Ubiquitous Resilience with Connected Infrastructure and Technology - - PowerPoint PPT Presentation

ubiquitous resilience with connected infrastructure
SMART_READER_LITE
LIVE PREVIEW

Ubiquitous Resilience with Connected Infrastructure and Technology - - PowerPoint PPT Presentation

FUTURe CITy Fostering Smart Urban Transformation and Ubiquitous Resilience with Connected Infrastructure and Technology Dr. Mohamed Abdel-Aty, PE Trustee Chair Pegasus Professor and Dept Chair Civil, Environmental and Construction Engineering


slide-1
SLIDE 1

FUTURe CITy Fostering Smart Urban Transformation and Ubiquitous Resilience with Connected Infrastructure and Technology

  • Dr. Mohamed Abdel-Aty, PE

Trustee Chair Pegasus Professor and Dept Chair Civil, Environmental and Construction Engineering

slide-2
SLIDE 2

FUTURe CITy

2

What makes a City?

Infrastructure

Operations

People

Cities and Urban Areas

1913: 10% of world’s population lived in cities

2013: 50% and 2050: 70% of world’s population in cities

80% of Americans reside in Urban areas (2010 Census)

Urban areas and metropolitan regions account for 76% of all economic activity and 85% of all scientific innovation.

slide-3
SLIDE 3

Urban Development, Needs and Opportunities

3

Integrating wide-ranging technological advances

Improving quality of life and economic vitality

Center of excellence for technologically, socially and organizationally balanced urban transformation

Improve quality of life for its citizens through technology, ultimately creating a sustainable environment

slide-4
SLIDE 4

Focus and Objectives

4

 What makes a Future City?

 Smart Infrastructure  Urban Operations  People – Public Policy and

Financing

(1) To build expertise in deployment of sensing, communication and data transfer network for the interconnected smart city infrastructure

(2) To employ expertise for advanced urban computing data analytics, information technology networks for smart city operations

(3) To develop proficiency in coordinating between technology development and policy formulation, social programs and their implementation

slide-5
SLIDE 5

FUTURe CITy Partners

5

Our initiative is supported by the following with strong recommendation

slide-6
SLIDE 6

Connecting the East Orlando Communities

Overview of the Advanced Transportation and Congestion Management Technologies Deployment (ATCMTD) Project

slide-7
SLIDE 7

Collaborative Effort

District Five

slide-8
SLIDE 8
slide-9
SLIDE 9

Latest and Greatest Roadside Technology

slide-10
SLIDE 10
slide-11
SLIDE 11

Latest and Greatest Smart Cities Technology

Transit Kiosk

Connected Vehicle Technology

Safety and Mobility Applications

Autonomous Vehicles

Solar Energy

Real-Time Multimodal Data

Bus Automated Vehicle Location

Parking Availability

Travel Times

Ride Share Availability

Transit Demand

slide-12
SLIDE 12

SmartCommunity

 Leveraging Aspects of

Smart Cities

 Developing Mobility on

Demand (MoD) framework

 Trying to pave way for

Mobility as a Service (MaaS)

slide-13
SLIDE 13

Research

 Evaluation and Value Addition  Big data analytics and IOT  Connected and Autonomous Vehicles

Public Acceptance

 Smart City Simulation and Visualization  Develop an autonomous vehicle for garage

parking management.

 Clean data from a person with multiple

devices.

 Develop algorithms for assigning the

number of vehicles on routes and route choice.

slide-14
SLIDE 14

Benefits for Local Agencies

 New forward compatible

standard for signal deployment, transit kiosks

 Demonstration of benefits  Coordinated decision making  Latest technology deployed

across the region

 Enhancement mobility and

safety for travelling public

slide-15
SLIDE 15

Benefits for UCF Students

 Applications for Smart Phone

 Parking, drive time vs

Shuttle comparison in real- time

 More efficient transit service,

connections controlled

 Automated vehicle connecting

Stadium and Recreation Center

 Connection ready for

connected vehicles

slide-16
SLIDE 16

Transferable Components

 New Standard in Signal Technology  Modular Software

 Route and Mode Choice Engine  Can be used connect LYNX, Votran,

Sunrail, Uber, LYFT, ZipCar, and Juice

 OBU software  Can be integrated into existing Apps to

get CV benefits or made standalone

slide-17
SLIDE 17

Big Data Applications, Safety, Simulation, Traffic Management

 ITS Traffic Detection

System

 Strength of ITS

High Deployment Density

Real-time Monitoring

 Congestion

Time duration

Congestion area

Congestion intensity

 Safety

Crash precursors

Crash’s effects

  • Dr. Mohamed Abdel-Aty, PE
slide-18
SLIDE 18

18

The Built Environment real-time streaming BIG DATA Understanding the City

Analytics Insights

Sensors & Devices

Mobility Innovation

Visit us: http://www.cecs.ucf.edu/shasan

  • Data-driven methods for

urban mobility & congestion

  • Real-time emergency

response

  • Infrastructure resilience in

delivering critical urban services

slide-19
SLIDE 19

UCF Civil, Environmental, and Construction Engineering

Civil Infrastructure Technologies for Resilience and Safety (CITRS)

Kevin Mackie Andrew Yun Omer Tatari Necati Catbas Boo Nam

CITRS Group

Reliability and probabilistic assessment Sustainable and green structures Novel and nanotech- based materials for civil infrastructure bridges, buildings, highway structures, pavements, roads, stadiums, convention centers, airports, ports, dams, tunnel, lifelines Structural health monitoring and identification with novel sensing, analysis, and predictive analysis approaches Material-, component-, and large-scale testing

Safe, resilient, smart, sustainable civil infrastructure systems

Non-destructive evaluation Advanced modeling and analysis, multiple hazard assessment Life-cycle assessment and life-cycle cost

slide-20
SLIDE 20

NETWORK-LEVEL ROAD PAVEMENT CONDITION ASSESSMENT USING DEEP LEARNING-BASED COMPUTER VISION TECHNIQUE

  • Dr. Hae-Bum “Andrew” Yun || Advanced Imaging, Monitoring & Sensing (AIMS) Lab || Hae-Bum.Yun@ucf.edu

FULLY AUTOMATED ANALYSIS FOR SAN ANTONIO ROAD NETWORK BASED ON ASTM D 6433-11 Multi-purpose road survey vehicle equipped with LRIS, right-of-way digital cameras, laser surface profiler, GPS, DMI, and on- board computer GPU-BASED HIGH-PERFORMANCE COMPUTING

NVIDIA TITAN X was used to process a high-volume image data at high speed (< 5 sec/ image) (PASCAL architecture, 3584 CUDA cores, 12GB GDDR5X) MULTI-SCALE CONVOLUTIONAL NEURAL NETWORK (CNN) is a Deep Learning algorithm developed to deal with various scale issues to detect 13 different road surface objects, such as crack, patch, manhole, marking, pothole, etc.

slide-21
SLIDE 21

UNDERGROUND SCANNING TECHNIQUE TO IDENTIFY MATERIAL PROPERTIES FOR VERY HIGH-SPATIAL RESOLUTION USING 3D GROUND PENETRATING RADAR

  • Dr. Hae-Bum “Andrew” Yun || Advanced Imaging, Monitoring & Sensing (AIMS) Lab || Hae-Bum.Yun@ucf.edu

RAW PROCESSED

NOVEL DIELECTRIC CONSTANT IDENTIFICATION ALGORITHM FOR VERY HIGH SPATIAL RESOLUTION

RETRACTABLE AIR-COUPLED 3D GPR CONTROLLING DECK MULTI-PURPOSE ROAD INSPECTION VAN

3D GPR HIGH-SPEED ROAD SURFACE IMAGING DEVICE

slide-22
SLIDE 22

Smart urban air quality surveillance and air pollution exposure management

 Connected, heterogeneous sensor network

Multi-pollutants, multi-platform, address specific community needs

Near real-time, secured data dissemination system

Smart phone app for travel and exercise planning

Community-engaged and stakeholder-involved

slide-23
SLIDE 23

Kelly Kibler, PhD

  • EcoHydraulics
  • Hydrologic /

Hydraulic Modeling

  • Flood Risk

Assessment

  • Flow Prediction

Talea Mayo, PhD

  • Numerical Model

Development

  • Uncertainty

Quantification

  • Risk Analysis

Steven Duranceau, PhD, PE

  • Water Quality and

Treatment

  • Corrosion
  • Direct Potable

Reuse

  • Disinfection

Byproducts

Dingbao Wang, PhD

  • Surface Water &

Groundwater Modeling

  • Water Resource

Systems

  • Contaminant

Transport Modeling

Arvind Singh, PhD

  • Sediment

Transport

  • Network

Dynamics

  • Geomorphology

A H M Anwar Sadmani, PhD

  • Membrane-based

& Hybrid Processes

  • Emerging

Pollutants of Concern

  • Water Reuse

Applications

  • Alternate

Sources

  • Potable Water

Stephen Medeiros, PhD, PE

  • Hydrology /

Hydraulics

  • Coastal

Hydrodynamics

  • Remote Sensing

& Lidar

  • Enginering /

Industry / Business

  • Sensors and

Instrumentation

Thomas Wahl, PhD

  • Sea level rise and

storm surges

  • Coastal flood and

erosion risk

  • Multi-hazards
  • Extreme value

analysis

WATER FIRST MULTIDISPLINARY TEAM

slide-24
SLIDE 24

SMART Water and Wastewater Management

  • Dr. Woo Hyoung Lee

In Situ Sensors for Water Quality Monitoring Renewable Energy Production from Wastewater

Heavy metal sensor for drinking water and groundwater quality monitoring Point-of-use sensor for drinking water Algal farming using CO2 from power plants Harmful algal blooms (HAB) monitoring sensors Oil spill & chemical detection microsensor Carbon sequestration

In situ heavy metal detection microsensor

Biomass production SMART wastewater treatment Renewable energy production (H2, biofuel, electricity) No mechanical aeration

Algae farm using CO2 from Stanton Energy Center (Orlando, FL)

Nutrient (N & P) recovery

Microbial fuel cells (MFCs) for electricity generation from wastewater

slide-25
SLIDE 25

Thank you

  • Dr. Mohamed Abdel-Aty, PE

m.aty@ucf.edu http://www.cece.ucf.edu/future-city-initiative/

25