Modelling Railway LOS with the Deterministic Transit Assignment

1. Modelling Railway LOS with the Deterministic Transit Assignment Spain Emme Users' Conference

2. 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

3. 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

4. Long distance demand forecast for Railway Services • 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

5. 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

6. 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.

7. 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

8. 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

9. 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 and simplification

10. 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 stations 4.000 services

11. 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.

12. 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

13. 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

14. 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

15. 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

16. 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

17. Thank you for your attention