scholarly journals A Novel Dynamic Dispatching Method for Bicycle-Sharing System

2019 ◽  
Vol 8 (3) ◽  
pp. 117 ◽  
Author(s):  
Dianhui Mao ◽  
Zhihao Hao ◽  
Yalei Wang ◽  
Shuting Fu
Keyword(s):  
Traffic Congestion ◽  
Dynamic Scheduling ◽  
Human Mobility ◽  
Rapid Development ◽  
Regression Tree ◽  
Gaussian Mixture ◽  
Gradient Boosting ◽  
Mobility Patterns ◽  
Scheduling Method ◽  
Spatio Temporal

With the rapid development of sharing bicycles, unreasonable dispatching methods are likely to cause a series of issues, such as resource waste and traffic congestion in the city. In this paper, a new dynamic scheduling method is proposed, named Tri-G, so as to solve the above problems. First of all, the whole visualization information of bike stations was built based on a Spatio-Temporal Graph (STG), then Gaussian Mixture Mode (GMM) was used to group individual stations into clusters according to their geographical locations and transition patterns, and the Gradient Boosting Regression Tree (GBRT) algorithm was adopted to predict the number of bikes inflow/outflow at each station in real time. This paper used New York’s bicycle commute data to build global STG visualization information to evaluate Tri-G. Finally, it is concluded that Tri-G is superior to the methods in control groups, which can be applied to various geographical scenarios. In addition, this paper also discovered some human mobility patterns as well as some rules, which are helpful for governments to improve urban planning.

scholarly journals Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data

2018 ◽  
Author(s):  
Yingzi Wang ◽  
Xiao Zhou ◽  
Anastasios Noulas ◽  
Cecilia Mascolo ◽  
Xing Xie ◽  
...  
Keyword(s):  
Chronic Diseases ◽  
Population Health ◽  
Large Scale ◽  
Human Life ◽  
Human Mobility ◽  
Gaussian Mixture ◽  
Disease Rate ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Spatio Temporal

Chronic diseases like cancer and diabetes are major threats to human life. Understanding the distribution and progression of chronic diseases of a population is important in assisting the allocation of medical resources as well as the design of policies in preemptive healthcare. Traditional methods to obtain large scale indicators on population health, e.g., surveys and statistical analysis, can be costly and time-consuming and often lead to a coarse spatio-temporal picture. In this paper, we leverage a dataset describing the human mobility patterns of citizens in a large metropolitan area. By viewing local human lifestyles we predict the evolution rate of several chronic diseases at the level of a city neighborhood. We apply the combination of a collaborative topic modeling (CTM) and a Gaussian mixture method (GMM) to tackle the data sparsity challenge and achieve robust predictions on health conditions simultaneously. Our method enables the analysis and prediction of disease rate evolution at fine spatio-temporal scales and demonstrates the potential of incorporating datasets from mobile web sources to improve population health monitoring. Evaluations using real-world check-in and chronic disease morbidity datasets in the city of London show that the proposed CTM+GMM model outperforms various baseline methods.

scholarly journals Analytical Models for Traffic Congestion and Accident Analysis

2021 ◽  
Author(s):  
Hongrui Liu ◽  
Rahul Ramachandra Shetty
Keyword(s):  
Traffic Congestion ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Gradient Boosting ◽  
Decision Tree Classifier ◽  
The Road ◽  
Tree Classifier ◽  
Extreme Gradient Boosting ◽  
On The Road

In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.

scholarly journals Deriving Spatio-Temporal Geographies of Human Mobility from GPS traces

2021 ◽  
Author(s):  
Alessia Calafiore ◽  
Nombuyisielo Murage ◽  
Andrea Nasuto ◽  
Francisco Rowe
Keyword(s):  
Structural Changes ◽  
Human Mobility ◽  
Gps Data ◽  
Mobility Patterns ◽  
Individual Level ◽  
Temporal Properties ◽  
Detection Approach ◽  
Collective Movements ◽  
Spatio Temporal ◽  
City Structure

This paper leverages on the opportunities presented by individual level GPS data to study human mobility. It develops a methodology to understand the spatio-temporal properties of collective movements using network science. Through a spatially-weighted community detection approach, we derived functional neighbourhoods from human mobility patterns from GPS data and analyse the extent to which they vary across time. The results show that while the overall city structure remains stable, functional neighbourhoods tend to contract and expand over the course of the day. This work proposes a methodological framework and emphasises the importance of detecting short-term structural changes in cities based on human mobility.

scholarly journals Identifying multiscale spatio-temporal patterns in human mobility using manifold learning

2020 ◽  
Vol 6 ◽  
pp. e276 ◽  
Author(s):  
James R. Watson ◽  
Zach Gelbaum ◽  
Mathew Titus ◽  
Grant Zoch ◽  
David Wrathall
Keyword(s):  
Manifold Learning ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Network ◽  
Human Migration ◽  
Mobility Patterns ◽  
Great Opportunity ◽  
Spectral Graph ◽  
Spectral Graph Wavelets ◽  
Spatio Temporal

When, where and how people move is a fundamental part of how human societies organize around every-day needs as well as how people adapt to risks, such as economic scarcity or instability, and natural disasters. Our ability to characterize and predict the diversity of human mobility patterns has been greatly expanded by the availability of Call Detail Records (CDR) from mobile phone cellular networks. The size and richness of these datasets is at the same time a blessing and a curse: while there is great opportunity to extract useful information from these datasets, it remains a challenge to do so in a meaningful way. In particular, human mobility is multiscale, meaning a diversity of patterns of mobility occur simultaneously, which vary according to timing, magnitude and spatial extent. To identify and characterize the main spatio-temporal scales and patterns of human mobility we examined CDR data from the Orange mobile network in Senegal using a new form of spectral graph wavelets, an approach from manifold learning. This unsupervised analysis reduces the dimensionality of the data to reveal seasonal changes in human mobility, as well as mobility patterns associated with large-scale but short-term religious events. The novel insight into human mobility patterns afforded by manifold learning methods like spectral graph wavelets have clear applications for urban planning, infrastructure design as well as hazard risk management, especially as climate change alters the biophysical landscape on which people work and live, leading to new patterns of human migration around the world.

scholarly journals Identifying Human Mobility via Trajectory Embeddings

2017 ◽  
Author(s):  
Qiang Gao ◽  
Fan Zhou ◽  
Kunpeng Zhang ◽  
Goce Trajcevski ◽  
Xucheng Luo ◽  
...  
Keyword(s):  
Human Mobility ◽  
Route Planning ◽  
Classification Problem ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Classification Problems ◽  
Trajectory Data ◽  
Trajectory Classification ◽  
Spatio Temporal ◽  
Real World Datasets

Understanding human trajectory patterns is an important task in many location based social networks (LBSNs) applications, such as personalized recommendation and preference-based route planning. Most of the existing methods classify a trajectory (or its segments) based on spatio-temporal values and activities, into some predefined categories, e.g., walking or jogging. We tackle a novel trajectory classification problem: we identify and link trajectories to users who generate them in the LBSNs, a problem called Trajectory-User Linking (TUL). Solving the TUL problem is not a trivial task because: (1) the number of the classes (i.e., users) is much larger than the number of motion patterns in the common trajectory classification problems; and (2) the location based trajectory data, especially the check-ins, are often extremely sparse. To address these challenges, a Recurrent Neural Networks (RNN) based semi-supervised learning model, called TULER (TUL via Embedding and RNN) is proposed, which exploits the spatio-temporal data to capture the underlying semantics of user mobility patterns. Experiments conducted on real-world datasets demonstrate that TULER achieves better accuracy than the existing methods.

scholarly journals Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences

2021 ◽  
Vol 13 (24) ◽  
pp. 13921
Author(s):  
Laiyun Wu ◽  
Samiul Hasan ◽  
Younshik Chung ◽  
Jee Eun Kang
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Mobility Models ◽  
Mobility Patterns ◽  
User Characteristics ◽  
Transit System ◽  
Demographic Groups ◽  
Urban Dynamics ◽  
User Groups ◽  
Spatio Temporal

Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics and modal preferences. We identified users’ commuting locations and estimated the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets.

scholarly journals Influence of sociodemographic characteristics on human mobility

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Maxime Lenormand ◽  
Thomas Louail ◽  
Oliva G. Cantú-Ros ◽  
Miguel Picornell ◽  
Ricardo Herranz ◽  
...  
Keyword(s):  
Digital Media ◽  
Credit Card ◽  
Human Mobility ◽  
Sociodemographic Characteristics ◽  
Mobility Patterns ◽  
Relevant Role ◽  
Males And Females ◽  
Spatio Temporal ◽  
Geotagged Photos ◽  
Displacement Patterns

Abstract Human mobility has been traditionally studied using surveys that deliver snapshots of population displacement patterns. The growing accessibility to ICT information from portable digital media has recently opened the possibility of exploring human behavior at high spatio-temporal resolutions. Mobile phone records, geolocated tweets, check-ins from Foursquare or geotagged photos, have contributed to this purpose at different scales, from cities to countries, in different world areas. Many previous works lacked, however, details on the individuals’ attributes such as age or gender. In this work, we analyze credit-card records from Barcelona and Madrid and by examining the geolocated credit-card transactions of individuals living in the two provinces, we find that the mobility patterns vary according to gender, age and occupation. Differences in distance traveled and travel purpose are observed between younger and older people, but, curiously, either between males and females of similar age. While mobility displays some generic features, here we show that sociodemographic characteristics play a relevant role and must be taken into account for mobility and epidemiological modelization.

scholarly journals FedVoting: A Cross-Silo Boosting Tree Construction Method for Privacy-Preserving Long-Term Human Mobility Prediction

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8282
Author(s):  
Yinghao Liu ◽  
Zipei Fan ◽  
Xuan Song ◽  
Ryosuke Shibasaki
Keyword(s):  
Traffic Congestion ◽  
Differential Privacy ◽  
Human Mobility ◽  
Great Accuracy ◽  
Construction Method ◽  
Gradient Boosting ◽  
Commercial Activities ◽  
Mobility Data ◽  
High Efficient

The prediction of human mobility can facilitate resolving many kinds of urban problems, such as reducing traffic congestion, and promote commercial activities, such as targeted advertising. However, the requisite personal GPS data face privacy issues. Related organizations can only collect limited data and they experience difficulties in sharing them. These data are in “isolated islands” and cannot collectively contribute to improving the performance of applications. Thus, the method of federated learning (FL) can be adopted, in which multiple entities collaborate to train a collective model with their raw data stored locally and, therefore, not exchanged or transferred. However, to predict long-term human mobility, the performance and practicality would be impaired if only some models were simply combined with FL, due to the irregularity and complexity of long-term mobility data. Therefore, we explored the optimized construction method based on the high-efficient gradient-boosting decision tree (GBDT) model with FL and propose the novel federated voting (FedVoting) mechanism, which aggregates the ensemble of differential privacy (DP)-protected GBDTs by the multiple training, cross-validation and voting processes to generate the optimal model and can achieve both good performance and privacy protection. The experiments show the great accuracy in long-term predictions of special event attendance and point-of-interest visits. Compared with training the model independently for each silo (organization) and state-of-art baselines, the FedVoting method achieves a significant accuracy improvement, almost comparable to the centralized training, at a negligible expense of privacy exposure.

scholarly journals Quantifying the economic impact of disasters on businesses using human mobility data: a Bayesian causal inference approach

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Yunchang Zhang ◽  
Satish V. Ukkusuri
Keyword(s):  
Business Performance ◽  
Large Scale ◽  
Human Mobility ◽  
Primary Source ◽  
Economic Cost ◽  
Economic Loss ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Causal Impact ◽  
Spatio Temporal

AbstractIn recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning. Conventionally, surveys have been the primary source of data used to quantify damages inflicted on businesses by disasters. However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. In this work, we use location data collected from mobile phones to estimate and analyze the causal impact of hurricanes on business performance. To quantify the causal impact of the disaster, we use a Bayesian structural time series model to predict the counterfactual performances of affected businesses (what if the disaster did not occur?), which may use performances of other businesses outside the disaster areas as covariates. The method is tested to quantify the resilience of 635 businesses across 9 categories in Puerto Rico after Hurricane Maria. Furthermore, hierarchical Bayesian models are used to reveal the effect of business characteristics such as location and category on the long-term resilience of businesses. The study presents a novel and more efficient method to quantify business resilience, which could assist policy makers in disaster preparation and relief processes.

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