Resources for Students and Instructors

ITE Student Chapter Series

The ITE Student Chapter series provides current ITS information to students at the undergraduate and graduate level within civil engineering/transportation programs. These presentations demonstrate to students the interdisciplinary nature of highways, transit, infrastructure, and other multimodal requirements within roadway systems.

Presentation 3
ITS Grand Challenges: How Can You Help Meet Them?

Note: The content on this page is a 508-compliant version of the PowerPoint presentation. The PowerPoint file and a PDF of the PowerPoint presentation are available for download to the right.

 

Slide 1: ITS Grand Challenges: How Can You Help Meet Them?

(Extended Text Description: Image of Slide 1 of this presentation. Across the top is a blue graphic header bar with the ITE Institute of Transportation Engineers logo on the left and a textbox on the right which reads, A Community of Transportation Professionals - Your source for expertise, knowledge and ideas. In the main body of the slide is the text: Developed for the ITS Joint Program Office - ITS Grand Challenges: How Can You Help Meet Them? ITE Student Chapter Series.)

 

Developed for the ITS Joint Program Office
ITE Student Chapter Series

 

Slide 2: The Ultimate ITS Vision?

• There is no consensus vision…

Image Source: ThinkStock/USDOT

• "Toward Zero Deaths" is a universal aspiration

• And most agree that the world of
  • Connected and
  • Autonomous Vehicles

is coming

Image Source: ThinkStock/USDOT

 

Slide 3: The Ultimate ITS Vision?

• But will this ultra-high tech, hyper-connected world be -
  • Livable?
  • Human scale?
• And will it be characterized by -
  • Social justice?
  • Environmental stewardship?

Image Source: ThinkStock/USDOT

 

Slide 4: Where We Stand

• Much of the foundational technology is in place
• Yet significant challenges remain
• There are still significant technological advances needed
• There are still significant institutional impediments

Image Source: ThinkStock/USDOT

 

Slide 5: Grand Challenges

• Refining the vision and making it reality will be challenging
• It is not a stretch to say Grand Challenges must be overcome
• Core ITS professionals must lead on challenges related to -
  • Technology
  • Human factors
• ITS professionals must play key supporting roles on challenges related to institutional issues such as -
  • Policy development
  • Risk assessment and management
  • Legal framework evolution
  • Liability and insurance system revolution

Image Source: ThinkStock/USDOT

 

Slide 6: Grand Challenges in Technology

• Big Data Management and Analytics
  • Wireless network capacity
  • Data fusion
  • Data mining
  • Accurate metrics for system benefits and costs
  • High fidelity, real-time, regional-scale modeling
• Data and system security
  • Sensitive and personal data security
  • Protection against malicious system attacks
• Achieving the system reliability needed for wide spread autonomous vehicle use

Image Source: ThinkStock/USDOT

 

Slide 7: Grand Challenges in Human Factors

• In the pre-autonomous, connected vehicle era -
  • "Connecting" vehicles without overloading drivers
  • Providing information in ways that are quickly and accurately understood

• Entering the fully-autonomous vehicle era -
  • Managing the transition from the driver perspective
  • Training in system operation
  • Driver takeover in emergency situations

Image Source: ThinkStock/USDOT

 

Slide 8: Institutional Grand Challenges

• Adapting public policy and laws for connected and ultimately autonomous vehicles
• Assessing and managing system risks
• Establishing new tort liability and insurance systems
• Commercial vehicle operations in the autonomous vehicle era

Image Source: ThinkStock/USDOT

 

Slide 9: Research - The Key to Addressing the Grand Challenges

• Targeted research is essential
• Basic and applied research is underway in academia and industry
• Research gaps persist
• But exciting new thrusts are beginning

Image Source: ThinkStock/USDOT

 

Slide 10: Research Case Studies

• The scope of exciting and impactful research is broad
• Three examples…the tip of the iceberg
  • Probe data for system monitoring and management
  • Location-based social networking for travel demand modeling
  • Google Car

Image Source: ThinkStock/USDOT

 

Slide 11: Indiana Mobility

• Researchers at Purdue are using high definition probe data from INRIX to produce powerful visualization tools for Indiana

(Extended Text Description: This figure contains an example of a color coded profile graph created by researchers at Purdue University for the Indiana Department of Transportation (INDOT) built upon analysis of high density probe vehicle data provided by INRIX, one of the world leaders in real time traffic data. This figure shows speed profiles for southbound I-65, January 2013-June 2014. The examples on this slide show how visualizations provide unprecedented informational detail that INDOT can use to evaluate and develop mobility improvement strategies. For example only.)

• The research also includes major work zone monitoring and operational support

(Extended Text Description: This figure contains an example of a color coded profile graph created by researchers at Purdue University for the Indiana Department of Transportation built upon analysis of high density probe vehicle data provided by INRIX. This figure shows speed profiles for various districts including LaPorte District, Crawfordsville District, Greenfield District and Seymour District in January 2014. For example only.)

Image Source: 2013-2014 Indiana Mobility Report

(Extended Text Description: This figure contains an example of a map with real time state wide probe data created by researchers at Purdue University for the Indiana Department of Transportation. This is an example of an Incident Detection Application from October 2014 showing a section of I-94 with filtering tabs to the right of the map showing real-time data including route, work zones, ISP districts, INDOT districts and delta speed. For example only.)

Image Source: Purdue University

 

Slide 12: Location-Based Social Networking (LBSN)

• Researchers at UT-Austin and Rutgers have been exploring the power in Foursquare™ data
• Results have been published on using the data to estimate urban origin-destination patterns
• Potential for fusing data across multiple LBSN providers is huge
• Privacy and safety issues must be addressed

(Extended Text Description: This figure is labeled Figure 2 Venue locations within study area by (a) individual location and (b) density (no. = number). The relevance of the figure is to show how research at UT-Austin and Rutgers used data from the Foursquare app to estimate urban origin-destination patterns. The result are very promising even with the data being from a single application.)

Image Source:
Jin, P.J., et al., "Location-Based Social Networking Data Exploration into Use of Doubly Constrained Gravity Model for Origin-Destination Estimation," Transportation Research Record: Journal of the Transportation Research Board, No. 2430, Transportation Research Board of the National Academies, Washington, D.C., 2014, pp. 72-82.

 

Slide 13: Google Car

• The most visible of the autonomous vehicle research efforts
• Fully-autonomous
• Testing is -
  • Legal in four states
  • Underway in Mountain View, CA and Austin, TX
  • Google says the car will be market-ready in 2020

Image Source: ThinkStock/USDOT

 

Slide 14: Research Gaps and the Big Gap

• The research examples just cited are all early stage efforts
• Other emphasis areas include commercial vehicles, transit, etc.
• Gaps exist in all areas
• A persistent soft-side gap is the need to develop a clear vision of the "Smart City" of the future
  • This vision will guide all other efforts
  • Developing this vision should be a priority
  • Collaboration across many disciplines is needed

Image Source: ThinkStock/USDOT

 

Slide 15: Emerging Research Thrusts

• Three examples of cutting edge ITS research that are truly breaking new ground
  • Advanced Research Projects Agency-Energy (ARPA-E) TRANSNET research program
  • Simulator-based research on driver emergency takeover in autonomous vehicles
  • Electric autonomous taxi systems

Image Source: ThinkStock/USDOT

 

Slide 16: ARPA-E's TRANSNET Program

• Advanced Research Projects Agency-Energy
• Traveler Response Architecture using Novel Signaling for Network Efficiency in Transportation (TRANSNET)
  • Five awards totaling $14.5 million
  • Research teams will create control architectures to encourage energy saving travel behavior
  • University of Maryland National University Transportation Center Team
    • Integrated, Personalized, Real-time Traveler Information and Incentive (iPretii)

Image Source: University of Maryland National Transportation Center

 

Slide 17: Emergency Takeover in Autonomous Vehicles

• What will happen when things go wrong in a self-driving car?
• Some visions include the need for travelers to take over driving
• This will be an entirely new travel situation

• Researchers in NC State's Department of Psychology are investigated emergency takeover using a driving simulator

Image Source: ThinkStock/USDOT

 

Slide 18: Autonomous Electric-Vehicle Taxis

• Imagine Uber with no drivers or exhaust
• Researchers at UT Austin and Lawrence Berkeley National Laboratory have found that the environmental and energy benefits could be huge
• The UT Austin researchers also investigated methods to model the impact of various vehicle staging schemes

Image Source: ThinkStock/USDOT

(Extended Text Description: This figure contains a bar chart labeled Figure 3 - GHG emissions intensities per mile for CDVs in 2014 and 2030, and ATs in 2030. Cost optimal vehicle technologies indicated by asterisks. Both full-sized (purple) and right sized (red) ATs are shown, each with three sets of electricity GHG intensity assumptions. Right sized ATs have per mile GHG emissions intensities 87-94% below 2014 ICEVs, and 63-82% below 2030 HEVs, depending on electricity GHG intensity. The data is approximately as follows: the y axis is GHG emissions, and the x axis is 2014 (CDV), 2030 (CDV), 2030 (AT, full-sized), 2030 (AT, right-sized). 2014 (CDV) shows 300 HEV and 300-475 ICEV*. 2030 (CDV) show 160 HEV* and 160-225 ICEV. 2030 (AT, full-sized) shows 50 BEV* CA, 75 EPA, 100 EIA. 2030 (AT, right-sized) shows 30 BEV* CA, 50 EPA, and 70 EIA.)

Source:
Reprinted by permission from Macmillan Publishers LTD: Nature Climate Change, Greenblatt, J.B. and S. Saxena, "Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles," Nature Climate Change 5 (2015): 860-63.

 

Slide 19: Joining the Grand Challenge Team

• The opportunities are vast and continuing to grow
• There are many ways to get in the game
  • See what the major research funding agencies are supporting
  • See what the major industry players are saying and doing
  • Search and read
  • Ask questions
  • Think big

Image Source: ThinkStock/USDOT

 

Slide 20: Joining the Grand Challenge Team

• Find an intersection between your skill set and a challenge that piques your interest
• Identify the academic and industry research programs that are seriously working to meet the challenge
• Create and execute an education plan to prepare you for the research
• Go for it!

Image Source: ThinkStock/USDOT

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