RAIL DEMAND FORECASTING
IN HONG KONG AND SHENZHEN
AUTHORS
Tim Martin and Stephen Rutherford
Atkins China Limited
16/F World Trade Centre, 280 Gloucester Road, Causeway Bay, Hong Kong
Tele. (852) 2972 1000, Fax. (852) 2881 0056
tmartin@atkins-china.com.hk, srutherford@atkins-china.com.hk
ABSTRACT
Many cities in Asia are looking to rail-based systems in order to solve urban transport
problems and to allow further development. Patronage forecasts for rail system planning
and design must be robust and yet sensitive to small differences in the attractiveness of
the available route choice options. The default EMME/2 transit assignment procedure
can produce travel cost matrices that are suitable for use in strategic modelling but
limitations arise when the default procedure is used for detailed rail patronage
forecasting. This paper describes a logit-based multi-routing public transport EMME/2
model structure that is appropriate for urban rail networks with low service intervals.
The model allows for choice of sub-mode (rail or other), choice of station, and choice of
rail route between station pairs. The necessary network coding and transit assignment
‘additional options’ features required are outlined.
INTRODUCTION
RAIL DEVELOPMENT
Hong Kong. The 6.5 million residents of Hong Kong make around 12 million trips
everyday with 80% made by public transport (i.e. transit). Rail accounts for 35% of the
public transport market share with bus at 40%, minibus 15%, and LRT or ferry or tram
10%. The rail network first opened in 1979 and three lines were operational by 1997.
Last year the Airport Rail Line (Airport Express and Tung Chung Line) started services
and a further three lines will be opened by 2004. This recent emphasis on rail
development is closely related to increases in future population projections and
recognition of the efficiency and environmental benefits of rail transportation. Rail
services run at service intervals of two to three minutes and trains are often full in peak
periods where line flows over 70,000 passengers per person per direction (pphpd) are
regularly carried on the MTR. Interchange stations passenger (walk) flows can reach up
to 20,000 passengers per hour. Urban and trunk bus services also operate at high
frequencies and with high occupancies.
Shenzhen.
Population within the Shenzhen economic zone is set to increase by
around 1 million people by 2010. At the same time, the population is becoming more
dispersed and more affluent. Major developments are planned in terms of reclamation
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areas, New Towns, port developments and industrial centres. To address current
highway congestion and to provide an attractive alternative to the car, a Metro system is
planned comprising underground services in the Central Area with radial routes to the
development corridors. The first section of the planned Metro System will be open by
year 2003 with construction beginning later this year. Atkins China Ltd has recently
been commissioned by the Shenzhen Urban Transport Planning Centre to undertake the
Shenzhen Comprehensive Transport Planning and Rail Development Study. This 12
month Study involves the planning and feasibility of alternative rail systems and
alignments, with an emphasis on integrating development with the rail stations.
THE NEED FOR RAIL FORECASTS
The planning and design of rail network development requires realistic and plausible
patronage forecasts for a range of development scenarios and rail networks. Rail strategy
and system design decisions will be made on the basis of the patronage forecasts. Given
the engineering cost of rail systems (Hong Kong will spend HK$110 billion on rail
development between now and 2004), the planning resources dedicated to optimising the
rail system can prove to be extremely cost effective.
RAIL FORECASTS WITHIN THE OVERALL FORECASTING APPROACH
Public transport assignment is only one stage of the overall demand forecasting approach
that is required to produce future year rail passenger flows. For Hong Kong, and now for
Shenzhen, we have developed an overall land-use strategic transport planning model
incorporating the traditional four-stage approach within EMME/2. This model takes
planning data, transport network data and socio-economic data (income etc.) and
produces daily and peak hour flows on highway and public transport services.
For rail forecasting, the critical outputs from the strategic transport model include (i)
daily public transport trip matrices and (ii) peak hour highway/bus travel times.
Although the strategic model is not detailed within this Paper, it is important to stress that
the two inputs (trip matrices and highway network speeds) must be thoroughly checked
to ensure that they are acceptable for use in the detailed public transport model.
TRANSIT ASSIGNMENT
The term assignment describes the building of paths through a network and the loading of
a trip matrix onto those paths. Key outputs include routeings through the network,
flows/usage information, and network performance i.e. level of congestion (especially for
highway applications). Transit assignment involves (i) choice of public transport submode (bus / rail / ferry) and (ii) choice of individual service. System design is generally
based on demand forecasts (assuming unlimited capacity) which means that successive
model iterations (used in highway assignments) are not required. For public transport,
the assignment must include all elements of door-to-door journey cost such as:



Walk access time
Waiting time
Fare (converted to minutes using a Value of Time (VOT)
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

In-vehicle time
Walk egress time to final destination
Actual travel time is ‘weighted’ such that a generalised journey cost is produced.
Typically walking and waiting time is perceived as unattractive and factored by around 2
relative to in-vehicle time. Boarding and interchange penalties are used to represent the
inconvenience of an in-direct journey. Additional waiting penalties at boarding nodes or
in-vehicle time factor on congested services can be used to represent congestion.
STANDARD TRANSIT ASSIGNMENT WITHIN EMME/2
The ‘default’ transit assignment within EMME/2 incorporates a multi-routeing algorithm
based on the computation of ‘optimal strategies’. For any journey a ‘strategy’ comprises
a selection of ‘sub-strategies’ representing all the attractive sub-paths such that the total
expected travel time is minimised. The path-building and assignment procedure is
essentially a two-stage procedure as follows:
i.
Selection of a ‘attractive’ path set. At any decision point, a path is excluded
from the attractive path set if a traveller can reach the destination more quickly by
waiting the full service interval for an alternative service.
ii.
Allocation of trips based on frequency. Trips are allocated to the sub-paths in
the attractive path set on the basis of service frequency.
This algorithm is suitable for strategic modelling as the concept of an ‘overall strategy’
ensures that zonal travel costs are minimised. These travel costs can then be used in the
four-stage model for input to main mode choice calculations and distribution modules.
LIMITATIONS OF THE DEFAULT TRANSIT ALGORITHM
However if detailed forecasts of loadings on individual lines and services are required,
then straightforward application of the default EMME/2 algorithm may not be
appropriate. For the purpose of system design (and particularly station sizing) it is
essential that the forecasts are ‘continuous’ in that small changes in travel time / cost lead
to reasonable changes in patronage. The following features of the EMME/2 pathbuilding/assignment algorithm need to be considered when using the default algorithm:
Selection of Paths. When the headway (service interval) of the ‘lowest cost’ path is small
(2 or 3 minutes) as is often the case in Hong Kong, the algorithm can tend to operate in a
similar manner to an All-or-Nothing (AoN) approach. Potential problems occur when
two alternative paths are similar in terms of time and cost in that small change in travel
time can result in extremely large differences in flow.
Treatment of walk choices at Rail Interchange. A particularly important aspect for station
planning is the forecasts of passenger interchange demands. Within the default EMME/2
algorithm, all passengers already on board a particular rail service and heading for a
particular destination, will tend to act as a single group and ‘herd’ together (see Figure 2).
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Although the network can be coded to introduce a ‘decision point’ and to control the split
of passengers (i.e. avoid the AON decision), this is not an ideal solution.
Treatment of walk choices. In the allocation of trips to reasonable paths, walk links are
treated as having an infinite frequency and will therefore take 100% of all trips. Where
two alternative walk choices are available (e.g. zone centroids to choice of zone
connectors), a single link will take 100% of trips between an O-D pair. For zone
connectors, EMME/2 options 5.34 and 5.35 (for detailed analysis of transit trips) allow
trips to be allocated to the connectors on the basis of journey cost. This option is limited
due to processing time and it also precludes the ‘additional options’ features.
Effect of Frequency in Line Loading. Journey cost elements (other than service
frequency) can only influence line loading via the selection of paths. Thus (for a
individual O-D pair) a change in fare or travel time for a service will either (i) have no
effect or (ii) can influence whether or not the path is included within the reasonable path
set. As line flows are made up of many O-D pairs, the total line loadings are much less
sensitive than the route choice for a single O-D pair.
TECHNIQUES TO ADDRESS THE ABOVE
Small zone sizes should be used to address the issue of loading of trips between zone
connectors. Following the assignment, critical route choices should be identified and the
‘additional options’ facilities used to investigate whether the result is acceptable for study
requirements. If necessary, the network coding and assignment parameters can be
adjusted to ensure that ‘correct’ routeings are achieved.
However, for most transit applications involving system design, it is preferable to use a
‘matrix’ based approach to sub-mode split in place of the simple default algorithm.
Under this approach, two or more sets of public transport ‘biased’ paths are built. Trips
are then allocated to sub-modes based on the difference in travel costs according to a
‘diversion curve’ such as a logit model. This approach removes some of the
‘discontinuities’ described above and will produce at least two paths for each O-D pair.
However, the sub-mode split approach alone is not sufficient to address the more
complex rail routing issues detailed above. The approach that we have adopted to
produce rail forecasts for system design is described below.
RAIL MODEL
STRUCTURE
Our rail forecasting approach is a three-stage procedure, given the inputs of a daily public
transport trip matrix and highway speeds. Modules such as path-building, travel cost and
daily to peak hour factors are included within the public transport model structure. The
primary assignment-related models are as follows:

Sub-mode split model to split total public transport trips into two categories - rail
and non-rail trips – based on differences in journey time and cost. A trip that uses rail
for any part of the journey is included within the rail category.
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
Station Choice Model to determine the (initial) origin and (final) destination station
for rail trips. Between an OD pair, trips may have a choice of several origin and
destination rail stations

Rail Route Choice Model to determine the route through the rail network when more
than one rail routing is available
SUB-MODE SPLIT MODEL
The sub-mode split model parameters define the relationship between relative journey
costs by mode and the proportion of trips using each mode. The output from the submode split model is the proportion of public transport trips travelling by either (i) rail or
(ii) other public transport modes. We apply a ‘binary’ logit model to divide trips into rail
or non-rail as this is the most basic choice that travellers make. Separate curves were
calibrated for short, medium and long trips.
Apart from the total public transport trip matrix, the basic inputs to the sub-mode split
model include travel cost skim matrices taken from ‘biased’ rail and non-rail
assignments. A weighting of 3 to 5 is applied to the in-vehicle time of the unfavoured
mode to produce the biased assignments. The model parameters include the slope i.e.
degree of sensitivity and mode constant i.e. bias (which can be expressed as minutes in
favour of a particular mode). The function employed in the PTM is as follows:
1
Pr
=
(1 + e ( (GCn - GCr)*a - b) )
where:
Pr =
GCn =
GCr =
a =
b =
the probability of choosing rail mode for any OD pair
generalised travel cost by non-rail modes from origin to destination
generalised travel cost by rail modes from origin to destination
slope parameter
mode constant (positive = in favour of rail)
The parameters a and b are ideally calibrated based on observed data. For cases with little
survey data (or with no existing rail system such as Shenzhen) then imported values need
to be used (subject to sensitivity tests).
STATION CHOICE MODEL
The station choice model determines the ultimate boarding station and final alighting
station of rail trips. In simple rail networks, the choice of station is straightforward. For
more complex networks, there may be a number of options available all of which involve
travel by rail (see Figure 1). Typically, people within walking distance (up to 700m) will
walk to the nearest station. For bus access trips, however, the route choice model will
determine the split of trips amongst the available stations. In Hong Kong only 50% of
journeys are single-leg ‘walk-in’/ ‘walk-out’ rail trips which shows the importance of bus
access trips.
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The modeller must identify the range of alternative choices within the network/corridor in
question. Path-building parameters within EMME/2 must be set so that the desired
routeing is achieved. Travel costs from each path are saved and manipulated in order to
remove the ‘bias’ and to give the actual travel cost. The multinomial logit diversion
function applied to the three sets of alternative rail paths is similar to the binary logit
function used in the sub-mode split model. The form of the model is as follows:
eUr1
Pr1
=
(eUr1 + eUr2 + eUr3 )
where Pr1
eUr1
=
=
the probability of choosing rail path 1 for any OD pair
a utility function for travel by rail path from origin to destination
For equal generalised journey cost, an equal proportion of trips will be allocated to each
alternative. For a situation with two alternative route choices, a difference of 10
generalised cost minutes will result in around 75% of trips using the shorter alternative.
Figure 1
Example of Available Route Choices: Shatin to Hong Kong Island
Choice of Origin Rail
Station (primarily for
bus access trips)
Choice of cross-harbour submode: ‘MTR’ or ‘KCR plus
cross-harbour bus’
Choice of Destination
Rail Station
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RAIL ROUTE CHOICE MODEL
For each station-to-station pair, the rail route choice (RCM) model determines the
proportion of rail trips travelling via each available rail route based on the travel cost of
each alternative. A station-to-station trip matrix is extracted from the full zone-to-zone
assignment following the station choice model. The RCM is applied at the station-tostation level in order to reduce processing time. The general form of the model is the
same as the station choice model although the model parameters are different in that the
route choice model is more sensitive to journey time differences. For a difference
between two alternative routes of 10 generalised cost minutes, it is estimated that around
90% of trips will use the shorter alternative.
A typical application of the RCM is illustrated in Figure 2 which shows the proposed
West Rail service in HK and the interchange stations with two existing lines. In the AM
peak, a major flow of passengers is forecast to travel on West Rail towards the Central
area which is also a primary interchange with other rail lines. In reality, with the small
differences in journey characteristics in terms of walk interchange time, wait time etc,
one would expect a limited amount of multi-routing between the two route options, but
that the route-choice would be highly sensitive to station design in terms of walkinterchange length at each station.
Figure 2
Route Choice Involving Rail Interchange Stations
NORTH WEST
NEW TERRITORIES
MEI FOO STATION
(6 MINUTE WALK)
WEST RAIL
(PLANNED)
TUNG CHUNG LINE
(EXISTING)
or
aj
M
TSUEN WAN LINE
(EXISTING)
r
ge
en
ss
Pa
YEN CHOW STREET
STATION
(3 MINUTE WALK)
d
an
m
De
Choice of Rail Line
and Interchange
Station
ow
Fl
HONG KONG
STATION
KEY
interchange station
CENTRAL STATION
This level of sensitivity is extremely difficult to achieve using sub-mode split techniques
alone. In the route choice model, one path will be built via the Tsuen Wan Line and one
path via the Tung Chung Line. The journey costs via each alternative are compared using
the logit model described above. The route specific matrices are then successively
assigned (using the ‘assign more trips’ options) to the network to give line loadings and
rail interchange flows. As with all logit models, a key issue in the design of the Rail
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Route Choice model is that only ‘true’ alternatives should be input. If required, further
levels in the hierarchy can be introduced to differentiate between two very similar but
slightly different paths.
SPECIAL MODEL FEATURES
RAIL NETWORK CODING
The rail network was coded so that a single entry/exit ‘gate’ link (node 8177-7177)
provided walk access/exit from each rail system as shown in Figure 3. The arrangement
for rail-rail interchange stations is more complex but operates on the same principle.
Figure 3
ZONE
CONNECTORS
Network Coding for Rail Stations
STATION ENTRY
Note:
Board Fare set by
length of entry ‘gate’
walk link. Set to zero
on outbound gate link
RAIL PLATFORM
BUS
LINK
BUS-RAIL WALK LINK
RAIL LINE
This network coding serves several purposes. Firstly, the initial boarding fare of a rail
trip can be represented while allowing subsequent boardings (i.e. rail interchanges) to
occur without fare penalty. Secondly, the gate link allows for the extraction of a stationto-station trip matrix for use in the route choice model and for calculation of system
revenue Thirdly, the interchange between bus and rail can be represented by a dedicated
walk link allowing alternative interchange penalties to be modelled.
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FARES MODELLING
Fares are included within the network as additional time penalties. Bus fares are
straightforward and are included in the line-specific ‘boarding penalty’ which is ideal for
‘flat’ fare systems. ‘Step-down’ fares need to be coded as ‘line and node’ specific option
which can be time-consuming to code due to the volume of bus routes.
Rail fares are represented as an initial boarding fare (represented by the ‘gate link’ on rail
system entry) plus a distance element per kilometre stored in a link attribute. The stationto-station fare represented in the network can be extracted using the ‘additional options
assignment’ and compared to the actual fare table. Adjustments to the boarding penalty
and the distance-related fares on individual links can then be made in an iterative manner
to ensure that fares are correctly represented.
EXTRACTION OF RAIL TRIP MATRIX (FOR ENTIRE SYSTEM OR BY OPERATOR)
The procedure for extraction of a station to station matrix requires use of the additional
options procedures and a short (Fortran) program. In the example below, 100 rail trips
travel between a single O-D pair and all use origin destination ‘188’ as shown below.
Some rail route choice occurs with 60% of trips alighting at station ‘177’ and 40% of
trips alighting at station ‘193’. The steps to produce a station to station matrix include
the following (see also Figure 4):
1. The link extra attribute (@RSTAT) on the walk gate-links is set as follows where
%ID% is a three-digit station identifier:

Entry links given the value: %ID% * 1000

Exit links are given the value: %ID%
2. A first ‘additional options’ assignment is undertaken to sum the auxiliary transit
attribute @RSTAT. The ‘maximum attribute sub-strategies’ are retained with the
path operator ‘+’ and a threshold of ‘1 to 999999’. The ‘additional strategy attribute’
and the ‘active demand’ are saved.
3. The link extra attribute (@RSTAT or Rail STATion) on the walk gate-links is reset:

Entry links are given the value: (-1000+%ID%)*1000

Exit links are given the value: -1000+%ID%
4. A second ‘additional options’ assignment is undertaken. This time the ‘minimum
attribute sub-strategies’ are retained with the path operator ‘+’ and a threshold of ‘999999 to 0’. The ‘additional strategy attribute’ and the ‘active demand’ are saved.
5. The four matrices are then converted to ASCII and a short program is used to produce
a station to station trip matrix. The values of RSTAT and the active demand output
from each assignment are shown in the Table below. The FORTRAN program then
re-converts @RSTAT back into the origin and destination station and appends the
number of trips using the route.
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Figure 4
1).
Extraction of Station to Station Matrix for a Rail System
Assignment Number 1: Maximum Attribute Retained
(not retained)
60% of trips
in
8188
177
7177
188000
177000
7188
188
193
40% of trips
(retained)
2).
7193
out
8193
193000
Assignment Number 2: Minimum Attribute Retained
-823
(retained)
60% of trips
in
8188
Key:
8188
-812000
8177
-823000
7188
Station number
-807
out
8193
40% of trips
7193
-807000
(not retained)
Notes:
(i) Length of station entry link
Rail service
Represents Boarding fare
188000 @Rstat: station entry
188
out
7177
-812
3).
out
8177
(ii) Zero length on station exit link
@Rstat: station exit
Information Extracted from the Two Additional Option Assignments
Add. Options
Assignment
1) Maximum
Sub-strategy
2) Minimum
Sub-strategy
Origin
Station
188
188
188
188
Destination
Station
177
193
177
193
Value of
@RSTAT
188177
188193
-812823
-812807
Active Demand
Retained (trips)
Not retained
40
60
Not retained
CONCLUSIONS
Realistic transit patronage forecasts can be produced for urban rail systems using
EMME/2. The exact approach needs to be tailored to the requirements of the particular
study. The default algorithm is appropriate for strategic modelling but more complex
‘diversion-curve’ models are more appropriate for rail forecasting, particularly in
complex urban networks.
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