidated by this study, forms a crucial element in policy formulation on epidemic control by indigenous Governing bodies. [ABSTRACT FROM AUTHOR]

The mid-Middle Paleolithic (late Marine Isotope Stage 6 and Marine Isotope Stage 5) is the least documented phase of the Levantine Middle Paleolithic (MP), especially concerning flint provisioning strategies. Our study of raw material exploitation at Nesher Ramla karst sinkhole (central Coastal Plain, Israel) provides an intriguing glimpse into the decision making of mid-Middle Paleolithic hominins. In this study we focused on the identification of the provisioning strategies derived from two horizons (which we dubbed the "Pelvis Horizon" and the "Stones Horizon") from the upper part of the Nesher Ramla sequence. We applied several analytical measures: a survey of raw material sources, material composition and typo-technological analyses of the archaeological assemblages, mainly using the chaîne opératoire approach and a taphonomic-taxonomic analysis of the faunal assemblages. Our study demonstrates that different lithic provisioning strategies were practiced in each horizon. The findings for the Pelvis Horizon are particularly interesting. They offer strong evidence for hunter-gatherers' personal toolkits associated with the provisioning of individuals strategy. The Stones Horizon, conversely, produced a mixed signal, containing evidence for both intensive in situ knapping and mobile toolkits. The faunal remains echo these differences: Compared to the Stones Horizon, the zooarchaeological assemblage of the Pelvis Horizon presents lower frequencies of bone fracturing, anthropogenic modifications, and burning. Accordingly, during the Pelvis Horizon, the site most likely functioned as an ephemeral hunting station whereas, during the Stones Horizon, it hosted a broader range of activities and longer occupations. These observations shed light on the variability of south Levantine MP mobility patterns, modes of occupation, and site function. [ABSTRACT FROM AUTHOR]

Internet of Thing (IoT) devices are rapidly becoming an indispensable part of our life with their increasing deployment in many promising areas, including tele-health, smart city, intelligent agriculture. Understanding the mobility of IoT devices is essential to improve quality of service in IoT applications, such as route planning in logistic management, infrastructure deployment, cellular network update and congestion detection in intelligent traffic. Despite its importance, there are not many results pertaining to the mobility of IoT devices. In this article, we aim to answer three research questions: (i) what are the mobility patterns of IoT device? (ii) what are the differences between IoT device and smartphone mobility patterns? (iii) how the IoT device mobility patterns differ among device types and usage scenarios? We present a comprehensive characterization of IoT device mobility patterns from the perspective of cellular data networks, using a 36-days long signal trace, including 1.5 million IoT devices and 0.425 million smartphones, collected from a nation-wide cellular network in China. We first investigate the basic patterns of IoT devices from two perspectives: temporal and spatial characteristics. Our study finds that IoT device mobility exhibits significantly different patterns compared with smartphones in multiple aspects. For instance, IoT devices move more frequently and have larger radius of gyration. Then we explore the essential mobility of IoT devices by utilizing two models that reveal the nature of human mobility, i.e., exploration and preferential return (EPR) model and entropy based predictability model. We find that IoT devices, with few exceptions, behave totally different from human, and we further derive a new formulation to describe their movement. We also find the gap mobility predictability and predictability limit between IoT and human is not as big as people expected. [ABSTRACT FROM AUTHOR]

Proximity attacks allow an adversary to uncover the location of a victim by repeatedly issuing queries with fake location data. These attacks have been mostly studied in scenarios where victims remain static and there are no constraints that limit the actions of the attacker. In such a setting, it is not difficult for the attacker to locate a particular victim and quantifying the effort for doing so is straightforward. However, it is far more realistic to consider scenarios where potential victims present a particular mobility pattern. In this article, we consider abstract (constrained and unconstrained) attacks on services that provide location information on other users in the proximity. We derive strategies for constrained and unconstrained attackers, and show that when unconstrained they can practically achieve success with theoretically optimal effort. We then propose a simple yet effective constraint that may be employed by a proximity service (for example, running in the cloud or using a suitable two-party protocol) as a countermeasure to increase the effort for the attacker several orders of magnitude both in simulated and real-world cases. [ABSTRACT FROM AUTHOR]

With the development of mobile positioning technology, a large number of Origin-Destination (OD) flow data with spatial and temporal details have been produced. These OD flow could give us a great opportunity to research geographical phenomena such as spatial interaction and mobility patterns. The OD flow clustering approach is an effective way to explore the main mobility patterns of the objects. At the same time, similarity measurement plays a key role in OD flow clustering. However, most of the previous OD flow similarity measurement methods failed to make full use of the spatial information of the flow including spatial proximity and geometric similarity. In this paper, we considered both position information and geometric properties of OD flow and propose a new method to measure the spatial similarity between OD flows. Specifically, the proposed method sets the neighbor threshold with the length of OD flows and the parameter $\alpha $ dynamically. Based on the constraint of the OD points’ location, the directions of the flows are implicitly restricted. The sole-parameter $\alpha $ has a practical value as it determines the maximum length difference and the maximum directional difference that can be tolerated between similar flows. The proposed method passed a simulation experiment with synthetic flows and a case study with 283,008 taxi trips in Beijing in one day. The results show that the proposed method can discover the dominant mobility pattern from a large number of flow data effectively. In the case study, the dominant flow clusters reveal the taxi mobility patterns of residents at different distances in Beijing.

With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collected smartphone sensing data using the Beiwe platform from two groups of undergraduate students at a major North American university, one from January to March of 2020 (74 participants), the other from May to August (52 participants), to observe the differences in students' daily life patterns before and after the start of the pandemic. In this paper, we focus on the mobility patterns evidenced by GPS signal tracking from the students' smartphones and report findings using several analytical methods including principal component analysis, circadian rhythm analysis, and predictive modeling of perceived sadness levels using mobility-based digital metrics. Our findings suggest that compared to the pre-COVID group, students in the mid-COVID group generally 1) registered a greater amount of midday movement than movement in the morning (8–10 a.m.) and in the evening (7–9 p.m.), as opposed to the other way around; 2) exhibited significantly less intradaily variability in their daily movement; 3) visited less places and stayed at home more everyday, and; 4) had a significant lower correlation between their mobility patterns and negative mood.

Summary: In vehicular ad hoc network (VANET) broadcasting is considered as the critical area of research. The vehicles are connecting in an ad hoc manner to create a network of a wider range. In an intelligent transport system (ITS), with vehicle to vehicle (V2V) communication in the VANET is used to support the network backbone and which the accident prevention technique has been deployed for ensuring the road safety. The major reason for the road accident is the abnormal driving behavior of human drivers. The movement pattern of the vehicle is the main factor of the network topology so that the impact of human driving pattern influences the performance and behavior of the network. Driving behavior can be classified as reckless, normal, drunken, and fatigue driving. During driving, the behavior of human drivers was predicted in this paper, thereby providing the performance analysis on the mobility of the network. For the improvement of a driver behavior, the hybridized mega‐trend diffusion (MTD) with optimized deep belief network–sunflower optimization (DBN‐SFO) is used. Here, the network performance of the adaptive deer hunting Optimized Link State Routing Protocol (ADHOLSR) is analyzed in NS2 simulation platform. [ABSTRACT FROM AUTHOR]

Multidimensional inequalities have been increasingly discussed in the transportation field, particularly in the case of bike riding. Although several studies focused on the gender differences in bike-share systems, only a few include socioeconomic inequalities and the dimension of race. This study considers a multidimensional perspective, relating the urban space characteristics, cycling infrastructure and bike-sharing mobility distribution patterns. We analyzed the data collected from bike-share systems of five major Brazilian cities to investigate the aspects of cycling behavior in terms of gender. Additionally, we examined the uneven access to cycling infrastructure, considering its socio-spatial inequalities. The obtained results confirm the lack of female bike-sharing cyclists at different levels. The gender difference decreased for bike-share trips with longer durations and recreational purposes. In seaside towns, women represented a higher participation percentage in bike-share cycling, particularly in recreational areas such as parks, seafronts, and lakesides. Although cycle lanes reduced the disparity, they did not ensure the gender balance as observed in recreational areas. The proportion of female bike-share cyclists reduced in areas lacking cycle lanes and near metro-rail stations. Furthermore, the peripheries with more Black and lower-class inhabitants lack bike-friendly spaces, which are vital for promoting more sustainable and equitable cities. • We revealed gender uneven access to bike-sharing systems in five Brazilian cities. • We mapped class and race inequalities related to bike system infrastructure. • The gender gap decreased for bike-sharing trips with recreational purposes. • In cities peripheries, blacks and lower-class inhabitants lack bike-friendly spaces. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for establishing vehicle usage patterns by Floating Car Data based on their daily mobility making. Vehicle trajectories were firstly sequenced into meaningful trips and then clustered into different trip types. Trips pertaining to each vehicle were aggregated as a vector of counts per type to obtain the mobility profile of the vehicle. Based on these profiles, a topic modeling approach using Latent Dirichlet Allocation was developed to discover the patterns of vehicle daily usage, thereby constituting a typology. An application was conducted for the Paris Region, which identified 3 vehicle usage types associated with local usage within specific areas and the other two holding hybrid patterns between different areas. The prevailing pattern of vehicle usage was found on short-medium trips around pericenter and near suburban areas. Overall, this study offered a data-driven framework to help understand vehicle daily usage patterns and their differentiation [ABSTRACT FROM AUTHOR]

This paper studies the relationship between trip chain complexity and daily travel behaviour of travellers. While trip chain complexity is conventionally investigated between travel modes, our scope is the more aggregated level of a person's activity-travel pattern. Using data from the Netherlands Mobility Panel, a latent class cluster analysis was performed to group people with similar mode choice behaviour in distinct mobility pattern classes. All trip chains were assigned to both a travel mode and the mobility pattern class of the traveller. Subsequently, differences in trip chain complexity distributions were analysed between travel modes and between mobility pattern classes. Results indicate considerable differences between travel modes, particularly between multimodal and unimodal trip chains, but also between the unimodal travel modes car, bicycle, walking and public transport trip chains. No substantial differences in trip chain complexity were found between mobility pattern classes. Independently of the included travel modes, the distributions of trip chain complexity degrees were similar across mobility pattern classes. This means that personal circumstances such as the number of working hours or household members are not systematically translated into specific mobility patterns. [ABSTRACT FROM AUTHOR]