Enhancing Active Transportationand Demand Management (ATDM) withAdvanced and Emerging Technologiesand Data Sources

FHWA Office of Operations and National Operations Center of Excellence (NOCoE) WebinarOctober 31, 2019

Today’s Webinar PresentersJames Colyar, P.E.Transportation SpecialistFHWA Office of Operations

David K. Hale, Ph.D., PMPSenior Transportation Project ManagerLeidos

Jiaqi MaAcademic Director, Advanced Transportation CollaborativeUniversity of Cincinnati

Dan Lukasik, P.E.Vice President, Intelligent Transportation SystemsParsons

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Presentation Overview

1. Webinar Objective and Introductions.2. ATDM and Project Overview.3. Guide High-Level Overview.4. Guide Walk Through.5. Question and Answer Session.

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ATDM and ProjectOverview

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The Foundation of ATDM

Active Management

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Source: FHWA

Moving Towards Active Management

ActivelyManaging

Operations

• Time of day• Set-it and forget it• Will work when there is limited variability

Static Management

• Respond to current conditions• Account for traffic impacts due to

conditions• Reduce time of degraded operation

Responsive Management

• Respond to predicted changes in supply and demand

• Ability to delay or eliminate breakdowns

Proactive Management

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Source: FHWA

ATDM Throughout the Trip Chain

DestinationChoice

Origin Destination

Time of DayChoice

ModeChoice

RouteChoice

Lane/FacilityUse/Choice

ATDM approaches provide travelers with choices throughout

the trip chain.

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Source: FHWA

What does ATDM include?

Active Demand Management (ADM): A suite of strategies intended to dynamically reduce or redistribute travel demand to alternate modes, routes, and/or times of day.

• Examples: Comparative multi-modal travel times, dynamic ride-sharing, pricing and incentives.

Active Traffic Management (ATM): A suite of strategies that actively manage traffic on a facility.

• Examples: Variable speed limits, dynamic shoulder use, dynamic lane control.

Active Parking Management (APM): A suite of strategies designed to affect the demand on parking capacity.

• Examples: Parking pricing, real-time parking availability and reservation systems.

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Project Objectives

• Educate current and future implementers of ATDM.• Increase awareness about linkages and synergies.• Investigate the potential impacts, opportunities,

efficiencies, and challenges.• Prepare agencies for deploying and operating ATDM.

• Ultimate end result is to see improvements to existing ATDM operations and deployments.

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• ATDM Data Sources:• Typical traditional sensors (e.g., inductive loops, radar sensors, cameras).• Many use third-party data (e.g., Inrix, Here, Waze).

• ATDM Technologies: • Active Traffic Management (ATM) Strategies:

• Dynamic Speed Limits, Part Time Shoulder Use, Dynamic Lane Use Control, etc.

• Active Demand Management (ADM) Strategies:• Dynamic Pricing and Predictive Traveler Information.

• Active Parking Management (APM) Strategies:• Dynamic Wayfinding and Dynamic Parking Capacity.

• Common emerging technologies:• Cloud computing, Internet of Things, big data and data analytics.

State of the Practice (Current)

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• ATDM Data Sources: • Many envision Wi-Fi/Bluetooth, high-definition signal data, dedicated short-

range communications (DSRC), social media, parking and weather data. • Some envision real-time trajectory data, real-time turning movement data,

high-definition signal data, high resolution map data, and social media data.

• ATDM Technologies:• Cloud computing, Internet of Things, big data and data analytics, voice drive

assistants, connected vehicles and autonomous vehicles.

• Anticipated Barriers: • Funding restraints and lack of resources/knowledge.

• Seeking Guidance: • Data fusion and validation and data analytics.

State of the Practice (Future)

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GUIDEHIGH-LEVEL OVERVIEW

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Guide High-Level Overview

Chapter Name1 Introduction

2 Emerging Technologies and Data Sources

3 ATDM Applications

4 Planning and Organizational Guidelines

5 Design and Deployment Elements and Methods

6 Operations and Maintenance Guidance

7 Case Studies

App. A References

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Purpose of Guide

• Guide agencies towards leveraging new technologies and data sources.

• Provide overview of viable emerging technologies and data sources.

• Identify deployment elements and methods.• Review considerations to enhance ATDM solutions.

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Guide Objectives

• Educate current and future implementers of ATDM concepts.• Increase awareness about ATDM and advanced and emerging

technologies and data sources.• Investigate potential impacts, opportunities, efficiencies, and

challenges.• Investigate how agencies can prepare for deploying and operating

ATDM in a fast-changing world.• End result: See improvements to existing ATDM operations.

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Target Audience

Agencies and companies that currently deploy ATDM and desire enhancements.Engineering consultants, technology companies, systems integrators, ATDM software solution providers.Legislative, executive and policy staff.City, County and State transportation planners, system managers and project development professionals.City, County and State project engineers, ATDM infrastructure designers, finance specialists, agency procurement departments and legal professionals.

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GUIDEWALK THROUGH

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• Identified emerging technologies and data sources that apply to any step within the Active Management Cycle.

• Conducted detailed literature reviews.• Interviewed various public agencies as well as private companies.• Data sources perspective:

• Immense list of sources on travelers, in vehicles, within infrastructure.

• Technologies perspective:• Data analytics and tools which allow fusion of diverse sources

to improve ADTM solutions.

Chapter 2 – Emerging Technologies and Data Sources

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Emerging Technologies Data technologies: • Light detection and ranging (LiDAR). • Lasers.• Automatic vehicle location (AVL).• GPS/phone-based probe data.• Crowdsourced data.• Internet of Things.• Cloud computing.• Big data technologies.• Block chain.• Data analytics.• Commercial transactional data.

Vehicle technologies: • Connected vehicles.• Automated vehicles.

Decision support system technologies: • Artificial intelligence (AI).

• Machine learning (ML).• Deep learning (DL).

• Cloud computing.• Edge computing.• Voice driven assistants.• Data analytics.

Sensor technologies: • Video analytics sensors (edge and

centralized).• Air quality monitoring sensors.• Smart lighting.• Gunshot detectors.• Bluetooth/WiFi sensors.

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Emerging Data SourcesConnected travelers data: • Crowd sourced data (e.g., speed incident,

event, and congestion data).• Connected citizen applications.• Crowd sourced video.

Connected vehicle data: • Basic safety messages (BSM).• Probe data messages.• Others.

Connected infrastructure data: • Roadside dedicated short-range

communications (DSRC)/BSM collection.• High-definition signal data.• ATM devices (e.g., signals, signs, cameras,

weather info systems).• Internet of Things.

Map Technologies: • Crowdsourced mapping data.• High resolution map data and other asset

management systems.• Real-time trajectory data.

Other data sources: • Real-time turning movement data.• Bluetooth re-identification.• Mobile sensors.• High-definition maps.

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• Details how each ATDM Solution (ATM, ADM, APM) is impacted by emerging technologies and data sources in the basic functions:

• Collect and monitor data.• Assess system performance.• Evaluate and recommend actions.• Implement the dynamic actions.

• Developed three use cases for current and future scenarios:• Recurring congestion on freeways and highways.• Major incident on freeway.• Parking near the airport.

Chapter 3 – ATDM Applications

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ATDM – Monitor System

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Source: Parsons

Source: FHWA

ATDM – Assess System Performance

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Source: FHWA

Source: Parsons

ATDM – Evaluate and Recommend

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Source: FHWA

Source: Parsons

ATDM – Implement Dynamic Actions

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Source: FHWA

Source: Parsons

Recurring Congestion Use Case (Current/Traditional

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Source: FHWA

Recurring Congestion Use Case

Congestion is detected more quickly and more accuratelyMore appropriate implementation actionsSystem learns and improves over time

Better analytics and reporting

Recurring freeway congestion is measurably reduced

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Future

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Source: FHWA

ATDM requires:1. Organizational capability.2. Planning for modified ATDM operations.3. Setting objectives and performance measures.4. Analysis, modeling and simulation.5. Programming and budgeting.

Chapter 4 – Planning and Organizational Considerations

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Planning for ATDM

1. Organizational Capability.

Step-wiseapproach towards Active Management

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Source: FHWA

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Planning for ATDM (continued)

2. Planning for modified ATDM operations involves:• Scenario planning.• New data sources.• New technology uses and expertise.• Training.

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3. Setting objectives and performance measures.

SMART: Specific, Measurable, Attainable, Realistic, Time-Bound

Planning for ATDM (continued)

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Objective Performance Measures

Reduce congestion(example)

Highway delay Person throughputTravel speed Number of

bottlenecksVehicle-hours of travel Customer

satisfactionPassenger travel times

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4. Analysis, modeling and simulation.• Methodologies to facilitate decision include:

• Sketch-planning methods.

• Post-processing methods.

• Multi-resolution/multi-scenario methods.

5. Programming and budgeting.

Planning for ATDM (continued)

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1. Design elements.2. Data sources.3. System platforms.4. Infrastructure: Field and geometric design

considerations.5. Technology testing.6. Public outreach.

Chapter 5 – Design and Deployment Elements and Methods

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• Design elements:– Logical data management.– Physical data management.

• Data sources:– Big data process model.– Acquisition.– Marshalling.– Analysis.– Data storage.– Data sharing.

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Design and Deployment Elements and Methods (continued)

• Data platforms– Cloud.– Premise.– Hybrid.

• Infrastructure:– Field elements.– Geometric design.

• Technology testing:– Automation.

• Public outreach.

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• Design speed• Lane width• Shoulder width• Horizontal alignment• Super-elevation• Vertical alignment• Grade• Stopping sight distance • Cross slope• Vertical clearance • Lateral offset to obstruction• Structural capacity of

bridges

Design and Deployment Elements and Methods (continued)

1. Routine issues:a) Cybersecurity and data privacy.b) Performance monitoring and maintenance.c) Enforcement.d) Costs.

2. Future proofing.

Chapter 6 – Operations and Maintenance Considerations

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Data Privacy Considerations• Crowdsourced data feeds.• Cloud computing.• Big data.• Connected and autonomous

vehicle data.• Video analytics.• Bluetooth/WiFi sensors.• Internet of Things• Mobile sensors.

Key Questions• Who will pay to collect, store, and

share the data? • Who (if anyone) can sell the data,

and to whom?• Are there any privacy issues in the

data that must be addressed?• Who is allowed to access the data,

and what data may they access• What purposes are the data allowed

to be used for?• What data use agreements are in

place?

Cybersecurity and Data Privacy

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Key Questions:• How will performance of new technologies and data sources be monitored?• What tools should be installed?• Who has the responsibility for this?• What type of key performance indicators (KPIs) will be used?

Performance Monitoring

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© Pi et al (2017)

• Future proofing:• Technology maturity and evolution assessments.• Technology flexibility and scalability.• Change control boards.• Future-proof information technology plans.• Education programs.• Working relationships.

Operations and Maintenance Considerations

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San Diego Integrated Corridor Management System:• ATDM Approach: ATM and ADM.• Emerging Technologies and Data: Artificial intelligence, decision support systems, real-

time microsimulation, on-line traffic prediction.

Stanford University’s Congestion and Parking Relief Incentives (CAPRI) Study:• ATDM Approach: ADM and APM.• Emerging Technologies and Data: ID sensing, smartphone apps, social media

CHARM Program in the Netherlands• ATDM Approach: ATM.• Emerging Technologies and Data: Connected vehicles, traffic prediction, in-car

applications, AI/learning algorithms.

Chapter 7 – Case Studies

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QUESTIONS AND ANSWERS

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Project Contacts

James Colyar Dan LukasikFHWA ParsonsJames.Colyar@dot.gov Daniel.Lukasik@parsons.com

David Hale Leidos, Inc.david.k.hale@leidos.com

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