scholarly journals Zone-based public transport route optimisation in an urban network

2020 ◽  
Author(s):  
Philipp Heyken Soares
Keyword(s):  
Real World ◽  
Public Transport ◽  
Hybrid Approach ◽  
Future Research ◽  
Real World Data ◽  
Core Element ◽  
Urban Network ◽  
Route Network ◽  
Input Dataset ◽  
Planning Processes

Abstract The majority of academic studies on the optimisation of public transport routes consider passenger trips to be fixed between pairs of stop points. This can lead to barriers in the use of the developed algorithms in real-world planning processes, as these usually utilise a zone-based trip representation. This study demonstrates the adaptation of a node-based optimisation procedure to work with zone-to-zone trips. A core element of this process is a hybrid approach to calculate zone-to-zone journey times through the use of node-based concepts. The resulting algorithm is applied to an input dataset generated from real-world data, with results showing significant improvements over the existing route network. The dataset is made publicly available to serve as a potential benchmark dataset for future research.

scholarly journals Strategic Planning for Integrated Mobility-on-Demand and Urban Public Bus Networks

2020 ◽  
Vol 54 (6) ◽  
pp. 1616-1639
Author(s):  
Konrad Steiner ◽  
Stefan Irnich
Keyword(s):  
Real World ◽  
Public Transport ◽  
Public Transportation ◽  
Network Planning ◽  
Transport Systems ◽  
Real World Data ◽  
Public Network ◽  
On Demand ◽  
Route Network ◽  
Bus Networks

App-based services and ridesharing in the field of mobility-on-demand (MoD) create a new mode of transport between motorized individual transport and public transportation whose long-term role in the urban mobility landscape and within public transport systems is not fully understood as of today. For the public transport industry, these new services offer new chances but also risks, making planning models and tools for integrated intermodal network planning indispensable. We develop a strategic network planning optimization model for bus lines that allows for intermodal trips with MoD as a first or last leg. Starting from an existing public transport network, we decide simultaneously on the use of existing line segments in the future fixed-route network, on areas of the city where an integrated MoD service should be offered, on how MoD interacts with the fixed-route network via transfer points, and on passenger routes fulfilling given service-level requirements. The main challenges from a modeling point of view are to capture the interplay between MoD services and the fixed public network, as well as the approximation of MoD costs taking into account that vehicle utilization is a key factor influencing these costs. We develop a path-based formulation and a branch-and-price algorithm, as well as an enhanced enumeration-based approach, to solve real-world instances to proven optimality. The solution methods are tested on instances generated with the help of real-world data from a medium-sized German city, Göttingen, that currently operates around 20 bus lines.

scholarly journals Incorporating Social-Marketing Insights Into Prejudice Research: Advancing Theory and Demonstrating Real-World Applications

2020 ◽  
Vol 15 (3) ◽  
pp. 608-629 ◽  
Author(s):  
Mitchell R. Campbell ◽  
Markus Brauer
Keyword(s):  
Social Marketing ◽  
Real World ◽  
Intergroup Relations ◽  
Hybrid Approach ◽  
Psychological Theory ◽  
Prejudice Reduction ◽  
Future Research ◽  
Theoretical Understanding ◽  
Real World Applications ◽  
Reduction Methods

Prejudice researchers have proposed a number of methods to reduce prejudice, drawing on and, in turn, contributing to our theoretical understanding of prejudice. Despite this progress, relatively few of these methods have been shown to reliably improve intergroup relations in real-world settings, resulting in a gap between our theoretical understanding of prejudice and real-world applications of prejudice-reduction methods. In this article, we suggest that incorporating principles from another field, social marketing, into prejudice research can help address this gap. Specifically, we describe three social-marketing principles and discuss how each could be used by prejudice researchers. Several areas for future research inspired by these principles are discussed. We suggest that a hybrid approach to research that uses both theory-based and problem-based principles can provide additional tools for field practitioners aiming to improve intergroup relations while leading to new advances in social-psychological theory.

Learning From Imbalanced Data

2018 ◽  
pp. 1825-1834 ◽  
Author(s):  
Lincy Mathews ◽  
Seetha Hari
Keyword(s):  
Real World ◽  
Imbalanced Data ◽  
Misclassification Rate ◽  
Future Research ◽  
Data Sets ◽  
Real World Data ◽  
Imbalanced Data Sets ◽  
Challenging Issue ◽  
Research Challenges ◽  
Balanced Distribution

A very challenging issue in real world data is that in many domains like medicine, finance, marketing, web, telecommunication, management etc., the distribution of data among classes is inherently imbalanced. A widely accepted researched issue is that the traditional classifier algorithms assume a balanced distribution among the classes. Data imbalance is evident when the number of instances representing the class of concern is much lesser than other classes. Hence, the classifiers tend to bias towards the well-represented class. This leads to a higher misclassification rate among the lesser represented class. Hence, there is a need of efficient learners to classify imbalanced data. This chapter aims to address the need, challenges, existing methods and evaluation metrics identified when learning from imbalanced data sets. Future research challenges and directions are highlighted.

Abstract WMP2: Trevo 2000: Real-World Experience in the First 1247 Patients

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Mandy J Binning ◽  
Ronald F Budzik ◽  
Blaise W Baxter ◽  
Bruno M Bartolini ◽  
David S Liebeskind ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Stroke ◽  
Real World ◽  
Stent Retriever ◽  
Future Research ◽  
Real World Data ◽  
Open Label ◽  
Eligibility Criteria ◽  
Secondary Endpoints ◽  
Stent Retrievers

Objective: The Trevo Registry is designed to assess real world outcomes of the Trevo Retriever in patients experiencing ischemic stroke. This is the largest prospective study for acute stroke intervention, with 1247 patients currently enrolled and 90 day outcomes in 1021 patients. The primary endpoint is revascularization status based on post-procedure TICI score and secondary endpoints include 90-day mRS, 90-day mortality, neurological deterioration at 24 hours and device/procedure related adverse events. Methods: The study is a prospective, open-label, consecutive enrollment, multi-center, international registry of patients undergoing mechanical thrombectomy for acute stroke using the Trevo stent retriever as the initial device. Enrollment is expected to reach 2000 subjects at up to 100 sites. Results: As of August 13, 2016 a total of 1247 patients were enrolled. The median NIHSS at admission was 16 (IQR 11-20). Most patients (66.2%) were treated at >/= 6 hours from last known normal with a median procedure time of 50 minutes (32-77). The occlusion site was M1 or M2 in 74.5%. General anesthesia was employed in 46.6% of procedures. TICI 2b or 3 revascularization was 92.8% with an average of 1.6 passes with the device. Median NIHSS at 24 hours and discharge was 6 and 4 respectively. Fifty-five percent of patients had mRS ≤2 at 3 months and the overall mortality rate was 15.4%. Patients treated after 8 hours of symptom onset had a 94.9% revascularization rate and 52.8% mRS ≤2 at 3 months. The symptomatic ICH rate was 1.2%. Patients who met the revised AHA criteria for thrombectomy were found to have 58.4% mRS 0-2 at 90 days. Conclusions: The Trevo Retriever Registry represents the first real world data with stent retriever use in the era of clinical trials showing the overwhelming benefit of stent retrievers to treat acute ischemic stroke. Due to the fact that this data represents real world use of the Trevo Retriever, (e.g. subjects pre-stroke mRS >1 (16.5%) and those treated 6-24 hours after stroke symptoms (33.8%), this data cannot be compared to the results from recent trials with restricted eligibility criteria. Future subgroup analysis of this large cohort will help to identify areas of future research to enhance outcomes further with this treatment modality.

Real-world treatment and outcomes of frontline chemotherapy in patients with metastatic pancreatic cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16249-e16249
Author(s):  
Salwan Al Mutar ◽  
Muhammad Shaalan Beg ◽  
Eric Hansen ◽  
Andrew J. Belli ◽  
Maegan Vaz ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Clinical Trial ◽  
Real World ◽  
Univariate Analysis ◽  
The United States ◽  
Metastatic Pancreatic Cancer ◽  
Future Research ◽  
Real World Data ◽  
The Real ◽  
The Impact

e16249 Background: The difference between the FOLFIRINOX and gemcitabine/nab-paclitaxel (GnP) regimens’ clinical trial designs limit the ability to generate cross-study comparisons. Therefore, there is a significant need to understand the impact of various demographic and clinical characteristics on the effectiveness of these systemic therapies in the real-world treatment setting. This study seeks to compare the real-world outcomes of patients with metastatic pancreatic cancer treated with frontline FOLFIRINOX or GnP. Methods: Patients with primary metastatic pancreatic cancer who received first-line (1L) FOLFIRINOX or GnP were identified in the COTA real-world database. The COTA database is a de-identified database of real-world data (RWD) derived from the electronic health records of healthcare providers in the United States. Real-world overall response rate (rwORR) was calculated as the proportion of patients achieving complete response (CR) or partial response (PR). Overall survival (OS) was calculated using the Kaplan-Meier method and multivariate analyses utilized Cox proportional hazards. Results: The overall qualified cohort (n=236) was stratified by 1L FOLFIRINOX (n=109) or GnP (n=127). Select patient characteristics are shown in table. Patients treated with 1L FOLFIRINOX showed greater rwORR as compared to those treated with GnP (68.8% vs. 55.9%, p=0.04). Additionally, patients treated with 1L FOLFIRINOX had longer median OS (14.4 vs 11.4 mos, respectively). In univariate analysis, patients treated with GnP had a greater chance of mortality (HR: 1.3, 95% CI: 1.0, 1.8, p=0.05). This relationship strengthened in multivariate analysis (GnP treated HR: 1.6, 95% CI: 1.1, 2.1, p=0.01). Conclusions: Due to lack of enrollment of representative patients in clinical trials and in the absence of a comparative clinical trial, real-world experience with chemotherapy regimens provide critical insights on the outcome of treatments. In our cohort, patients treated with frontline GnP had a significantly greater chance of mortality as compared to patients treated with frontline FOLFIRINOX. The FOLFIRINOX cohort also showed greater rwORR. Future research will continue to expand on treatment patterns in subsequent lines of therapy, as well as emerging therapy types, in order to better understand the optimal treatment sequence in metastatic pancreatic cancer.[Table: see text]

scholarly journals A Systematic Methodology on Class Imbalanced Problems involved in the Classification of Real-World Datasets

2019 ◽  
Vol 8 (3) ◽  
pp. 7071-7081
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Imbalanced Data ◽  
Past Research ◽  
Future Research ◽  
Data Sets ◽  
Time Data ◽  
Real World Data ◽  
The Past ◽  
Research Studies

Current generation real-world data sets processed through machine learning are imbalanced by nature. This imbalanced data enables the researchers with a challenging scenario in the context of perdition for both the machine learning and data mining algorithms. It is observed from the past research studies most of the imbalanced data sets consists of the major classes and minor classes and the major class leads the minor class. Several standards and hybrid prediction algorithms are proposed in various application domains but in most of the real-time data sets analyzed in the studies are imbalanced by nature thereby affecting the accuracy of the prediction. This paper presents a systematic survey of the past research studies to analyze intrinsic data characteristics and techniques utilized for handling class-imbalanced data. In addition, this study reveals the research gaps, trends and patterns in existing studies and discusses briefly on future research directions

scholarly journals A survey on feature weighting based K-Means algorithms

2020 ◽  
Author(s):  
Renato Cordeiro de Amorim
Keyword(s):  
Machine Learning ◽  
Empirical Evidence ◽  
Real World ◽  
Clustering Algorithm ◽  
Machine Learning Algorithms ◽  
Feature Weighting ◽  
Future Research ◽  
Real World Data ◽  
Data Set ◽  
Partitional Clustering

In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means

Learning From Imbalanced Data

2019 ◽  
pp. 403-414 ◽  
Author(s):  
Lincy Mathews ◽  
Seetha Hari
Keyword(s):  
Real World ◽  
Imbalanced Data ◽  
Misclassification Rate ◽  
Future Research ◽  
Data Sets ◽  
Real World Data ◽  
Imbalanced Data Sets ◽  
Challenging Issue ◽  
Research Challenges ◽  
Balanced Distribution

A very challenging issue in real-world data is that in many domains like medicine, finance, marketing, web, telecommunication, management, etc. the distribution of data among classes is inherently imbalanced. A widely accepted researched issue is that the traditional classifier algorithms assume a balanced distribution among the classes. Data imbalance is evident when the number of instances representing the class of concern is much lesser than other classes. Hence, the classifiers tend to bias towards the well-represented class. This leads to a higher misclassification rate among the lesser represented class. Hence, there is a need of efficient learners to classify imbalanced data. This chapter aims to address the need, challenges, existing methods, and evaluation metrics identified when learning from imbalanced data sets. Future research challenges and directions are highlighted.

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