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Intermodal trip composition: the
MyWay meta-planning approach
MyWay Final Workshop – Barcelona Activa - 18th February 2016
Michal Jakob
Artificial Intelligence Center
Czech Technical University in Prague
http://agents4its.net
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We’ve built an intermodal trip planner that requires (almost) no data
Tram Bus Train Metro
(Shared) Bike Electric Scooter Private car Car sharing
Bike Taxi Ride sharing Ferry
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Intermodal Plans
• Combining different (possibly both public and private)
means of transport within one trip
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Car Train Bike sharing
Intermodal Planners: State of Practice
• Algorithms for intermodal trip planning have been
recently introduced
– e.g.: Delling, Daniel, et al. "Computing multimodal journeys in
practice." Experimental Algorithms. Springer Berlin Heidelberg,
2013. 260-271.
• However, there are hardly any intermodal trip planners
out there
• Why?
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STANDARD APPROACH
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car
PT
bike
Route planning algorithm
trip plan query
suggested plans
planning graph
…
walk
bike sharing
De
taile
d in
form
atio
n a
bo
ut a
ll mo
de
s
METAPLANNING APPROACH
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Car route
planner API
PT trip
planner API
Bike route
planner API
BS route
planner APIMetaplan
Refinement
suggested plans
Metaplanning
trip plan query
metaplans
Metagraphconstruction
transport metagraph
…
approximate but intermodal
single-modal
subplannersA novel way of
planner integration
Trip Metaplanning
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~6 min ~25 min transfer: ~5 min ~8 min ~4 min
Viladecans Passeig de Gracia
Walk Train Shared bike Walk
Met
ap
lan
Metaplan Refinement Example
~6 min ~25 min transfer: ~5 min ~8 min ~4 min
Viladecans Passeig de Gracia
metaplan
refinement
Walk Train Shared bike Walk
Ref
ined
det
aile
d p
lan
Met
ap
lan
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TripPlan(id=1, time=2520, legs=5, departure=2014-11-04T17:28:00+01:00)
• TripLeg(id=1, transportMode="WALK", steps=2, duration=480, distance=289)
– TripStep(id=0; loc=41.310896, 2.024552)
– TripStep(id=1; name=Viladecans (Estació de Tren) ; loc=41.309424, 2.027405; timeFromPreviousStep=480)
• TripLeg(id=2, transportMode="TRAIN", steps=5, duration=1380, distance=14734)
– TripStep(id=0; name=Viladecans (Estació de Tren) ; type=TrainStation; loc=41.309424, 2.027405)
– ....
– TripStep(id=4; name=Passeig de Gracia ; type=TrainStation; loc=41.392525, 2.164728; timeFromPreviousStep=360)
• TripLeg(id=3, transportMode="WALK", steps=5, duration=136, distance=188)
– TripStep(id=0; loc=41.392280, 2.164990)
– TripStep(id=1; loc=41.392180, 2.164880; timeFromPreviousStep=10)
– ....
• TripLeg(id=4, transportMode="SHARED_BIKE", steps=31, duration=403, distance=1890)
– TripStep(id=0; loc=41.393106, 2.163399)
– TripStep(id=1; loc=41.392950, 2.163670; timeFromPreviousStep=6)
– ....
– TripStep(id=30; loc=41.397952, 2.180042; timeFromPreviousStep=6)
• TripLeg(id=5, transportMode="WALK", steps=5, duration=121, distance=168)
– .....
Public transport
subplanner
Bike sharing
subplanner
Metagraph Construction
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An algorithm that automatically builds an approximate intermodal model of the transport systems by querying
single-modal trip planners.
Metagraph: Model
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Cell diameter:
0.2 – 5 km
Metaedges
Real routes
Voronoi cell
Road junction
Metanode
PT stop
Bike sharing station
based on Generalized time-dependent graph representation*
* J. Hrncir and M. Jakob: Generalised Time-Dependent Graphs for Fully
Multimodal Journey Planning. In IEEE Intelligent Transportation Systems
Conference (ITSC). 2013.
Metaedges weights: travel time, cost,
emissions, physical effort
Metagraph: Construction
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Planner APIsSubplanner APIs
Fixed mode stops
Service availability zones
Nodes
Metagraph
Edges
Nykl, J. - Hrnčíř, J. - Jakob, M. Achieving Full Plan Multimodality by Integrating Multiple Incomplete Journey
Planners In: Proceedings of the 18th IEEE International Conference on Intelligent Transportation Systems . 2015,
p. 1430-1435.
103-104 API callshours of computation
Voronoi cells with
adaptive density
Edges creation
and time precomputation
Local properties
and mode location
Road network
smart subplanner querying
Metagraph Example
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# metanodes # metaedges
Catalonia 10 000 151 000
Berlin 5 000 88 000
Trikala 500 9 000
Metagraph
sizes
Ca
talo
nia
fra
gm
en
t
~1/100 nodes of a
fully detailed graph
Metasearch and Refinement
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MetaplanRefinement
up to 8 detailed plans
Metaplanning
trip plan query
20+ metaplans
Intermodal trip planning algorithm
Runtime: hundreds miliseconds
Select most diverse metaplans to refine
Invoke subplanners
Runtime: seconds
Benefit 1: Fully intermodal plans
faster than public transport-only plans
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1
2
21
3
3
Not rule-based – discovered by the metaplanning algorithm
Benefit 2: Fully intermodal plans where no
public transport-only plans exist
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Statistical Quality Evaluation
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Catalonia
Berlin
Trikala
Increased)Usage)of)Public)Transport
Only:Intermodal:PT Other
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Catalonia
Berlin
Trikala
Intermodal+Journey+Plans+Group+Split+53+Criteria
Dominating Pareto Dominated
Computational Statistics
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Metasearch (ms)
Refinement(ms)
Total(ms)
Metagraph size (#nodes + #edges)
Catalonia 547.4 2523 3080 161 000
Trikala 48.2 200 248.9 9 500
Berlin 1371.2 (9000) (10000) 93 000
One trip planning requests results, on average, in the following number
of subplanner requests (Cat): 1.45 (car), 2.81 (PT), 1.47 (bike)
A number of speed-up techniques can be applied.
* for constrained single-criteria shortest path metasearch algorithm
Pros and Cons
• Full intermodality
• Rapid deployment
• High customizability (facilitates mobility policy injection)
• Flexibility: new transport modes / services easy to add
• Service-oriented approach: reuse of existing planning
capabilities (incl. their data maintenance processes)
• Disadvantage: slightly slower response times
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Summary
• A novel approach to intermodal planning
• Substitutes access to data with access to planning APIs
• Employs AI instead of fixed rules for intermodal
integration
• Successfully tested in three diverse living labs sites
• A good basis for mobility-as-a-service planning solutions
• Ready for deployment in new locations/areas
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