Transportation Planning, Transportation Demand Analysis Land Use-Transportation Interaction...
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Transcript of Transportation Planning, Transportation Demand Analysis Land Use-Transportation Interaction...
Transportation Planning, Transportation Demand Analysis
• Land Use-Transportation Interaction
• Transportation Planning Framework
• Transportation Demand Analysis
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
Land Use-Transportation-Environment Interaction
Land use
Transport
Environment
..
ZonesUrban Area
Change in land use over time (i.e. change in residential units, commercial land use, industrial land use, retail land use, etc.
Land Use Patterns, Bid RentPressure for growth
Demand for land Bid rent
Land use pattern Location of
activities
CBD
Bid rent $/sq.km
PopulationJobs
Distance from CBD
CBD Distance
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..
Modelling Travel DecisionsUser 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
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.
TransportationNetwork & Service
Attributes
Four Stages of Urban Travel Demand Modelling
I JTrip GenerationOi
Dj
I JTrip Distribution Tij
J
I JMode Split
Tij,auto
Tij, transit
I
J
Traffic assignment
Path of flow Tij,auto through the auto network
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.
The Traffic Prediction Process
Trip generation P & A
Transit network Road network
Trip distribution
Modal split
Transit person trips Auto person trips
Occupancy Occupancy
Transit vehicle trips Auto vehicle trips
Freight & other vehicles
Transit traffic assignment Road traffic assignment
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
Trip Productions & Attractions
Pi = Trip productions of zone i = f(land use, socio-economic characteristics of zone i)
Aj = Attractions of zone j = f(land use, socio-economic characteristics of zone j)
Regression Model Examples: (P.M. Peak Period Work Trips)
Pi = 0.4572 emp - 138 (R2 = 0.87)
Aj = 0.1848 pop + 9 (R2 = 0.90)
Where emp is total employment
pop is total population
Trip Productions & Attractions (Continued)
Regression Model Examples: (P.M. Peak Period Non-work trips)
Pi = 0.1346pop+0.2897emp+0.0043GLA (R2 = 0.76)
Aj = 0.0888emp+ 0.6204DWEL+0.0045GLA+221 (R2 = 0.80)
Where emp: is total employment
pop: is total population
GLA: shopping centre gross leasable area (ft2)
DWEL: Dwelling units
Trip Productions & Attractions (Continued)
RegressionModel Development
Data Required
Zone Pi*
Aj*
pop emp GLA DWEL
1 …. .… …. …. …. …..
2 …. .… …. …. …. …..
.
_____________________________________________
* from O-D survey
Data on other variables obtained from city data base
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)
Trip Productions & Attractions (Continued)RegressionModel Development (Continued):
Check on :
- R Multiple correlation coefficient (max. value of 1.0)
- R2 Coefficient of multiple determination (max. value of 1.0)
- Standard Error of Estimate (for the dependent variable - e.g. for Pi)
Its value can be checked against the estimated values of the dependent variable.
Example: A range of Pi values: 1,000-5,000; St. Error of 100 (very low!)
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
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.
Trip Distribution Models• Many models; most common is gravity model
Zone i
Pi
Zone jAj
Zone j Aj
Zone j Aj
Tij
Trip Distribution Models
Origin-Constrained Gravity Model
Tij = Pi [ Aj Fij Kij
Σ for j(Aj Fij Kij)
]
Where
Tij = Trips produced in zone I and attracted to zone j
Pi = Trips produced by zone i
Aj = Trips attracted to zone j
Fij = Impedance of travel from zone I to zone j (a travel time factor -- expressing an area-wide effect of distance)
Kij = A zone-to -zone adjustment factor
Trip Distribution Models
Destination-Constrained Gravity Model
Tij = Aj [ Pi Fij Kij
Σ for i(Pi Fij Kij)
]
Where
Tij = Trips produced in zone I and attracted to zone j
Pi = Trips produced by zone i
Aj = Trips attracted to zone j
Fij = Impedance of travel from zone I to zone j (a travel time factor -- expressing an area-wide effect of distance)
Kij = A zone-to -zone adjustment factor
Gravity ModelThe Fij is usually a some function of the travel time or
generalized cost of travel between zones
Fij = C-α ij or Fij = t-α ij Fij
tij or Cij
Where α is the calibration constant
Fij = Travel time factor
C ij = Generalized cost function
t ij = Travel time
Kij = A zone-to-zone adjustment factor (takes into account special characteristics of ij combinations
Zone 1 Zone 2
River Example
Gravity ModelNote:
Pi = Σ for j Tij
Aj = Σ for i Tij
Pi
Aj
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
__________________________________________________
Gravity ModelHere, Pi 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 Cij,, and Aj, we find:
______________________________________________
Zone Aj Cij C-α AjC- α ij Tij
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
Gravity Model
• Following iteration 1 of finding Tij from every zone to all zones, check to see if Ajs match the known values
• If yes, the trip distribution problem is solved.
• If not, the Ajs have to be adjusted.
• The adjustment process is an iterative one (not covered here)