Acknowledgments
This project was funded
jointly by Mack Blackwell Transportation Center (MBTC) with matching funds from
the Kansas Department of Transportation (KDOT) through the Kansas
Transportation Research and New Developments (KTRAN) research program. MBTC is
a national center at the
Disclaimer
The contents of this report
reflect the views of the authors who are responsible for the facts and accuracy
of the data presented herein. The contents do not necessarily reflect the views
or the policies of
Quick Response Community Planning
Abstract
The objective of this report
is to present the outcomes of a research project conducted for formulating a
unique and shortcut procedure for travel demand modeling and forecasting future
traffic for small cities or urban areas with population less than 15,000, using
a GIS platform. The TransCAD academic version 3.2 was used as the GIS platform. The major focus has been to reduce the time
and cost for the overall travel demand modeling process. Also, the level of detail of the network
required for building an appropriate travel demand model for such small areas
was determined. As a case study for application
of the method, McPherson, a small city in
Two travel demand models were
developed using a different number of Traffic Analysis Zones (TAZs). One consisted of 21 TAZs within the study
area and was called the low-density-zoning scheme. The second consisted of 63 TAZs and was
called the high-density-zoning scheme.
From the resulting values of a screenline analysis and average daily
vehicle distances of travel estimated from the traffic volumes assigned to the
network by the models, it was concluded that the travel demand model developed
from the high-density level of network provided better results as compared to
the low-density network. However, in
regard to the values of traffic volume assigned to the proposed bypass, the
travel demand models for the two levels essentially performed the same.
It was determined that the GIS/TransCAD platform provides useful tools for data organization and analysis of results through graphical features. It was concluded that there is a need of updating technical resources for applying these modern methods of travel demand modeling.
TABLE OF CONTENTS |
PAGE NO. |
List of Figures
|
ii |
List of Tables
|
iv |
Acknowledgements
|
v |
Chapter 1:
Introduction, Background and Objectives
|
1 |
|
Chapter 2: Selection of Software |
10 |
|
Chapter 3: Information Collection
and Data Preparation |
12 |
Chapter 4:
Developing Traffic Analysis Zones
|
19 |
|
Chapter 5: Building A Travel Demand Model |
31 |
|
Chapter 6: Results |
35 |
|
Chapter 7: Use of QRSII in
Modeling Small Urban Area Travel |
48 |
Chapter 8: Discussion and Conclusions |
58 |
|
References |
61 |
Appendix 1:
Socioeconomic Data for Census Tracts Within Study Area
|
63 |
|
Appendix 2: Graphs from KDOT
Corridor Study |
69 |
|
Appendix 3: Vehicle Kilometers
Travel Data for McPherson |
70 |
LIST OF TABLES |
PAGE NO. |
Table 3.1
Employment Categories Based on SIC Code
|
16 |
Table 3.2
Network Attributes
|
18 |
|
Table
3.3 Capacity for Links Based on Functional Classification |
18 |
|
Table
6.1 Demographic and Socioeconomic Data of TAZs for the Low Density Level of
Zoning |
35 |
|
Table
6.2 Demographic and Socioeconomic Data of TAZs for the High Density Level of
Zoning |
36 |
Table 6.3 Screenline Analysis Results
for Assigned Traffic Volumes From Original TAZ Data
|
40 |
Table 6.4
Screenline Analysis Results after Increasing Trip Productions
|
41 |
Table 6.5 Total
Length of Streets According to Functional Classification
|
42 |
Table 6.6
Average Daily Vehicle Miles of Travel
|
43 |
|
Table 6.7 Average
Daily Vehicle Miles of Travel for Links With Traffic Counts for Low Density
Level of Zoning |
43 |
|
Table
6.8 Average Daily Vehicle Miles of Travel for Links With Traffic Counts for High
Density Level of Zoning |
44 |
|
Table 6.9 Linear
Regression Analysis for Low Density Level of Zoning |
45 |
|
Table 6.10 Linear
Regression Analysis for High Density Level of Zoning |
46 |
Table 6.11 Comparison of Traffic
Assignments on the North Bypass
|
47 |
Table 7.1 Comparison of Traffic Assigned
to North Bypass for Low and High Levels
|
54 |
Table 7.2 Screenline Analysis for the
QRSII Models
|
57 |
LIST
OF FIGURES
|
PAGE NO. |
Figure 1.1 Traditional Travel Demand
Forecasting Process [Source: UTPS (2)]
|
1 |
|
Figure
1.2 Using A Calibrated Travel Demand Model for Forecasting Future Trips [Source: UTPS (2)] |
2 |
|
Figure 1.3 Street Network
Within the City Limits of McPherson and Proposed Bypass |
9 |
|
Figure
3.1 TransCAD Window for Locating Employers by Address Matching |
15 |
|
Figure
3.2 TransCAD Window for Preparing Population and Housing Data for Census
Blocks |
17 |
|
Figure
4.1 TAZs for the Low-Density-Zoning Scheme |
20 |
Figure 4.2 TAZs for the High-Density-Zoning Scheme |
21 |
|
Figure
4.3 TransCAD Window for Preparing Population and Household Data for TAZs for
Low Density Zoning Scheme |
23 |
|
Figure
4.4 Microsoft Excel Window for Calculating the Number of Employees for
Different Employment Types for Each TAZ in Low-Density-Zoning Scheme |
25 |
|
Figure
4.5 TAZs and External Stations for Low Density Zoning |
26 |
|
Figure
4.6 TAZs and External Stations for High Density Zoning |
27 |
|
Figure
4.7 Street Network for Low Density Zoning |
28 |
|
Figure 4.8a Street Network for High Density Zoning
for the Whole Study Area |
29 |
|
Figure 4.8b Street Network for High Density
Zoning; Expanded View for the Central
Area. |
30 |
|
Figure
5.1 Screenline Used for Model Evaluation |
33 |
|
Figure
6.1 Scaled Representation of Links According to Directional Speed |
38 |
|
|
|
|
|
|
LIST
OF FIGURES
|
PAGE NO. |
|
Figure
6.2 Scaled Representation of Links According to Directional Capacity |
39 |
|
Figure
6.3 Plot of Assigned Traffic Volumes vs. Traffic Counts on Streets on the
Screenline and the Respective Regression Lines |
41 |
Figure 6.4 Plot of Assigned Traffic Volumes vs. Traffic Counts on Streets on the Screenline and the Respective Regression Lines for Increased Trip Productions |
42 |
|
Figure
6.5 Plot of Assigned Traffic Volume vs. Traffic Counts and Regression Line
for Low Density Network of Zoning |
45 |
|
Figure
6.6 Plot of Assigned Traffic Volume vs. Traffic Counts and Regression Line
for the High Density Level of Zoning |
46 |
|
Figure 7.1 Street Network with CBD
Bypass |
49 |
|
Figure
7.2 Existing Network with Traffic Volumes for the Low Density Network |
50 |
|
Figure
7.3 Existing Street Network with Traffic Volumes for the High Density Network |
51 |
|
Figure
7.4 Low Density Network with North Bypass Included |
52 |
|
Figure 7.5 High Density Network with North
Bypass Included |
53 |
|
Figure 7.6 Low Density Network with CDB Bypass Included |
55 |
|
Figure 7.7 High Density Network with CDB Bypass Included |
56 |
Chapter 1: Introduction, Background and Objectives
Introduction
Urban travel demand forecasting is the process of
predicting the impact of various changes in a study area on future travel
characteristics and demand. The changes
may be due to various policies and programs for development of the study area,
which results in change in socioeconomic characteristics, improvement and
changes in transportation facilities, and other factors. In general, travel demand forecasting
attempts to quantify the amount of travel on transportation system, where, the
travel demand is created by separation of urban activities and transportation
system is represented by characteristics of highway and transit network. The Traditional process for travel demand
forecasting consists of four steps: Trip Generation, Trip Distribution, Mode
Split and Traffic Assignment. Figure 1.1
below describes the four step modeling process along with information needs and
the feedback process.
Figure 1.1 Traditional Travel Demand Forecasting Process [Source: UTPS
(2)]
As shown in the Figure 1.1, the inputs to the
four-step model are the urban activities and highway and transit network
information for the study area. These
parameters may be modified after evaluating the impact of traffic volumes
assigned to the highway and transit networks making changes to land use
forecasts necessary. The modified
parameters again serve as input to the model and the process is carried out
until no further changes are made to the input parameters. The four-step travel demand model is
calibrated using existing transportation characteristics of the study area and
then is used to forecast and study the transportation characteristics in a
future year. Figure 1.2 below shows the
use of a calibrated model for future trip forecasting.
Figure 1.2 Using A Calibrated Travel Demand Model for Forecasting Future Trips
[Source: UTPS (2)]
It is important to realize that modification in
transportation system policies and street networks have impact on land use
changes and economic growth (1)*.
Therefore the process of feedback of modified input parameters to the
model and evaluating the system is necessary for any transportation planning
process.
Information
Requirements:
The information required as data input to build the traditional travel demand
forecasting model are divided into four broad categories (2) as follows:
· Study area
·Urban activities
· Transportation system
· Travel
* Numbers in Parentheses Indicate References.
The information on the study area should be known
before proceeding with collecting any other data. The boundary of the study must be defined at
the beginning. The boundary must
consider the scope of the project for which the study is being conducted. A general urban study usually includes the
surrounding area that is expected to be urban within twenty years. Project specific studies must include the
points of decision for routing traffic.
The study area has to be divided into several
Traffic Analysis Zones (TAZ s) according to land use, physical barriers and
street classification as well as the scope and limit of projects. Urban activities consist of demographic and
socioeconomic data of the study area.
Some of the specific data include population, dwelling units, automobile
ownership, income, household size and employment locations and
characteristics. The transportation
system consists of the present street classification and characteristics. This information is used to build a transportation
network for the study area. Travel information includes how, when and
where people are currently travelling.
The information is studied to determine the underlying factors causing
people to make certain travel decisions so that models can be calibrated and
used to forecast how people will travel in the future.
It was realized that conducting manual surveys for
collecting this necessary information is expensive and time consuming. Literature reveals the efforts made to
develop different techniques for the travel demand forecasting process (3, 4,
5, 7, 8, 10 and 11) to reduce time and money spent for data collection. The advent of computers with larger storage
capacities and faster processing speeds helped in a proliferation of different
techniques which provide efficient means of data management. The following section discusses some of the
present methods for travel demand forecasting.
Background
Quick Response Methods and Computer Application:
National Cooperative Highway Research Program
(NCHRP) Report 187 (3) provides
different transferable parameters, factors and manual techniques to enable
simplified and quick response urban travel demand estimation without conducting
extensive surveys for data collection.
The parameters provided by the report were based on several research
results and can be used for computerized methods. Various shortcut manual techniques are
suggested in this report.
NCHRP 187 was updated in NCHRP Report 365 (4) , and extensive manual procedures discussed
in the former report were excluded. The
report provided transferable parameters that can be applied in any available
travel demand softwares. Also the use of
geographic information systems (GIS) for building travel demand forecasting
model database are discussed in the report.
Smith and McFarlene (5) evaluated an internal volume
forecasting (IVF) model as a replacement for conventional urban travel demand
models in small and medium sized areas.
The model used interzonal trip probability factors as a measurement of
the magnitude of the trip between zones.
The trip probability factors were calculated as a function of production
and attraction variables of zones, interzonal travel time impedance and related
exponential factors. Instead of
conducting surveys and home interviews for estimating productions and
attractions for each zone, the model used production and attraction variables
such as population, employment and rating for commercial and recreational use
of each zone. The model was applied to
calibrate a travel demand model for the
V =
181 + 0.031 ´ Pi Pj
----------------------------------------------------(Equation 1.1)
Where, Pi is the
population of zone i and Pj is the population of zone j.
The model was used to forecast traffic volumes for
future years using trip probability factors, which were calculated from
forecasted changes in production and attraction variables. The model used linear regression method for
calibration.
Other non-linear models have been proposed by
Hogberg (6). Also, the model did not
consider intrazonal travel time impedance.
The inclusion of origin-zone accessibility was recommended by the
study. The major advantage of this
approach was the elimination of conducting extensive surveys for collecting
socioeconomic data for estimating zonal productions and attractions, and using
ground traffic counts for calibration.
The study concluded that the model was well suited for application in
small urban areas with population less than 50,000.
McPherson, Heimbach and Goode (7) set forth a
computerized method for updating data bases for travel demand forecasting. The study focused on development of a
computerized system capable of monitoring and recording changes in urban
activities that could be used to update transportation planning data bases
periodically. Two major analytical
techniques were used to develop the model, as follows:
i)
Computerized Geocoding:This technique provided the
mechanism for linking together a variety of diverse data sources that may have
only one common link (e.g., street addresses).
The process provided a convenient framework for continuous monitoring
and updating transportation planning data.
ii)
Income Model Construction
Using Harmonic Analysis: Harmonic analysis was used to express income-estimating model as a
Fourier series. Data for calibrating
income models were available from the 1970-1980 census urban transportation
planning packages.
The study expressed the necessity of developing an income model because of confidentiality requirements by law prohibited state and federal revenue agencies from releasing income tax return files to other agencies. Conclusions of this study were supported by the findings from the
system
implemented and tested in
Rutherford and Pennock (8) discussed the features
and application of the software ‘Quick Response Microcomputer System’ (QRS)
developed by Comsis Corporation, to incorporate the techniques of travel demand
forecasting presented in NCHRP 187 (3).
The software could be used for rapid calculation of zonal productions
and attractions. Some of the limitations
of the original software were:
· maximum number of TAZs was limited to 50,
· maximum number of assignment links was
limited to 800, and
· maximum travel time allowed was 40 minutes,
The socioeconomic data, such as number of dwelling
units, income, auto-ownership, retail and non-retail employment were input to
the trip generation procedure. The
travel times were calculated from zone types, coordinates of zone centroids,
map scale and a circuity factor to convert airline distance to over-the-road
distance. Rutherford and Pennock
believed that the mode choice model of the software was not a particularly
useful tool, as it required inputs which might be less than logical.
Goulias and Kitamura (9) presented a model for
forecasting travel demand based on microanalytic simulation and dynamic
analysis of travel characteristics. The
system consisted of two components: a microsimulator of household socioeconomic
and demographic data and a dynamic model system of household auto ownership and
mobility. Each component comprised
interlinked models formulated at the household level. The interactions and causal paths that
underlie life cycle evolution of individual households were replicated in the
socioeconomic and demographic microsimulator.
Simulation units evolved from year to year, experiencing changes in
urban activities. Employment, income,
education level, household size were among the variables that were generated in
the microsimulation. The parameters of
the model for generating the socioeconomic and demographic data were estimated
from observed data from Dutch National Mobility Panel Data. The model system provided a flexible tool for
forecasting travel demand; however, the process was complex and required a
large amount of data for estimating the parameter which triggered the
simulation process.
Application of
Geographic Information Systems (GIS):
Computer based GIS integrates data from diverse
disciplines and various formats to generate useful information about an area of
earth or physical features on earth such as street networks, location of
employment centers, etc. GIS software
packages are capable of capturing, organizing, processing and analyzing
spatially referenced data. The physical
features are associated with related databases and are represented graphically
to scale. In recent years, the Census
bureau has made census data available to be used with GIS, by producing TIGER
(topologically integrated geographic encoding and referencing) files. GIS shows promise as an important tool for
transportation professionals.
Mugler and Quinn (10) explained the use of GIS
technologies for preparation of demographic forecasts in the Denver Regional
Council of Governments (DRCOG). They
subdivided the 1990 census tracts based on land use to create TAZs. Demographic attributes of the TAZs were
extracted from TIGER files. GIS
processing was used to create population and employment estimates from state
income tax roles, census data, electric utility records and privately developed
databases. The data were geocoded on the
TAZ polygon layer and the road base map.
Raley (11) expressed the efforts of Delaware
Department of Transportation (DelDOT) in using GIS based census data and TIGER
files for cost effective transportation planning. DelDOT had developed modified planning grids
in 1960s, which were comprised largely of census geography. The grids were updated to provide current
TIGER geography, and were used as building blocks for TAZs. Census data were used for assignment of population
and employment forecasts to the modified grid level.
Gan (12) formulated a GIS-aided procedure for
creating TAZs from census data and TIGER files.
He expressed the problem concerning data disaggregation when census and
TAZ boundaries are non-coterminous. For
presentation purposes he used the TAZ system of
Slavin (13) proposed a fundamental reformulation of
the travel demand modeling process based upon a richer spatio-temporal
conceptual and empirical approach. The
proposed approach focussed on improvements and extensions to traffic assignment
models, and combined the following four elements:
· modeling joint choices as super-networks,
· dynamic, stochastic network equilibrium
models,
· integration of traffic engineering models,
and
· GIS technology for database management and
model integration.
He discussed the extensive use of GIS software for
defining TAZs and developing zonal attribute
data prior to modeling by polygon overlay and
spatial aggregation functions. He also
mentioned that GIS offers an effective means of improving network
characteristics.
Other Methods for Travel Demand Modeling
Neumann, Halkias and Elrazaz (14) proposed a method
for estimating trip rates from traffic counts on the links of a network. The socioeconomic variables causally related
to trip production, such as zonal population and dwelling units, were
distributed by gravity model and assigned to the links of the network. Regression analysis was carried out to build
a linear regression model for estimating traffic counts on the links from
assigned socioeconomic variables on each link, using observed link counts as
the dependent variable and socioeconomic variables as independent
variables. Trip rates by purpose were
calculated from the estimated proportion of trips by purpose of the study area.
Several methods (15, 16, 17, 18, 19) were proposed
for estimating an origin-destination (O-D) matrix from traffic counts on the
links of a network. One of the problems
associated with this technique was that there was no unique O-D matrix which
could result in the observed traffic counts.
In other words, several O-D matrices could possibly exhibit the same
traffic count on the links. Turnquist
and Gur (15) expressed the necessity of a good estimate of an initial O-D
matrix fed into the iterative process of the calculation in order to arrive at
better results.
Need for
Small Urban Area Planning Methodology:
Several methods for travel demand forecasting
discussed in the previous section are applicable to medium to large urban areas
with population over 50,000. The NCHRP
reports (3, 4) for quick response methods in travel demand modeling provide
transferable parameters for such areas.
Availability of demographic and socioeconomic data such as
auto-ownership, as classified by income groups and person trips per households,
as cross classified by income and auto-ownership, etc., are limited to small
areas where there has been a study conducted previously. GIS based Census Transportation Planing
Packages (CTPP) provides useful data for large urban area planning. Unavailability of the census socioeconomic
data, geocoded for less than block group level, makes the task of applying GIS
for travel demand modeling for small urban areas difficult. In small towns with population less than
15,000 and without major industrial activity, there may be less than 20 block
groups, which may not provide a significant level of detail for TAZs. One of the reasons for unavailability of
adequate data for small urban areas is that there may not be a significant
effect of changes in socioeconomic activities in such areas on the
transportation system considered in large scale. Some of the available literatures concerning
travel demand modeling for small urban areas are briefly discussed below.
Travel Demand Modeling in Small Areas
Souleyrette and Anderson (20) addressed the barriers
to technical expertise and the problem of inadequate resources for applying
modern methods of travel demand modeling to smaller cities or rural areas. Their study proposed a method for applying
GIS to develop travel demand models for small urban areas and implemented it in
Socioeconomic and demographic data aggregation from
census block group level to TAZ was performed using GIS. TRANPLAN was used to run the trip generation
model. TIGER files, Iowa DOT computer
aided design (CAD) files and local area CAD files were used for defining the
network. Transferable parameters for
travel demand modeling as provided in NCHRP 187 (3) were used for preparing
TRANPLAN files to create and calibrate the travel demand model. The advantages of the visualization tools
provided by GIS, such as representing the assigned traffic volumes on links
using different bandwidths and colors for identifying problem areas, were
expressed in the study. Visualization
tools were useful in calibrating the model by comparing the assigned traffic
volumes with ground counts. Model
calibration was done using screen line analysis, i.e., comparing the computer
traffic output on the links to key ground counts.
Schrank and
Farnsworth (1) presented a traffic modeling template for small cities in
Transportation network data were available from the
Texas Department of Transportation (TxDOT) district traffic map and databases,
which provided current and forecasted traffic volumes and the percentage of
trucks. Sites which generated higher
than average traffic volumes such as industrials parks, retail centers,
etc. were located. After data assembling, level of service (LOS)
for the street network was analyzed and possible locations of improvement were
identified. Although the study did not
provide any method for travel demand modeling and forecasting for small areas,
its methodology may be used for initial preparation of future networks based on
present network characteristics and socioeconomic activities.
Objectives
of This Study:
As previously mentioned, there are several problems
associated with formulating a shortcut procedure for travel demand forecasting
for small cities or urban areas with population less than 50,000, such as lack
of expertise and resources and unavailability of socioeconomic and demographic
data in adequate detail. Urban areas
with population more than 50,000 have the benefit of metropolitan planning
organizations (MPOs) which were created as a part of the Federal-Aid Highway Act
of 1973, and are responsible for transportation planning and programming for
such areas. Small urban areas lack this
capacity.
Since the regional transportation system may not be
significantly affected, little resources are dedicated for travel demand
modeling and forecasting for small urban areas except on a case-by-case
basis. This leads to the prime objective of the present research:
formulating a shortcut procedure for
travel demand modeling for small cities or urban areas with population less than
15,000, which will not require much expertise and resources. As a case study for application of the
method, McPherson, a small city in
Major milestones of the research were as follows:
i)
Selection of software: One of the objectives of the research was to
perform the same steps with several models and compare the ease of use as well
as the results obtained. GIS based
softwares TransCAD and Maptitude and quick response based software QRSII were
selected for the research purpose.
ii)
Information collection and data preparation: This involved obtaining
socioeconomic and demographic data, information on street network
characteristics and traffic counts and information on present and future land
use and transportation policies and preparing it for use.
iii)
Creating travel demand model: This involved creation of TAZs based on land
use and socioeconomic activities.
Appropriate level of detail required for creating the model was
determined by comparison of the model output from using different levels of
detail in TAZs. Calibration of the model
was done using screenline analysis, i.e., comparison with ground traffic
counts. A traditional four step travel
demand model was prepared.
iv)
Traffic Volume Prediction on Bypass: The city proposed a bypass
on the north side of
the city, to divert through
traffic on US56 highway (as shown in Figure 1.3) from passing through the CBD
of the city. The travel demand model was
used to predict the traffic volumes that would use the bypass.
Figure 1.3 Street Network Within the City Limits of McPherson and Proposed Bypass
Chapter 2: Selection of Software
The selection of software for this research project took into
consideration researcher familiarity and technical resources of the sponsoring
organization, the Kansas Department of Transportation (KDOT). As the mainframe models of the FHWA Urban
Planning Battery and the USDOT TRANPLAN were being phased out, KDOT first
purchased TModel; however, they made little use of it. The staff then became involved with QRS II
and beta tested a number of versions.
KDOT has had considerable experience with QRS II over the years. Their interest in this research was to see if
TransCAD, with its GIS features, would have advantages over the QRS II
routines. The software packages
evaluated in this research are Quick Response System/II (QRS II) version 5.1,
TransCAD academic version 3.2 and Maptitude version 4.1, EMME/2 and TModel.
Quick Response System II
QRS II is based on quick response methods (3, 4). A comparison program
which is called the General Network Editor (GNE) is used to build the street
network, centroids of traffic analysis zones (TAZs) and the centroid
connectors. GNE does not support importing GIS layers for street network and
census data. An image file containing
the streets within the study area may be used as a background on which the
street network is drawn. The scale of
the network can be set appropriately.
The zone centroids are located and connected to the street network using
centroid connectors. The demographic and
socioeconomic data, such as population, households and employment can be
entered as centroid attributes for their respective TAZs.
The software provides no method for data organization to prepare TAZ
data. It is necessary to collect
information and organize the data to estimate TAZ socioeconomic and demographic
data previous to building the network in GNE, for further use for building the
travel demand model with QRS II. The
software is based on quick response methods (3, 4) and ARSII contains the
routines to perform the four usual steps of travel demand modeling, namely,
Trip Generation, Trip Distribution, Model Split, and Traffic Assignment. Other balancing converstion routine also are
included. It is also familiar to the
researchers involved in this study and technical personnel of KDOT.
TransCAD and Maptitude
TransCAD and Maptitude contain GIS routines that can be used to import,
organize and prepare GIS based data useful for travel demand modeling, such as
census data and street network data. It
can also be used for geocoding data, such as locating records on a geographic
layer by matching address with a street network layer.
Maptitude provides a useful tool for preparing socioeconomic and
demographic data for TAZs, which may be further used for building a travel
demand model using QRS II or any other software designed for this purpose. Maptitude was used to estimate TAZ and street
network data, which was then fed to the GNE, and finally to QRS II for building
a travel demand model.
TransCAD, other than providing GIS features, also provides tools for
building a travel demand model. It is
window based GIS software designed for transportation engineering applications. TransCAD academic version 3.2 did not provide
all GIS features of the complete commercial software. Therefore Maptitude version 4.1 was purchased
to perform tasks using enhanced GIS tools.
It was determined from the research that TransCAD is a powerful software
package for travel demand modeling.
However, it took a considerable amount of time to gain familiarity in
using it. A training course conducted
for KDOT by Caliper Corporation, which markets the TransCAD and Maptitude
software accelerated the learning process for the research. It was realized that after gaining
familiarity in using the software, it could simplify the overall process of
travel demand modeling in regards to time and cost of data preparation.
Unlike QRS II, TransCAD is not pre-designed to use quick response methods
or transferable parameters (3, 4). The
user inputs values of the parameters required for building travel demand
models, such as trip production and attraction rates, friction factor developing
methods, etc., but at the same time the transferable parameters as provided in
the quick response methods (3, 4) may be used without any restriction.
TransCAD proved to be useful in meeting the goals of the research by
providing GIS tools for easy data organization, visual tools for developing
street network and TAZs, and results that can be easily analyzed.
The other softwares that were evaluated were EMME/2 and TModel. They were not used to model the McPherson
Area Network; however, each model was used during the project period on
classroom projects. Based on that
experience, EMME/2 proved to be a very comprehensive model and would be very
useful when several modes were being analyzed.
However, it is too complex and the learning curve is too long for use in
small urban areas. TModel is a much
easier model to use, similar to QRS II, but it did not handle External-Local
and Through Trips easily. The through
trips had to be manually assigned to the network and treated as base traffic. These procedures did not lend themselves to
an analysis of a bypass.
Chapter 3: Information Collection and Data
Preparation
As mentioned in Chapter 1, the four broad categories of information required for building a travel demand model (2) are the study area, the urban activities, the transportation system and the travel information. In this chapter, a detailed discussion is presented of sources for collecting this information and using it to prepare data for building a travel demand model using TransCAD.
Information Collection
Study Area: Information on a study area
includes the location of the cordon line, the boundaries of census tracts, the
census block groups and blocks, the land use data and the location of major
streets and physical barriers. This
section presents a list of sources of information on the study area.
i)
City Comprehensive Plan (21) provided the following
information:
1. McPherson Planning Area Map provided information for defining the cordon line for the study area.
2. Development Influence Map provided the location of
physical features within the study area such as streams, railway tracks and
major streets.
3. Present and future land use
information and relevant maps were used for defining TAZs.
4. Functional Classification
Plan was used for defining the network and TAZs.
ii)
McPherson County General Highway Map Aerial Photographs provided by KDOT for the
city of McPherson were used for identifying the locations of major employers
and residential areas and their accessibility to major streets. Also, the location of physical features such
as streams, railway tracks and streets were identified from the photographs.
Urban
Activities:
The information on urban activities included demographic data such as
population and housing and socioeconomic data such as income auto-ownership and
employment.
This section presents the sources of such
information and data available from them.
i)
1990 Census Data (22) for population and households provided the
data on population and household units for each census block within the study
area. The census blocks were coded according
to the census code.
ii)
TIGER/LINE 1995 Compact Disc (CD) (23) was the source of information on location
of census blocks and their census codes.
TransCAD was used to import the geographic area layer for census blocks
from the CD.
iii)
Employment Data. The data were available from
American Business Lists, Inc., Omaha, Nebraska.
Search for other vendors providing the information on business research
tools or statistics, may be done through the Internet. The data included street addresses, number of
employees and standard industrial classification (SIC) codes. KDOT purchased these data and provided them
for the project.
Transportation
System: The
information on the transportation system included location, functional
classification and characteristics of streets and highways within study
area. This section provides the sources
for such information and data available from them.
i)
ii)
General Highway Map for
iii)
City Comprehensive Plan (21) provided the functional street classification
map for the streets within the city limits.
The McPherson Public Works Department, provided the geometric
characteristics of streets. The data
consisted of number of lanes, width and average daily traffic (ADT) counts for
the major streets and intersection type for all intersections within city.
iv)
KDOT/KTA Major Corridor Study (25) provided the graphical representation
of estimated daily LOS of arterials, rural highways, freeways and
expressways. The capacity of the links
of the network was assumed to be the service volume at LOS B for each type of
street or highway.
Travel Information: Vehicles using the streets and highways are the result of the travel behavior of people. The traffic counts are compared to the results of the modeling process to determine whether the models are accurately reproducing choices made in daily travel. This section presents the sources of travel information and relevant data.
i) City Comprehensive Plan (21) provided the data on traffic counts for major street within the study area.
ii) KDOT provided the data on daily vehicle miles of travel on streets in urban areas according to functional street classification.
Data Preparation
TransCAD was used for preparing the employment and demographic data, which were further used for building data for TAZs.
Employment Data: The employment data, made available by KDOT, provided the street address for all employers. It was then necessary to locate them as point features within the study area. This was done by executing the ‘Locate by Address’ process in TransCAD. This section presents the sequential method for locating the employers within the study area and creating a point geographic layer in TransCAD for the employment locations.
1. The employment data file, which was available as a Microsoft Excel file, was first converted to dBASE (*.dbf) file format.
2. The geographic line layer representing the streets within the study area, which was imported from US Streets 97 (24) (as mentioned in earlier section), was opened in TransCAD or Maptitude. The data were checked to ensure that the associated data for the streets within the city limits had the left zip, right zip, starting and ending block numbers for left and right side of each street. These data were required for locating the employers on the correct street and side.
3. The dBASE file containing the employment data were opened in the same workspace. The data file contained 614 records. It was observed that the street name in the address field of the data contained different names for the same street section for many records. For example, the names “Old Highway”, “Old 81 Bypass” and “Bypass”, in many records in the employment data referred to the same street, the 81 Bypass on the west side of McPherson. All such street addresses were changed to the single name that was used in the dataview associated with the line geographic layer of streets.
4. The ‘Locate by Address’ command in TransCAD was used to locate the employers within the study area as a geographic point layer. A total of 526 employers were located in the process. Figure 3.1 shows the TransCAD window containing the workspace for locating employers.
5. There were 88 employers whose addresses could not be matched and thus TransCAD failed to locate them. Out of these records, there were only nine employers with ten or more employees. Their location was determined by either calling the company (the phone number for each employer was available as a data field) or by using the web site “http://www.anywho.com”. The remaining employers (with ten or fewer employees) were discarded.
The employers were located as points on the existing point geographic layer of located records, as new records. Finally a total of 535 records were used as employment data for building the travel demand model.
Figure 3.1 TransCAD Window for Locating
Employers by Address Matching
6. A new field, to specify the employment type for each employer, was added to the dataview of the point geographic layer for employment locations. The employment type consisted of three categories: Non Retail, Service and Retail. The classification was done based on SIC codes available for each record. Table 3.1 provides the employment classification based on SIC codes as specified in NCHRP Report 365 (4). The SIC codes were revised in 1997. Make sure the correct version is used.
Table 3.1 Employment Categories Based on SIC Code
|
Employment Category |
SIC Code (1997) |
|
Non-retail |
100000 through 519999 900001 through 909999 |
|
Service |
600000 through 909999 |
|
Retail |
520000 through 599999 |
Population and Housing Data: Population and households of the census blocks for the 1990 census were available from the Census Bureau as mentioned earlier. A data field named “code” containing the census code for each block was present in the data. It was then necessary to form an area geographic layer of census blocks which contained the population and household data in the associated dataview in TransCAD. This section describes the sequential method for the process of creating the area geographic layer of census blocks containing population and household data.
1. The area geographic layer for census blocks, which was imported from the TIGER/LINE 1995 CD (23), was opened and the census blocks within the study area were selected and exported to the new area geographic layer. The fields in the associated dataview of the geographic layer contained the area and census code for each block within the study area.
2. The population and housing data were available as a Comma Delimited Text (*.csv) file and was opened in Microsoft Excel. The file type was converted to a dBASE
(*.dbf) file format.
3. The dBASE file containing the population and housing data were opened in the same
workspace where the area geographic layer of census blocks already existed in TransCAD.
4. The ‘Dataview-join’ command in TransCAD was used to overlay the area geographic layer of census blocks with the database of population and households, using census block codes as the common field between the two data sets. Figure 3.2 shows the TransCAD window for this process.
5. The ‘Tools-export’ command was then used to export the joined dataview to a new area geographic layer of census blocks, which contained the population and household data in the associated dataview.
Street Network: The line geographic layer imported from US Streets 97 CD (24) consisted of essentially all public roads within the nation. The network was built using links representing major and minor arterials, collectors, freeways and expressways, rural highways and some of the local roads. In this section, the procedure is discussed for building the network using available data.
1. The line geographic layer imported from US Streets 97 CD was opened and the selected portion bounded by the cordon line was exported to a new line layer. This was done to reduce the size of the file to a manageable proportion and to keep the previously imported layer unchanged while building the network using the new layer. All links not included in the network were deleted. TransCAD, ‘Tools-Map-Editing’ command was used to make any required modification in the links of the network. Note that the selected portion must be exported rather than saved to eliminate the remainder of the national network.
2. The TransCAD ‘Tools-Selection’ feature was used to select the links of same functional classification and represent them with same color and width.
3. The network attributes were added by modifying the dataview associated with the line geographic layer and adding new fields. Table 3.2 gives the list of attributes
added to the links for building the network.
Figure 3.2 TransCAD Window for Preparing Population and Housing Data
for Census
Blocks
Table 3.2 Network Attributes
|
Field |
Description |
|
Link Type |
Functional Classification |
|
AB and BA Speed |
Directional speed for each link in miles/hour |
|
AB and BA Travel Time |
Directional travel time for each link in minutes |
|
AB and BA Capacity |
Directional capacity in vehicles per day (vpd) |
|
AB and BA Count |
Directional traffic counts as observed in vpd |
|
Alpha and Beta |
Parameters for BPR function used for traffic assignment |
4. The directional speed data for each record of the line layer was assigned with the posted speed limit for the corresponding street represented by the link. Available traffic count data were assigned the observed traffic counts for corresponding streets if available, as provided by the city comprehensive plan (21), it was left blank for those streets were it was not available. The BPR parameters alpha and beta were assigned values of 0.15 and 4.0, respectively, as specified in NCHRP Report 365 (4). The directional free flow travel times for each link were calculated as follows:
Travel Time = Length *60/AB_Speed -------------------- (Equation 2.1)
5. The directional capacity was determined from the 24-hour service volume at LOS B for different streets according to functional classification in graphs representing the service volume at various LOS provided by KDOT/KTA Corridor study (25). Table 3.3 provides the capacities along each direction for each link type in the network.
Table 3.3 Capacity for Links Based on
Functional Classification
|
Code |
Functional Classification |
Capacity Direction (vpd) |
|
1 |
Major Arterial |
8,500 |
|
2 |
Minor Arterial |
3,500 |
|
3 |
Collector Road |
2,800 |
|
4 |
Interstate |
16,000 |
|
5 |
Rural Highway |
1,600 |
Chapter 4:
Developing Traffic Analysis Zones
In the last chapter the collection and preparation
of the data needed for building a travel demand model were discussed. The next step for building a travel demand
model is to develop traffic analysis zones (TAZs) In this chapter the procedure for
defining zone boundairies is
discussed.
Defining TAZs
As recommended by Garber and Hoel (26), TAZs were
defined in such a way that the land use and socioeconomic activities within them
were as homogeneous as possible.
Physical features such as major streets, railway tracks and streams were
used as zone boundaries and where possible the TAZ boundaries were made
coterminous with census block boundaries.
The information used for defining the TAZs was discussed in Chapter 3.
TAZs were created as an area geographic layer in
TransCAD. The line layer representing
streets within the study area, and area geographic layer for census blocks,
were overlayed, with the area geographic layer used for defining the TAZs.
Level of Detail and Zone Numbering
One of the objectives of this research was to determine how many zones were necessary to produce satisfactory results for different types of projects. Three levels of detail were initially attempted to analyze the level of detail that would be adequate for various projects that might be encountered in a small urban area. However, because of the numerous streams and railroads that bisect the developed area of McPherson, only two levels of networks were developed. One of them consisted of 21 TAZs within the study area, which was named the low-density-zoning scheme and the other consisted of 63 TAZs, and was named the high-density-zoning scheme. It was not possible to include all natural zone barriers such as railway tracks and streams for defining TAZ boundaries in the low-density-zoning scheme, whereas it was done in the high-density-zoning scheme. A more detailed network would have been far too complex for a small urban area and would have taken an excessive amount of time to develop.
For the high-density-zoning scheme, or any area that
has a large number of zones, a ring-sector scheme can be used. The rings are numbered from "0" for
the CBD out to Ring 4, if necessary. The
sectors are numbered clockwise from "1" (North) to "8"
(Northwest). A zone in the CBD would be
numbered "001" and a zone to the southeast would be "141"
or "241." TAZs for each
density-zoning scheme were developed in TransCAD as area geographic
layers. Figures 4.1, and 4.2 present the
TAZs for low-density-scheme and high-density-scheme respectively.
Figure 4.1 TAZs for the
Low-Density-Zoning Scheme
Figure 4.2
TAZs for the High-Density-Zoning Scheme
Data Preparation for TAZs
Population and
Housing Data:
In Chapter 3 it was discussed how the population and
housing data for census blocks was prepared from available database and area
geographic layer for census blocks from TIGER/LINE 95 CD (23). To prepare the demographic data for the TAZs,
the population and number of households for census blocks within each TAZ was
aggregated to produce the total population and number of households for each
TAZ. This was done in TransCAD by using
the “Merge-by-value” feature for creating ‘districts’ (27). The procedure for preparing demographic data
for the TAZs is given as follows:
1. The area geographic layer
for the TAZs for the density scheme for which TAZ data were being prepared was
opened in TransCAD.
2. The area geographic layer
for census blocks with the associated database containing population and household
data for each block within the study area was opened as a second layer using
the TransCAD “Map-Layers-Add Layer” command.
The census blocks were made the active layer. The colors of the two layers that were opened
were made different so as to identify them.
3. The associated dataview in
TransCAD for the census blocks geographic layer was modified using the TransCAD
“Dataview-Modify Table”, and a new integer field named ‘zone_no’ was
added. This field was used to give zone
numbers to the census blocks.
4. The TransCAD selection tool
was activated, and for each TAZ the census blocks within it were selected and
the field ‘zone_no’ in the associated dataview was filled with the zone number
of the corresponding TAZ number using the TransCAD “Edit-Fill” command in the
dataview for the selected records. In
this way, all the census blocks within the study area were designated with the
zone number of the corresponding TAZ to which they belonged.
5. The area geographic layer
for census blocks was checked to be sure it was the current working layer. The TransCAD “Tools-Merge by Value” feature
was used to merge the census blocks based on ‘zone_no’ and to create a new area
geographic layer which merged the area of all the census blocks having the same
zone number, i.e., belonging to the same TAZ.
The associated database consisted of one record for each zone number
containing the total population and households of census blocks of that
zone. This process is termed ‘creating
districts’ in TransCAD (27). Figure 4.3
shows TransCAD window for creating districts for low-density-zoning scheme from
census blocks.
6. The area geographic layer
for the TAZs was joined with the dataview of area geographic layer for census
block districts developed in step 5 and exported as new area geographic layer
for TAZs containing population and household data using the ‘Tools-Export’
command.
Figure 4.3 TransCAD Window for Preparing Population and Household Data
for TAZs for Low Density Zoning Scheme
Employment
Data:
The procedure for locating employment records based
on address was discussed in Chapter 3.
The results of the locating procedure was a point geographic layer
representing the locations of employers within the study area. The associated database consisted of records
for each employer. Then the employers
were assigned to the respective zones to which they belonged. Thereafter the number of employees in each
TAZ was determined according to employment type. In this section, the procedure of assigning
employers to their respective zones and determining employment data for the
TAZs is discussed.
1. The area geographic layer
defining the TAZs for the density scheme for which the TAZ data were being
prepared was opened in TransCAD.
2. The point geographic layer
representing employment locations was opened as a second layer using the
TransCAD “Map-Layers-Add Layer” command.
The associated dataview of the point geographic layer was modified using
the TransCAD “Dataview-Modify Table” command and a new integer field named
‘zone_no’ was added. This field was used
to assign aTAZ number to each employment record of the TAZ to which an employer
belonged.
3. The point geographic layer
representing employment locations was made the current working layer. The TransCAD selection tool from the
“Tools-Toolbox” was activated and for each TAZ the employers within it were
selected and the dataview field ‘zone_no’ was filled with the corresponding TAZ
number using the TransCAD “Edit-Fill” command.
This assigned the corresponding TAZ number to each employment
record. The dataview was saved as a
dBase file using the “File-Save-as” command.
4. The dBase file saved in step
3 was opened in Microsoft Excel. All the
fields except those specifying the TAZ number, the number of employees and the
employment type for each record were deleted.
5. The Microsoft Excel
“Data-filter-Auto filter” command was used to categorize the data according to
zone number and employment type. The
employment type was specified by 0, 1 or 2 for non-retail, service and retail employment
respectively for each record.
6. For each zone number, the
total number of employees of each employment type was determined by selecting
each type of employment from the scroll-down list in the data field for the
employment type in the MS-Excel worksheet, and then summing up the total number
of employees for that employment type.
7. The area geographic layer
for the TAZs containing population and household data were opened in TransCAD,
and the associated dataview was modified by adding three integer fields named
‘Non-Retail’, ‘Service’ and ‘Retail’ and was filled with the employment data
obtained from step 6 for each zone.
Figure 4.4 shows the MS-Excel worksheet for calculating the number of
retail employees (employment type = 1) for TAZ number 10, which is equal to 28.
This completed the preparation of TAZs and their
associated data for the two different levels of zoning scheme that would be
used for building the travel demand model.
The associated dataview for the area geographic layer developed in step
7 contained the fields: TAZ number, total population, total households,
non-retail, service and retail employees for each TAZ, for two different levels
of TAZs.
Developing
the Street Network
Building the street network using different links from the line geographic file imported from US Streets 97 CD was discussed in Chapter 3. After defining the TAZs, centroids and centroid connectors were added to the network. In this section, the procedure for modifying the network by adding zone centroids and centroid connectors is discussed.
1. The street network that was
developed as discussed in Chapter 3 was opened in TransCAD. The line geographic layer representing the
streets was associated with a point geographic layer representing the
intersections. The point layer was made
the current working layer and a formula field named ‘temp’ was added to the
associated dataview using the TransCAD “Dataview-Formula fields” command. The line geographic layer was then made the
current layer and was exported to two new geographic layers to be used for
modifying networks for two different levels of zoning. The point layer was modified in order to make
the associated dataview for intersections layer in the two new street layers
modifiable, so that new fields could be added to the dataview of the associated
point geographic layer. The field ‘temp’
was renamed as ‘zone_no’ in both the networks.
Figure 4.4 Microsoft Excel Window for Calculating the Number of Employees for Different Employment Types for Each TAZ in Low - Density Zoning Scheme
2. The area geographic layer
for the TAZs for the level of zoning for which the network was being prepared
was opened as a second layer using the TransCAD “Map-Layers-Add Layer”
command. The line geographic layer was made
the current working layer.
3. The aerial photographs,
employment locations and residential zones within each TAZ was used to
determine the position of the zone centroid and location of centroid
connectors. The centroid for each TAZ
was located at the apparent center of activity of each TAZ.
4. The network was modified by
adding centroid connectors and updating the associated database of the line
geographic layer. A travel speed of 15
miles per hour was assigned to the links representing centroid connectors. The ‘zone_no’ data field in the associated
dataview for the points representing centroids was assigned the corresponding
TAZ number.
5. The TransCAD
‘Network-Create’ command was used to create network from the line geographic
layer and its associated intersection layer.
Addition of
External Stations:
A total of eleven external stations for external-internal and through trips was selected for the study based on information provided for the transportation network and travel activities. The major streets extending through the cordon line connecting to the external stations were identified from the street network geographic layer as being present in the US Streets 97 CD (24), McPherson planning area map in the city comprehensive plan (21) and aerial photographs. The area geographic layer for TAZs was opened in TransCAD and dummy areas for the external station were added to the layer using the ‘Tools-Map editing’ feature in TransCAD. The street network was modified by adding centroids to the external stations and connecting them to the network using centroid connectors. Figures 4.5 and 4.6 show the TAZs with external stations added and Figures 4.7 and 4.8 show the network for low-density-zoning and high-density zoning schemes respectively.
Figure 4.5 TAZs and External Stations for Low Density Zoning
Figure 4.6 TAZs and External Stations for High Density Zoning
Figure 4.7 Street Network for Low Density Zoning
Figure 4.8a Steet Network for High Density Zoning; for
the Whole Study Area
Figure 4.8b Steet Network for High Density Zoning;
Expanded view for the Central Area.
Chapter 5: Building A Travel Demand Model
After defining and preparing the data for the TAZs and building the street network for two different levels of zoning, as discussed in Chapter 4, the next step was to build the travel demand models for two levels of detail in TAZs and calibrate them using available information. In this chapter, the sequential procedures followed to build and calibrate the travel demand models for the study area is discussed.
Trip Generation
Trip
Production:
The trip rates by trip purpose used for estimating trips productions were
obtained from Table 9 of NCHRP Report 365 (4) using the parameters for urban
area with population size 50,000 – 199,999.
Although the study area for this research had total population of
approximately 13,000, the parameters for trip rates were not available for
urban areas with such low population range, therefore the parameters for
population range 50,000 – 199,999 were used.
It was observed that the median income for all TAZs was within the range
of $20,000 to $39,999, which was categorized as medium income level by the
table. The following data were used for
internal trip production by trip purpose for all TAZs, for both levels of
detail:
i)
9.3 average daily person trips per household, and
ii)
percentage average daily person trips by purpose:
Home Based Work (HBW); 21%
Home Based Other (HBO); 56%
Non - Home Based (NHB); 23%
The external-internal trips produced by external
stations were categorized as NHB, and the data were available from KDOT.
Trip
Attractions:
Trip attraction rates for estimating zonal trip attractions were available from
Table 8 of NCHRP Report 365 (4). The trips attractions for TAZs were estimated
from the employment and household data and the trip rate parameters. The external stations were assumed to have no
attractions.
NHB Trip
Productions for External Stations: The non-home-based trips produced by the external
stations were available from KDOT (29) and the values were added to the
records.
Balancing
Productions and Attractions: The productions and attractions of both internal
trips and external-internal trips were balanced by holding productions constant
and balancing attractions to equate the total attraction to production for each
trip purpose. TransCAD (27) was used for
this procedure.
Trip Distribution
Friction
Factors:
Friction factors were estimated by applying a gamma function to zone-to-zone
impedance (4). The zone-to-zone
impedance matrix was developed by applying multiple shortest path procedures on
the network. The network for each level
of detail was opened and the
centroids of all TAZs were selected from the
associated intersection layer with the line layer representing the street
network.
TransCAD “Networks/Paths-Multiple paths” procedure
was run to develop a matrix consisting of minimum free-flow travel time between
each pair of centroids. This matrix was
used as the impedance matrix. The gamma
functions for each trip purpose, obtained from Table 14 of the NCHRP Report 365
(4), were applied to the values in each cell of the matrix and three new
matrices were obtained containing friction factors for each pair of centroids,
i.e., zone-to-zone friction factors for each respective trip purpose. The TransCAD ‘Planning-Trip Distribution-
Synthetic friction factors’ procedure was run to generate the friction factor.
Trip
Distribution:
The gravity model was used for trip distribution using zonal productions and
attractions and zone-to-zone friction factors, for both levels of TAZ. The TransCAD “Planning-Gravity evaluation”
command was used to run the procedure and the resulting production-attraction
(P-A) matrices were obtained consisting of zone-to-zone productions and
attractions for each of the three trip purposes. The TransCAD “Matrix-Quick sum” command was
used for cell-by-cell addition of the three matrices and obtain a single P-A
matrix.
P-A to O-D
Conversion:
The 24 hour P-A matrix obtained from trip distribution procedure was converted
to a 24 hour origin-destination (O-D) matrix by using the TransCAD “Planning-PA
to OD” command. As discussed in an
earlier section of this chapter, the trip rates used to estimate productions
and attractions were average daily person trips per household. It was necessary to incorporate the option of
converting person trips to vehicle trips in the P-A to O-D conversion procedure
in TransCAD. The factor for this
conversion used in this research was 1.15, which was obtained by dividing the
person trip rate, taken from Table 9 of the NCHRP Report 365 (4) as discussion
in trip productions, by the corresponding vehicle trip rate.
Adding Through
Trips: The
values for the station-to-station truck and auto trips were obtained from KDOT
for the external stations (29). An O-D
matrix for the through trips between external stations was prepared and was
added to the previously prepared O-D matrix of the internal and
external-internal trips. This final O-D
matrix was used for traffic assignment as discussed in the next section.
Traffic Assignment
The TransCAD workspace for the street network for
each detail and the respective O-D matrices were used for running the traffic
assignment procedure. The user
equilibrium method for traffic assignment (27) was applied through TransCAD and
the default values for the parameters alpha and beta in the BPR function for
volume-to-capacity analysis were assigned 0.15 and 4.0, respectively, as
obtained from NCHRP Report 365 (4).
Model Evaluation
Screenline
Analysis:
Screenline analysis was done by comparing the traffic counts for the streets
crossed by the railway track passing through the CBD of the city, with the
assigned traffic volumes from the model, as shown in Figure 5.1.
Figure 5.1 Screenline Used for Model Evaluation
The streets
with volumes considerably higher or lower than the respective traffic count
were noted and some modifications were made to the network by adding or
removing centroid connectors to those streets from nearby TAZs, to make the
trips from the zones more realistic. It
was observed that the Old 81 Bypass on the west part of the town had low
traffic volumes assigned because the centroids of the adjacent TAZs were closer
to the US 56 highway, and thus the trips were loaded via that street. But in reality, there existed a number of
employers along the two sides of the bypass, and for them the trips were made
via that street. To account for this
situation, the respective centroids were located closer to the bypass. Also, it was observed that
It was observed that the total traffic volume on the
streets on the screenline, as assigned by the model, was less than the total
traffic count. The trip productions for
both levels of detail in TAZs were increased by a factor equal to the ratio of
the total volume from traffic counts on the streets on the screenline to the
assigned volume, and the models were rerun to assign the increased trips to
their respective networks. The results
are discussed in Chapter 6.
Miles and
Daily Vehicle Miles of Travel: The total length of different streets according to
their functional classification and corresponding total daily vehicle miles of
travel, as obtained from traffic volumes loaded by the model, was compared with
the observed data for McPherson which was available from KDOT (28). The daily vehicle miles from the model was
estimated by multiplying the length of each link times the associated
directional volumes loaded, for each type of street within the study area
according to their functional classification,.
The results are discussed in Chapter 6.
Regression
Analysis:
Regression analysis was done to estimate the slope of the least square line and
the value of R-Square for the data set, using traffic counts as the independent
variable and the assigned traffic volumes on the corresponding links as the
dependent variables. The intercept of
the regression line was forced through zero in the analysis. This was done to avoid non-negative
intercepts of the regression line.
Chapter 6:
Results
Demographic
and Socioeconomic Data of TAZs:
The socioeconomic data such as auto-ownership,
income and employment, and the demographic data such as total population and
households for each TAZ in both levels of detail of the zoning schemes are
shown in Tables 6.1 and 6.2.
Table
6.1 Demographic and Socioeconomic Data for the Low Density Level of Zoning
|
Zone No |
Pop-ulation |
House-holds |
Non-Retail Emp |
Retail Emp |
Service Emp |
Income |
Autos per HH |
|
1 |
151 |
56 |
27 |
0 |
0 |
29015 |
2.03 |
|
2 |
113 |
45 |
376 |
0 |
0 |
28448 |
2.11 |
|
3 |
47 |
15 |
0 |
0 |
0 |
26430 |
1.64 |
|
4 |
30 |
9 |
0 |
0 |
0 |
26511 |
1.98 |
|
5 |
5 |
1 |
0 |
2 |
17 |
26430 |
1.64 |
|
6 |
770 |
266 |
615 |
8 |
157 |
26430 |
1.64 |
|
7 |
6 |
3 |
105 |
2 |
45 |
26430 |
1.64 |
|
8 |
627 |
218 |
23 |
306 |
67 |
26430 |
1.64 |
|
9 |
262 |
105 |
89 |
28 |
39 |
26430 |
1.64 |
|
10 |
1017 |
450 |
126 |
54 |
39 |
26430 |
1.64 |
|
11 |
1440 |
655 |
84 |
4 |
164 |
23802 |
1.62 |
|
12 |
1392 |
602 |
49 |
654 |
100 |
23802 |
1.62 |
|
13 |
333 |
165 |
205 |
117 |
634 |
23802 |
1.62 |
|
14 |
506 |
214 |
572 |
164 |
142 |
26430 |
1.64 |
|
15 |
2903 |
1237 |
100 |
24 |
650 |
33333 |
1.71 |
|
16 |
1528 |
559 |
441 |
0 |
405 |
33333 |
1.71 |
|
17 |
0 |
0 |
373 |
0 |
0 |
26430 |
1.64 |
|
18 |
90 |
33 |
27 |
0 |
0 |
26430 |
1.64 |
|
19 |
252 |
86 |
5 |
0 |
1 |
33333 |
1.71 |
|
20 |
31 |
6 |
0 |
225 |
0 |
33333 |
1.71 |
|
21 |
1634 |
645 |
719 |
|
219 |
33333 |
1.71 |
Table
6.2 Demographic and Socioeconomic Data for the High Density Level of Zoning
|
Zone No |
Pop- ulation |
House- holds |
Non Retail
Emp |
Retail Emp |
Service Emp |
Income |
Autos per HH |
|
101 |
227 |
106 |
164 |
490 |
553 |
23802 |
1.62 |
|
111 |
232 |
91 |
22 |
0 |
18 |
23802 |
1.62 |
|
112 |
106 |
59 |
41 |
172 |
81 |
23802 |
1.62 |
|
113 |
44 |
26 |
17 |
5 |
8 |
23802 |
1.62 |
|
121 |
369 |
161 |
6 |
0 |
7 |
33333 |
1.71 |
|
122 |
270 |
122 |
14 |
0 |
42 |
33333 |
1.71 |
|
123 |
151 |
69 |
35 |
0 |
3 |
26430 |
1.98 |
|
124 |
191 |
84 |
0 |
0 |
25 |
26430 |
1.98 |
|
125 |
110 |
39 |
0 |
0 |
3 |
26430 |
1.98 |
|
126 |
83 |
29 |
0 |
0 |
16 |
26430 |
1.98 |
|
127 |
132 |
54 |
0 |
0 |
0 |
26430 |
1.98 |
|
128 |
86 |
44 |
0 |
0 |
4 |
26430 |
1.98 |
|
131 |
0 |
0 |
0 |
0 |
0 |
26430 |
1.98 |
|
132 |
235 |
120 |
3 |
25 |
13 |
26430 |
1.98 |
|
133 |
827 |
373 |
0 |
12 |
141 |
26430 |
1.98 |
|
134 |
378 |
162 |
81 |
17 |
10 |
23802 |
1.62 |
|
141 |
463 |
183 |
52 |
3 |
16 |
23802 |
1.62 |
|
211 |
149 |
68 |
71 |
109 |
92 |
26430 |
1.98 |
|
212 |
81 |
29 |
462 |
3 |
28 |
26430 |
1.98 |
|
213 |
0 |
0 |
623 |
0 |
0 |
26430 |
1.98 |
|
214 |
1329 |
511 |
191 |
24 |
349 |
33333 |
1.71 |
|
215 |
199 |
48 |
0 |
0 |
50 |
33333 |
1.71 |
|
216 |
252 |
86 |
5 |
0 |
1 |
33333 |
1.71 |
|
217 |
240 |
58 |
0 |
0 |
131 |
33333 |
1.71 |
|
218 |
1300 |
500 |
0 |
149 |
54 |
33333 |
1.71 |
|
219 |
876 |
389 |
3 |
0 |
455 |
33333 |
1.71 |
|
220 |
487 |
290 |
97 |
7 |
12 |
33333 |
1.71 |
|
221 |
31 |
6 |
0 |
0 |
0 |
33333 |
1.71 |
|
222 |
956 |
379 |
80 |
13 |
145 |
26430 |
1.98 |
|
223 |
1 |
1 |
80 |
0 |
0 |
26430 |
1.98 |
|
225 |
8 |
2 |
0 |
0 |
0 |
33333 |
1.71 |
|
226 |
17 |
4 |
0 |
0 |
0 |
33333 |
1.71 |
|
227 |
2 |
1 |
0 |
80 |
0 |
26430 |
1.98 |
|
228 |
2 |
1 |
0 |
125 |
68 |
26430 |
1.98 |
|
229 |
647 |
256 |
61 |
7 |
6 |
26430 |
1.98 |
|
230 |
0 |
0 |
498 |
0 |
0 |
33333 |
1.71 |
|
231 |
0 |
0 |
60 |
9 |
4 |
26430 |
1.98 |
|
232 |
2 |
1 |
10 |
266 |
21 |
26430 |
1.98 |
|
233 |
2 |
1 |
15 |
31 |
14 |
26430 |
1.98 |
|
234 |
4 |
3 |
0 |
0 |
0 |
26430 |
1.98 |
|
235 |
221 |
90 |
4 |
0 |
0 |
26430 |
1.98 |
|
236 |
33 |
10 |
0 |
0 |
0 |
26430 |
1.98 |
|
237 |
4 |
3 |
0 |
0 |
6 |
26430 |
1.98 |
|
238 |
623 |
215 |
23 |
2 |
61 |
26430 |
1.98 |
|
242 |
472 |
230 |
59 |
25 |
23 |
26430 |
1.98 |
|
243 |
731 |
248 |
2 |
2 |
157 |
26430 |
1.98 |
|
244 |
0 |
0 |
432 |
0 |
0 |
26430 |
1.98 |
|
245 |
39 |
18 |
256 |
0 |
0 |
26430 |
1.98 |
|
246 |
0 |
0 |
0 |
0 |
0 |
26430 |
1.98 |
|
311 |
34 |
12 |
0 |
0 |
0 |
26430 |
1.98 |
|
312 |
28 |
8 |
0 |
0 |
0 |
26430 |
1.98 |
|
313 |
28 |
11 |
27 |
0 |
0 |
33333 |
1.71 |
|
314 |
21 |
10 |
0 |
0 |
0 |
29015 |
2.03 |