urban transport modeling (based on these two sources)

Urban Transport Modeling(based on these two sources)

A Transportation Modeling Primer

May, 1995

Edward A. BeimbornCenter for Urban Transportation StudiesUniversity of Wisconsin-Milwaukee

http://www.uwm.edu/Dept/CUTS/primer.htm

Modelling Transport: Theory and PracticeFrank McCabe, Dublin Transportation Office

http://www.icetact.tcd.ie/icetact/news/transport/mccabe.html

What’s in the Black Box?

Beimborn, Edward A. 1995. A Transportation Modeling Primer.Center for UrbanTransportation Studies, University of Wisconsin-Milwaukee: http://www.uwm.edu/Dept/CUTS/primer.htm (last accessed 11/14/04).



Travel Demand Modeling

First developed in the late 1950s

Helps make decision on future development of (urban) transport systems

Forecasts travel patterns 15-25 years ahead

Model is predictive, but used prescriptively

A clear understanding of the modeling process and assumptions is essential to understanding transportation plans.

Limitations of Urban Transport Modeling

Only considers factors and alternatives explicitly included in the equations.If models are not sensitive to certain factors, they will not show any effect of them.This could lead to a conclusion that the factors are ineffective.E.g., bicycle or pedestrianIt is therefore critical to consider the assumptions before decisions are made.

Define problem

Define goals and criteria

Collect data

Forecasting (modeling)

Develop alternatives

Evaluate

Finalize an implementation plan

How do models fit in the transport planning process?

How is Travel Modeled?1. What will our community look like in

the future? How many people? (population forecasts) What will they do? (economic forecasts) Where will they do it? (land use pattern)

2. What are the travel patterns in the future? How many trips? (trip generation) Where will the trips go? (trip distribution) What modes will they use? (mode split) What routes will they take? (traffic assignment) What will be the effects of this travel? (impact analysis)



Population Forecasts

Birth rates

Death rates

Migration rates

Ages

Often use forecasts from other agencies

Economic Forecasts

Employment levelsForecasted in conjunction with population Basic employmentEconomic multipliers used to estimate nonbasic employmentOften use forecasts from other agencies

Land Use Forecasts

Allocate population and economic growth

1. Establish land use goals and land use rates

2. Allocate to specific locations—models can be used to predict nonbasic and residential from basic

Land Use Forecasting:Limitations

No feedback with transportation plans

Current development is fixed – considers only vacant land

Mixed-use benefits not considered

Existing Land Use of Metro Manila (1996)

Study Area

The area within which trip patterns will be significantly affected by the implementation of transport proposals.

Aggregate Modeling

McCabe, Frank. Modelling Transport: Theory and Practice. Dublin Transportation Office: http://www.icetact.tcd.ie/icetact/news/transport/mccabe.html (last accessed 11/14/04).


Zoning System of Greater Metro Manila

Using EMME for the UTP of Iloilo City: An Example

Node

Link

CPU

SM City

from/to Guimaras

from/to South

from/to Airport

Trip Generation Example: Iloilo City

Trip Generation

Done separately for each trip purpose

Two main methods:Multiple regressionCross-classification (a.k.a. category

analysis)

Trip Generation byMultiple Regression, e.g.:

Home-to-work trips (R = 0.99)Oi = -43.6 + 0.097 Population + .773 Employed

Residents - 351 Number of Households + .504 Num Cars Trips to shopping (R = 0.95)Oi = -17.9 + 1.19 Area Res. Land + .266 Number of

Cars Light Commercial Vehicle Trips:Oi = 75.9 + .367Number HHs + .267 Total Empl -.339

Office Empl - .0188 Industrial Empl

Trip Generation byCross-Classification

Advantages Disadvantages

Multiple Regression

1. Familiar methodology2. Statistical significance3. Estimates effect holding other variables constant

1. Assumes linearity2. Aggregate data (ecological fallacy)3. Coefficients not stable over time, or after improvements4. Multicollinearity

Category Analysis

1. Individual data good for predicting individual behavior. 2. Interaction effects 3. Doesn’t assume linearity

1. Needs individual data2. Hard to resurvey individuals for more variables3. Best with naturally discrete variables.

Common Limitation of Trip Generation Models

Independent decisions

Limited trip purposes

Limited independent variables

Trip-chaining is ignored

Lacks feedback with trip distribution, modal split, traffic assignment


O-D Matrix (12 zones, Iloilo City)

Trips Attracted (from other zones ) to (Zone 12 – CPU and adjacent areas)

Common Limitations of Trip Distribution Models

Constant trip lengths (in minutes) into the futureUse of car travel times in gravity model denominatorIgnores socio-cultural-economic factors for O-D pairsLacking feedback with trip generation, modal split, network congestion

Common Limitations of Modal Split Models

Mode choice affected only by time and cost characteristics

Omitted factors (amenities, crime, safety, security) have no effect

No consideration of ease of walking and comfort of waiting for transit

Modal Split Ignores Pedestrian Friendliness


Traffic Assignmentalso known as

Network assignment

Route assignment

Network loading

Trip assignment

Wardrop Equilibrium"Under equilibrium conditions, traffic arranges itself in congested networks in such a way that no individual trip maker can reduce his path costs by switching routes".

All used routes between any Origin / Destination pair have equal and minimum costs, while all unused routes have greater or equal costs."

Algorithms

A mechanistic iterative procedure with a:

Starting rule

Iteration rule

Stopping rule

Wardrop Equilibrium Algorithm

Starting rule: set all arc flows to 0, compute all arc costs from cost-flow curve.

Iteration (for each O-D pair):Use shortest path algorithm to find the cheapest route for

each O-D pairShift a fraction of the flow from the old route(s) to the

new cheapest route.Recalculate the new arc flows and arc costs.

Check for convergence:

high-cost route – low cost route < (i.e., tolerance)

Common Limitations of Traffic Assignment Models

Intersection delay is ignoredTravel only on simplified networkIntrazonal travel ignored (affects pollution estimates)Capacities are simplified as a function of number of lanes and type of roadModel ignores time of day. Peak-hour adjustment factor is critical.Peak spreading is not considered.

Final Outputs from Traffic Assignment

Amount of travel, congestion, speed per link

Total transport cost, time, VMT

By applying other coefficients:AccidentsAir pollution

Improving UTMSBetter data

Biking and walking

Auto occupancy

Time of day factors

More trip purposes

Transit-friendly and bike/walk-friendly design

Land use feedback

Intersection delays

Other Critiques to UTMS

Ignores non-transport impacts (energy, neighborhoods, etc.)

Sequential 4-step model doesn’t always match transport decision-making process.

Ecological fallacy (of aggregate data)

Disaggregate Methods

Parcel-based microsimulation

Household Activity Travel Simulation (HATS)

urban transport modeling (based on these two sources)

Documents