Metropia App and Implications for Travel Demand Management

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Center for Transportation Research - UT Austin

2015 D-STOP Symposium session by Yi-Chang Chiu, Metropia founder. Watch the presentation at http://youtu.be/sSotvhKg3Wc?t=12m59s Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/

Technology

Metropia App and Implications for Travel
Demand Management
Yi-Chang Chiu, PhD
University of Arizona, Metropia Inc.
D-STOP Symposium
University of Texas at Austin,
March 2, 2015

Colorado Study:
I-70 Mountain - A State-Wide Treasure

Predictive Optimal Routing and Load
Balancing
Reserve Validate

Metropia’s Gamification Features
• Earning Points can be exchanged for local merchant rewards
or to plant trees

Metropia’s Gamification Features
• Users also get CO2 and Time Savings and Driving Scores

Metropia Multi-Modal “Lead Generation” Concept
Metropia
Drivers
Alternative
Mode
Attractive
Alternative
Mode
Feasible

Metropia Multi-Mode “LeadGen” Example
(Portland Metro Network - PMPeriod)

Metropia Multi-Mode “Lead Gen” Example
(PortlandMetro Network–PM Period)
Potential Travel Shed for Mode
Shift with Lead Gen (Simulated)
Alternative Mode Feasible:
• SOV trips with direct transit
services between origin and
destination
• 280,000 trips captured (15%)
Alternative Mode Attractive:
• SOV trips with direct transit
services between origin and
destination
• Wait time < 15 minutes
• Walk distance < 0.5 Miles
• 10,000 trips captured (0.5%)

Activity Pattern
• Activity History • Activity Prism
Work
Home
Shopping
Home
Shopping Work
Activity Based Model

Intersection Delays
Congestion
Management System

Data Coverage (Week 1-16)
171 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Model Calibration

Data Coverage (Week 1-16)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Stable links 304 438 807 1157 1460 1682 1856 2266 2517 2734 3097 3416 3564 3767 4094 4259 4377
Updated links 2305 3798 7278 9599 10602 11548 12172 13426 14374 15145 16006 17328 17748 18274 19150 19525 19772
0
5000
10000
15000
20000
25000
30000
#oflinks
week

Austin Weekend PH Congestion
Travel Time/
Reliabiilty Study

YC Weekday Home 8 AM
Kids School
Metropia
U of AZ
Emission
Test
Tucson
Airport
Destination Choice
Modeling

YC Weekday UA 6 PM
Home
Metropia
To Go
Restaurant
Kid’s
Martial Art

YC Weekend Home 11 AM
Brunch
Dim Sum
Restaurant
Tucson
Airport
Kid’s
Martial Art
Metropia

Conclusions
• Metropia focuses on Multi-Modal Active
Demand Management
• Metropia data support various travel demand
management purposes
– Multi-modal opportunities
– Congestion/performance measures
– Activity patterns
– Distraction/safety analysis

Metropia App and Implications for Travel Demand Management

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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject. We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup. Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved. The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring. The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise. By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.

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Metropia App and Implications for Travel Demand Management

1. Metropia App and Implications for Travel Demand Management Yi-Chang Chiu, PhD University of Arizona, Metropia Inc. D-STOP Symposium University of Texas at Austin, March 2, 2015

2. Colorado Study: I-70 Mountain - A State-Wide Treasure

3. Great Fun in the Mountain

4. Long Way Home….

5. Long Way Home….

6. $10M $20M $200M $2B

7. Predictive Optimal Routing and Load Balancing Reserve Validate

8. Metropia’s Gamification Features • Earning Points can be exchanged for local merchant rewards or to plant trees

9. Metropia’s Gamification Features • Users also get CO2 and Time Savings and Driving Scores

10. Metropia Multi-Modal “Lead Generation” Concept Metropia Drivers Alternative Mode Attractive Alternative Mode Feasible

11. Metropia Multi-Mode “LeadGen” Example (Portland Metro Network - PMPeriod)

12. Metropia Multi-Mode “Lead Gen” Example (PortlandMetro Network–PM Period) Potential Travel Shed for Mode Shift with Lead Gen (Simulated) Alternative Mode Feasible: • SOV trips with direct transit services between origin and destination • 280,000 trips captured (15%) Alternative Mode Attractive: • SOV trips with direct transit services between origin and destination • Wait time < 15 minutes • Walk distance < 0.5 Miles • 10,000 trips captured (0.5%)

13. GPS Trajectories

14. GPS Trajectories

15. Activity Pattern • Activity History • Activity Prism Work Home Shopping Home Shopping Work Activity Based Model

16. Intersection Delays Congestion Management System

17. Data Coverage

18. Data Coverage (Week 1-16) 171 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Model Calibration

19. Data Coverage (Week 1-16) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Stable links 304 438 807 1157 1460 1682 1856 2266 2517 2734 3097 3416 3564 3767 4094 4259 4377 Updated links 2305 3798 7278 9599 10602 11548 12172 13426 14374 15145 16006 17328 17748 18274 19150 19525 19772 0 5000 10000 15000 20000 25000 30000 #oflinks week

20. Data Intensity

21. Data Intensity

22. Data Intensity (YC)

23. Data Intensity (YC)

24. Congestion Pattern

25. Austin Weekend PH Congestion Travel Time/ Reliabiilty Study

26. Austin Weekday PH Congestion

27. Origin-Destination Pattern

28. Austin User OD Pattern OD Survey

29. Tucson User OD Pattern

30. YC OD Pattern and Intensity

31. Predicted Destinations

32. YC Weekday Home 8 AM Kids School Metropia U of AZ Emission Test Tucson Airport Destination Choice Modeling

33. YC Weekday UA 6 PM Home Metropia To Go Restaurant Kid’s Martial Art

34. YC Weekend Home 11 AM Brunch Dim Sum Restaurant Tucson Airport Kid’s Martial Art Metropia

35. Sensor Data

36. Accelerometer Data

37. Gyroscope Data

38. Conclusions • Metropia focuses on Multi-Modal Active Demand Management • Metropia data support various travel demand management purposes – Multi-modal opportunities – Congestion/performance measures – Activity patterns – Distraction/safety analysis

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