bCharge: Data-Driven Real-Time Charging Scheduling for Large-Scale Electric Bus Fleets

2018 ◽  
Author(s):  
Guang Wang ◽  
Xiaoyang Xie ◽  
Fan Zhang ◽  
Yunhuai Liu ◽  
Desheng Zhang
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Data Driven ◽  
Electric Bus

scholarly journals Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoxuan Chen ◽  
Xia Wan ◽  
Fan Ding ◽  
Qing Li ◽  
Charlie McCarthy ◽  
...  
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Network ◽  
Feature Model ◽  
Data Driven ◽  
Experimental Result ◽  
Prediction System ◽  
Urban Network ◽  
Flow Prediction

Cellular probe data, which is collected by cellular network operators, has emerged as a critical data source for human-trace inference in large-scale urban areas. However, because cellular probe data of individual mobile phone users is temporally and spatially sparse (unlike GPS data), few studies predicted people-flow using cellular probe data in real-time. In addition, it is hard to validate the prediction method at a large scale. This paper proposed a data-driven method for dynamic people-flow prediction, which contains four models. The first model is a cellular probe data preprocessing module, which removes the inaccurate and duplicated records of cellular data. The second module is a grid-based data transformation and data integration module, which is proposed to integrate multiple data sources, including transportation network data, point-of-interest data, and people movement inferred from real-time cellular probe data. The third module is a trip-chain based human-daily-trajectory generation module, which provides the base dataset for data-driven model validation. The fourth module is for dynamic people-flow prediction, which is developed based on an online inferring machine-learning model (random forest). The feasibility of dynamic people-flow prediction using real-time cellular probe data is investigated. The experimental result shows that the proposed people-flow prediction system could provide prediction precision of 76.8% and 70% for outbound and inbound people, respectively. This is much higher than the single-feature model, which provides prediction precision around 50%.

scholarly journals Integrated Incident Decision-Support using Traffic Simulation and Data-Driven Models

2018 ◽  
Vol 2672 (42) ◽  
pp. 247-256
Author(s):  
Tao Wen ◽  
Adriana-Simona Mihăiţă ◽  
Hoang Nguyen ◽  
Chen Cai ◽  
Fang Chen
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Case Studies ◽  
Situation Awareness ◽  
Large Scale ◽  
Traffic Simulation ◽  
Data Driven ◽  
Incident Management ◽  
Management Platform ◽  
Transport Management

This paper introduces the framework of an innovative incident management platform with the main objective of providing decision-support and situation awareness for transport management purposes on a real-time basis. The logic of the platform is to detect and then classify incidents into two types: recurrent and non-recurrent, based on their frequency and characteristics. Under this logic, recurrent incidents trigger the data-driven machine learning module which can predict and analyze the incident impact, in order to facilitate informed decisions for transport management operators. Non-recurrent incidents activate the simulation module, which then evaluates quantitatively the performance of candidate response plans in parallel. The simulation output is used for choosing the most appropriate response plan for incident management. The current platform uses a data processing module to integrate complementary data sets, for the purpose of improving modeling outputs. Two real-world case studies are presented: 1) for recurrent incident management using a data-driven model, and 2) for non-recurrent incident management using traffic simulation with parallel scenario evaluation. The case studies demonstrate the viability of the proposed incident management framework, which provides an integrated approach for real-time incident decision-support on large-scale networks.

scholarly journals Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Peter O’Donovan ◽  
Ken Bruton ◽  
Dominic T.J. O’Sullivan
Keyword(s):  
Information Technology ◽  
Real Time ◽  
Large Scale ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Reference Architecture ◽  
Air Handling Unit ◽  
Significant Effort

Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a large-scale Air Handling Unit (AHU).

scholarly journals Using data-driven approaches to improve delivery of animal health care interventions for public health

2021 ◽  
Vol 118 (5) ◽  
pp. e2003722118
Author(s):  
Stella Mazeri ◽  
Jordana L. Burdon Bailey ◽  
Dagmar Mayer ◽  
Patrick Chikungwa ◽  
Julius Chulu ◽  
...  
Keyword(s):  
Real Time ◽  
Vaccination Coverage ◽  
Large Scale ◽  
Animal Health ◽  
Vaccination Campaign ◽  
Sub Saharan Africa ◽  
Data Driven ◽  
Time Data ◽  
Real Time Analysis ◽  
Vaccination Campaigns

Rabies kills ∼60,000 people per year. Annual vaccination of at least 70% of dogs has been shown to eliminate rabies in both human and canine populations. However, delivery of large-scale mass dog vaccination campaigns remains a challenge in many rabies-endemic countries. In sub-Saharan Africa, where the vast majority of dogs are owned, mass vaccination campaigns have typically depended on a combination of static point (SP) and door-to-door (D2D) approaches since SP-only campaigns often fail to achieve 70% vaccination coverage. However, D2D approaches are expensive, labor-intensive, and logistically challenging, raising the need to develop approaches that increase attendance at SPs. Here, we report a real-time, data-driven approach to improve efficiency of an urban dog vaccination campaign. Historically, we vaccinated ∼35,000 dogs in Blantyre city, Malawi, every year over a 20-d period each year using combined fixed SP (FSP) and D2D approaches. To enhance cost effectiveness, we used our historical vaccination dataset to define the barriers to FSP attendance. Guided by these insights, we redesigned our vaccination campaign by increasing the number of FSPs and eliminating the expensive and labor-intensive D2D component. Combined with roaming SPs, whose locations were defined through the real-time analysis of vaccination coverage data, this approach resulted in the vaccination of near-identical numbers of dogs in only 11 d. This approach has the potential to act as a template for successful and sustainable future urban SP-only dog vaccination campaigns.

Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems

2016 ◽  
Author(s):  
Dazhong Wu ◽  
Janis Terpenny ◽  
Li Zhang ◽  
Robert Gao ◽  
Thomas Kurfess
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Manufacturing Systems ◽  
Large Scale ◽  
Failure Detection ◽  
Remote Access ◽  
Data Driven ◽  
Time Data ◽  
Real Time Data ◽  
Geographically Distributed

Over the past few decades, both small- and medium-sized manufacturers as well as large original equipment manufacturers (OEMs) have been faced with an increasing need for low cost and scalable intelligent manufacturing machines. Capabilities are needed for collecting and processing large volumes of real-time data generated from manufacturing machines and processes as well as for diagnosing the root cause of identified defects, predicting their progression, and forecasting maintenance actions proactively to minimize unexpected machine down times. Although cloud computing enables ubiquitous and instant remote access to scalable information and communication technology (ICT) infrastructures and high volume data storage, it has limitations in latency-sensitive applications such as high performance computing and real-time stream analytics. The emergence of fog computing, Internet of Things (IoT), and cyber-physical systems (CPS) represent radical changes in the way sensing systems, along with ICT infrastructures, collect and analyze large volumes of real-time data streams in geographically distributed environments. Ultimately, such technological approaches enable machines to function as an agent that is capable of intelligent behaviors such as automatic fault and failure detection, self-diagnosis, and preventative maintenance scheduling. The objective of this research is to introduce a fog-enabled architecture that consists of smart sensor networks, communication protocols, parallel machine learning software, and private and public clouds. The fog-enabled architecture will have the potential to enable large-scale, geographically distributed online machine and process monitoring, diagnosis, and prognosis that require low latency and high bandwidth in the context of data-driven cyber-manufacturing systems.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

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