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Cinzia Cirillo Facultes Universitaires Notre Dame de la Paix – FUNDP

La modélisation de la demande de transport: méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires Notre Dame de la Paix – FUNDP Transportation Research Group – GRT Namur BELGIUM ccir@math.fundp.ac.be. The activity based approach.

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Cinzia Cirillo Facultes Universitaires Notre Dame de la Paix – FUNDP

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  1. La modélisation de la demande de transport:méthodes avancées appliquées aux chaînes d’activités. Cinzia Cirillo Facultes Universitaires Notre Dame de la Paix – FUNDP Transportation Research Group – GRT Namur BELGIUM ccir@math.fundp.ac.be FUNDP Namur 19 Avril 2004

  2. The activity based approach The analysis of transport demand in Belgium (MOBEL) FUNDP Namur 19 Avril 2004

  3. Modeling framework • The scheduling model system for workers Pattern, tour and stop models. • The Mode choice model Value of Time (VOT) study. • The destination choice model Combining temporal and spatial aspects in the mobility analysis. • Advanced models to measure travel behavior Mixed Logit & AMLET. FUNDP Namur 19 Avril 2004

  4. An econometric simulator for daily activity travel patterns Aggregate Demographics (forecast year) Model parameters Medium-term Choice Simulator Synthetic Population Generator Individual & Household Demographics (forecast year) Individual Medium-term Decision (forecast year) Transportation system Characteristics (forecast year) Individual Activity-travel Patterns (forecast year) Activity-travel simulator Activity- Environment Characteristics (forecast year) Model parameters FUNDP Namur 19 Avril 2004

  5. Daily pattern simulation for each individual of the household FUNDP Namur 19 Avril 2004

  6. The scheduling model system FUNDP Namur 19 Avril 2004

  7. Scheduling model system for workersPattern model system alternatives FUNDP Namur 19 Avril 2004

  8. FUNDP Namur 19 Avril 2004

  9. Scheduling model system for workersTour model system alternatives FUNDP Namur 19 Avril 2004

  10. FUNDP Namur 19 Avril 2004

  11. Scheduling model system for workersStop model system alternatives FUNDP Namur 19 Avril 2004

  12. FUNDP Namur 19 Avril 2004

  13. Mode choice model: Mobidrive data FUNDP Namur 19 Avril 2004

  14. Mode choice model: variables FUNDP Namur 19 Avril 2004

  15. Goodness of fit FUNDP Namur 19 Avril 2004

  16. VOT: Value Of Time study Confidence interval (Armstrong et al., 2001) ( ) ( ) ( ) 2 2 2 2 2 2 2 r - - - - æ ö æ ö q - r q t t t t t t t t t t t t t c t c t c t c t c ç ÷ ç ÷ ( ) = ± V ç ÷ ç ÷ S , I q q 2 2 2 2 t t - - è ø è ø t t t t c t c t c c FUNDP Namur 19 Avril 2004

  17. VOT by socio-economic characteristics FUNDP Namur 19 Avril 2004

  18. VOT per tour tipe VOT distribution for non-workers per tour type VOT distribution for workers per tour type FUNDP Namur 19 Avril 2004

  19. Travel time FUNDP Namur 19 Avril 2004

  20. Travel cost FUNDP Namur 19 Avril 2004

  21. VOT FUNDP Namur 19 Avril 2004

  22. TT: BPA FUNDP Namur 19 Avril 2004

  23. TT: Principal pattern NW FUNDP Namur 19 Avril 2004

  24. TT: Evening pattern NW FUNDP Namur 19 Avril 2004

  25. TT: commute pattern W FUNDP Namur 19 Avril 2004

  26. TT: Evening pattern W FUNDP Namur 19 Avril 2004

  27. Destination choice model. • Discrete choice methods to model out-of-home and out-of-work activity location choice • Alternative size: Statistical Sector • Sampling of alternatives : Action Space * (*) Dijst and Vidakovic (1997) FUNDP Namur 19 Avril 2004

  28. Data • Data sources: MOBEL, the Belgian National Mobility Survey (1999)  1484 geocoded daily activity chains achieved in the Flemish Region  1950 out-of-home and out-of-work activities FUNDP Namur 19 Avril 2004

  29. Alternatives generation process Action space theory FUNDP Namur 19 Avril 2004

  30. Action Space Equation (*) where, • T : time-budget; • V : travel speed; • L : distance between bases (home-work); • τ: travel time ratio ; • x, y : coordinates of points belonging to the action-space. (*) Dijst and Vidakovic (1997) FUNDP Namur 19 Avril 2004

  31. Worker’s daily activity chain • Morning commute • Midday tour • Evening commute • After tour • Non worker’s daily activity chain • Before tour • Main tour (main activity) • After tour FUNDP Namur 19 Avril 2004

  32. Worker’s action spaces • Non worker’s action spaces S H W S S W S W H S S H 1 stop 2 stops 1 stop 2 stops ss H mas H s H s s 2 stops main activity (ma) + 2 stops 2 stops FUNDP Namur 19 Avril 2004

  33. For each tour and commute, building an action space • For each observed stop, creating a set of max 19 alternatives: nine randomly selected destinations + actual destination chosen by the individual + the other destination chosen in the activity chain  all in action space FUNDP Namur 19 Avril 2004

  34. Group I: • Home • Work (workers) • Principal activity stop (non-workers)  • Group II • Main stop in the morning tour • Main stop in the evening tour • Group III • 1 Secondary stop in the morning tour • 1 Secondary stop in the evening tour2 Secondary stops in principal tour FUNDP Namur 19 Avril 2004

  35. Before main tour action space FUNDP Namur 19 Avril 2004

  36. Morning commute action space FUNDP Namur 19 Avril 2004

  37. Variables description FUNDP Namur 19 Avril 2004

  38.  LOS variables Impedance variables: • in-vehicle travel time; • cost; Impedance = IVTT +COST (VOT = value of time = 7 Euro/hour)* * Recent model developped for the Walloon Region FUNDP Namur 19 Avril 2004

  39. Land use variables • Statistical sector area (total geographical area of the sector [m²]) ; • densely-built housing ; • built-up housing ; • housing and other developments ; • industrial / commercial / port area ; • agriculture and meadowland (agriculture and open space, meadowland and orchards). • green / nature area (broad-leaved, coniferous and/or mixed forests, municipal parks, heath land and moors, dunes and beaches, water); • infrastructure (highways, district roads, airport and/or railway infrastructure and so on); FUNDP Namur 19 Avril 2004

  40. Activity variables • shopping variable; • financial variable (banks); • hotel / restaurant / café; • cinemas; • sport activities; • cultural, recreational and leisure activities (museum, library, school of music, zoo, nature reserve, theatres, casino and so on); • car retail; • personal service (beauty center and so on). FUNDP Namur 19 Avril 2004

  41. Random utility • Utility function • yz : land use zone-specific variables, • λ: coefficients fixed across all zones; • : size measure of alternative z, • Mzk : kth size variable for zone z, • βk : corresponding coefficient, • μ: positive scale parameter; • xizj: exogenous accessibility variables for individual i in zone z , • γ: vector of random parameters; • εiz: error termsindependently and identically Gumbel distributed. FUNDP Namur 19 Avril 2004

  42. Model results FUNDP Namur 19 Avril 2004

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  44. FUNDP Namur 19 Avril 2004

  45. Estimation of mixed logit • Probability choice where, : normally distributed random vector; θ : means and standard deviation of ; Liz: logit formula. FUNDP Namur 19 Avril 2004

  46. Maximizing the log-likelihood function • Monte-Carlo Simulation where R is the number of random draws δr, taken from the distribution function of δ. FUNDP Namur 19 Avril 2004

  47. Computing θ as the solution of the simulated log-likelihood problem: FUNDP Namur 19 Avril 2004

  48. Software to estimate mixed logit • Gauss (special routine written by K. Train) • Biogeme (M. Bierlaire) • Alogit (A. Daly) • LIMDEP (W. Greene) • AMLET (F. Bastin) FUNDP Namur 19 Avril 2004

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