Predictive Analytics for Transportation in a High Dimensional Heterogeneous Data World
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This document discusses predictive analytics for transportation in a high dimensional heterogeneous data world. It covers several topics: 1) The transportation world is generating large amounts of high dimensional data from sources like cameras, GPS, cell phones, and probe vehicles that needs to be combined and analyzed. 2) Connected and automated vehicles will generate huge amounts of detailed data on vehicle movements, passenger activities and intentions that can be used to infer travel patterns and predict crashes. 3) Applying machine learning, advanced computation and domain knowledge is necessary to make sense of this non-standardized, high volume data.
Predictive Analytics for Transportation in a High Dimensional Heterogeneous Data World
- 1. Predictive Analytics for Transportation in a High Dimensional Heterogeneous Data World Dr. Chandra Bhat Center for Transportation Research The University of Texas at Austin Acknowledgments: D-STOP, Humboldt Award, Dr. Ram Pendyala, Dr. Kostas Goulias, all my graduate/undergraduate students
- 2. World of high dimensional heterogeneous data • Providing accurate traffic information is becoming an imperative • Cameras, GPS, cell phone tracking, and probe vehicles are used to supplement the information provided by conventional measurement systems. • Methodologies to combine and aggregate high dimensional heterogeneous data are needed
- 3. Connected/Automated Vehicles (CAVs) and big data • The car of the near future will be a part of a gigantic data-collection engine. • Vehicles have embedded computers, GPS receivers, short-range wireless network interfaces, in-car sensors, cameras, and internet. • Vehicles interact with Roadside wireless sensor networks, passenger’s wireless devices, and other cars.
- 4. Data required to keep a CAV 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 other road users, such as… jaywalking pedestrians, cars coming out of hidden driveways, a stop sign held up by a crossing guard, and cyclist making signs of riding intent.
- 5. What can be inferred from CAVs and smartphones data • Where people drive, • when people drive, • what route people take, • where people stop, • what people put in their car, • why, how and when people take decisions on the fly and change their activity plan route, and • detailed crashes data (speed, position, and intention at the moment of the accident).
- 6. Data Science • Not enough humans to process • Machine learning, visualization, and advanced computation techniques • Statistics, social sciences, and domain knowledge • High-dimensional heterogeneous data
- 7. DOMAIN KNOWLEDGE IN THE CONTEXT OF TRAVEL DEMAND MODELING
- 8. Exogenous Variable Vector X Model • Conceptual/Theoretical/ Methods/Tools and Techniques • Specification and Definition of Alternatives Activity-based Paradigm Trip-based Paradigm Outputs
- 9. Five Pillars of ABM Design • Based on sound behavioral theory/paradigm • Computationally feasible and tractable – Model estimation – Model implementation • Optimal use of available data (present and future) • ABM should be both an Activity-Based Model and an Agent-Based Model • Sensitive to policy issues and planning applications of interest
- 10. Behavioral Basis of ABM • Decision hierarchies and choice processes – A variety of behavioral decision structures possible – Virtually all models assume a sequential decision structure similar to traditional four-step models for computational convenience • Considerable evidence of simultaneity in behavioral choice mechanisms – Several choices made simultaneously as a lifestyle package
- 11. Behavioral Basis of ABM • Examples of simultaneous choice packages – Residential location, vehicle ownership, mode to pre- planned activities (e.g., work) – Activity type, activity duration, and activity timing (scheduling) • Behavioral heterogeneity – Differences in choice processes across market segments – Identify market segments both exogenously and endogenously (latent market segments)
- 12. Time-Space Interactions Home Work Activity 1 (Fixed) Activity 2 (Fixed) Time Urban Space 1 v Home Activity A Activity at Location A Activity 1 Activity 2
- 13. Agent Interactions I have a client meeting today; so I will take the car I have to pick up Jane from School and go shopping later; I need the car. My meeting is in the morning. I can pick up Jane from school today. And we can go shopping together in the evening. OK, that sounds good. I’ll go ahead and take light rail today to work. See you later. Hey, Mom and Dad, don’t forget; you have to drop me off at Johnny’s house in the evening today Don’t worry Jane; we’ll drop you off on the way to the store and pick you up later. Run along now, you’ll miss the bus.
- 14. Definition of an Activity • Disaggregate activity purpose definition – Challenge traditional notion of mandatory and discretionary activities/trips – Movie, ball game, and child’s tennis lesson or soccer game often have spatial and/or temporal fixity – Characterize activities and trips by level of spatial and temporal fixity/constraints (besides purpose) – Can be accomplished using concepts of time-space geography – Automated method to add attributes describing degrees of freedom according to set of spatial/temporal fixity criteria to activity records in data set
- 15. Central Role of Time Use • Notion of time is central to activity-based modeling – Explicit modeling of activity durations (daily activity time allocation and individual episode duration) – Treat time as “continuous” and not as “discrete choice” blocks • Activity engagement is the focus of attention – Travel patterns are inferred as an outcome of activity participation and time use decisions – Continuous treatment of time dimension allows explicit consideration of time constraints on human activities • Reconcile activity durations with network travel durations (feedback processes)
- 16. In Summary • ABM should… – Capture the central role of activities, time, and space in a continuum – Explicitly recognize constraints and interactions – Represent simultaneity in behavioral choice processes – Account for heterogeneity in behavioral decision hierarchies – Incorporate feedback processes to facilitate integration with land use and network models • SimAGENT does it all and more…
- 17. SIMAGENT (SIMULATOR OF ACTIVITIES, GREENHOUSE GAS EMISSIONS, ENERGY, NETWORKS, AND TRAVEL): AN OVERVIEW
- 18. SimAGENT Activity-travel environment characteristics (base year) Detailed individual-level socio- demographics (base year) Activity-travel simulator (CEMDAP) Individual activity-travel patterns Link volumes and speeds Dynamic Traffic Assignment (DTA) Socio-economics, land-use and transportation system characteristics simulator (CEMSELTS) Socio- demographics and activity-travel environment SimAGENT Aggregate socio- demographics (base year) Synthetic population generator (PopGen) Base Year Inputs Forecast Year Outputs and GHG Emissions Prediction
- 19. CEMDAP A COMPREHENSIVE ECONOMETRIC MICROSIMULATOR OF DAILY ACTIVITY-TRAVEL PATTERNS
- 20. CEMDAP – The Core ABM in SimAGENT Socio-Economic Data PopGen CEMSELTS CEMDAP • Simulates activity schedule and travel characteristics for each individual of the region • Core module of SimAGENT • 52 sub-models. • Developed by UT Austin
- 21. Features of CEMDAP (continued) • Changes in the activity-travel pattern of one individual in a household may bring about changes in activity-travel patterns of other household members • MDCEV approach facilitates modeling activity participation at a household level with joint activity participation incorporated in a simple fashion – MDCEV – Multiple Discrete Continuous Extreme Value econometric choice modeling method • Includes a model of household vehicle ownership by type and make/model, and primary driver assignment
- 22. INNOVATION: COMPREHENSIVE REPRESENTATION OF INTRA-HOUSEHOLD INTERACTIONS
- 23. Joint Activities and Household Interactions MDCEV Model • Most activity based models accommodate activity type choice as a series of models for each individual in the household • These approaches do not explicitly recognize that activity participation is a collective decision of household members • MDCEV approach – simple and relatively inexpensive for modeling activity participation at a household level • SimAGENT now features MDCEV modeling methodology to capture household-level activity participation
- 24. Joint Activities and Interactions MDCEV Model • Conventional discrete choice frameworks need to generate mutually exclusive alternatives results in an explosion in the number of alternatives • MDCEV allows us to tackle the problem by considering activity participation as a household decision • MDCEV offers substantial computational and behavioral advantages – Employ one model to generate activities – Accommodate substitution/complementarity in activity participation and household member dimensions
- 25. MDCEV Model P1 P2 P1 P2 None None None A1 None None A2 None None A1 A2 None None P1 P2 P1 P2 None None A1 A1 None A1 A2 None A1 A1 A2 None A1 P1 P2 P1 P2 None None A2 A1 None A2 A2 None A2 A1 A2 None A2 P1 P2 P1 P2 None None A1 A2 A1 None A1 A2 A2 None A1 A2 A1 A2 None A1 A2 P1 P2 P1 P2 None A1 None A1 A1 None A2 A1 None A1 A2 A1 None P1 P2 P1 P2 None A1 A1 A1 A1 A1 A2 A1 A1 A1 A2 A1 A1 P1 P2 P1 P2 None A1 A2 A1 A1 A2 A2 A1 A2 A1 A2 A1 A2 P1 P2 P1 P2 None A1 A1 A2 A1 A1 A1 A2 A2 A1 A1 A2 A1 A2 A1 A1 A2 P1 P2 P1 P2 None A2 None A1 A2 None A2 A2 None A1 A2 A2 None P1 P2 P1 P2 None A2 A1 A1 A2 A1 A2 A2 A1 A1 A2 A2 A1 P1 P2 P1 P2 None A2 A2 A1 A2 A2 A2 A2 A2 A1 A2 A2 A2 P1 P2 P1 P2 None A2 A1 A2 A1 A2 A1 A2 A2 A2 A1 A2 A1 A2 A2 A1 A2 P1 P2 P1 P2 None A1 A2 None A1 A1 A2 None A2 A1 A2 None A1 A2 A1 A2 None P1 P2 P1 P2 None A1 A2 A1 A1 A1 A2 A1 A2 A1 A2 A1 A1 A2 A1 A2 A1 P1 P2 P1 P2 None A1 A2 A2 A1 A1 A2 A2 A2 A1 A2 A2 A1 A2 A1 A2 A2 P1 P2 P1 P2 None A1 A2 A1 A2 A1 A1 A2 A1 A2 A2 A1 A2 A1 A2 A1 A2 A1 A2 A1 A2 Each box represents an alternative
- 26. MDCEV Model A1 P1 A1 P2 A1 P1P2 A2 P1 A2 P2 A2 P1P2 Each box represents an alternative None+ Alternatives - Total 7alternatives versus 64in traditional case
- 27. INNOVATION: HOUSEHOLD VEHICLE COMPOSITION AND DRIVER ASSIGNMENT
- 28. Vehicle Type Choice Simulation Component • Vehicle type choice determines vehicle fleet mix; critical to energy and emissions analysis • SimAGENT incorporates joint vehicle type choice and primary driver allocation model which jointly determines: – Multiple vehicle holdings – Body type (Sub-compact, Compact car, Mid-sized car, Large car, Small SUV, Mid-sized SUV, Large SUV, Van, and Pickup) – Age (Less than 2 years old, 2 to 3 years old, 4 to 5 years old, 6 to 9 years old, 10 to 12 years old, Older than 12 years) – Make/model and use (miles) – Primary driver of each vehicle
- 29. Vehicle Holdings and Use Vehicle Type/ Vintage 33 makes/models 21 makes/models 24 makes/models 25 makes/models 7 makes/models 10 makes/models 23 makes/models 19 makes/models 16 makes/models 12 makes/models 13 makes/models 13 makes/models 23 makes/models 15 makes/models 12 makes/models 23 makes/models 12 makes/models 5 makes/models 6 makes/models 15 makes/models Coupe Old Sedan Mid-size New Sedan Mid-size Old Sedan Compact Old Sedan Mini/Subcompact New Sedan Mini/Subcompact Old Coupe New Sedan Compact New Sedan Large Old Sedan Large New Minivan Old Pickup Truck New SUV New SUV Old Hatchback/Station Wagon New Hatchback/Station Wagon Old Pickup Truck Old Van New Van Old Minivan New Non-motorized vehicles
- 30. COMPUTATIONAL TECHNIQUES AND INTEGRATION POTENTIAL
- 31. Portable & Flexible Software Architecture ODBC Run-Time Data Objects Household Person Zone Data LOS Data Pattern Tour Stop Output Files Simulation Coordinator Modeling Modules … . . . Decision to Work Model Work Start/End Time model Input Database Application Driver Data Queries Zone to Zone Data Coordinator
- 32. Ability to Integrate and Enhance • Successfully interfaced with – Multi-period static assignment (the current four-step approach of SCAG) – TRANSIMS and MATSim (second by second assignment of people and vehicles on networks), and • Continuous-time evolutionary framework facilitates real-time dynamic integration of ABM and DTA models
- 33. • SimAGENT is successfully implemented in the LA region • Existing SimAGENT code (CEMDAP, PopGen, CEMSELTS) is open source • Being implemented currently in the New York region; selected based on behavioral realism and ability to accommodate CAVs • Elements of system being used for long distance travel modeling by CDOT; UT-Austin working with CDOT
- 35. Why joint modeling of data is important? • Borrows information on other outcomes • Able to answer intrinsically multivariate questions, such as the effect of a covariate on a multidimensional outcome • Obviates the need for multiple tests and facilitates global tests, offering superior testing power and better control of Type 1 error rates • If some endogenous outcomes are used to explain other endogenous outcomes, and if the outcomes are not modeled jointly, the result can be inconsistent estimation of the effects of one endogenous outcome on another. • Problem? Mixed data, high-dimensional data
- 36. A Way-Out • The new Spatial Generalized Heterogeneous Data Model (GHDM); Bhat (2015) • Correlation across various dimensions (of the dependent variables) are captured using latent constructs. • Accommodates all possible types of data (dependent variables). • Dimension of integration is independent of number of latent constructs. • Bhat’s Maximum Approximate Composite Marginal Likelihood (MACML) estimation approach is used for estimation of GHDM.
- 37. Conceptual diagram of structural relationships in the empirical model