This document provides an overview of transport modelling conducted at Swiss Federal Railways (SBB). It discusses the following key points: 1. SBB uses an agent-based simulation model called SIMBA MOBi to forecast demand and model mobility behavior. The model is used to analyze the impact of changes to transport supply on demand. 2. SBB collects data from surveys like the Mobility and Transport Microcensus to inform the models. This includes data on individual travel behavior and characteristics. 3. SBB conducts transport modelling to support various uses like service planning, infrastructure planning, and scenario analysis. The models help evaluate the effects of projects like new stations or train lines.

1. 1
Antonin Danalet
EPFL, 06.04.2023
Transport
modelling at
SBB.

2. 2
Research and Teaching Assistant.
Transport and Mobility Laboratory
(TRANSP-OR)
Co-head of the National Transport Modeling
Unit (VM-UVEK).
Federal Office for Spatial Development (ARE)
Transport modeller.
– Passenger Services Markets (MP)
– Long-Distance Services (FV)
– Offer Planning (APL)
– Transportation Planning (VPL)

3. Organizational Structure.
3
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

4. 4
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.

5. 5
1. Data for transport modelling
– Transport and Mobility Microcensus (MTMC)
– Stated Preference (SP) survey on mobility behaviour
2. Transport modelling landscape at SBB & in Switzerland
3. SIMBA MOBi
– Introduction to SBB’s agent-based simulation model
– Use cases and applications
Outline.

6. What this course is not about (even if it exists at SBB).
6
– Train operations
– Freight demand
– Pedestrian flow in railway stations

7. 7
1. Data for transport
modelling
Transport and Mobility Microcensus (MTMC)

8. A timeline.
8
1974 1994 2015 2020 2021 2025
2023
First computer-
assisted telephone
interview (CATI) data
collection procedure.
Most recent data
collection (till 2pm this
afternoon), published
in 2017.
Planed data collection.
Took place from
January to March.
Then pandemic.
Most recent data
collection (starting
today from 2pm). Took
place all year long.
First edition.
Since then, every 5
years (in principle).
Publication of the
2021 results today.
Next planed data
collection.

9. Content.
9
– Socioeconomic characteristics
– Mobility tools
– Trips on a given reference day
– Occasional journeys
– Attitudes towards transport policy in Switzerland

10. 10
Since 1994 until today (2021): telephone interviews
Tested in 2022: app for smartphones
Data collection method.
Source photo: Federal Statistical Office (FSO)

11. Inputs for transport models:
Reference data for calibration and validation.
11
– Activity types per population group
– Number of tours, activities, trips per population group
– Activity duration
– Trip distance
– Modal split per mobility tool and by region
– Trips by time of day
– Transfer frequencies
– …

12. Inputs for transport models:
Estimation of model parameters.
12
– Choice of tour and trip frequency
– Distribution of activity duration
– Activity start times
– Choice of mobility tools:
– Driving license
– Number of cars per household
– Public transport season ticket

13. To know more
13
– Wait until 2pm
– Check
– www.mtmc.bfs.admin.ch
– www.are.admin.ch/mtmc
– More than 50’000 interviews
– Daily distance and travel time, means of transport used,
trip purposes, annual mobility, …

14. 14
1. Data for transport
modelling
Swiss Stated Preference (SP) survey on mobility behaviour

15. Choice data.
15
– Revealed preferences (RP):
– Observe actual behaviour
– Example: Mobility and Transport Microcensus
– Stated preferences (SP):
– Hypothetical situations
– Advantages:
– Exploring options which are not yet available
– Disentangle characteristics closely linked, such as
cost & time
– Drawback: what people answer is not what they do

16. Example of an SP question.
16

17. How do we make SP surveys more realistic?
17
– Base SP questionnaire on trips declared in an RP survey
– Discrete choice models estimated on both RP & SP data
– Concretely in Switzerland:
4000 adults recruited during the MTMC for an SP survey

18. Example of an SP question based on an RP trip.
18
For the trip from Bern train station to Wankdorf Stadium that you did by public transport on March 17th, what
would you choose in the context presented below?

19. Outputs
20
– Value of time
– Some results of 2015:
– CHF 13 per hour for private motorised transport
– CHF 12 per hour for public transport (CHF 2/10 min)
– Elasticities
– Some results of 2015:
– Drivers less sensitive to variation in duration and cost
than those who travel by public transport.
– Public transport: -1% in travelling time increases
demand more than -1% in the price.

20. 21
2. Transport
modelling
landscape

21. 22
– Service planning
– Fleet and infrastructure planning
– Financial planning
– Corporate strategy
Goal of travel demand
modelling at SBB.

22. 23
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

23. 24
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

24. 25
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%

25. Zurich
Transport modelling landscape in Switzerland:
Models.
26
Cantonal
models
National
Passenger
Transport
Model
Federal Office for Spatial
Development
MATSim
Model
ETH
Geneva
…
Zug

26. TransSol
BAK
Transport modelling landscape in Switzerland:
Engineering Consultants.
27
Rapp
Transoptima
Transitec
Strittmatter
Partner
Prognos
CITEC
…
EBP
PTV
INFRAS
Stratec
RGR

27. ZHAW
Transport modelling landscape in Switzerland:
Research.
28
ETHZ
IVT
EPFL
LUTS, TRANSP-OR
HEG
FHNW
HEIG-
VD
…

28. 29
3. SIMBA MOBi
Introduction to SBB’s agent-based simulation model

29. Transport demand modelling:
Balancing supply and mobility behaviour.
30
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

30. SIMBA MOBi.
31
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

31. Video SIMBA MOBi
32

32. 33
– 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

33. 34
– Activity and transport choices repeated for all individuals
– Sum of routes and activities compared with empirical data
– Model parameters adjusted in order 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

34. Main model steps.
35
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

35. 36
– 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

36. Home locations: 3.8m households, 8.6m people.
37

37. Activity locations: 0.7m businesses and other activity
attractions, 5.0m jobs.
38

38. MOBi.plans: Generating synthetic plans.
39
Employee,
24-h day plan:
H-L-S-W-S-H

39. MOBi.plans: A series of discrete choices.
40
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

40. MOBi.Sim: MATSim at SBB.
41
– 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

41. 42
3. SIMBA MOBi
Use Cases and Applications

42. Use cases for SIMBA MOBi.
43
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

43. 44
Dimensioning of new railway
stations

44. Dimensioning of new Railway Stations.
45
– 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

45. Moving and re-dimensioning St. Gallen Bruggen Station
46

46. 47
- 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

47. Passenger Development
48
Bruggen Haggen Total
Base case 1’500 2’800 4’300
New Station &
New Developments
2’400 3’400 5’800

48. Where do people travel to from the new stations?
49

49. 50
Access and egress at railway
stations

50. 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
51
Walk ↔ Rail
Bike ↔ Rail
Park & Ride
↔ Rail
Kiss & Ride ↔
Rail
Bus ↔ Rail
5'384
Rail ↔ Rail

51. 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

52. 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

53. 54
Post-Covid rail demand forecast

54. Goal: prediction of future rail demand.
55
2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
COVID-19 outbreak
Extroplation of existing
behaviour
???

55. Tool: SIMBA MOBi.
56
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

56. Tool: SIMBA MOBi – work from home extension.
57
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

57. Daily Scheduling in MOBi Plans.
58
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

58. Methodology: prediction of working from home.
59
Basis: Synthetic population.
– Persons with household
location and socio-
demographic attributes.
– Workplace for all employed
persons.

59. Methodology: prediction of working from home.
60
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

60. Individual preferences for working from home.
61
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+

61. Work from home day
Re-scheduling of daily mobility choices: example.
62
Office day

62. Long term assumptions on Working from Home.
63
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

63. Case study – modal split.
64
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

64. Case study – Impact on rail demand.
65

65. 66
Joschka Bischoff
Product Owner
Annette Knupp Davi Guggisberg
Lukas Sieber Antonin Danalet
The team

66. 67
Contact: antonin.danalet@sbb.ch
Questions?