1. MaaS meets Travel Demand Modeling
This car is the I.D. Concept, unveiled by VW at this year’s Paris Motor Show
It is all-electric with a range of 600km and has a retractable steering wheel for AV mode
VW claim that this car will be released in 2020
MaaS meets Travel Demand Modeling
Klaus Noekel
Michael Oliver
ptvgroup.com

2. How does mobility as a service affect your city?
How many vehicles left on the road?
How to co-ordinate MaaS with transit?
How much parking will be freed up?
How best to regulate MaaS companies?
What additional infrastructure for pick- up/drop/off?
Can the city profitably run its own mobility service?
Can you forecast a ‘most-likely’ future that includes vehicle and ride-sharing?
Do you know what travel behavior changes vehicle and ride-sharing will bring?
Does your transit plan include autonomous on-demand buses?
Have you considered how to integrate vehicle and ride-sharing in to your transit plan?
Is your scheme still profitable under these conditions?
Do you know how to simulate mixed AV and regular traffic?
Are you confident that your planned transit line will be profitable in 20 years’ time?
Have you considered the reduced parking need in your land-use forecast?
Will the introduction of shared, autonomous and electric fleets allow my city to meet its safety and environmental objectives?
Do we have sufficient charging infrastructure?
CITY HALL
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3. Fitting maas into the model architecture
Trip Generation
Destination choice
Mode choice
Car
Assignment
Bike
Public Transport
Skims

4. Fitting MAAS into the model architecture
Trip Generation
Destination choice
Mode choice
Car
Assignment
Bike
Public Transport
MaaS 1
MaaS 2
Skims

5. Flavors of shared mobility systems
General principle
Alternative forms of mobility that do not require exclusive access (or exclusive ownership) of a means of transport
Vehicle sharing (cars, bikes)
One vehicle is shared sequentially by several travellers. Each traveller has exclusive use of the vehicle for a certain time.
Ride pooling
One vehicle is shared simultaneously by several travellers. Travellers travel together in one vehicle.

6. Ridesharing: model input
DATA INPUT
BASE = STANDARD TDM
Street network
Transit networks
Key city hubs and interchanges
City travel demand
Typical traveler behavior, e.g. mode choice
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7. Ridesharing: model input
SELECT PACKAGE 1 ($$$ - $):
CAPACITY
WAITING TIME
DEVI TIME
AREA TYPE: DROP OFF ONLY
PICK UP ONLY
DROP OFF & PICK UP 8
2.0 20
0.2 10 7
DATA INPUT
SERVICE SPECIFICATION
Pre-booking time
Departure time window
Max. Detour time
Fare
Vehicle capacity
Max. fleet size
Boarding/alighting time
Pick-up/drop-off points
Geographical coverage
Wie viel Zeit?
Wie griß
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8. ridesharing: assignment
Model steps
Generate trip requests from OD demand by spatial and temporal disaggregation
Solve dial-a-ride-problem (DARP)  set of schedules for vehicles and assignment of passengers (= trip requests) to vehicles
Visualize optimization result: inspect tours
Extract user cost components for feedback into mode choice
Calculate operating KPIs (fleet size, veh-km, empty veh-km, …) from operator perspective  economic evaluation

9. ridesharing : map method to software tools
Model steps
Generate trip requests from OD demand by spatial and temporal disaggregation
Solve dial-a-ride-problem (DARP)  set of schedules for vehicles and assignment of passengers (= trip requests) to vehicles
Visualize optimization result: inspect tours
Extract user cost components for feedback into mode choice
Calculate operating KPIs (fleet size, veh-km, empty veh-km, …) from operator perspective  economic evaluation
scripted
Tour planning
optimizer
TDM software

10. Ridesharing: Dynamic dial-a-ride-Problem
dt = small time slice (5-15 min) Tourplan= empty for t = t_start to t_end step dt TR = all trip requests with birthtime in [t; t+dt) Freeze each tour in Tourplan until first event after t Tourplan = TourPlanOptimizer(Tourplan, TR) Import Schedule into PTV Visum for post-analysis Formulate task as a vehicle routing problem with pickup and delivery and with time windows. Solve it by very large-scale neighborhood search. Basis: R.K. Ahuja, J.B. Orlin, D. Sharma: Very large-scale neighborhood search, Intl. Trans. in Op. Research 7 (2000)

11. Ridesharing: Example result

12. Ridesharing: Example result

13. Ridesharing: Example result

14. Ridesharing: Example result

15. Ridesharing: model output KPI‘s
Passenger perspective  Service quality
Waiting time
Travel time
Journey time
Revenue
Unserved demand
Max. number of other passengers in vehicle during trip
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16. Ridesharing: model output KPI‘s
Operator perspective  Efficiency
Required fleet size
Tour plan for each vehicle
Operating time (loaded, idle)
Drive time
Board/alight time
Vehicle wait time
Operating cost
Revenue
Cost coverage
Spart Chart to show the KPIs – different scenarios that we are comparing
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17. Status & next steps Completed
Tour optimization module for ride pooling
KPIs from user perspective, operator perspective
Next objectives
Estimate and apply extended mode choice model
Complete embedding in travel demand model
BUT: scarce data!
Best solution for now: use scenario technique, systematically vary unknown parameters, look at range of outcomes
Important: try out and improve – it can‘t wait!
There is a real challenge facing everyone involved in transportation that mobility is changing faster than we can plan for it
Traditional tools are not sufficient, and so we need to develop new tools and fat
We have a vision of how our software components can build business analysis and technology frameworks that plan, implement and control shared fleets
We have solutions now that support the planning process and through collaboration with all stakeholders involved we will launch these and together we can create an equitable future