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History of Urban Development by historyman

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									 History of Urban Development
• Early cities supported by links to water
  (harbors & rivers) and later rail & highways
• City core + moderate density residential;
  radial highway and transit links (streetcars,
  rail, & later bus)
• Add low-density suburbs; urban & suburban
  freeways (1960s & 70s)
• Add suburban and exurban centers; arterial
  & freeway expansion, decline of transit
  Trans-based Services & Costs
• Many transportation-related services in
  urban areas + public transit + private auto
  use
• Costs of the services tend to increase with
  city size, but opportunities for jobs, etc. also
  increase: Optimal city size?
• Externalities – pollution and congestion
     Urban Transportation Modes
•   Private auto – “driver only”, carpool
•   Van-pool; bus-pool
•   Taxi
•   Buses
•   Light Rail – at-grade/some grade separation
•   Rail Rapid Transit
•   Other: Commuter Rail, Ferry
     Urban Transportation Issues
•   Traffic congestion
•   Peaking (inefficient use of facilities)
•   Infrastructure financing
•   Specialized transit (elderly & disabled)
•   Environment
•   Safety
•   Technological Innovation
Transportation Problem Solutions
• Pricing – roadway tolls, parking costs, transit
  fares, fuel taxes, regional road pricing
• Management & policy – ramp-metering, land
  use controls, flexible work hours, bus/ped
  malls, parking/delivery controls, improved
  public transit, high-occupancy veh incentives
• Advanced technology
       Advanced Technology
• Informatics (Intelligent Transportation
  Systems – Driver Information Services)
  – Variable Message Signs (VMS), route
    guidance, in-vehicle navigation
• Safety – collision warning/avoidance, night
  vision
• Capacity – headway control (liability
  issues), bus rapid transit
  Advanced Technology – con’t
• Pricing – Electronic Toll Collection (ETC),
  congestion pricing (using ETC) – equity
  issue, but large system benefits
• Environment – emissions controls,
  alternative fuels (fuel cells), hybrid vehicles
• Telecommunications – Telecommuting,
  teleshopping, teleconferencing
     ITS – User Services Groups
•   Travel and Transportation Management
•   Public Transit Operations
•   Electronic Payment
•   Commercial Vehicle Operations (CVO)
•   Emergency Management
•   Vehicle Control and Safety Systems
Urban Trans Planning Process
•   Base Data: trans sys, activity sys, demand
•   Problem Definition        UTP Models
•   Goals & Objectives
•   Alternative Solutions
•   Evaluate Solutions        UTP Models
•   Select Best Plan
•   Implement
•   Monitor
    Urban Trans Planning Models
• Activity System (land use)
    – Population and Employment by zone
•   Trip Generation: How many trips?
•   Trip Distribution: Which destination?
•   Modal Choice:       Which mode?
•   Assignment:          Which route?
•   Output: Level of Service by mode
TRANSPORTATION PLANNING


     i                      j

         Trip Generation
Pi                         Aj
                                            k
                           Qik
                                      Qij
                       i                            j

                                Trip Distribution
TRANSPORTATION PLANNING
      Qijm
         CAR
 i       BUS        j
       SUBWAY

     Modal Choice
                             Qijmp


          i                              j


                    Network Assignment
                    (Trip Assignment)
    Trip Generation Models

• Objective: forecast number of person trips that
  begin or end in a travel analysis zone for a
  typical day in a target year

 Future Land        Calibrated      Trip Ends
 Use & Socio-       Trip            QI and QJ
 Econ Activity      Generation          or
 by Zone            Model           PI and AJ
• Prior to application, model must be calibrated
  using travel data for base year
• Model output - trip ends by zone
• Trip ends are estimated by trip purpose:
   – 1) work
   – 2) school
   – 3) shopping
   – 4) social/recreation
   – 5) personal business/medical
     Time-of-day distribution of
          trips by purpose

1) “purpose to” definition - going to work, to
  school, to home
2) peaking
        AM with work and school trip
        PM with “to home” and all other trips
3) peak spreading in PM
  Zonal vs household-based models

1) zonal: direct estimate of total trips in
   travel analysis year based on zonal
   attributes

2) household: combine contributions of
   groups of similar households to obtain
   total trips for a zone
Productions and Attractions
1) trips are assumed to be produced at the “home”
  zone and attracted to the “non-home” zone, e.g.
  Home-based Work

2) trip origin & destination is based on the
  direction of a trip (from “origin” to “dest.”)

     Home                       Work

     Home                       Work
 2 Productions              2 Attractions
Productions and Attractions
3) generally will have some productions, PI,
  and attractions, AJ, in each zone

4) Trip with neither “end” at “home” is a
  Non-Home Based (Non-HB) trip

 Home         Work        Shop         Home
 Zone 1      Zone 2      Zone 3        Zone 1

      HB-Work     Non-HB          HB-Shop
  Regression Models
• Yi = a0 + a1*X1i + a2*X2i + … + aN*XNi
  where:
  Yi - trip ends for the ith household or zone
  XNi - Nth attribute of the ith household or zone

1) Model calibrated using travel survey data for the
   base year.
2) Apply model to estimate future “trip ends” based
   on projections of the values of the independent
   variables, XNi.
• Must be linearly related to the dependent
  variable

• Must be highly correlated with the
  dependent variable

• Must not be highly correlated between
  themselves

• Must lend themselves to relatively easy
  projection
 Typical Independent Variables

1) Income
2) Auto ownership
3) Household size
4) Land use density
5) Number of employees (attractions)
6) Sq. feet of floor area (attractions)
The correlation matrix contains the simple
correlation coefficients between pairs of
variables computed by Eq C.3.4 using base-year
data. Which explanatory variables X should be
included in a linear multiple regression?

            Y      X1     X2     X3     X4
     Y      1.00   0.32   0.92   0.95   0.62
     X1            1.00   0.25   0.19   0.03
     X2                   1.00   0.99   0.29
     X3                          1.00   0.33
     X4                                 1.00
  Trip Rate Analysis
• Trip rates are expressed with respect to the
  intensity of use at specific traffic generators

• PI = Σ Rk * Xk         where:

• PI - total trip productions in zone I
• Rk - trip prod. per unit of the kth land use type
• Xk - number of units of the kth land use type

- ITE TG Manual example - Office Park
     Cross-classification Models
• Represent extension   of simple trip rate
models
• generally developed at household level
• stratify the “trip ends” by the independent
variables (typically two) and estimate the
mean trip rate within each cell
• See Table 8.2.4 - Total HB-NonWork Trip
Rates
  Cross-classification Models-con’t

• Estimate total hsld trip productions in zone I
  as:

PI = ∑ nqr * Rqr where:

nqr - no. households of category qr

Rqr - daily trip rate for category qr
   Cross-classification Models-con’t

Estimate the trip rate in the qr th cell of the cross-
classification matrix by:
Rqr = [∑ Pqrk ]/nk where:

Pqrk - no. trip productions by kth household in
  the qr th category

nk - no. households in the category qr
            Trip Distribution
• Objective: estimate future trip volumes, QIJ,
  between zones I and J
• Assumptions:
  – All attraction zones, J, in competition w/others
  – Trips to J are proportional to “attractions” AJ
  – Trips I to J are inversely proportional to WIJ,
    the impedance (time, distance, cost) from I to J
            Gravity Model
    QIJ = k * PI*AJ/WIJc     where:
  PI – number of trip productions in zone I
 AJ – number of trip attractions in zone J or
 relative attractiveness index
 WIJ – impedance (time, distance, or cost)
 between zones I and J
k, c – calibration parameters
     Gravity Model Derivation
• Trip production balance equation is:
      PI = ∑X QIX
Thus, all trip productions in zone I are
  “distributed” to attraction zones
• Substitute the trip interchange expression
  into the trip-production balance equation so:
        PI = k * PI * [∑X AX / WIXc ]
 Gravity Model Derivation-con’t
• Solving for k: k = 1/ [∑X AX / WIXc ] so
  QIJ = PI {(AJ / WIJc )/ [∑X AX / WIXc ]}
Note: the term in brackets is not affected by
  multiplying all the A by a constant. So
  “zonal attractiveness” can be measured by
  employment or GFA where appropriate. Also
QIJ = PI {AJ FIJ/ [∑X AX FIX]} with FIJ =1 / WIJc
where FIJ is the friction factor or travel time
  factor
 Gravity Model Derivation-con’t
• Trip attractions to zone J from GM output:
           AJ* = ∑X QXJ
In general AJ* ≠ AJ , thus, must “balance attr.”
• Add inter-zonal “soc-economic”factors, KIJ
  as selective adjustments to the Gravity Model
  QIJ = PI {AJ FIJ KIJ /[∑X AX FIX KIX]}
   QIJ = PI * pIJ where:
   pIJ - proportion of trips attracted to zone J
GM Application – Example 8.4

    Zone   Productions Attractiveness
     1        1500           0
     2         0             3
     3        2600           2
     4         0             5
Inter-zonal Impedances (minutes)
        J   Z1   Z2   Z3   Z4
   I
       Z1   5    10   15   20

       Z2   10   5    10   15

       Z3   15   10   5    10

       Z4   20   15   10   5
    GM Calculations- I=1;P1=1500
J      AJ   F1J   AJ F1J p1J   Q1J
1      0    .0400 0      0       0
2      3    .0100 .0300 .584    875
3      2    .0044 .0089 .173    260
4      5    .0025 .0125 .243    365
Sum               .0514 1.00   1500
    GM Calculations- I=3;P1=2600
J      A w3J F3J   AJ F3J p3J    Q3J
1      0 15
2      3 10
3      2   5
4      5 10
Sum                       1.00   2600
     GM Output – “Trip Table”
I        J   Z1   Z2    Z3     Z4
    Z1       0    875   260    365   1500
    Z2       0     0     0      0     0
    Z3       0    488   1300   812   2600
     Z4      0      0     0     0     0
    Sum      0     1363 1560 1177 4100
             0    33.2% 38.0% 28.7% 100%
    AJ       0      3     2     5    10
      Trip Frequency Distribution
wIJ     5     10     15    20    Total   Avg.
        1300 875     260   365
              488
              812
GMSum 1300 2175      260   365   4100    9.6
OD      870   2390   290   550   4100    10.6
      Gravity Model Calibration
1) Compare average trip length – GM vs OD
   Survey trip frequency distributions
2) Reasonable fit?
    1) Yes – model calibrated
    2) No – Adjust “friction factor” curve F vs w
    Fnewk = Fpriork * [OD tripsk/GM tripsk]
    Where k – kth interval of the impedance w
•    Compute new trip table and go to 1)
Trip Generation+GM Example 8.5
• Home-based Shopping Trips – 3 residential
  zones (1, 2, 3) & 2 commercial zones (4,5)
• Trip Generation-Trip Production Model
  – HB Shop Productions – cross-classification
  – Matrix of trip rates (trip productions per person)
    for persons per household vs # of autos stratified
    by income level (level I or level II)
  – Apply household data for residential zones to
    estimate zonal trip productions
• Trip Generation – Trip Attraction Model
  – “Attractiveness” model based on simple
    weighting of :
  – 1) shopping floor space (acres) – Xa
  – 2) parking area (acres) – Xb
  – For a zone the attractiveness, A, is:
            A = 5* Xa + 3* Xb
• Gravity Model – Friction Factor & K values
     F = 1/wIJ
    Gravity Model Input Values
I          Z4 - CBD            Z5 - Center      PI
J
      w      F      K     w       F      K

Z1    20     .05    1.0   5       .20    .09   670

Z2    10     .10    .09   10      .10    1.2   850

Z3    5      .20    1.0   20      .05    1.0   1640

AJ           22.5                 19.0
   GM Calculation – Zone 1
J AJ*F1J*K1J              p1J     Q1J


4 22.5*0.05*1.0 = 1.125   0.248   166


5 19.0*0.20*0.9 = 3.420   0.752   504


   Sums           4.545   1.00    670
     GM Results – Trip Table
I J 4 (CBD)       5 (Center)   PI
1    166          504          670
2    400          450          850
3    1354         286          1640
AJ* 1920 (61%)    1240 (39%) 3160 (100%)
AJ   22.5 (54%)   19.0 (46%)   41.5 (100%)
   Discussion of TG/TD Model
• Able to estimate future travel patterns given
  forecasts of population and land use
• Trip Generation Limitations:
  – Are trip rates stable over time?
  – Precision of estimate of population & land use?
• Gravity Model
  – Attractions do not balance
  – Includes primary factors, PI, AJ, wIJ
  – Models completely new development (synthetic)
   Trip Distribution - Summary
• Estimate QIJ given PI, AJ, wIJ, FIJ, KIJ
• Use Gravity Model
   – Distribute all productions (share model)
   – Attractions don’t balance [AJ* ≠ AJ ]
   – AJ and FIJ are scale independent
   – Calibrate by adjusting FIJ to match the GM trip
     length frequency distribution with the OD data
   – Synthetic model – apply to new development
  Mode Choice – Which mode?
• Important for :
• 1) Investment decisions
  – Madison
  – Milwaukee
  – Chicago
• 2) Operational decisions
  – Route changes
  – Frequency of service (headway)
       Mode Choice Behavior
• Depends on “attributes” of:
1) transportation system – time, cost, comfort,
  convenience
2) tripmaker – auto available, income,
  employed, family needs
3) trip – purpose, time-of-day
     Transit Ridership Groups
• Transit Captives
  – Lack access to private auto
  – Who? – elderly, young, disabled
  – Estimate as percentage of trips in a zone
• Choice Riders
  – Choose between public transit & private auto
  – Apply “mode choice” model
    Mode Choice Model Types
• Pre-distribution (trip-end)
  – Household based
  – Focus on transit captives
  – Transportation system attributes – accessibility
• Post-distribution (trip interchange)
  – Consider individual traveler
  – Trans. System attributes – transit vs auto
  – Consider alternative modes
      Post-Distr – Logit Model
• Probability-based model
• Describe alternative modes by a utility
  function
• Probability of choosing an alternative
  involves an exponential function of utility
            Utility Function
• Measures degree of satisfaction
• Disutility measures generalized cost
  (analogous to impedance)
• Value of utility depends on attributes of:
  – Mode
  – Trip-maker
  – Trip
• Calibrate at individual trip level
  (disaggregate) to obtain choice behavior –
  “behavioral model”
• Assume a linear utility function:
• U = a0 + ∑ i ai * Xi where
  – Xi attributes of trip, trip-maker and mode
  – ai – model parameters
• Model parameters can be “mode-specific”
  or “attribute specific” (mode abstract)
• Consideration of new modes requires
  attribute specific model
     Multinomial Logit Model
 p(k) = exp(Uk)/ ∑ x exp(Ux) where
 p(k) – proportion of trips made by mode k
 Uk – utility function for mode k
• For two alternative modes, have binomial
  logit model represented graphically as a
  sigmoidal (logistics) curve
    P(k=1) = 1/[1 + exp(U2-U1)]
      Example 8.9 – Logit Model
• Utility equation:
 Uk = ak-0.025X1-0.032X2-0.015X3-0.002X4
• Where:
  –   X1 – walk (at trip begin & end) time (minutes)
  –   X2 –wait time (minutes)
  –   X3 – line-haul (in-vehicle) time (minutes)
  –   X4 – out-of-pocket costs (cents)
  –   ak – mode specific constant (bias term)
       Mode Attribute Values
Attribute        Auto    Bus   RTransit
Access time      5       10    10
Wait time        0       15     5
Line-haul time   20      40    30
Cost             100     50    75
Constant term    -0.12   -0.56 -0.41
        Estimated Market Share
• For auto vs bus:
  –   U(A) = -0.745 and U(B) = -1.990 so
  –   p(A) = 0.78 and p(B) = 0.22 & for QIJ=5000
  –   QIJ(A) = 0.78*5000 = 3900 trips/day and
  –   QIJ(B) = 0.22*5000 = 1100 trips/day
• Transit revenue
  – 1100trips/day * $0.50/trip = $550 per day
   Market Share – Three Modes
• For auto vs bus vs rapid transit (new mode)
 U(A)= -0.745 U(B)= -1.990 U(RT)=-1.420
 p(A) = 0.56, p(B) = 0.16 and p(RT) = 0.28
  – QIJ(A) = 0.56*5000 = 2800 trips/day and
  – QIJ(B) = 0.16*5000 = 800 trips/day
  – QIJ(RT) = 0.22*5000 = 1400 trips/day
• Transit revenue
  – 800*$0.50 + 1400*$0.75 = $1450 per day
       Logit Model Properties
• Relative use of auto vs bus is independent
  of other modes:
    p(B)/p(A) = exp(UB)/exp(UA)
• Implied “value of travel time” (cents/min):
  Line-haul time – a3/a4 [(1/min)/(1/cents)] = 7.5
  Wait time – a2/a4 [(1/min)/(1/cents)]=16
• Value of wait time = 2 * (line-haul time)
        Network Assignment
• Concerned with choice of path
• Solution represents equilibrium point
  between supply and demand curves
• Supply curve represents reciprocal of speed-
  volume curve
• Analyze equilibrium flows using capacity
  analysis to obtain LOS
   Input to Assignment Models
• Person trips vs vehicle trips
  – Person trips needed for transit assignment
  – Vehicle trips needed for highway assignment
• Peaking of trips
  – Trips usually estimated for “average day”
  – Need peak-hour volumes to estimate LOS
  – Solution: 1) factor “avg. day” or 2) model peak-
    hour volumes directly (e.g., HB Work trips)
   Input to Assignment Models
• Trip direction
  – Account for directional flow
  – Use factors based on historical data
        Diversion Curve Models
•   Developed in 1950s and 60s (empirical)
•   Estimate diversion from arterial to freeway
•   Not used today
•   Unable to handle:
    – Multiple path choices
    – Alternative path with mix of freeway and
      arterial
  Network Assignment Methods
• Require:
  1) way of coding a network model
  2) understanding of factors influencing trip-
  maker’s preferences (route choice)
  3) computer algorithm able to generate
  preferred paths (route generation)
     Highway Network Model
• Typical street map (all streets)
• Coded traffic assignment network
  – Excludes local and minor streets
  – System of links and nodes – link attributes
    (capacity, volume, speed, travel time)
  – Travel analysis zones:
    1) coded as zonal “centroids”
    2) connected to network–“centroid connectors”
       Route-Choice Behavior
• Trip-makers consider impedance of
  competing paths in selecting travel path
• Alternative assignment methods:
• 1) all-or-nothing (minimum time path)
  – Often not realistic
  – Expect equilibrium where travel times are equal
    for competing paths
 Route-Choice Behavior – con’t
• Alternative Assignment methods:
• 2) multipath
  – Allocates trips to “all reasonable paths”
• 3) Capacity Restrained vs unrestrained
  – Link travel time is a function of the link volume
    to capacity ratio
  – Requires iterative solution
    Minimum Path Algorithms
• Minimum tree = all interzonal minimum
  paths that begin with a given zone
• Minimum time path only requires:
  – 1) time to node (from the origin node)
  – 2) prior node on the path (direction of
    approach)
  – 3) computer representation using a “tree table”
• Example 5 node network & assignment
Capacity Restrained Assignment
• 1) All-or-nothing assignment (20% of trips)
• 2) Adjust link travel times based on
  assigned link volume to link capacity ratio
  using: t = t0[1 + 0.15(q/cap)**4]
• 3) Perform new assignment with revised
  travel times and next 20% of trips and go to
  step 2) (4 iterations)
• 4) Add assigned volumes (five assignments)

								
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