10th European EMME/2 User’s Group Conference
Thessaloniki, Greece
The Use of EMME/2, SATURN, MapInfo, ACCESS, EXCEL and
ALOGIT in the CHUMMS Model
by
Jason, Jinsong Zhou
Oscar Faber
St Albans, United Kingdom
May 31 – June 01, 2001
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The Use of EMME/2, SATURN, MapInfo, ACCESS, EXCEL and
ALOGIT in the CHUMMS Model
1
Introduction
The Cambridge to Huntingdon Multi-Modal Study, CHUMMS, is among the first tranche of multimodal studies commissioned by the Department of the Environment, Transport and the Regions,
DETR, after the announcement of the Government’s Integrated Transport White Paper in 1998. Figure
1 shows the CHUMMS study area.
Figure 1: CHUMMS Study Area
The study considered solutions to congestion and safety problems in the Huntingdon and Cambridge
corridor, which also had substantial development pressure. More specifically, the study focused on the
A14 east-west trunk road between Cambridge and Hunting which is the pinch point of the whole A14
route between Felixstowe and the M1. The following problems were specified for the corridor and the
A14:

congestion, which is likely to be exacerbated by potential developments to the north and west
of Cambridge;

the shared use by local traffic with inter regional and international traffic on the A14;

alignment constraints and environmentally sensitive locations, especially around Huntingdon;
and

the dual strategic role of the A14 both east/west (A14 to A1 and M1 and east coast) and
north/south (A1(M) to M11).
Its objective, therefore, was to recommend multi-modal transport plans, which would provide
sustainable solutions to address the most urgent problems in the corridor, looking in particular at
opportunities for modal shift from the car. A variety of transport modes were assessed, including
heavy rail, light rail and guided bus systems. Extensive new park & ride site provisions were also
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under serious consideration. The study also examined the interaction between transport and land use,
and sought to develop a land use and transport system that would sustain and enhance the vitality and
viability of the Cambridge area in the long run.
Oscar Faber, a member of the consortium, was commissioned to develop a transport model which
would enable us to assess the various land use and transport options considered. Given the complex
nature of the study, it was clear that the CHUMMS model would require the effective use of the
software available to us. As a result we adopted EMME/2 and SATURN as the main transport
planning software, and MapInfo, SPSS, ALOGIT, EXCEL and ACCESS to help facilitate the
development of the fully functional model.
2
Overall Model Structure
The overall structure of the CHUMMS model process is illustrated in Figure 2. The fundamental
concept was to use a four-stage model with the classic elements, namely:




trip generation;
trip distribution;
modal choice; and
assignment.
The reasons for taking this route for the CHUMMS were as follows:
Figure 2: Overall Structure of the CHUMMS Model Process
Trip Generation &
Distribution
(MENTOR)
Daily PA Demands by Purpose
and Traveller Type
Mode Choice Model
(EMME/2)
Slow Modes
Demand
Car Demand
External –
External Highway
Demand
P&R
Demand
Conventional
Bus Demand
Demand for
New Mode
Total Demand for
Travel on the Highway
Total Demand for Travel by
Public Transport
Highway Assignment
by Period
(SATURN)
Public Transport Assignment
by Period
(EMME/2)
Heavy Goods
Vehicle Demand
Highway Network
Network and Economic
Assessment
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Public Transport
Network
First, the study involved long time scale of demand forecasts. Clearly there will be significant changes
in trip patterns, eg trip length distributions, trips costs, and modal choices, which are not captured in
simpler approaches.
Second, the study involved the issue of future housing and employment development in the study area.
The interaction between that development and the transport system pointed to the use of a trip
generation/distribution model.
Third, the availability of existing models for the study area, namely the MENTOR (land use) and
SATURN (highways) models made the development of a strategic multi-modal land use /
transportation modelling system possible within the required timescale.
And last, the process adopted complied with the requirements specified in the government’s Guidance
on the Methodology for Multi-modal Studies (GOMMMS).
Main features of the model process are:

an integrated land-use transportation modelling. In particular, the MENTOR programme,
developed by MEAP, a member of the consortium, was used to generate trips for different land use
dispositions, taking into account changes in generalised cost between transport options;

hierarchical, or nested, logit type mode choice models, programmed in EMME/2. Alternative nest
structures and coefficients were applied to trips with different travel purpose and traveller type
combinations. Park-and-ride mode was treated as an integrated part of the model process;

multi-class stochastic user equilibrium highway assignments and junction simulations carried out
in SATURN;

public transport assignments carried out in EMME/2. This was to take advantage of the efficient
assignment algorithms provided in EMME/2, and the ease with which various elements of travel
attribute matrices can be skimmed off. The assignment itself was carried out in an incremental
manner so that demand matrices were assigned in sequence. In the light rail option, for example,
the demand for LRT was assigned after the assignment of traditional bus demand; and

a highly automated process, which enabled us to undertake the complex tasks of the model, eg
mode choice models, highway assignments (in SATURN), PT assignments, cost and disutility
calculations for the MENTOR programme, and so forth, by simply keying in a single command
line, such that human errors in the process were limited.
It is worth emphasising the important feature of the CHUMMS model that it adopted an iterative
approach whereby the effects of transport strategies can be fed back to the trip generation/distribution
stage. The generalised cost (or disutility) matrices by mode were produced at the end of each iteration
and passed back to the MENTOR model for the next iteration. The iterative process has ensured that
the interaction between the land use patterns and transport system can be assessed in a robust and
consist way.
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Mode Choice Model
The mode choice element formed a particularly important part in the CHUMMS model since one
essential requirement of the model was to be able to consistently assess people’s choices of mode when
facing different transport strategies. In order to establish the appropriate model structures and
coefficients, a stated preference survey exercise was carried out as part of the study. Another purpose
of the survey was to derive parameters reflecting people’s preference towards new modes, such as
LRT, guided bus, and so forth, as well as to existing modes. We successfully collected the survey data,
and undertook the exercises of data analysis and model testing using SPSS and ALOGIT as analysis
tools. This led to the adoption of the hierarchical logit type of mode choice model, the parameters of
which were derived from the ALOGIT model estimations.
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The reason for this - in addition to our judgement in favour of this model form, based on our experience
- was that SP survey results had shown a closer analytical link between public transport modes, eg bus
and rail, than that between car and bus. In addition, a model with a nested structure has an advantage
over multi-nomial type models in that it is better able to address sub-mode choice issues, such as the
choice within the PT hierarchy (or nest) between bus and LRT or bus and guided bus.
In general, two sets of mode choice model nest structures were used in CHUMMS, depending on which
traveller type the trips belong to. These two structures are illustrated in Figures 3 and 4 respectively.
For the purpose of the mode choice model, trips were segmented by two traveller categories. These
two categories are:


car available category; and
non car available category.
Note that the MENTOR model actually produces trips with three traveller categories. In addition to the
two mentioned above, the third is the part-car available category. Trips in this category were split into
either non-car or car available categories for the mode choice model, the proportions applied in the
model being derived from the survey data.
Figure 3: Modal Choice Model Structure 1 – Car Available
Motorised trips
Car
Slow mode trips
P&R
PT
Walk
Bus
Guided bus/LRT
Rail
Bike
Figure 4: Modal Choice Model Structure 2 – Non-car Available
PT
Bus
Guided bus/LRT
Slow mode trips
Rail
Walk
Bike
The MENTOR model produced 16 24-hour Production/Attraction trips, segmented by trip purpose and
traveller type. Again for the purpose of the mode choice model, these trip purposes were aggregated
into 4 main purpose categories, each having a different set of model coefficients. These four travel
purposes are:

Home based work (HBW) – trips between place of work and home;

Home based education (HBEd) – trips between home and place of education;

Home based other (HBO) – trips between home and other activities, eg visiting friends
and relatives (VFR), shopping, personal business, etc, but excluding home-based
employer’s business; and

Other purposes (OTH) – all non-home-based (NHB) trips plus home-based employer’s
business.
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The mode choice models were developed in EMME/2 to make best use of its efficient macro language
and power matrix calculation facility.
An important feature of the model is that the park & ride mode is an integrated part of the logit model,
the utilities of both trip legs being calculated separately and combined using the matrix convolution
module, 3.23, in EMME/2. This enabled us to test schemes that involved extensive new park & ride
site provisions.
Slow modes, ie walking and cycling, are also included as part of the modal choice model. This is
important since existing data has suggested that slow modes contribute a significant proportion to the
total trips in the corridor. Missing out this proportion of trips in the model may lead to potential
misjudgements of the future year demand forecasts. At the same time, it is desirable to have a model
that is able to assess the impact of transport/land use strategies on the slow modes.
It is worth mentioning that as part of the survey, individuals were shown photo images of different
public transport modes, and then asked to express their opinion towards each mode, which was to be
measured by a number, ranging from, 1, very poor, to 9, very good.
The resultant mean scores tend to be clustered around the central point, but the relative values seem to
be intuitively correct. For example, LRT is valued most highly, followed by low floor bus, and both
preferred to traditional bus and heavy rail. The order of preference varies across sub groups, with car
available work & education respondents favouring guided bus over low floor bus, and those without a
car having greater preference for rail than guided bus.
Interestingly the new (low floor) bus and guided bus were valued very close to LRT. All of them were
preferred to traditional bus, which was scored very low. It appeared that people’s preferences were
strongly influenced by the image of a mode. This may be particularly relevant in the UK context,
where the traditional bus has been generally regarded as a less privileged mode. So the modern images
of new PT modes could play an important role in providing a quality alternative and attracting people
away from the car use.
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Data Exchanges between Software
Data exchanges between different software in the CHUMMS model can be illustrated in Figure 5.
Figure 5: Data Exchanges between Software
SATURN
New highway demand
matrices
ALOGIT/SPSS
Highway time, distance
matrices
Mode choice model
structure and coefficients
EMME/2
Network, transit lines
Network derived data
ACCESS/EXCEL/ VBA
Macros
Spatial data, network
and transit line data
MapInfo
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It is apparent that the availability of right software and expertise has been essential for the development
of the CHUMMS model to meet the requirements of the study of this kind. We were fortunate in that
we had all the required software at hand at the time the project started, and a team with deep working
knowledge and experience of these software, so that we were able to consolidate the strengths, whilst at
the same time avoiding the weaknesses of each of these software, to the advantage of the study.
As the data exchange between ALOGIT/SPSS and EMME/2 is relatively straightforward and has been
covered to some extent in the previous section, the description here will concentrate on data exchanges
between:



EMME/2 and SATURN through Fortran utility interfaces;
EMME/2 and MapInfo; and
EMME/2 and ACCESS/EXCEL for transit line coding.
It should be stressed that data exchanges may very often involve more than two software at a time.
Data from MapInfo, for example, were actually processed in ACCESS/EXCEL through VBA macros
before being converted to EMME/2 input files.
4.1
EMME/2 and SATURN
The CHUMMS model adopted SATURN for the highway assignments/simulations, and EMME/2 for
the public transport assignments. EMME/2 has acted as a ‘glue’, which combines different elements of
the model together.
The data exchanges between SATURN and EMME/2 were fully automated. From within the EMME/2
macro environment the model:

executes the SATURN model, which carries out multi-class stochastic user equilibrium
highway assignments and simulations, and produces distance and journey time matrices;

converts automatically the highway distance and time matrices to EMME/2 format through an
interface utility created using the Fortran programme, and then batches into EMME/2
databank;

undertakes PT assignments and mode choice models, and outputs new highway demand
matrices; and

converts highway demand matrices automatically to the SATURN model, through another
Fortran programmed interface utility, for the next iteration of highway assignment.
One issue involved in the automation process was that of synchronisation. The SATURN version 5
runs in the MS DOS environment, where several SATURN routines can run in a batch mode. But
unlike EMME/2, the model could actually skip the current routine when it encountered an error, and
continued to activate the next routine in the batch file, thus could pose a potential danger of missing
calculation. To counter this, a comprehensive error trapping mechanism has been set up for the model
so that in case of any undue module skipping, it can be detected and paused for correction, thus
eliminates the potential untraceable errors.
The carefully constructed EMME/2 macro structures, SATURN scripts and data exchange interfaces
have made it possible to run the whole model in an automatic manner, and as such have allowed us to
test various land use and transport strategies successfully in a tight time scale.
4.2
EMME/2 and MapInfo
MapInfo has played an important role in the development of the CHUMMS model, not only that it
provides high quality mappings and spatial data displays, but that as a technique it has helped facilitate
the model development process. We decided to use MapInfo as the tool to code the physical networks
for the model, which could later be converted to EMME/2 input files. The conversion itself was a
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highly automated process with ACCESS/EXCEL as the media. The approach involved the following
steps:
1.
zones and sectors were geo-coded in MapInfo, in the form of region object;
2.
the physical networks were also geo-coded in MapInfo in the forms of node and link objects.
These included junctions, bus stops, rail stations and P&R sites, and roads.
3.
centroid connectors were then created as link objects;
4.
user-defined fields attaching to these objects were also created in MapInfo. Examples of these
user attributes are:



5.
zonal user attributes: zone number, x and y coordinates, area indicator, etc.
node user attributes: node number, x and y coordinates, area indicator, stop indicator etc.
link user attributes: link type, mode, distance, area indicator, etc;
an ACCESS database file, having been specifically structured for this purpose, was used as the
media to convert the network information in MapInfo into EMME/2 input files, through a set
of VBA macros in ACCESS/EXCEL.
In addition, the mechanism has been set up so that network information in MapInfo can be converted
into either node/link attributes or vectors to facilitate network/matrix calculations.
The advantage of this approach is that the coding and future modifications of network schemes can be
achieved relatively easily using MapInfo, and the highly automated conversion approach means that the
task of network coding has been less tedious and less time consuming.
4.3
EMME/2 and ACCESS/EXCEL/VBA
One particular case of using ACCESS/EXCEL in the development of the model was the automated
process of converting base year bus timetable into emme/2 transit line input files. During the study, we
obtained the base-year bus timetable data, which was in MS Word format, and successfully converted
them into ACCESS/EXCEL, where the database had been carefully structured, and VBA macros
created, so that the timetable information could be converted into EMME/2 transit line files
automatically. The process entailed the following steps:
1.
the base-year timetable information in MS Word format was converted to ACCESS, where a
database with proper field structure was set up;
2.
a correspondence list was created between node numbers in EMME/2 network and station
names in the timetable. This matching process has been achieved efficiently in MapInfo since
station names were coded as a node user field already;
3.
service stopping patterns were defined according to stop indicators coded in MapInfo;
4.
services frequencies (headways) were defined by definition of modelling periods;
5.
segment travel times were derived from the timetable. Although segment distances were not
essential for the study, they were created from the information in MapInfo, and later used to
calculate average speeds; and
6.
the database was then converted to EMME/2 transit line files using VBA macros in ACCESS.
The mechanism has been set up in such a way that different criteria can be used. For example, transit
lines within any given time threshold can be selected and converted very easily. This has certainly
expedited the transit coding process, which would normally require a substantial amount of time from
an experienced EMME/2 user.
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5
Conclusion
Overall we have developed a model for the CHUMMS study that enabled us to assess the land use and
transport strategies considered in ways which are efficient and robust. This has been achieved through
the construction of a sensible model structure, the choice and best use of the software available, and the
careful design of EMME/2 macros and other data exchange interfaces.
By late 2000, we have successfully completed the assessments of the first batch of land use and
transport strategies, the results of which have been put forward for public consultations.
Reference
Akiva, B., et al, (1985), Discrete Choice Analysis.
Cambridgeshire County Council, (1999), Traffic Monitoring Report.
DETR, (2000), Guidance on the Methodology for Multi-modal Studies.
INRO Consultants Inc, (1998), EMME/2 User’s Manual, Release 9.
Spiess, H,. (1996), A Logit Parking Choice Model with Explicit capacities.
Vliet, D.V., (1998), SATURN User’s Manual.
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