SI-5141 & SJ-5122 Urban Travel Forecasting History
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Transcript of SI-5141 & SJ-5122 Urban Travel Forecasting History
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Urban Travel Forecasting:What Was Learned in the Past 50 Years?
How Should We Proceed in the Future?
Professor David Boyce
Department of Civil and Environmental EngineeringNorthwestern University, Evanston, Illinois, USA
Computational Transportation Science Seminar
University of Illinois at ChicagoApril 25, 2007
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Origins of travel forecasting models Travel forecasting, as we know it today, began in
the early 1950s:
In practice, to provide a basis for designing post-warfreeway systems, as an outgrowth of earlier surveys ofurban travel patterns;
In research, as an ingenious idea suggested by a new,
on allocation of scarce resources in a post-war civildefense project.
The former took hold, and was disseminated;the latter was lost for 15 years, and has had little
impact on the field, despite its far-sightedimplications.
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Travel Forecasting Procedure Based on Detroit Study Experience(Carroll and Bevis, Papers of the Regional Science Assn, 1957)
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The description of thetransportation planningprocess, as found onpage 9 of the Final Report
of the Chicago AreaTransportation Study,Volume I, 1959.
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Fig. 20. Recommended Expressway Plan for the Chicago Study Area
I-355
Crosstown
Expressway
This plan was recommended by the
Chicago Area Transportation Study in1962. The solid red lines were agreedupon before the Study began in 1955.
The dashed lines were proposed additionsto that system. Only one facility on this
map, I-355, was built; an extension is nowunder construction. The CrosstownExpressway became highly controversial
in the early 1970s, although funding wasavailable to build this facility. Those funds
Source: CATS (1962, Map 13, p. 64)
were later used for arterial roads andtransit lines. This plan was the first andlast attempt to utilize optimizing methodsto design a plan.
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Martin Beckmanns user-equilibrium travel
model with variable demand formulated as aconstrained, nonlinear optimization problem1
1. Beckmann, M., C. B. McGuire and C. B. Winsten (1956)Studies in the Economics of Transportation, Yale University Press.
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Urban Location and Transportation Systems
Urban activities may be viewed as a spatial system: land area, floor area and layout requirements of households, firms
and public agencies and services desire for spatial separation, light, clean air and environmental
amenities land availability and suitability for location requirements
Transportation provides connections among activities: high density activities require higher capacity systems (e.g. rail
low density, extensive activities require lower capacity, moreflexible systems (e.g. cars on an arterial network)
Travel times and costs partially determine the relativespacing of activities:
households and workplaces households and retail firms and services employment and business services and package delivery
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Relation of travel times/costs to spatial interactions:
unit travel costs increasing with network flows (roads) unit travel costs decreasing with network flows (some transit)
ability of different modes to serve spatially intensive vs. spatiallydecentralized patterns
Land market and regulations: an imperfect mechanism forcoordinating land development, density and thereby travel.
Major cohesive forces that causes large cities to grow: transportation services
agglomeration and localization economies (availability of specializedservices at one location)
business and public services (why did Boeing move to Chicago?)
Major forces that cause large cities to disperse:
need to satisfy space requirements at lower cost desire to move closer to skilled labor force or to employ labor with different
attributes (why did Sears move to the outlying suburbs?)
reluctance and lack of incentives to recycle previously used land(reuse of brownfields)
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One attempt to represent
the relationships amongurban activities andtransportation modes.
PTV America, Inc.
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Lets examine from first principles the attributesof these phenomena, as might be the situationin a place with no prior modeling experience.
Unconstrained by prior research and practice;
Unconstrained by theory and data requirements.
Note: This may be dangerous! But it may offerus some new insights into the phenomena.
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C. Network characteristics
Vehicles (single-packet-flow) Relation of travel delay to:
> flows on links
> clock time
B. Basic dimensions of travel choices
Frequency of travel
Departure time
Origin-destination flow
Framework for the design of
a travel forecasting model asa three-dimensional matrix ofmodel attributes.
A. Basic model primitives Location of households,
employment, urban activities,land use
Travel activities
Traveler classes Clock time
Transportation technologies(modes)
Networks
Mode choice structures Route choice structures and
travel time perceptions
Structure of travel choices
Traveler market segmentation
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A. Basic model primitives Location of households,
employment, urban activities,
land use Travel activities
Clock time
Transportation technologies
(modes)
Networks
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Location of households, employment, urban activities and
land use Locations and land development defined by small areas
Locations and land development defined by land parcel
Locations and land development defined on a small grid
Travel activities
Trips from origins to destinations
Tours, or sequences of trips
Connections between activities (activity-based model) Traveler classes
Socio-economic classes (households classified by number ofpersons, number of workers, income, number of cars)
Trip purposes, for trip-based and tour-based models
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Frequency of travel
Trips or tours per time period
Departure time
Uniform rate during modeling period
Dependent on desired arrival time, or congested travel time Dependent upon avoiding congested travel conditions
Origin-destination flow
Demand function for each OD air Beckmanns formulation
Constrained by total number of departures or arrivals (known as adoubly-constrained gravity model)
Destination choice function determined by variables describingdestination, and segmented by classes
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Route choice structures and assumptions about
perceptions of travel time Cost minimizing based on perfect information(deterministic user-equilibrium)
Cost minimizing based on perfect information with randomperception errors (stochastic user-equilibrium)
Cost minimizing based on stochastic link/intersection travel timeswith assumption about attitude towards risk
Structure of travel choices (e.g. mode choice)
Sequential (sequence of choices, each dependent on the previous) Hierarchical (choices conditional on other information)
Traveler market segmentation Tour type, designating the trip chain in which an individual trip
occurs: work tour, at-work tour, and non-work tour Chauffeured tours and non-chauffeured tours
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C. Network characteristics
Vehicles (single-packet-flow)
Relation of travel delay to:
> flows on links
> clock time
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Attributes of traditionaltravel forecasting models
Basic primitives Activity locations defined by traffic analysis zones Trip-based, origin to destination
Classes defined by trip purposes, with socio-economic segmentation
Daily (24 hour) or Period, such as peak-period Sometimes vehicular travel only, including trucks
Networks defined by nodes, links with travel time/cost
Basic dimensions: four models solved se uentiall /feedback
Trips per time period with implied uniform departure rate Origin-destination flow constrained by number of departures and
arrivals (doubly-constrained gravity model)
Nested logit mode choice model
Cost minimizing route choice (deterministic user-equilibrium) Basic network characteristics
Continuous flows of vehicles
Delay depends on each links own flow (separable)
Delay depends on current flow only
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Attributes of integratedtravel forecasting models
Basic primitives Activity locations defined by traffic analysis zones
Trip-based or tour-based, origin to destination
Classes defined by trip purposes, with socio-economic segmentation
Multiple periods, such as peak and shoulder periods Person and vehicular travel, including trucks
Networks defined by nodes, links with travel time/cost
Trips per time period, exogenous or endogenous Origin-destination, mode and time period choices defined as flows
and constrained by number of departures and arrivals
Cost minimizing route choice by period (deterministic user-equilibrium)
Solved by an iterative algorithm to precise convergence Basic network characteristics
Continuous flows of vehicles
Delay depends on each links own flow (separable or non-separable)
Delay depends on current flow only
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Dest Choice / Mode Choice /
Period Choice / Route Choice
Activity Frequency
(Trip Generation)
Destination Choice
(Trip Distribution)
Activity Frequency(Trip Generation)
Sequential Procedure Integrated Model
Mode Choice
Route Choice
(Traffic Assignment)
Feedback
Consistent levels ofservice with a precise
user-equilibrium solution
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Input data:( )
iO and jD by trip purpose
Road network
Compute the initial solution for 1:=
k Initialize travel costs ( )1ijc
Solve Trip Distribution ( )1)1( ijij de Assign ( )1ijd to road network ( )1af
Legend:k Loop indexW Weight for averaging matricesE Feedback convergence targetCW Constant WeightsMSA Method of Successive AveragesTMF Total Misplaced FlowRSE Root Squared Error
Feedback by Averaging of OD Matrices
Compute the solution for 1: += kk
Compute average OD cost ( )kcij
Solve Trip Distribution ( )keij Check convergence of ( )keij to ( )1kdij :
TMF = ( ) ( ) E1 ij
ijij kekd , or
RSE = ( ) ( )( ) E1
2/1
2
ijijij kekd
If converged, then STOP; if not, continue.
Assign ( )kdij to road network to desired level
of convergence of excess route costs ( )kfa
Average trip matrices ( )1kdij and ( )keij :
CW: ( ) ( ) ( ) ( )kekdkd ijij += W11W ,
or
MSA: ( ) ( ) ( )kek
kdk
kkd ijij
+
=
11
1
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Problems requiring travel forecasts
for transportation systems planning
Systems or network planning:
Determine system layout or configuration
Determine spacing of facilities by type (e.g., freeway, arterial,collector; rail, bus, shuttle)
Determine overall capacities of facilities (vehicles, persons per hour)
Determine intersection lane capacities, signal system design
Coordinate signal system design
Determine transit frequencies (headways), vehicle size
Coordinate transit services among submodes
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Staging of facility and service improvements:
Determine annual and multi-year improvement programs
Find optimal staging of project implementation
Assessment of environmental, energy and social
consequences of transportation systems Determine total emissions (NOx, CO2, SO2) and energy
consumption by year, facility type, and subregions
justice in USA) Determine which travel classes, trips, time periods are impacted by
a given system improvement
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Relationship to Location and Land Use Planning
Extent and scale of transportation systems is determinedby location, density and scale of land use pattern and theassociated pattern of urban activities;
Effectiveness and efficiency (cost) of alternativetransportation technologies (modes) depends on theextent, densit and la out clusterin or dis ersion of
urban activities; To be most effective, land use and transportation systems
planning must be coordinated and undertaken jointly.
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Constraints imposed by software systems
Nearly all software is based on the traditional sequentialprocedure view of travel forecasting;
As a result, the capabilities offered are basically toolkits forimplementing and solving specific models, and sequences
of models, as found in practice; These capabilities are linked together by menus, scripts
and other ad hoc methods;
solution procedure based on the integrated model concept(MCTs ESTRAUS); General purpose solvers for integrated models, formulated
as optimization problems, are not efficient for the large-scale implementations found in this field; micro-simulationremains impractical and may omit important relationships.
Professional practice and training of practitioners isincreasingly related to one or more of these softwaresystems, which are often seen as black boxes by users.
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Prospects for the Future
Need for better informed decisions is increasing (globalwarming, resource shortages, equity around the world);
Implications of bad decisions are not confined to wastedresources, since system equilibria will adjust to the realities,
and the least efficient urban cities will decline (St. Louis,Detroit in the US; Russia, Britain in the world economy);
Opportunities to create more livable and productive urbanenvironments may be lost, if decisions are not improved;
Progress in advancing travel and location models is slowand evolutionary, but capability to apply accumulatedknowledge through improving computer hardware andsoftware appears to expand at an increasing rate;
Progress will ultimately depend upon improved training ofprofessionals and researchers, which is relatively slow;
Therefore, investment in education and research is the keyto exploiting the technological advances that computer
engineering and science is providing to us.