Intermodal trip composition: the MyWay meta-planning … · Intermodal trip composition: the MyWay...

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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213/1/2016

michal.jakob@fel.cvut.cz