1. Transportation Planning, Transportation Demand Analysis Land Use-Transportation Interaction Transportation Planning Framework Transportation Demand Analysis

2. Land Use-Transportation Interaction Change in Land use Change in Trip generation Change in travel needs Change in transportation supply (added services & facilities) Accessibility Land Values Transportation serves land uses Transportation shapes land uses

3. Land Use-Transportation-Environment Interaction Land use Transport Environment.. Zones Urban Area Change in land use over time (i.e. change in residential units, commercial land use, industrial land use, retail land use, etc.

4. Land Use Patterns, Bid Rent Pressure for growth Demand for land Bid rent Land use pattern Location of activities CBD Bid rent $/sq.km Population Jobs Distance from CBD CBD Distance

5. Purpose of Land Use Models To explain /predict: Change in land use as a function of: - accessibility to employment - land value - percent of urban level available vacant land in a zone - public transit accessibility - quality of water & sewer services - etc..

6. Modelling Travel Decisions User Decisions 1. To travel (for a given trip purpose at a given time)? (Trip generation) 2. Destination? (Trip distribution) 3. Mode? (Modal Choice) 4. Route? (Assignment of trip to network) Modelling Approaches Four-stage urban transportation modelling system (UTMS) Unified approaches

7. Urban Transportation Demand Modelling: Four- Stage Modeling System Population & Employment Forecasts Trip Generation Trip Distribution Modal Split Trip Assignment Link & O-D Flows, Times, Costs, Etc. Transportation Network & Service Attributes

8. Four Stages of Urban Travel Demand Modelling I J Trip Generation OiOi Dj I J Trip Distribution T ij J I J Mode Split T ij,auto T ij, transit I J Traffic assignment Path of flow T ij,auto through the auto network

9. Multiple Trip Purposes HW HS NWS Generation Generation Generation Distribution Distribution Distribution Modal Split Modal Split Modal Split Road Assignment Transit Assignment Population Employment Trip Rates, etc. Transport Network Link & O-D volumes, times, costs, v/c ratios, etc.

10. The Traffic Prediction Process Trip generation P & A Transit network Road network Trip distribution Modal split Transit person trips Auto person trips Occupancy Transit vehicle trips Auto vehicle trips Freight & other vehicles Transit traffic assignment Road traffic assignment

11. Trip Generation Modelling Methods Linear regression method Cross-classification (category analysis) method/trip rate method _______________________________________________________ Trip generation Productions & Attractions Home-based & non-home based trips J I Zones

12. Trip Productions & Attractions P i = Trip productions of zone i = f(land use, socio- economic characteristics of zone i) A j = Attractions of zone j = f(land use, socio-economic characteristics of zone j) Regression Model Examples: (P.M. Peak Period Work Trips) P i = 0.4572 emp - 138 (R 2 = 0.87) A j = 0.1848 pop + 9 (R 2 = 0.90) Where emp is total employment pop is total population

13. Trip Productions & Attractions (Continued) Regression Model Examples: (P.M. Peak Period Non- work trips) P i = 0.1346pop+0.2897emp+0.0043GLA (R 2 = 0.76) A j = 0.0888emp+ 0.6204DWEL+0.0045GLA+221 (R 2 = 0.80) Where emp: is total employment pop: is total population GLA: shopping centre gross leasable area (ft 2 ) DWEL: Dwelling units

14. Trip Productions & Attractions (Continued) RegressionModel Development Data Required Zone P i * A j * pop emp GLA DWEL 1 …..… …. …. …. ….. 2 …..… …. …. …. …... _____________________________________________ * from O-D survey Data on other variables obtained from city data base

15. Trip Productions & Attractions (Continued) RegressionModel Development (Continued): Check on : - Partial correlation coefficient (r). Should be high between P (the dependent variable) & other variables (the independent variables) & Should be high between A (the dependent variable) & other variables (the independent variables). Should be low between pop, emp, GLA, DWEL (I.e. between independent variables) - Other statistical measures (“t” statistic for each independent variable)

16. Trip Productions & Attractions (Continued) RegressionModel Development (Continued): Check on : - R Multiple correlation coefficient (max. value of 1.0) - R 2 Coefficient of multiple determination (max. value of 1.0) - Standard Error of Estimate (for the dependent variable - e.g. for P i ) Its value can be checked against the estimated values of the dependent variable. Example: A range of P i values: 1,000-5,000; St. Error of 100 (very low!)

17. Trip Productions & Attractions (Continued) Trip Generation Rates (Cross Classification Approach) Trip Production: Step 1 Family Size Auto Ownership 0 1 2 or more 1 Trips/household/day 2 3 4 or more

18. Trip Productions & Attractions (Continued) Trip Generation Rates (Cross Classification Approach) Trip Production: Step 2 Trip productions for Zone i = (Trips/household/day) x (No. of households of that classification). Trips/household/day: is based on O-D survey No of households of a given classification: to be forecasted.

19. Trip Distribution Models Many models; most common is gravity model Zone i P i Zone j A j Zone j A j Zone j A j T ij

20. Trip Distribution Models Origin-Constrained Gravity Model T ij = P i [ A j F ij K ij Σ for j( A j F ij K ij ) ] Where T ij = Trips produced in zone I and attracted to zone j P i = Trips produced by zone i A j = Trips attracted to zone j F ij = Impedance of travel from zone I to zone j (a travel time factor -- expressing an area-wide effect of distance) K ij = A zone-to -zone adjustment factor

21. Trip Distribution Models Destination-Constrained Gravity Model T ij = A j [ P i F ij K ij Σ for i(P i F ij K ij ) ] Where T ij = Trips produced in zone I and attracted to zone j P i = Trips produced by zone i A j = Trips attracted to zone j F ij = Impedance of travel from zone I to zone j (a travel time factor -- expressing an area-wide effect of distance) K ij = A zone-to -zone adjustment factor

22. Gravity Model The F ij is usually a some function of the travel time or generalized cost of travel between zones F ij = C -α ij or F ij = t -α ij F ij t ij or C ij Where α is the calibration constant F ij = Travel time factor C ij = Generalized cost function t ij = Travel time K ij = A zone-to-zone adjustment factor (takes into account special characteristics of ij combinations Zone 1Zone 2 River Example

23. Gravity Model Note: P i = Σ for j T ij A j = Σ for i T ij PiPi AjAj

24. Gravity Model Example Using a gravity model with an impedance term of the form C -α, estimate the number of of trips from zone 1 to all other zones. α = 1.80. Other inputs are shown below. Zone Travel time to zone 1 (min) Productions Attractions 1 -- 5000 1000 2 10 2000 4000 3 20 4000 5000 4 15 3000 4000 __________________________________________________

25. Gravity Model Here, P i for i = zone 1 are to be distributed to other zones by using the gravity model. Assume all K = 1 For α = 1.80 and given travel times C ij,, and A j, we find: ______________________________________________ Zone A j C ij C -α A j C - α ij T ij 1 1000 -- -- -- -- 2 4000 10 1/63.1 63.40 2716* 3 5000 20 1/219.7 22.76 975 4 4000 15 1/130.91 30.56 1309 Sum 116.72 5000 * T from 1 to 2= 5000(63.40/116.72) = 2716

26. Gravity Model Following iteration 1 of finding T ij from every zone to all zones, check to see if A j s match the known values If yes, the trip distribution problem is solved. If not, the A j s have to be adjusted. The adjustment process is an iterative one (not covered here)