scholarly journals Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China

2020 ◽  
Vol 12 (4) ◽  
pp. 1426
Author(s):  
Yang Liu ◽  
Yanjie Ji ◽  
Tao Feng ◽  
Zhuangbin Shi
Keyword(s):  
Traffic Congestion ◽  
Logistic Regression Model ◽  
Low Frequency ◽  
Gps Data ◽  
Spatiotemporal Information ◽  
Ordered Logistic Regression ◽  
Intercept Survey ◽  
The Times ◽  
Combined Mode ◽  
Significant Factors

Promoting a transition in individuals’ travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that results mainly from commute trips performed by individual automobiles. This paper focuses on the use frequency of an integrated metro–bikeshare by individuals, and presents empirical evidence from Nanjing, China. Using one-week GPS data collected from the Mobike company, the spatiotemporal characteristics of origin/destination for cyclists who would likely to use shared bike as a feeder mode to metro are examined. Three areas of travel-related spatiotemporal information were extracted including (1) the distribution of walking distances between metro stations and shared bike parking lots; (2) the distribution of cycling times between origins/destinations and metro stations; and (3) the times when metro–bikeshare users pick up/drop off shared bikes to transfer to/from a metro. Incorporating these three features into a questionnaire design, an intercept survey of possible factors on the use of the combined mode was conducted at seven functional metro stations. An ordered logistic regression model was used to examine the significant factors that influence groupings of metro passengers. Results showed that the high-, medium- and low-frequency groups of metro–bikeshare users accounted for 9.92%, 21.98% and 68.1%, respectively. Education, individual income, travel purpose, travel time on the metro, workplace location and bike lane infrastructure were found to have significant impacts on metro passengers’ use frequency of integrated metro–bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems.

scholarly journals A Proposed Framework for Identification of Indicators to Model High-Frequency Cities

2021 ◽  
Vol 10 (5) ◽  
pp. 317
Author(s):  
Ahmad M. Senousi ◽  
Junwei Zhang ◽  
Wenzhong Shi ◽  
Xintao Liu
Keyword(s):  
Urban Studies ◽  
Traffic Congestion ◽  
High Frequency ◽  
Urban Policy ◽  
Low Frequency ◽  
Highway Design ◽  
City Life ◽  
Cultural Affiliation ◽  
Internal Mobility ◽  
The City

A city is a complex system that never sleeps; it constantly changes, and its internal mobility (people, vehicles, goods, information, etc.) continues to accelerate and intensify. These changes and mobility vary in terms of the attributes of the city, such as space, time and cultural affiliation, which characterise to some extent how the city functions. Traditional urban studies have successfully modelled the ‘low-frequency city’ and have provided solutions such as urban planning and highway design for long-term urban development. Nevertheless, the existing urban studies and theories are insufficient to model the dynamics of a city’s intense mobility and rapid changes, so they cannot tackle short-term urban problems such as traffic congestion, real-time transport scheduling and resource management. The advent of information and communication technology and big data presents opportunities to model cities with unprecedented resolution. Since 2018, a paradigm shift from modelling the ‘low-frequency city’ to the so-called ‘high-frequency city’ has been introduced, but hardly any research investigated methods to estimate a city’s frequency. This work aims to propose a framework for the identification and analysis of indicators to model and better understand the concept of a high-frequency city in a systematic manner. The methodology for this work was based on a content analysis-based review, taking into account specific criteria to ensure the selection of indicator sets that are consistent with the concept of the frequency of cities. Twenty-two indicators in five groups were selected as indicators for a high-frequency city, and a framework was proposed to assess frequency at both the intra-city and inter-city levels. This work would serve as a pilot study to further illuminate the ways that urban policy and operations can be adjusted to improve the quality of city life in the context of a smart city.

scholarly journals TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA

2019 ◽  
Vol 20 (1) ◽  
pp. 108-118 ◽  
Author(s):  
Wiam Elleuch ◽  
Ali Wali ◽  
Adel M. Alimi
Keyword(s):  
Traffic Congestion ◽  
Prediction Method ◽  
Traffic Information ◽  
Gps Data ◽  
Traffic Patterns ◽  
Dynamic Changes ◽  
Traffic Conditions ◽  
Congestion Prediction ◽  
Different Types ◽  
Nonparametric Techniques

ABSTRACT: The prediction of accurate traffic information such as speed, travel time, and congestion state is a very important task in many Intelligent Transportations Systems (ITS) applications. However, the dynamic changes in traffic conditions make this task harder. In fact, the type of road, such as the freeways and the highways in urban regions, can influence the driving speeds and the congestion state of the corresponding road. In this paper, we present a NNs-based model to predict the congestion state in roads. Our model handles new inputs and distinguishes the dynamic traffic patterns in two different types of roads: highways and freeways. The model has been tested using a big GPS database gathered from vehicles circulating in Tunisia. The NNs-based model has shown their capabilities of detecting the nonlinearity of dynamic changes and different patterns of roads compared to other nonparametric techniques from the literature. ABSTRAK: Ramalan maklumat trafik yang tepat seperti kelajuan, masa perjalanan dan keadaan kesesakan adalah tugas yang sangat penting dalam banyak aplikasi Sistem Pengangkutan Pintar (ITS). Walau bagaimanapun, perubahan keadaan lalu lintas yang dinamik menjadikan tugas ini menjadi lebih sukar. Malah, jenis jalan raya, seperti jalan raya dan lebuh raya di kawasan bandar, boleh mempengaruhi kelajuan memandu dan keadaan kesesakan jalan yang sama. Dalam makalah ini, kami membentangkan model berasaskan NN untuk meramalkan keadaan kesesakan di jalan raya. Model kami mengendalikan input baru dan membezakan corak trafik dinamik dalam dua jenis jalan raya yang lebuh raya dan jalan raya. Model ini telah diuji menggunakan pangkalan data GPS yang besar yang dikumpulkan dari kenderaan yang beredar di Tunisia. Model berasaskan NNs telah menunjukkan keupayaan mereka untuk mengesan ketiadaan perubahan dinamik dan pola jalan yang berbeza berbanding dengan teknik nonparametrik yang lain dari kesusasteraan.

scholarly journals Vehicle classification from low-frequency GPS data with recurrent neural networks

2018 ◽  
Vol 91 ◽  
pp. 176-191 ◽  
Author(s):  
Matteo Simoncini ◽  
Leonardo Taccari ◽  
Francesco Sambo ◽  
Luca Bravi ◽  
Samuele Salti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Low Frequency ◽  
Vehicle Classification ◽  
Gps Data

scholarly journals Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors

2020 ◽  
Vol 17 (13) ◽  
pp. 4852
Author(s):  
Pouya Gholizadeh ◽  
Behzad Esmaeili
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Odds Ratio ◽  
Logistic Regression Model ◽  
The Body ◽  
Occupational Accidents ◽  
Multivariate Logistic Regression Model ◽  
Ratio Analysis ◽  
Accident Scenarios ◽  
Significant Factors

The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most significant factors that can explain the fatality rates in different scenarios. Thereafter, this study performed an odds ratio analysis among significant factors to determine which factors increase the likelihood of fatality. For example, it was found that having a fatal accident is 4.4 times more likely when the source is a “vehicle” than when it is a “tool, instrument, or equipment”. After validating the consistency of the model, 105 accident scenarios were developed and assessed using the model. The findings revealed which severe accident scenarios happen commonly to people in this trade, with nine scenarios having fatality rates of 50% or more. The highest fatality rates occurred in “fencing, installing lights, signs, etc.” tasks in “alteration and rehabilitation” projects where the source of injury was “parts and materials”. The proposed analysis/modeling approach can be applied among all specialty contracting companies to identify and prioritize more hazardous situations within specific trades. The proposed model-development process also contributes to the body of knowledge around accident analysis by providing a framework for analyzing accident reports through a multivariate logistic regression model.

scholarly journals Continuous partial trends and low-frequency oscillations of time series

2005 ◽  
Vol 12 (4) ◽  
pp. 451-460 ◽  
Author(s):  
A. R. Tomé ◽  
P. M. A. Miranda
Keyword(s):  
Time Series ◽  
Low Frequency ◽  
Distributed Data ◽  
Spatially Distributed ◽  
Geophysical Time Series ◽  
Slow Evolution ◽  
Using Data ◽  
The Times ◽  
Data Subject

Abstract. This paper presents a recent methodology developed for the analysis of the slow evolution of geophysical time series. The method is based on least-squares fitting of continuous line segments to the data, subject to flexible conditions, and is able to objectively locate the times of significant change in the series tendencies. The time distribution of these breakpoints may be an important set of parameters for the analysis of the long term evolution of some geophysical data, simplifying the intercomparison between datasets and offering a new way for the analysis of time varying spatially distributed data. Several application examples, using data that is important in the context of global warming studies, are presented and briefly discussed.

scholarly journals Factors Affecting Mortality in Patients Admitted to the Hospital by Emergency Physicians despite Disagreement with Other Specialties

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Engin Ozakin ◽  
Arif Alper Cevik ◽  
Filiz Baloglu Kaya ◽  
Nurdan Acar ◽  
Fikri M. Abu-Zidan
Keyword(s):  
Blood Pressure ◽  
Logistic Regression ◽  
Systolic Blood Pressure ◽  
Logistic Regression Model ◽  
Admission Diagnosis ◽  
Emergency Physicians ◽  
Factors Affecting ◽  
Admission Decisions ◽  
Service Departments ◽  
Significant Factors

Background. Emergency physicians (EPs) face critical admission decisions, and their judgments are questioned in some developing systems. This study aims to define the factors affecting mortality in patients admitted to the hospital by EPs against in-service departments’ decision and evaluate EPs’ admission diagnosis with final discharge diagnosis. Methods. This is a retrospective analysis of prospectively collected data of ten consecutive years (2008–2017) of an emergency department of a university medical center. Adult patients (≥18 years-old) who were admitted to the hospital by EPs against in-service departments’ decision were enrolled in the study. Significant factors affecting mortality were defined by the backward logistic regression model. Results. 369 consecutive patients were studied, and 195 (52.8%) were males. The mean (SD) age was 65.5 (17.3) years. The logistic regression model showed that significant factors affecting mortality were intubation (p<0.0001), low systolic blood pressure (p=0.006), increased age (p=0.013), and having a comorbidity (p=0.024). There was no significant difference between EPs’ primary admission diagnosis and patient’s final primary diagnosis at the time of disposition from the admitted departments (McNemar–Bowker test, p=0.45). 96% of the primary admission diagnoses of EPs were correct. Conclusions. Intubation, low systolic blood pressure on presentation, increased age, and having a comorbidity increased the mortality. EPs admission diagnoses were highly correlated with the final diagnosis. EPs make difficult admission decisions with high accuracy, if needed.

scholarly journals Employment Transitions and Mental Health in a Cohort of 45 Years and Older Australians

2021 ◽  
Vol 18 (17) ◽  
pp. 9030
Author(s):  
Vikas Arya ◽  
Sandro Sperandei ◽  
Matthew J. Spittal ◽  
Andrew Page
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Psychological Distress ◽  
Old Age ◽  
Logistic Regression Model ◽  
Labour Force ◽  
Employment Transitions ◽  
Ordered Logistic Regression ◽  
Large Prospective Cohort

Background: This study investigated the associations between employment transitions and psychological distress among a cohort of 45 years and older Australians. Methods: This study was based on the 45 and Up Study, a large prospective cohort of participants aged 45 years and older (N = 267,153), followed up over the period 2006–2015. The risk of psychological distress was compared between various employment transitions categories by specifying an ordered logistic regression model adjusting for confounders. Results: Compared to participants who remained employed at baseline and follow-up, higher psychological distress was found among those who transitioned from being employed to unemployed (OR = 2.68, 95%CI 2.13–3.33) and to not being in the labour force or retired (OR = 2.21, 95%CI 1.85–2.62). Higher psychological distress was also evident among those who remained unemployed from baseline to follow-up (OR = 2.00, 95%CI 1.10–3.43), and those who transitioned from being retired to being unemployed (OR = 1.55, 95%CI 1.03–2.27). Conversely, lower psychological distress was found among those who transitioned from being unemployed to being employed (OR = 0.35, 95%CI 0.25–0.51). In general, lower psychological distress was found among ‘positive’ employment transitions (transitioning to being employed or retired). Conclusions: Policies focussing on re-employment in older age, as well as unemployment schemes, might be helpful in reducing psychological distress among middle- and old-age Australians.

scholarly journals Assessment of factors that affect the bankruptcy of an organization

2020 ◽  
Vol 19 (1) ◽  
pp. 57-71
Author(s):  
Irina Vinnikova
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Timely Manner ◽  
Financial Situation ◽  
Significant Factors

Analysis of factors that influence the company's bankruptcy is one of the main tasks for companies that want to assess their financial situation and prevent possible bankruptcy in a timely manner. This article analyzes the factors that affect the company's bankruptcy. A logistic regression model was constructed based on the indicators of both bankrupt and financially stable companies. During the development of the model, significant factors were identified for predicting the bankruptcy of the organization. The results will be useful both for future bankruptcy researchers and for those companies that want to assess their financial situation.

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