SI-5141 & SJ-5122 Urban Travel Forecasting History

8/7/2019 SI-5141 & SJ-5122 Urban Travel Forecasting History

1/30

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


2/30


3/30

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.


4/30

Travel Forecasting Procedure Based on Detroit Study Experience(Carroll and Bevis, Papers of the Regional Science Assn, 1957)


5/30

The description of thetransportation planningprocess, as found onpage 9 of the Final Report

of the Chicago AreaTransportation Study,Volume I, 1959.


6/30

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.


7/30

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.


8/30

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


9/30

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)


10/30

One attempt to represent

the relationships amongurban activities andtransportation modes.

PTV America, Inc.


11/30

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.


12/30

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


13/30

A. Basic model primitives Location of households,

employment, urban activities,

land use Travel activities

Clock time

Transportation technologies

(modes)

Networks


14/30

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


15/30


16/30


17/30

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


18/30

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


19/30

C. Network characteristics

Vehicles (single-packet-flow)

Relation of travel delay to:

> flows on links

> clock time


20/30


21/30

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


22/30

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


23/30

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


24/30

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


25/30

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


26/30

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


27/30

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.


28/30


29/30

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.


30/30

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.

SI-5141 & SJ-5122 Urban Travel Forecasting History

Documents

Transcript of SI-5141 & SJ-5122 Urban Travel Forecasting History