2015 D-STOP Symposium session by D-STOP Director Chandra Bhat. Watch the presentation at http://youtu.be/y2kYLM8GdbI?t=2m2s Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/

1. A New Generalized Mixed Data Model with
Applications to Transport Analysis
Chandra Bhat
Research partially supported by
• The U.S. DOT through the Data-Supported Transportation Operations and Planning (D-STOP) Tier 1 Center
• Alexander von Humboldt Foundation, Germany

2. Introduction and Motivation

3. • Growing interest in joint modeling of data with mixed types of
dependent variables in several fields
• Clinical biology: effectiveness of depression medication in
reducing occurrence, frequency, and intensity of depression
• Health: occurrence, frequency, and intensity of specific health
problems, as well as ordinal quality of life
• Transportation: Translating voluminous amounts of data into
information in near-real time or for planning purposes to take
proactive action

4. Data Science
• Not enough humans to process
• Machine learning, visualization, and advanced computation
techniques
• Statistics, social sciences, and domain knowledge

5. Why joint modeling is important?
• Borrows information on other outcomes
• Able to answer intrinsically multivariate questions, such as the
effect of a covariate on a multidimensional outcome
• Is able to integrate data to increase accuracy as well as
precision of information extraction.
• Helps causal effects to be distinguished from associative
effects.

6. • The new Generalized Heterogeneous Data Model (GHDM).
• Correlation across various dimensions are captured using latent
constructs.
• Accommodates all types of data (independent and dependent
variables).
• Bhat (2014) on Composite Marginal Likelihood (MACML)
• High dimensional independent variable setting (operations)
• High dimensional dependent variable setting (planning)

7. Connected vehicles technology provides high
dimensional heterogeneous data
 Vehicles have embedded
 Computers and GPS receivers
 short-range wireless network interfaces
 in-car sensors, cameras, and internet
 Vehicles interact with
 Roadside wireless sensor networks
 other cars
 Other road-users.
 Localized versus Central Data Processing and
Analysis
 Methodologies to translate data into
information

8. COLLABORATE. INNOVATE. EDUCATE.
Data required to keep vehicle safely on the road
 Highly detailed maps information:
 Shape and elevation of roadways,
 lane lines,
 intersections,
 crosswalks,
 speed limits, and
 traffic signals.
 Position, speed and intentions of other vehicles and pedestrians.
 Position, speed and intentions of unexpected obstacles, such as,
 jaywalking pedestrians,
 cars lunching out of hidden driveways,
 a stop sign held up by a crossing guard, and
 cyclist making gestures.

9. A simple example (operations)
• Assume two vehicles and an isolated non-signalized intersection
• Assume all measurements captured precisely

10. Position of Vehicle 1
(binary/continuous)
Speed of Vehicle 2
(continuous)
Position of Vehicle 2
(binary/continuous)
Speed of Vehicle 1
(continuous)
Direction
and angle of
progress of
Veh. 1
Direction and
angle of
progress of
Veh. 2
Vehicle 1
type/Age
(nominal, binary)
Vehicle 2
type/age
(nominal,
binary)
Weather
conditions
Convergence
rate index
Vehicle
separability index
Crash Occurrence
(yes/no)

11. • Position/trajectories of other vehicles
• Human in the loop
• Probability model (multi-index decision variable modeling)
• Projection: Principal components of a covariance matrix
constructed from the sub-samples of crashes and no crashes
• Estimation: Parametric or non-parametric choice modeling

12. COLLABORATE. INNOVATE. EDUCATE.
Lane-departure detection
Mechanism to detect when another vehicle begins to move
out of its lane.
Minimize accidents by addressing the main cause of collisions,
driving errors, and distractions.

13. COLLABORATE. INNOVATE. EDUCATE.
Automatic braking
Sensor to detect an imminent collision with another vehicle,
person or obstacle.
 Car actives the brakes itself.

14. COLLABORATE. INNOVATE. EDUCATE.
Self-parking
Car parks itself.
Drivers do not need to worry about finding a parking spot.

15. A simple example (planning)
• Consider residential choice and activity-travel behavior today
• Expansion in focus: Proactive, demand reducing, short-term,
sustainability-oriented
• Emphasis on land-use and transportation
policies to shape travel behavior
• Over the past decade
• Increasing attention on the causal vs.
associative nature of the relationship
• Residential self-selection (or sorting) effects
• Growing body of literature on this topic

16. Latent Variables
• Green lifestyle propensity
• Luxury lifestyle propensity

17. Commute
Mode choice
(nominal)
Housing Type
(nominal)
Density of
Neighborhood (nominal)
Housing Cost
(grouped)
Average Commute
Distance (grouped)
Household
Vehicle
Type/Size
Number of
Bathrooms
(count)
Number of
Bedrooms
(count)
Unit-Square
Footage
(grouped)
Lot Size
(grouped)
Green Lifestyle
propensity
Luxury Lifestyle
propensity
Framework for Housing Choices and Activity Travel Behavior

18. Impact of Connected/Autonomous Transportation
• Safety enhancement
• Virtual elimination of driver error – factor in 80-90% of crashes
• No drowsy, impaired, stressed, or aggressive drivers
• Reduced incidents and network disruptions
• Offsetting behavior on part of driver
• Capacity enhancement
• Platooning reduces headways and improves flow at transitions
• Vehicle positioning (lateral control) allows reduced lane widths and utilization of
shoulders; accurate mapping critical
• Optimized route choice
• Energy and environmental benefits
• Increased fuel efficiency and reduced pollutant emissions
• Clean fuel vehicles/Car-sharing

19. Impacts on Land-Use Patterns
 Live and work farther away
 Use travel time productively
 Access more desirable and higher paying job
 Attend better school/college
 Visit destinations farther away
 Access more desirable destinations for various activities
 Reduced impact of distances and time on activity participation
 Influence on developers
 Sprawled cities?
 Impacts on community/regional planning and urban design

20. Impacts on Household Vehicle Fleet
 Potential to redefine vehicle ownership
 No longer own personal vehicles; move toward car sharing enterprise where rental vehicles
come to traveler
 More efficient vehicle ownership and sharing scheme may reduce the need for additional
infrastructure
 Reduced demand for parking
 Desire to work and be productive in vehicle
 More use of personal vehicle for long distance travel
 Purchase large multi-purpose vehicle with amenities to work and play in vehicle

22. Impacts on Mode Choice
Automated vehicles combine the advantages of public transportation with that of
traditional private vehicles
 Catching up on news
 Texting friends
 Reading novels
 Flexibility
 Comfort
 Convenience
What will happen to public transportation?
Also Automated vehicles may result in lesser walking and bicycling shares
Time less of a consideration
So, will Cost be the main policy tool
to influence behavior?

23. Impacts on Mode Choice
 Traditional transit captive market segments now able to use auto (e.g., elderly, disabled)
 Reduced reliance/usage of public transit?
 However, autonomous vehicles may present an opportunity for public transit and car
sharing
 Lower cost of operation (driverless) and can cut out low volume routes
 More personalized and reliable service - smaller vehicles providing demand-
responsive transit service
 No parking needed – kiss-and-ride; no vehicles “sitting” around
 20-80% of urban land area can be reclaimed
 Chaining may not discourage transit use

24. COLLABORATE. INNOVATE. EDUCATE.
Individual attitudes regarding to autonomous
vehicles
 There are several individual lifestyle, personality, and attitudinal factors that may impact the decision
of owning/renting a connected/autonomous vehicle and use:
 Green lifestyle
 Multitasking inclination
 Tech-savvy people or geeks
 Stressed drivers
 For example, individuals who have a green lifestyle
 may search for locations that offer high accessibility to green areas,
 may own fewer autos,
 and may rent/ride autonomous vehicles (as public transportation or shared service) often.

25. The Bottom Line
 Data to information – an important data science
 Uncertainty, Uncertainty, Uncertainty
 More uncertainty implies more need for analysis/planning
 But analysis/planning must recognize the uncertainty (need a
change in current thinking and philosophy)