Transportation Planning and Traffic Estimation

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Transportation Planningand Traffic Estimation

• CE 453 Lecture 5

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Objectives (Primarily Review)

• 1. Identify highway system components
• 2. Define transportation planning
• 3. Recall the transportation planning process and
  its design purposes
• 4. 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.

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Highway System Components

• 1. Vehicle
• 2. Driver (and peds./bikes)
• 3. Roadway
• 4. Consider characteristics, capabilities, and
  interrelationships in design
• Start with demand (number of lanes?)

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Transportation Planning (one definition)

• Activities that
• 1. Collect information on performance
• 2. Identify existing and forecast future system
  performance levels
• 3. Identify solutions
•  
• Focus meet existing and forecast travel demand

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Where does planning fit in?
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Transportation Planning in Highway Design

• 1. identify deficiencies in system
• 2. identify and evaluate alternative alignment
  impacts on system
• 3. 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)

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Truck Traffic
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Four Steps of Conventional Transportation
Modeling

• 1. Trip Generation
• 2. Trip Distribution
• 3. Mode Split
• 4. Trip Assignment

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Study Area

• Clearly define the area under consideration
• Where does one entity end?
• May be defined by county boundaries,
  jurisdiction, town centers

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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

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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

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www.sanbag.ca.gov/ planning/subr_ctp_taz.html
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Four Steps of Conventional Transportation
Modeling

• Divide study area into study zones
• 4 steps (steps 1 and2)
• Trip Generation
• -- decision to travel for a specific purpose
  (eat lunch)
• Trip Distribution
• -- choice of destination (a particular
  restaurant? The nearest restaurant?)

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Four Steps of Conventional Transportation
Modeling

• 4 steps (steps 3 and 4)
• Mode Choice
• -- choice of travel mode (by bike)
• Network Assignment
• -- choice of route or path (University to
  Lincoln Way to US 69)

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Trip Generation
Model Step 1
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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

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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

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Trip Generation
ModelManager 2000
Caliper Corp.
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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

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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 regular (time of day)
• Recreational shopping - highly irregular

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Trip Generation

• Forecast of trips that are produced or
  attracted by each TAZ for a typical day
• Usually focus on Monday Friday
• Forecast function of other variables
• Attraction
• Number and types of retail facilities
• Number of employees
• Land use
• Production
• Car ownership
• Income
• Population (employment characteristics)

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Trip Purpose

• 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

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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

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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
• Wont 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

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Trip attractions
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Example Trip-rate analysis models
For 100 employees in a retail shopping center,
calculate the total number of trips Home-based
work (HBW) 100 employees x 1.7 trips/employee
170 Home-based Other (HBO) 100 employees x
10 trips/employee 1,000 Non-home-based (NHB)
100 employees x 5 trips/employee 500 Total
170 1000 500 1,670 daily trips
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Trip Distribution
Model Step 2
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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

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Gravity Model
Tij Pi AjFijKij
S AjFijKij
Tij 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
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Mode Choice
Model Step 3
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Mode Choice/Split

• In most situations, a traveler has a choice of
  modes
• Transit, walk, bike, carpool, motorcycle, drive
  alone
• Mode choice determines of trips between zones
  made by auto or other mode, usually transit

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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

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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

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Mode Choice Modeling

• A numerical method to describe how people choose
  among competing alternatives (dont confuse model
  and modal)
• Highly dependent on characteristics of region
• Model may be separated by trip purposes

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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 transformation
• U a0 a1X1 a2X2 .. arXr
• U utility derived from choice
• Xr attributes
• ar model parameters

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Logit Models

• Calculates the probability of selecting a
  particular mode
• p(K) ____eUk__
• ? eUk
• p probability of selecting mode k

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Logit Model Example 1
Utility 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)
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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
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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
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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

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Logit Model Example 2 (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 Ubike -0.07 0.35(35)
0.08(0) 0.005(0) -12.32
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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
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Traffic Assignment (Route Choice)
Model Step 4
Caliper Corp.
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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

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Person Trips vs. Vehicle Trips

• Trip generation total person trips
• Trip assignment vehicle (not person) trips
• Need to adjust person trips to reflect vehicle
  trips
• Understand units during trip generation phase

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Person Trips vs. Vehicle Trips Example

• Usually adjust by average auto occupancy
• Example
• 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

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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

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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

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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

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Minimum Tree

• Starts at zone and selects minimum path to each
  successive set of nodes
• Until it reaches destination node

Path from 1 to 5
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Minimum Tree

• Path from 1 to 5 first passes thru 4
• First select minimum path from 1 to 4
• Path 1-2-4 has impedance of 5
• Path 1-3-4 has impedance of 8
• Select 1-2-4

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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

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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

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Capacity Restraint

• Once vehicles begin selecting the minimum path
  between a set of nodes, volumes 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

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Sizing Facilities
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Sizing Facilities
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Sizing Facilities
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Homework Assignment
Travel characteristics between two zones
Using the methodology on slides 40 - 45, do a
sensitivity analysis of the impact of reducing
the transit wait time (t2) to zero by increments
of 2 minutes. Show what effect this has on mode
split. Now hold the transit wait time at 6
minutes and reduce the transit cost (c) to zero
by increments of 20 cents. Due on Wednesday