An introduction into microscopic transport modelling at SBB, dealing with agent based modeling and some sample applications of activity based transport models at SBB.

1. 1
Joschka Bischoff, Antonin Danalet
28.04.2023
Transport
modelling at SBB.

2. 2
1. SBB: Facts and figures
2. Who are we?
3. Transport Modelling at SBB
4. SIMBA MOBi – methodology
5. SIMBA MOBi – typical applications
Contents

3. 3
SBB: Facts and Figures

4. SBB CFF FFS: The Swiss Federal Railways
4
• Founded in 1902
• 3230 km train network, all-electric, mostly standard-gauge
• 11,338 trains per days
• 35,000 employees
• 2022: 17.3 billion passenger kilometers
• 795 stations
• International connection into all neighboring countries
• The largest train operator in Switzerland (non-competitive system)

5. Public Transport in Switzerland
5
• Relies heavily on cooperation
between different companies
• Generally offers seamless
connections between operators
• Main goal: Provide good connections
at reasonable speed
• There is an integrated ticketing
system for the whole country
• Several subscription plans available
that allow long-distance commuting
https://commons.wikimedia.org/wiki/File:Postauto_Enviro500.jpg
https://commons.wikimedia.org/wiki/File:SVB04wiki.jpg

6. 6
Who are we?

7. Multi Modal Transport Modeling Team
SIMBA MOBi
7
Joschka
Bischoff
Product Owner
MATSim expert
Annette
Knupp
Transport
planner
Davi
Guggisberg
Programmer
Lukas Sieber
Transport
planner
Antonin
Danalet
Empirical Data
and Choice
Models

8. Organizational Structure.
8
CEO
Vincent Ducrot
Passenger Services Markets
Long-Distance Services
Infrastructure Real Estate
Passenger Services
Production
Freight Section
Offer Planning
Product and Services Market and Competition
Price and revenue
management
Marketing and sales
management
Rolling stock development

9. 9
Transport Planning
Transport Planning
West East & Tessin
National «Mitte»
Offer Planning
• Analysis of the customer needs
• Forecasting demand in scenarios
• Assessment of mobility and rolling
stock deployment concepts
• Planning the offer in different time horizons
• Conception of concrete timetables
• Project work with infrastructure operators and politics
Modelling team at SBB: ca. 17 employees.

10. Transport modelling
landscape at SBB
10

11. 11
– Service planning
– Fleet and infrastructure planning
– Financial planning
– Corporate strategy
Goal of travel demand
modelling at SBB.

12. 12
SIMBA Bahn
Rail planning and optimization
– Long-time experience
– High-precision demand
– Macroscopic
SIMBA MOBi
Forecasts and behaviour
– New simulation approach
– Multimodal
– Microscopic, door-to-door
– 24h plans & activities
SBB’s simulation landscape SIMBA:
combining macro- and microscopic transport modelling.
All modes
SIMBA.MOBi
Public transport
SIMBA.MOBi
Rail
SIMBA.Bahn

13. 13
What is the change in demand resulting from a
change in supply, such as:
– Shorter travel time?
– More stops?
– More frequent trains?
Decision aid tool: comparison of different scenarios.
Reference
scenario
Option 1
Option 2
Option 3

14. 14
Lötschberg Base Tunnel opened in June 2007.
How did the model (NSVM) predict the effect of
the new offer?
These number represents the traffic flow on:
– Lötschberg railway line (opened in 1913)
– Lötschberg Base Tunnel
Prediction success.
2007, measured 2007, modelled 2008, measured 2008, forecasted
Traffic
volume,
Monday
to
Sunday
Exogenous growth
Modelled new routes 2008
Modelled new traffic 2008
Observed growth 2008
Traffic volume 2007
+59%
+67%
+48%
+18%
+1.5%

15. SIMBA MOBi
15
Introduction to SBB’s agent-based simulation model

16. Transport demand modelling:
Balancing supply and mobility behaviour.
16
Demand
Mode, route & departure time choices
Activity & destination choices
Choice of mobility tools/resources
Supply
Road network & capacities
Public transport timetable & capacity
Costs & travel times

17. SIMBA MOBi.
17
Synthetic
population
Mobility and
Transport
Microcensus
Road & rail
supply
Count data Individual
daily activity
plans
Time-sharp traffic
loads
Distances,
number of trips,
…
Mode choice for
each trip
Versatility.
Forecasting ability. Operational capability.
Input data Results

18. Video SIMBA MOBi
18

19. 19
– Average week day (Mo-Fr)
– On that day, the model decides:
– What activities a person will do,
– When,
– Where, and finally
– With which mean of transport.
– Decisions influenced by:
– Characteristics of the person:
– Age,
– Work-time percentage,
– Ownership of public
transport season tickets,
– …
– Work and leisure locations,
– Public transport offer and road
network,
– …
From a single person...
35 y
60% Job
Road & public transport
networks
Public transport
timetable
Population data
Inputs
Commuter matrix and
work locations
Leisure
locations

20. 20
– Activity and transport choices repeated for all individuals
– Sum of routes and activities compared with empirical data
– Model parameters adjusted to fit the count data
... to Switzerland as a
whole.
Aggregation
 Number of trips
 Distances
 Entry & exits at
station
 Road & rail loads
 …
Validation
Calibration
Mobility and Transport Microcensus
SBB count data
Road counts

21. Main model steps.
21
MOBi.synpop
(FSO, ARE, FaLC)
Synthetic
population
MOBi.plans
(SBB & PTV Visum)
Activity-based demand
as 24h daily plans
MOBi.sim
(MATSim)
Traffic flow simulation
for car and public transport
Route choice
Adaptation of the plans
(mode, time of day, activity duration)
All steps are:
– Person-specific (microscopic)
– Iterative

22. 22
– Developed in collaboration with the Federal Office for
Spatial Development (ARE) and Strittmatter, based on
register data from the Federal Statistical Office (FSO)
– A version for 2017, 2030, 2040 and 2050 exists
– Basis for the Transport Outlook 2050
– An Open Data, anonymised Synthetic Population for
2017 is available
MOBi.synpop: Synthetic Population of Switzerland.
Source:
Report
“Synthetische
Population
2017
Modellierung
mit
dem
Flächennutzungsmodell
FaLC,
2019,
www.are.admin.ch/flnm
Data sources
Persons, households,
and companies
Education, employment,
household structure
Assignment employee to
workplace
Employee qualification
Income
Mobility tools/resources
Register data
Models

23. Home locations: 3.8m households, 8.6m people.
23

24. Activity locations: 0.7m businesses and other activity
attractions, 5.0m jobs.
24

25. MOBi.plans: Generating synthetic plans.
25
Employee,
24-h day plan:
H-L-S-W-S-H

26. MOBi.plans: A series of discrete choices.
26
Ownership:
car,
PT subscription
Location choice
(work,
education)
Work from
home
Tour
frequency
choice
Stop
frequency
choice
Activity choice
(secondary)
Destination
choice
(secondary)
Mode choice
Activity
duration and
start time
Daily choices
Long-term choices

27. MOBi.Sim: MATSim at SBB.
27
– The open source software MATSim, including specific
software extensions of SBB:
– Simulates route choice & traffic flow
– Simulates mode choice for rail access
– Updates mode choice
– Update departure time choice

28. SIMBA MOBi
28
Use Cases and Applications

29. Use cases for SIMBA MOBi.
29
Development of rail and public transports services: Construction of new stations or new lines, e.g.
Analysis of the direct environment of railway stations
Mobility and land use, including impact on real estate
Future scenarios: Forecasting mobility depending on demographic, spatial and supply development

30. Dimensioning of
new railway stations
30

31. Dimensioning of new Railway Stations.
31
– New stations:
– Many dozens expected to be opened in Switzerland
– Mostly along existing lines
– Built to connect newly developed areas
(workplaces or housing)
– SIMBA MOBi allows estimating the impact of these
developments on the overall transport system

32. Moving and re-dimensioning St. Gallen Bruggen Station
32

33. 33
- Inputs to the model
- 4000 new inhabitants
- 3000 new work places
- Outputs of the model
- Daily plan for each agent
- Mobility tools
- Mode choice
- Trips
New developments in the area

34. Passenger Development
34
Bruggen Haggen Total
Base case 1’500 2’800 4’300
New Station &
New Developments
2’400 3’400 5’800

35. Where do people travel to from the new stations?
35

36. Access and egress at railway
stations
36

37. 19’808 passengers boarding or
alighting.
9’186 (46 %) use other modes
than walk to connect to or from
the station
Intermodal connections at Neuchâtel
Source: SIMBA MOBi (Monday-Friday)
Population data: 2017
Public transport offer: 2020
Maps © Thunderforest, Data © OpenStreetMap contributors
37
Walk ↔ Rail
Bike ↔ Rail
Park & Ride
↔ Rail
Kiss & Ride ↔
Rail
Bus ↔ Rail
5'384
Rail ↔ Rail

38. Morning peak: (06:00-09:00):
5’052 rail passengers
Evening peak: (16:00-19:00):
5126 rail passengers
Diurnal Variation
0
500
1000
1500
2000
2500
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2
Passengers
Arrival at Station
Neuchâtel
Railway
Bus
Kiss & Ride
Park & Ride
Bicycle
On foot
Contains all trips that board or alight a train at the station or change between two trains (simply counted) Source: SIMBA MOBi (Monday-Friday)
Population data: 2017
Public transport offer: 2020

39. Origins and destinations of railway passengers
Neuchâtel Station
Source: SIMBA MOBi (Monday-Friday)
Population data: 2017
Public transport offer: 2020
Maps © Thunderforest, Data © OpenStreetMap contributors

40. Post-Covid rail demand forecast
40

41. Goal: prediction of future rail demand.
41
2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
COVID-19 outbreak
Extroplation of existing
behaviour
???

42. Tool: SIMBA MOBi.
42
MOBi.sim
(MATSim)
network flow simulation,
car + public transport
rescheduling
(mode, time, duration),
route choice
MOBi.plans
(PTV Visum)
synthetic
population
activity-based travel
demand,
full-day plan scheduling

43. Tool: SIMBA MOBi – work from home extension.
43
MOBi.sim
(MATSim)
network flow simulation,
car + public transport
rescheduling
(mode, time, duration),
route choice
MOBi.plans
(PTV Visum)
synthetic
population
activity-based travel
demand,
full-day plan scheduling
Work from home

44. Daily Scheduling in MOBi Plans.
44
Decision to work from home is made for each day:
– Each person is still assigned a work place
– Each person decides whether to work from home

45. Methodology: prediction of working from home.
45
Basis: Synthetic population.
– Persons with household
location and socio-
demographic attributes.
– Workplace for all employed
persons.

46. Methodology: prediction of working from home.
46
Basis: Synthetic population.
– Persons with household
location and socio-
demographic attributes.
– Workplace for all employed
persons.
Modelling steps:
– Which persons?
– How much?
– Impact on daily mobility choices
– Network simulation

47. Individual preferences for working from home.
47
Dataset: Mobility and Transport Microcensus 2015
Big influence:
• Age
• Management position
• Work distance
• Working sector
Moderate influence:
• Being a student
• Number of children
• Mobility Tool
• Accessibility
• Workplace is rural
60+

48. Work from home day
Re-scheduling of daily mobility choices: example.
48
Office day

49. Long term assumptions on Working from Home.
49
Potential share of people
working from home
Share of employed people in Switzerland
55%
28%
in 2015*
46%** 64%**
How often do they working from home?
Share of working days
40%
27%
in 2015
22%** 58%**
Overall effect of Working from Home
Average share of people working from home.
22%
7%
im 2015
37%
10%
x
=
* Mobility and Transport Microcensus 2015
** Range based on literature research
+ 15%
Base case
WfH case

50. Case study – modal split.
50
Small modal split changes with a
big impact:
Total distance travelled in trains
drops by 8.3%
-1.5%
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
motorized
individual
non-motorized public
transport
thereof rail
Changes
in
modal
split
[%]
Delta between WfH case and Base case
distance
trips

51. Case study – Impact on rail demand.
51

52. 52
Joschka Bischoff
Product Owner
Annette Knupp Davi Guggisberg
Lukas Sieber Antonin Danalet
The team

53. 53
Contact: joschka.bischoff@sbb.ch
Questions?