Comparative Analysis of Metro Passengers’Mobility Patterns and Jobs-housing Balance of Metropolitan

The advent of the big data era has provided many types of transportation datasets,such as metro smart card data,for studying residents’mobility and understanding how their mobility has been shaped and is shaping the urban space.In this paper,we use metro smart card data from two Chinese metropolises,Shanghai and Shenzhen.Five metro mobility indicators are introduced,and association rules are established to explore the mobility patterns.The proportion of people entering and exiting the station is used to measure the jobs-housing balance.It is found that the average travel distance and duration of Shanghai passengers are higher than those of Shenzhen,and the proportion of metro commuters in Shanghai is higher than that of Shenzhen.The jobs-housing spatial relationship in Shenzhen based on metro travel is more balanced than that in Shanghai.The fundamental reason for the differences between the two cities is the difference in urban morphology.Compared with the monocentric structure of Shanghai,the polycentric structure of Shenzhen results in more scattered travel hotspots and more diverse travel routes,which helps Shenzhen to have a better jobs-housing balance.This paper fills a gap in comparative research among Chinese cities based on transportation big data analysis.The results provide support for planning metro routes,adjusting urban structure and land use to form a more reasonable metro network,and balancing the jobs-housing spatial relationship.

Pig Reproductive and Respiratory Syndrome Virus (PRRSV) Does Not Attach to Boar Sperm;It Affects Only the Velocity Pattern and the Mobility Pattern

The purpose of the study was to evaluate the sperm viability of semen infected with PRRSV viral particles, observing the effect of the Virus on the motility of boar sperm. The work was carried out at the FMVZ-BUAP Genetics and Reproduction Laboratory. 5 stallions were used. Each sample contained 1 × 106 sperm, the PRRS virus strain was ATCC-VR-2332 (0, 102, 104 and 106 copies of RNA/mL in triplicate), it was observed daily at the CASA;Hamilton Thorne®. Cells with MT (P < 0.05) on days 1, 3, 5, 7 and 10 of evaluation with 201 ± 7.3, 167 ± 10.1, 165 ± 14.6, 134 ± 8.2 and 120 ± 8.8, respectively. The % MP between control and virus concentrations (P ≥ 0.05). The LCV on day 1 and 7 PI at 10X2 and 10X6 (P < 0.05) vs control. In the Correlation Matrix, where it is observed that there is a correlation between VSL and VAP, VSL and VCL, VCL and ALH, VAP with ALH. There is a correlation of VSL and ALH, STR and ALH. In this study there were (P ≤ 0.01) in the VCL, in the concentrations (102) 162.81 ± 10.65 and (106) 177.12 ± 5.77 vs 193.04 ± 4.62 of control. This indicates that altering these parameters would be related to fertility and the PRRS virus affects the LCV. Regarding the VSL, it was observed that the sperm infected with viruses 102, 104 and 106 of 48.00 ± 3.38, 49.88 ± 1.83 and 50.55 ± 2.24 Vs. 56.66 ± 1.68 of control respectively, the control would have greater possibilities of fertilizing the oocyte. In this study, it was found (P ≤ 0.01) in the VAP with 102 of 77.26 ± 5.16, 104 with 83.35 ± 2.41 and 106 with 81.29 ± 3.14 vs the control with 90.56 ± 2.07. Regarding the ALH there is (P < 0.05) a 104 with 8.70 ± .26 and 106 with 9.64 ± 0.23 vs control 8.50 ± 0.27. The presence of different concentrations of PRRSV in boar semen induces changes in different types of sperm motility. Infection of ejaculates with the PRRS virus affects sperm motility on days 1, 3, 5, 7, and 10 post-infections.

Analysis of Mobility Patterns of Science and Technology Innovation Talents in China:An Empirical Study of Science and Technology Innovation Leaders Based on China's Ten Thousand Talents Program

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.

Mobility Pattern of Taxi Passengers at Intra-Urban Scale:Empirical Study of Three Cities

The study of human mobility patterns is of both theoretical and practical values in many aspects. For long-distance travel, a few research endeavors have shown that the displacements of human travels follow a power-law distribution. However, controversies remain regarding the issue of the scaling laws of human mobility in intra-urban areas. In this work, we focus on the mobility pattern of taxi passengers by examining five datasets of three metropolitans. Through statistical analysis, we find that the lognormal distribution with a power-law tail can best approximate both the displacement and the duration time of taxi trips in all the examined cities. The universality of the scaling laws of human mobility is subsequently discussed, in view of the analysis of the data. The consistency of the statistical properties of the selected datasets that cover different cities and study periods suggests that, the identified pattern of taxi-based intra-urban travels seems to be ubiquitous over cities and time periods.

OceanRoute: Vessel Mobility Data Processing and Analyzing Model Based on MapReduce

The network coverage is a big problem in ocean communication, and there is no low-cost solution in the short term. Based on the knowledge of Mobile Delay Tolerant Network(MDTN), the mobility of vessels can create the chances of end-to-end communication. The mobility pattern of vessel is one of the key metrics on ocean MDTN network. Because of the high cost, few experiments have focused on research of vessel mobility pattern for the moment. In this paper, we study the traces of more than 4000 fishing and freight vessels. Firstly, to solve the data noise and sparsity problem, we design two algorithms to filter the noise and complement the missing data based on the vessel's turning feature. Secondly, after studying the traces of vessels, we observe that the vessel's traces are confined by invisible boundary. Thirdly, through defining the distance between traces, we design MR-Similarity algorithm to find the mobility pattern of vessels. Finally, we realize our algorithm on cluster and evaluate the performance and accuracy. Our results can provide the guidelines on design of data routing protocols on ocean MDTN.

The Analysis of Frequency Reuse Irregular Patterns in Mobile Telephone Network

Some frequency reuse irregular patterns in radionetwork design are proposed,the characteristic and applica-tion measures of these patterns are analyzed.Then this paperaccounts that frequency reuse irregular patterns is a usefulway to impove spectrum efficiency and it is significative forartificial intelligence to be applied in this field.

Complete Your Mobility： Linking Trajectories Across Heterogeneous Mobility Data Sources

Nowadays, human activities and movements are recorded by a variety of tools, forming different trajectory sets which are usually isolated from one another. Thus, it is very important to link different trajectories of one person in different sets to provide massive information for facilitating trajectory mining tasks. Most prior work took advantages of only one dimensional information to link trajectories and can link trajectories in a one-to-many manner （providing several candidate trajectories to link to one specific trajectory）. In this paper, we propose a novel approach called one-to-one constraint trajectory linking with multi-dimensional information （OCTL） that links the corresponding trajectories of one person in different sets in a one-to-one manner. We extract multidimensional features from different trajectory datasets for corresponding relationships prediction, including spatial, temporal and spatio-temporal information, which jointly describe the relationships between trajectories. Using these features, we calculate the corresponding probabilities between trajectories in different datasets. Then, we formulate the link inference problem as a bipartite graph matching problem and employ effective methods to link one trajectory to another. Moreover, the advantages of our approach are empirically verified on two real-world trajectory sets with convincing results.

A high-precision heuristic model to detect home and work locations from smart card data

Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers.Processing and analyzing these data open new opportunities in urban modeling and travel behavior research.This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations.The model uses journey counts as an indicator of usage regularity,visit-frequency to identify activity locations for regular commuters,and stay-time for the classification of work and home locations and activities.London is taken as a case study,and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey.Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision.This study offers a new and cost-effective approach to travel behavior and demand research.

MViewer： mobile phone spatiotemporal data viewer

Nowadays movement patterns and people＇s be- havioral models are needed for traffic engineers and city plan- ners. These observations could be used to reason about mobil- ity and its sustainability and to support decision makers with reliable information. The very same knowledge about human diaspora and behavior extracted from these data is also valu- able to the urban planner, so as to localize new services, orga- nize logistics systems and to detect changes as they occur in the movement behavior. Moreover, it is interesting to inves- tigate movement in places like a shopping area or a working district either for commercial purposes or for improving the service quality. These kinds of tracking data are made avail- able by wireless and mobile communication technologies. It is now possible to record and collect a large amount of mobile phone calls in a city. Technologies for object tracking have recently become affordable and reliable and hence we were able to collect mobile phone data from a city in China from January 1, 2008 to December 31, 2008. The large amount of phone call records from mobile operators can be considered as life mates and sensors of persons to inform how many peo- ple are present in any given area and how many are entering or leaving. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by an- alyzing mobile phone data. Through mobile phones, we can learn the information about locations, communications be- tween mobile phone users during their daily lives.In this work, we propose a comprehensive visual analysissystem named as MViewer, Mobile phone spatiotemporal data Viewer, which is the first system to visualize and analyze the population＇s mobility patterns from millions of phone call records. Our system consists of three major components： 1） visual analysis of user groups in a base station; 2） visual anal- ysis of the mobility patterns on different user groups mak- ing phone calls in certain base stations; 3） visual analysis of handoff phone call records. Some well-established visu- alization techniques such as parallel coordinates and pixel- based representations have been integrated into our system. We also develop a novel visualization schemes, Voronoi- diagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone datasets that are kindly provided by our project partners and obtained some interesting findings regarding people＇s mobility patterns.

How machine learning informs ride-hailing services:A survey

In recent years,online ride-hailing services have emerged as an important component of urban transportation system,which not only provide significant ease for residents’travel activities,but also shape new travel behavior and diversify urban mobility patterns.This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services.The importance of on-demand ride-hailing services in the spatiotemporal dynamics of urban traffic is first highlighted,with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design,planning,operation,and control of urban intelligent transportation systems.Then,the research on travel behavior from the perspective of individual mobility patterns,including carpooling behavior and modal choice behavior,is summarized.In addition,existing studies on order matching and vehicle dispatching strategies,which are among the most important components of on-line ridehailing systems,are collected and summarized.Finally,some of the critical challenges and opportunities in ridehailing services are discussed.

Home location inference from sparse and noisy data:models and applications

Accurate home location is increasingly important for urban computing. Existing methods either rely on continuous(and expensive) Global Positioning System(GPS) data or suffer from poor accuracy. In particular, the sparse and noisy nature of social media data poses serious challenges in pinpointing where people live at scale. We revisit this research topic and infer home location within 100 m×100 m squares at 70% accuracy for 76% and 71%of active users in New York City and the Bay Area, respectively. To the best of our knowledge, this is the first time home location has been detected at such a fine granularity using sparse and noisy data. Since people spend a large portion of their time at home, our model enables novel applications. As an example, we focus on modeling people's health at scale by linking their home locations with publicly available statistics, such as education disparity. Results in multiple geographic regions demonstrate both the effectiveness and added value of our home localization method and reveal insights that eluded earlier studies. In addition, we are able to discover the real buzz in the communities where people live.