Transportation Planningand Traffic Estimation
CE 453 Lecture 5
Objectives
1. Identify highway system components 2. Define transportation planning3. Recall the transportation planning process
and its design purposes4. Identify the four steps of transportation
demand modeling and describe modeling basics.
5. Explain how transportation planning and modeling process results are used in highway design.
Highway System Components
1. Vehicle 2. Driver (and peds./bikes)3. Roadway4. Consider characteristics, capabilities,
and interrelationships in design
Start with demand needs (number of lanes?)
Transportation Planning (one definition) Activities that:1. Collect information on performance2. Identify existing and forecast future
system performance levels3. Identify solutions Focus: meet existing and forecast travel
demand
Where does planning fit in?
Transportation Planning in Highway Design
1. identify deficiencies in system2. identify and evaluate alternative
alignment impacts on system3. predict volumes for alternatives
• in urban areas … model? … smaller cities may not need (few options)
• in rural areas … use statewide model if available … else: see lab 3-type approach (note Iowa is developing a statewide model)
Truck Traffic
Planning at 3 levels State … STIP Statewide
Transportation Improvement Program (list of projects)
Regional … MPO Metropolitan Planning Organization (>50,000 pop.), 25 year long range plan and TIP (states now also do LRP)
Local …project identification and prioritization
Four Steps of Conventional Transportation Modeling
1. Trip Generation 2. Trip Distribution 3. Mode Split4. Trip Assignment
Study Area
Clearly define the area under consideration• Where does one entity end?
• May be defined by county boundaries, jurisdiction, town centers
Study Area
May be regional Metropolitan area – Des Moines including
suburbs, Ankeny, etc.• Overall impact to major street/highway network
Local – e.g., impact of trips to new Ames mall•Impact on local street/highway system
•Impact on intersections•Need for turning lane or new signal – can a
model do this level of detail?
Study Area Links and nodes Simple representation of the geometry of
the transportation systems (usually major roads or transportation routes)
Links: sections of roadway (or railway) Nodes: intersection of 2+ links Centroids: center of TAZs Centroid connectors: centroid to roadway
network where trips load onto the network
Travel Analysis Zones (TAZs)
Homogenous urban activities (generate same types of trips)
•Residential
•Commercial
•Industrial May be as small as one city block or as large
as 10 sq. miles Natural boundaries --- major roads, rivers,
airport boundaries Sized so only 10-15% of trips are intrazonal
www.sanbag.ca.gov/ planning/subr_ctp_taz.html
Four Steps of Conventional Transportation Modeling
Divide study area into study zones 4 steps
• Trip Generation • -- decision to travel for a specific purpose (eat lunch)
• Trip Distribution• -- choice of destination (a particular restaurant? The
nearest restaurant?)
• Mode Choice• -- choice of travel mode (by bike)
• Network Assignment• -- choice of route or path (Elwood to Lincoln to US 69)
Trip Generation
Model Step #1…
Trip Generation Calculate number of trips generated
in each zone•500 Households each making 2 morning
trips to work (avg. trip ends ~ 10/day!)
•Worker leaving job for lunch Calculate number of trips attracted
to each zone•Industrial center attracting 500 workers
•McDonalds attracting 200 lunch trips
Trip Generation Number of trips that begin from or
end in each TAZ Trips for a “typical” day Trips are produced or attracted # of trips is a function of:
• TAZs land use activities
•Socioeconomic characteristics of TAZ population
Trip Generation
ModelManager 2000™ Caliper Corp.
Trip Generation 3 variables related to the factors that influence trip
production and attraction (measurable variables) • Density of land use affects production & attraction
•Number of dwellings, employees, etc. per unit of land
•Higher density usually = more trips
• Social and socioeconomic characters of users influence production•Average family income
•Education
•Car ownership
• Location•Traffic congestion
•Environmental conditions
Trip Generation
Trip purpose•Zonal trip making estimated separately by
trip purpose•School trips
•Work trips
•Shopping trips
•Recreational trips
•Travel behavior depends on trip purpose •School & work trips are regular (time of day)
•Recreational trips highly irregular
Trip Generation Forecast # of trips that produced or attracted by
each TAZ for a “typical” day Usually focuses on Monday - Friday # of trips is forecast as a function of other variables Attraction
• Number and types of retail facilities
• Number of employees
• Land use Production
• Car ownership
• Income
• Population (employment characteristics)
Trip Purpose Trips are estimated by purpose (categories)
• Work
• School
• Shopping
• Social or recreational
• Others (medical) Travel behavior of trip-makers depends somewhat on trip purpose
• Work trips
• regular
• Often during peak periods
• Usually same origin/destination
• School trips
• Regular
• Same origin/destination
• Shopping recreational
• Highly variable by origin and destination, number, and time of day
Household Based Trips based on “households” rather than individual Individual too complex Theory assumes households with similar characteristics
have similar trip making characteristics However
• Concept of what constitutes a “household” (i.e. 2-parent family, kids, hamster) has changed dramatically
•Domestic partnerships
•Extended family arrangements
•Single parents
•Singles
•roommates
Trip Generation Analysis 3 techniques
• Cross-classification
• Covered in 355
• Multiple regression analysis
• Mathematical equation that describes trips as a function of another variable
• Similar in theory to trip rate
• Won’t go into
• Trip-rate analysis models
• Average trip-production or trip-attraction rates for specific types of producers and attractors
• More suited to trip attractions
Trip attractions
Example: Trip-rate analysis models
For 100 employees in a retail shopping center, calculate the total number of tripsHome-based work (HBW) =
100 employees x 1.7 trips/employee = 170 Home-based Other (HBO) =
100 employees x 10 trips/employee = 1,000Non-home-based (NHB) =
100 employees x 5 trips/employee = 500
Total = 170 + 1000 + 500 = 1,670 daily trips
Trip Distribution
Model Step #2…
Trip Distribution Predicts where trips go from each TAZ Determines trips between pairs of zones
•Tij: trips from TAZ i going to TAZ j Function of attractiveness of TAZ j
•Size of TAZ j
•Distance to TAZ j
•If 2 malls are similar (in the same trip purpose), travelers will tend to go to closest
Different methods but gravity model is most popular
Trip Distribution
Determines trips between pairs of zones
•Tij: trips from TAZ i going to TAZ j Function of attractiveness of TAZ j
•Size of TAZ j
•Distance to TAZ j
•If 2 malls are similar, travelers will tend to go to closest
Different methods but gravity model is most popular
Trip Distribution
Maricopa CountyCaliper Corp.
Gravity ModelTij = Pi AjFijKij Σ AjFijKij
Qij = total trips from i to j
Pi = total number of trips produced in zone i, from trip generation
Aj = number of trips attracted to zone j, from trip generation
Fij = impedance (usually inverse of travel time), calculated
Kij = socioeconomic adjustment factor for pair ij
Mode Choice
Model Step #3…
Mode Choice In most situations, a traveler has a
choice of modes•Transit, walk, bike, carpool, motorcycle,
drive alone Mode choice/mode split determines
# of trips between zones made by auto or other mode, usually transit
39
Characteristics Influencing Mode Choice Availability of parking Income Availability of transit Auto ownership Type of trip
• Work trip more likely transit
• Special trip – trip to airport or baseball stadium served by transit
• Shopping, recreational trips by auto Stage in life
• Old and young are more likely to be transit dependent
40
Characteristics Influencing Mode Choice Cost
• Parking costs, gas prices, maintenance?
• Transit fare Safety Time
• Transit usually more time consuming (not in NYC or DC …)
Image• In some areas perception is that only poor ride
transit
• In others (NY) everyone rides transit
Mode Choice Modeling A numerical method to describe
how people choose among competing alternatives (don’t confuse model and modal)
Highly dependent on characteristics of region
Model may be separated by trip purposes
Utility and Disutility Functions Utility function: measures satisfaction derived
from choices Disutility function: represents generalized costs
of each choice Usually expressed as the linear weighted sum of
the independent variables of their transformationU = a0 + a1X1 + a2X2 + ….. + arXr
U: utility derived from choiceXr: attributes
ar: model parameters
Logit Models Calculates the probability of
selecting a particular mode
p(K) = ____eUk__ eUk
p: probability of selecting mode k
Logit Model Example 1Utility functions for auto and transit
U = ak– 0.35t1 – 0.08t2 – 0.005c
ak = mode specific variable
t1 = total travel time (minutes)
t2 = waiting time (minutes)
c = cost (cents)
Do you agree with the relative
magnitude of the time parameters? Is
there double counting/colinearity
?
Logit Model Example 1 (cont)
Travel characteristics between two zones
Uauto = -0.46 – 0.35(20) – 0.08(8) – 0.005(320) = -9.70
Utransit = -0.07 – 0.35(30) – 0.08(6) – 0.005(100) = -11.55
Variable Auto Transitak -0.46 -0.07
t 1 20 30
t 2 8 6
c 320 100
Do you agree with the relative
magnitude of the mode specific
parameters? How much effect does
cost have?
Logit Model Example 1 (cont)
Uauto = -9.70
Utransit = -11.55
Logit Model:
p(auto) = ___eUa __ = _____e-9.70 ____ = 0.86 eUa + eUt e-9.70 + e-11.55
p(transit) = ___eUt __ = _____e-11.55 ____ = 0.14 eUa + eUt e-9.70 + e-11.55
Logit Model Example 2
The city decides to spend money to create and improve bike trails so that biking becomes a viable option, what percent of the trips will be by bike?Assume:• A bike trip is similar to a transit trip• A bike trip takes 5 minutes more than a transit trip but with no waiting time• After the initial purchase of the bike, the trip is “free”
Travel characteristics between two zones
Uauto = -0.46 – 0.35(20) – 0.08(8) – 0.005(320) = -9.70
Utransit = -0.07 – 0.35(30) – 0.08(6) – 0.005(100) = -11.55
Ubike = -0.07 – 0.35(35) – 0.08(0) – 0.005(0) = -12.32
Variable Auto Transit Bikeak -0.46 -0.07 -0.07
t 1 20 30 35
t 2 8 6 0
c 320 100 0
Logit Model Example 2 (cont)
Uauto = -9.70, Utransit = -11.55, Ubike = -12.32
Logit Model:
p(auto) = _____eUa ____ = _______e-9.70 ______ = 0.81 eUa + eUt +eUb e-9.70 + e-11.55 + e-12.32
p(transit) = _____eUt__ __ = ______e-11.55 ______ = 0.13 eUa + eUt +eUb e-9.70 + e-11.55 + e-12.32
p(bike) = _____eUt__ __ = ________e-11.55 ______ = 0.06 eUa + eUt +eUb e-9.70 + e-11.55 + e-12.32
Notice that auto lost share even
though its “utility” stayed the same
Logit Model Example 2 (cont)
Traffic Assignment (Route Choice)
Caliper Corp.
Model Step #4…
Trip Assignment
Trip makers choice of path between origin and destination
Path: streets selected Transit: usually set by route Results in estimate of traffic
volumes on each roadway in the network
Person Trips vs. Vehicle Trips Trip generation step calculated total
person trips Trip assignment deals with volume not
person trips Need to adjust person trips to reflect
vehicle trips Understand units during trip generation
phase
Person Trips vs. Vehicle Trips ExampleUsually adjust by average auto occupancyExample:If: average auto occupancy = 1.2 number of person trips from zone 1 = 550
So:Vehicle trips = 550 person trips/1.2 persons per
vehicle = 458.33 vehicle trips
Time of Day Patterns Trip generation usually based on
24-hour period LOS calculations usually based on
hourly time period Hour, particularly peak, is often of
more interest than daily
Time of Day Patterns Common time periods
• Morning peak
• Afternoon peak
• Off-peak Calculation of trips by time of day
• Use of factors (e.g., morning peak may be 11% of daily traffic)
• Estimate trip generation by hour
Minimum Path Theory: users will select the quickest
route between any origin and destination
Several route choice models (all based on some “minimum” path)•All or nothing
•Multipath
•Capacity restraint
Minimum Tree Starts at zone and selects minimum path
to each successive set of nodes Until it reaches destination node
1
2
3
45
(3)
(4)
(2)
(4)
(7)
Path from 1 to 5
Minimum Tree
1
2
3
45
(3)
(4)
(2)
(4)
(7)
1. Path from 1 to 5 first passes thru 4
2. First select minimum path from 1 to 4
3. Path 1-2-4 has impedance of 5
4. Path 1-3-4 has impedance of 8
5. Select 1-2-4
See CE451/551 notes for more on
shortest path computations –
several methods are available
All or Nothing Allocates all volume between zones
to minimum path based on free-flow link impedances
Does not update as the network loads
Becomes unreliable as volumes and travel time increases
Multi-Path Assumes that all traffic will not use shortest
path Assumes that traffic will allocate itself to
alternative paths between a pair of nodes based on costs
Uses some method to allocate percentage of trips based on cost• Utility functions (logit)
• Or some other relationship based on cost As cost increases, probability that the route will
be chosen decreases
Capacity Restraint Once vehicles begin selecting the
minimum path between a set of nodes, volume increase and so do travel times
Original minimum paths may no longer be the minimum path
Capacity restraint assigns traffic iteratively, updating impedance at each step
Sizing Facilities
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