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SARA 1 Final Presentation
Allocation of Autonomous VehiclesJonas Hatzenbühler, KTH Urban Mobility Group
22018-10-19 © Integrated Transport Research Lab
Creating new Modes of Transportation
Motivation
Improving Existing Systems
Improvement should benefit every stakeholder
1. beneficial for operator, service provider & users 2. economically efficient & improve service
Learning & Outcomes5
Results4
How? 3
What? 2
Why? 1
32018-10-19 © Integrated Transport Research Lab
Research QuestionVehicle Allocation
Tradeoff between investment costs and service provided
Improvement through Autonomous Buses
Autonomous Vehicles Public Transport System
+
1st Step: Vehicle Allocation on existing bus lines!
Learning & Outcomes5
Results4
How? 3
What? 2
Why? 1
42018-10-19 © Integrated Transport Research Lab
Research QuestionTransition Phase
Method: Selective Mesoscopic Multi Agent Simulation - BusMezzo
§ All vehicles have the same speed§ All vehicles can operate on the entire
network§ People perceive all vehicles the same
Assumptions
Where should AB systems be deployedbased on route network, bus capacity, busfrequency, operator demand and passengerdemand?
Question
Learning & Outcomes5
Results4
How? 3
What? 2
Why? 1
52018-10-19 © Integrated Transport Research Lab
Framework Overview
Learning & Outcomes5
Results4
How? 3
What? 2
Why? 1
Simulation§ Peak Hour
Demand§ Existing
Database of Stockholm
BusMezzo
Operator Cost (𝐶"#$%&'"%)
Operating Cost2
Capital Cost1
User Cost (𝐶()$%)
Vehicle Crowding
Travel TimeTransfers Denied Boarding
Waiting Time
Strategy DecisionDecision Variables:• Vehicle Type• Capacity• Frequency
Evaluation
Definition
62018-10-19 © Integrated Transport Research Lab
Model Specification Operator Cost - Overview
Learning & Outcomes5
Results4
How? 3
What? 2
Why? 1
Trade off between Vehicle Type, Capacity and Frequency
72018-10-19 © Integrated Transport Research Lab
Case Study “Kista Area”Representation
§ New AB lines§ 4 Busstops
§ ”Representative Lines”§ Replacement of red lines
(Sollentuna <> Helenelund <> Kista <> Husby)§ Approximation of Passenger Flow based on Demand
Data available on other Stops (Interpolation)§ OD Models of Sollentuna, Helenelund, Kista, Husby
§ Simulation § entire Stockholm Network§ Optimization focused on the Kista area§ Include spillover and network
effects in the analysis
”Extended” Kista AreaKista Area
2
1
4
3
Sollentuna
Helenelund
Kista
Husby
Stockholm
Learning & Outcomes5
Results4
How? 3
What? 2
Why? 1
PublicTransitNetworkKista Area– ABLinemarkedingreen
82018-10-19 © Integrated Transport Research Lab
Experiment DesignSimplified Strategy Discussion
Determining Frequency and Capacity
Learning & Outcomes5
Results4
How? 3
What? 2
Why? 1
• allBusesareconventional• bestFrequencyandCapacity
Baseline Strategy
Strategy 1
Line1
Strategy 2
Line2
Strategy 4
Line4
Strategy 3
Line3
2
3
1
“Autopiloten Strategy”
Vehicle Type Bad Service by Design
92018-10-19 © Integrated Transport Research Lab
Results Case StudyAllocation of Vehicles
Learning & Outcomes5
Results4
How? 3
What? 2
Why? 1
High Passenger Load favors deployment of AB Systems
AllAutonomous
AutonomousonLine1&Line2
“Autopiloten Strategy”
102018-10-19 © Integrated Transport Research Lab
Learnings & Consequences
What can we learn for the future of deploying Autonomous Buses?
High passenger load is a good indicator for potential autonomous bus lines
No Compensation of poor service by design
21
Learning & Outcomes5
Results4
How? 3
What? 2
Why? 1
Thank you for your attention!
Jonas Hatzenbühler, M.Sc.
PhD Candidate - Autonomous Transport [email protected] KTH StockholmUrban Mobility GroupTransport Planning, Economics and Engineering (TEE)
More Information:
https://www.byv.kth.se/en/avd/tethttps://www.itrl.kth.se/research/projects/sara1
