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A New Generalized Mixed Data Model with Applications to Transport Analysis


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2015 D-STOP Symposium session by D-STOP Director Chandra Bhat. Watch the presentation at

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A New Generalized Mixed Data Model with Applications to Transport Analysis

  1. 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. 2. Introduction and Motivation
  3. 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. 4. Data Science • Not enough humans to process • Machine learning, visualization, and advanced computation techniques • Statistics, social sciences, and domain knowledge
  5. 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. 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. 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. 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. 9. A simple example (operations) • Assume two vehicles and an isolated non-signalized intersection • Assume all measurements captured precisely
  10. 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. 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. 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. 13. COLLABORATE. INNOVATE. EDUCATE. Automatic braking Sensor to detect an imminent collision with another vehicle, person or obstacle.  Car actives the brakes itself.
  14. 14. COLLABORATE. INNOVATE. EDUCATE. Self-parking Car parks itself. Drivers do not need to worry about finding a parking spot.
  15. 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. 16. Latent Variables • Green lifestyle propensity • Luxury lifestyle propensity
  17. 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. 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. 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. 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
  21. 21. 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?
  22. 22. 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
  23. 23. 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.
  24. 24. 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)