Thursday, 25 February 2010 Modelling Railway LOS with the Deterministic Transit Assignment Spain...

Thursday, 25 February 2010

Modelling Railway LOS with the Deterministic Transit Assignment

Spain Emme Users' Conference


Contents

Long distance demand forecast for Railway Services

Why use the deterministic transit assignment?

Network building• Data sources• Network building and simplifications• Geo-referencing and clean-up• Fares• Transit line coding for module 5.36

Level of Service Calculation• Times and Fares• Frequencies


Contents







Demand forecast for railway services that will use the new Spanish-French high-speed connection Figueras-Perpignan

Different service scenarios with trains from Barcelona and Madrid to Toulouse, Marseille, Lyon, Paris and even Brussels and Geneva

Existing service are night-trains, Madrid to Paris, and Barcelona to Paris, Zurich and Milano

This method has also been applied in other models, e.g. for the Madrid-Valencia and Madrid-Lisbon lines


Contents






Why use deterministic and not standard transit assignment?

Standard transit assignment uses a “optimal strategies” approach, where frequencies of transit lines play an important roll

It also assumes waiting time to be proportional to headway

This approach works well, when most headway are small (1 hour or less), and arrivals of passengers at stops are uniform

In long-distance railway networks neither conditions is met• Headways are big (usually 1 hour or more, even 24 hours)• Arrival of passengers is not uniform, but usually 20 - 10 min before

scheduled departure. In transfers waiting time is defined by the timetable

Trains are not easily grouped in lines. • Stopping patterns, and station to station time vary among trains even in the

main corridors• Outside this corridors it is common to have five or less trains per day, and

all with different stopping pattern.


Contents






Network building: data sources

For French trains: SNCF train information on CD

Use script (AutoHotKey) to collect information about train schedules for approx. 150 relevant stations into two files

• Arrivals and Departures for each station

• List of trains with their complete schedule

For Spanish trains: Renfe website Use script (Python) to collect similar

information as above Both processes are “semi”-

automatic, as websites and GUIs tend to break down at some stage

Preferable to try to get the data in “decent” formats


Network building and simplification

Python script to process the textfiles into

• a list of all services• a list of all stations• a representation of each service,

with all its stops and times

Simplify to reduce number of stations

• Delete stations, where all trains come from the same previous station and go to the same next stations

• No relevant reduction of transfer boarding opportunities

• Reduces number of stations from 2.217 to 647 (French network)

Build links• Loop over trains and create a

link (if it does not exist already) for each pair of stations

• No relation to “real” railway infrastructure, not even coordinates yet

• Creates more than a few “ghost-links”, from services with few stops

• Once we have coordinates, the links will require manual revision


Network building: Geo-referencing and clean-up

Create a list of relevant stations for geo-referencing (especially main terminals, end of lines)

Match this list with a GIS layer (e.g. shape with European railway stations), and fill in any missing stations by hand

Interpolate the remaining stations

Now import the stations and links to emme, and clean-up ghost-links. It helps to have a map of the “real” railway network.

The final model• is exact for times and tranfers

opportunities• approximates distances• has no information on which

physical tracks are being used

1.400 stations4.000 services


Network building: Fares

Railway fares depend on• Service type

• Distance, but usually the price per kilometer decreases for long distances

• Discount, e.g. for older people, students, families, special promotions on the internet

In this case average revenues data by service-type for many station to station pairs was available

This was used to estimate prices per kilometer, service-type and class

When revenues data is not available, fare data can be collected from the internet. However, it is very difficult to take into account the different discounts.


Network building: transit line coding

a'f6561' h 4 999 80 'PARIS GARE DE LYON-GENEVE t' 430 1 0 path=yes ttf=1 dwt=#0 us1=0.01 7576 8217 tus1=91 tdwt=2 7948 tus1=24 tdwt=3 7670 tus1=65 tdwt=3 85444 tus1=27 tdwt=1 lay=0


Contents






Level of Service Calculation

Deterministic Transit Assignment – module 5.36 – was used to calculate Level of Services

Module 5.36 executes a timetable-based generalized-cost assignment, that identifies, for each OD and a given departure or arrival time specification, the least GC “path”, i.e. combination of services

In this particular case, journeys can be very long, and it is not uncommon (especial in the base year scenario) to use trains on two different days (e.g. a night-train to get to Paris, and a day-train to continue to Lille)

This requires to code many services for two (or even three) days, as headway is limited to 999 min, i.e. less than 24 hours

LOS are calculated just for one direction, and from origins on the Iberian Peninsula to destinations north of the Pyrenees

All assignment procedure were implemented by macros


Level of Service Calculation: Times and Fares

Get “best daily connection” for each OD pair 5.36 assignment of unity matrix with a departure time

specification of 24h The LOS data is written to report (further model steps are

spreadsheet based, so we need it outside Emme anyway)• Time and its components• Number of boardings• Generalized cost• Some mode-specific times (e.g. in-vehicle time night-trains)

LOS calculation is done for• First and Second class fares• With and without night-trains crossing the Spanish-French border,

in order to get LOS data for this two “sub-modes”

In other models “average daily connections” were used


Level of Service Calculation: Frequencies

Obtain an estimate of “Frequency” (makes only sense for day-trains)

Frequency was defined as “number of hours with feasible connection”

Repeat 5.36 assignments 24 times with a departure time specification of one hour (example 6h00+60)

Module 5.36 tracks “lateness” (selected departure time later than target departure time). If, this trip is assigned to a service leaving at 7h05, lateness becomes 5 min, and thus allows to identify that there was no feasible connection between 6h and 7h

Feasible connections are those with lateness = 0

This frequency is used in the utility function of the mode-split model

The detailed trip-reports of module 5.36 are very useful for post-processing, e.g. for station to station matrices

Thursday, 25 February 2010

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