Introduction to Large Scale Modeling Systems

1. Introduction toLarge Scale Modeling Systems CinziaCirillo, Ph.D., Associate Professor Department of Civil and Environmental Engineering University of Maryland Sept 3rd, 2013 Heart 2013 – Summer School Stockholm

2. About myself MS Civil Engineering Universita “Federico II” – Naples (ITALY) PhD Transportation Engineering Politecnicodi Torino – Torino (ITALY) Stagiare and Consultant Hague Consulting Group The Hague (NL) and Cambridge (UK) Post Doc – Marie Curie fellowship (EU) Applied MATH - University of Namur (BELGIUM) Assistant and now Associate Professor (with tenure) Department of Civil and Environmental Engineering University of Maryland (USA) This year on sabbatical leave at TU-Delft (NL)

3. My students at UMD…(Pratt, Michael, JM, Nayel, Renting, Me, Yangwen)

4. Table of Contents Terminology Four-step models Tour-based models Activity-based models Integrated land use and transportation models

5. Terminology of Network Representation Zones (Centroid, Centroid Connectors) Nodes Links

7. Four-Step Trip-Based Travel Demand Model

8. What are the Four Steps? Trip Generation (Ti) – Number of trips produced in and attracted to zone “I” [Number of trips that will be generated] Trip Distribution (Tij) – Number of trips produced in zone “i” and attracted to zone “j” [Where the trips might go] Mode Split (Tijm) – Number of trips produced in zone “i” and attracted to zone “j” traveling by mode “m” [Which mode of transportation do travelers choose – automobile, rail, bus, bicycle, etc.] Traffic Assignment (Tijmr) – Number of trips produced in zone “i” and attracted to zone “j” traveling by mode “m” over route “r” [Predicts the path the trips will take]

9. Trip Generation Model: Terminology Trip Trip Ends Tours Home-Based Trip Non-Home-Based Trip Trip Production Trip Attraction Trip Generation Trip Purpose

10. Methods for Trip Generation Modeling • Growth Factor Analysis • Often used for external trip generation modeling • Cross Classification Methods • Most widely used in current practices • Regression Models • Zone-level regression • Household level regression • Combining Cross-Classification and Regression Methods • Trip Rate Analysis • Often used for special trip generators • ITE Trip Generation Manual • Matching Trip Generations and Attractions

11. Cross Classification Models

12. Zone-Level Regression Analysis: Example Pi = 22.4 + 1.87HHi + 0.22Ai Production Aj = 57.2 + 0.87Ej + 0.15Rj Attraction Pi: Total number of HBW trips produced from zone i Aj: Total number of HBS trips attracted to zone j HHi: Total number of households in zone i Ai: Total number of automobiles in zone i Ej: Total employment in zone j Rj: Total retail space in zone j

13. Matching Productions and Attractions Pi: number of trips produced from zone i Aj: number of trips attracted to zone j P’i adjusted number of trips produced from zone i A’j: adjusted #trips attracted to zone j i, j: index of zones

14. Methods for Trip Distribution Modeling • Growth Factor Analysis • Use it only if there are no other better feasible method • Synthetic Model (e.g. Gravity Model) • Most widely used in practice • Discrete Choice Model • More flexible model structure and behaviorally rich • Gravity model can be shown as a special case of discrete choice model • Statistical/Optimization Methods for Estimating OD Trip Tables from Traffic Counts • Intervening Opportunity Model • Etc.

15. Basic Gravity Model • Idea comes from Newton’s Law of Gravitation • Where: • Tij: Number of trips from to Pi: Productions at i • Aj: Attractions at j • Fij: A function of travel time, distance, and/or cost • e.g. Fij = 1/(Cij)2 , or Fij = exp(b*Cij) • Kij: Socioeconomic factor (specified by the modeler)

16. Model Choice • Decision maker • Alternatives • Attributes of alternatives • Decision rule • Conjunctive rules, e.g. Satisfaction • Disjunctive rules, e.g. A set of if-then rules • Lexicographical rules, e.g. Dominance • Compensatory rules, e.g. Utility maximization • Combination of rules, e.g. Elimination by aspects • Other Heuristic Decision Rules • Etc

17. Utility Maximization Theory U1n = U(t1n, c1n) = b1t1n + b2c1n U2n = U(t2n, c2n) = b1t2n + b2c2n Individual n chooses alternative 1 if U1n>U2n When there are multiple alternatives, individual n chooses alternative 1 if U1n > Uin of all other alternatives.

18. Random Utility Maximization Theory U1n = U(t1n, c1n) = b1t1n + b2c1n + e1n U2n = U(t2n, c2n) = b1t2n + b2c2n + e2n Let V1n = b1t1n + b2c1n V2n = b1t2n + b2c2n U1n = V1n + e1n U2n = V2n + e2n V: Systematic utility e: Random utility

19. Discrete Choice Models Binary: Prob (1) = P(U1 > U2) = P (V1n + e1n > V2n + e2n) = P (e2n – e1n < V1n – V2n ) Multinomial: Prob (i) = P(Ui > Max Uj,j≠i) If e is assumed to be normally distributed, a Probit choice model is obtained. If e is assumed to be logistically distributed, a Logit choice model is obtained.

20. Traffic Assignment / Equilibrium Supply: Travel cost = f (Travel demand) Demand: Travel demand = f (Travel cost) • An equilibrium is achieved when both supply and demand equations are simultaneously satisfied • Wardrop’s Two Traffic Equilibrium Principles • First Principle: User Equilibrium (UE) Each user acts to minimize his/her own travel cost. At UE, all used routes between each OD pair have equal travel costs, while all unused routes have higher travel costs. • Second Principle: System Optimal (SO) Each user acts to minimize the total travel cost in the system. At SO, the lowest total system travel cost is achieved.

21. Classification of Traffic Assignment

22. Tour-Based and Activity-Based Models

23. Activity-Based Models Recognize… Travel is a derived demand Spatial, temporal, transportation and interpersonal interdependencies constrain activity/travel behavior Household and other social factors/structures influence travel and activity behavior Activity-based approaches aim at predicting which activities are conducted where, when, for how long, with whom, the transport mode involved and ideally also the implied route decisions.

24. Typical Specification of ABM: Type 1 Population synthesis and updating Mobility-lifestyle choices (auto, home location etc.) Day-level activity pattern generation (List of Activities with or without sequencing) Scheduling of activities Activity or tour-based mode and destination choices

25. Typical Specification of ABM: Type 2 Population synthesis and updating Mobility-lifestyle choices (auto, home location etc.) Day-level activity pattern generation (Primary and secondary tours and their sequencing) Tour-level primary activity destination, mode, and scheduling choices Stop-level secondary activity destination, mode, and scheduling choices

26. ABM History HATS (Jones 1979) CARLA (Jones et al. 1983) STARCHILD (Reckeret al. 1986a, 1986b) SCHEDULER (Garling et al. 1989) SMASH (Ettemaet al. 1993) SAMS and AMOS (Kitamura et al. 1993, RDC Inc. 1995, Kitamura et al. 1996) MIDAS (Kitamura and Goulias 1989, Goulias and Kitamura 1996) SMART (Stopher et al. 1996) GISICAS (Kwan 1997) PCATS (Kitamura and Fujii 1998) ALBATROSS (Arentze and Timmermans 2000) PETRA (Fosgerau 2001) SIMAP (Kulkarni and McNally 2001) TASHA (Miller and Roorda 2003) CEMDAP ( Bhatet al. 2004) FAMOS (Pendyala et al. 2004) TRANSIMS (Los Alamos National Laboratory 2005)

27. ABM in Practice • U.S. • Atlanta, GA • Boston, MA • Columbus, OH • Dallas, TX • Denver, CO • New York, NY • Portland, OR • Sacramento, CA • San Francisco, CA • Southeast Florida • Statewide in Oregon • International • Netherlands • Swiss • Germany • Chile • Etc

28. ABM Benefits Predicts travel behavior along a continuous time axis and scheduling adjustments; Assesses the impact of sophisticated travel demand management measures; Can be easily modified to evaluate policy scenarios with or without new SP surveys (e.g. extended transit service, dynamic pricing, daycare facilities at work, flexible work hours); Produces results with desired level of spatial and temporal accuracy using synthetic population sample; More comprehensively evaluates the impact of transportation projects and policies on the entire activity-travel pattern not trip travel pattern, just on a trip.

29. ABM Data Needs • Demand Side • Longitudinal and geographic information on household or individual time use (e.g. type of activities, travel, activity locations, activity duration, scheduling); • Socio-demographic information (e.g. household composition, age, gender, job, income, housing); • Auto-ownership and other household mobility and lifestyle choices; • Activity-travel pattern changes/shifts over time and in response to transportation system changes; • Household characteristics with regard to telecommunication.

30. ABM Data Needs • Supply Side • Transportation networks coded to the activity-stop level; • Level of service of the transportation network by time of day (this could be endogenous with DTA); • Daily, day-of-the-week, and seasonal activity time windows (e.g. store open hours, periods during which specific activities can be pursued); • Spatial and non-spatial inventory of activity locations, land use, and economic data.

31. ALBATROSS (Arentze and Timmermans 2000, 2004) • Albatross: A learning based transportation oriented simulation system • The model predicts which activities are conducted when, where, for how long, with whom and also transport mode • Decision tree is proposed as a formalism to model the heuristic choice • Considers various constraints on behavior: • Situational constraints: can’t be in two places at the same time • Institutional constraints: such as opening hours • Household constraints: such as bringing children to school • Spatial constraints: e.g. particular activities cannot be performed at particular locations • Time constraints: activities require some minimum duration • Spatial temporal: constraints an Spatial-individual cannot be at a particular location at the right time to conduct a particular activity

32. ALBATROSS (Arentze and Timmermans 2000, 2004) • Albatross assumes that choice behavior is based on rules that are formed and continuously adapted through learning while the individual is interacting with the environment (reinforcement learning) or communicating with others (social learning). • Options for rule-based behavior representation: • Decision trees (used in Albatross) • Classification rules • Bayesian network • Etc.

33. Albatross Model Flowchart Each oval represents a decision tree

35. CEMDAP (Bhat et al. 2003) CEMDAP: Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns” A system of econometric models that represent the activity-travel decision-making behavior of individuals. Input: Various land-use, socio-demographic, activity system, and transportation level-of-service attributes Output: Complete daily activity-travel patterns for each individual in the household.

36. Daily Activity-Travel Pattern: Worker

37. Daily Activity-Travel Pattern: Non-Worker

38. CEMDAP Modeling Framework

39. Activity Generation-Allocation Module

40. Activity Generation-Allocation Models

41. Pattern/Tour/Stop-Level Scheduling Modules

42. Pattern/Tour/Stop-Level Scheduling Models

43. Activity-Based Model Applications Effects of development patterns on travel behavior Sensitivity to price and behavioral changes Effects of transportation system and system condition Need for improved validity and reliability Ability to evaluate policy initiatives Better analysis of freight movement Ability to show environmental effects Modeling low-share alternatives Better ability to evaluate effects on specific subgroups Reflect non-system policy changes (TDM, ITS)

44. Transportation Eras and Urban Growth Patterns

45. Integrated land use and transportation models

46. Population Density vs. Distance to City Center

47. Population Density vs. %Transit Mode Share

48. Transportation and Land Use

49. A More Detailed Theoretical Framework

50. Land Use Model Components Input: Total population and total employment by type in the study area Output: Population and employment by type in each spatial analysis unit Typical Spatial Analysis Unit: TAZ, Census tract, Parcel, Block, Grid cell Demand Modules: Household location choice, Employment location choice, and/or Household/employment relocation choice Supply Modules: Housing development, business real estate development Balancing Supply and Demand: No balancing, Price and equilibrium, Disequilibrium