Intelligent route planning for sustainable mobility

MICHAL JAKOB ET AL. | HTTP://TRANSPORT.FELK.CVUT.CZ

Intelligent route planning for sustainable mobilityMichal Jakob*Artificial Intelligence CenterFaculty of Electrical EngineeringCzech Technical University in Praguehttp://transport.felk.cvut.cz

*joint work primarily with Jan Hrnčíř, Pavol Žilecký and Jan Nykl

http://transport.felk.cvut.cz/

AI for Transport and Mobility

M. JAKOB ET AL. | HTTP://AGENTS4ITS.NET

Data

Common models

To

ols

sharing => acceleration

Simulation and Modelling

Journey planning and routing

Intelligent transport marketplaces

Transport and mobility data analysis

1.Understand transport and mobility2.Improve transport and mobility

Michal Jakob http://transport.felk.cvut.cz

Brief Introduction to Route Planning


Joruney/Route/Trip Planning


?

Brief History of Routing


Road network routing

1960s

Public transport routing / journey planning

1980s

Intermodal routing

2005+

Walk / Bike routing

2010+

Routing for EVs

…

IntermodalReal-time context-aware

Multi-criteria

PersonalizedResource-

awareBehaviourchanging

Next-Generation (Urban) Trip Planners


Building Blocks


Network models (routing graph): Directed weighted graphs

Typical sizes – number of nodes:(typical node degree 2-4)

105− 106: urban scale106− 107: national scale107+ : continental scale

Algorithms: Variants of shortest paths algorithms

Extensions over SP algorithms: • speed ups• multi-criteria search• constrained shortest pathPractical response times: 1+ms to <10s


Multi-Criteria (Urban) Bicycle Routing


Cycling is viewed as a key component of sustainable urban mobility.

Finding good cycling routes in complex urban environments is difficult.

Cyclists consider many route-choice factors.

Challenging AI problem because of the multiple route planning objectives and rich representations required.

Numerous online bicycle route planners exist but mediocre route quality

typically only a configuration of generic (road network-oriented) routing engines

very limited information about their inner working

Underexplored topic in routing (esp. compared to road and PT routing).

Very few research papers on models and algorithms for bicycle routing.


Bike Routing: Motivation and Specific Features

Model: Multi-Criteria Bicycle Routing Problem

Directed multi-weighted graph 𝐺 = (𝑉,𝐸, 𝒄)

Tri-criteria bicycle routing problem (based on transport behaviour research):


𝑐timetime (s) needed ( slope, surface, obstracles, …)

𝑐stress stress (S.U.) induced ( infrastructure measures, traffic intensity, traffic speed, …)

𝑐effort physical effort (kJ) exerted ( slope, wind, surface,…)

criteria models

Multi-criteria shortest path problem: Given an origin and destination node, find a Pareto set of paths.

(Recorded real trajectories)

Elevation data (SRTM)

Transport network maps

OSM tag categories: surface, obstacles, crossing, for_bicycles, motor_roads

Algorithms: Multi-criteria Shortest Path search

Multi-criteria label setting (MLS) algorithm with heuristics Ellipse Pruning

𝜖-dominance

Buckets

Dijsktra’s bouding

Cost-based and ration-based prunning

Multi-objective A* algorithm (NAMOA*)


Multi-criteria shortest path problem: Given an origin and destination node, find a Pareto set of paths. infeasible

region

feasible region

1 (1+𝜀) Criterion X

Cri

teri

on

Y

𝜖-dominance

CriterionX

Cri

teri

on

Y

1

23

4

5

1 2 3 4 5 6

buckets ellipse pruning

Evaluation


Runtime: Average runtime in ms

Quality: Average distance of the heuristicPareto set Π from the optimal Pareto setΠ∗ in the criteria space

𝑑𝑐 Π∗, Π ≔

1

Π∗

𝜋∗∈Π∗

min𝜋∈Π

𝑑𝑐(𝜋, 𝜋∗)

Intuitively, 𝑑𝑐 = 0.1 ~ 6% diff. in eachcriterion (for three criteria)

Additional quality metrics: ─ Jaccard distance 𝑑𝐽

─ percentage of Pareto optimum path Π%.

Scenario Metrics

300 requests + 100 scale-up requests

Prague cyclegraph

Prague B Prague A

Prague C

Whole Prague

Results


Best practical tradeoff

MLS, optimal Pareto set with 532 routes, 90 s

HMLS+Ellipse+Buckets, 26 routes, 623 ms

Scale-up (whole Prague) MLS: no results within 15 mins HMLS+Ellipse+Epsilon: 5s NAMOA*: 106s (optimum / full Pareto set)

Speed vs Quality Trade-offs

Applications


http://its.felk.cvut.cz/cycleplanner2

Hrnčíř, J. - Žilecký, P. - Song, Q. - Jakob, M. Practical Multi-Criteria Urban Bicycle Routing. In: IEEE Transactions on Intelligent Transportation System (in print)

http://its.felk.cvut.cz/cycleplanner2/#responseId=48471

http://its.felk.cvut.cz/cycleplanner2/#responseId=48471

http://its.felk.cvut.cz/cycleplanner2


Metaplanning-based Intermodal Trip Planning



Tram Bus Train Metro

(Shared) Bike Electric Scooter Private car Car sharing

Bike Taxi Ride sharing Ferry

Many transport modes/means now available

4/6/2016 16 | Page

Intermodal Trip Planning

Combining multiple different (possibly both public and private) means of transport within a single trip

Algorithmically now possible (e.g. [1]) but hardly any intermodal planners in practise

4/6/2016 17 | Page

Car Train Bike sharing

[1] Delling, Daniel, et al. "Computing multimodal journeys in practice." Experimental Algorithms. Springer Berlin Heidelberg, 2013. 260-271.

Standard data-oriented approach

4/6/2016 18 | Page

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-based Service-oriented Approach

4/6/2016 19 | Page

Car route planner API

PT trip planner API

Bike route planner API

BS route planner API

MetaplanRefinement

suggested plans

Metaplanning

trip plan query

metaplans

Metagraphconstruction

transport metagraph

…

approximate but intermodal

single-modal subplanners

Trip Metaplanning

4/6/2016 20 | Page

~6 min ~25 min transfer: ~5 min ~8 min ~4 min

Viladecans Passeig de Gracia

Walk Train Shared bike Walk

Me

tap

lan

Metaplan Refinement Example

4/6/2016 21 | Page

~6 min ~25 min transfer: ~5 min ~8 min ~4 min

Viladecans Passeig de Gracia

metaplan

refinement

Walk Train Shared bike Walk

Re

fin

ed

de

taile

d p

lan

Me

tap

lan

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)


– 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)


– ....



– .....

Public transport subplanner

Bike sharing subplanner

Metagraph


Cell diameter:

0.2 – 5 km

Metaedges

Real routes

Voronoi cell

Road junction

Metanode

PT stop

Bike sharing stationM

od

el

Co

ns

tru

ctio

n

Planner APIsSubplanner APIs

Fixed mode stops

Service availability zones

Nodes

Metagraph

Edges

Voronoi cells with adaptive

density

Edges creation and time

precomputation

Local properties and mode

location

Road network

smart subplanner querying

Metagraph Example


Catalonia: 106nodes, node degree: ~3

Catalonia: 104 nodes, node degree ~15

Full graph Metagraph

Building the Catalonia metagraph requires ~103 calls to the public transport subplanner.

Expanding Travel Options

4/6/2016 25 | Page

1

2

21

3

3In 23% / 50% of trips a competitive intermodal option(s) added

(all / long trips > 1hr)Average intermodal speedup of 20% over PT-only trip

http://transport.felk.cvut.cz/metaplannerui2/#city=barcelona&originLat=41.4667&originLon=2.2401&destinationLat=41.376&destinationLon=2.1558&departureTime=1442476380000

http://transport.felk.cvut.cz/metaplannerui2/#city=barcelona&originLat=41.4667&originLon=2.2401&destinationLat=41.376&destinationLon=2.1558&departureTime=1442476380000

Summary

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)

Disadvantages: (slightly) slower response times

greater dependency on third party service providers

optimality guarantees difficult to achieve

4/6/2016 26 | Page

Nykl, J. - Hrnčíř, J. - Jakob, M. Achieving Full Plan Multimodality by Integrating Multiple Incomplete Journey Planners In: Proccedings of the 18th IEEE International Conference on Intelligent Transportation Systems. 2015, p. 1430-1435.


Other Problems



Transport Accessibility Analysis

Generalization of routing single-origin multiple-destination

multiple-origin multiple-destination problems

Routing graph remain identical

Algorithms adapted goal-oriented techniques less useful

Applications data-drive assessment of public transport

network

facility location, property development, event management, …

Demo : http://transportanalyser.com


http://transportanalyser.com/

http://transport.felk.cvut.cz/TransportAnalyser/

http://transport.felk.cvut.cz/TransportAnalyser/

Travel Planning for Electric Vehicles

Energy recuperation may lead to negative weight edges.

Routing may need to be combined with charging scheduling.

Charging scheduling may need to take into account limited charging capacity (both in space and time).

ELECTRIFIC project (2016-2019) GV.8-2015. Electric vehicles’ enhanced performance

and integration into the transport system and the grid



Engineering Aspects



Engineering Process


Quality Assurance


Preliminary routing graph

Data importers and Routing graph builder

Final routing graph

Anomalydetection

Constraints checkingTime

tables

Elevation

Traffic

…

Maps

Hig

h-q

ua

lity

rou

tin

g g

rap

hs

Grabage in, garbage out

Route planning request

generator

Tested planner

Resultsevaluator and comparator

Detailed planner performance statistics

Test scenario specs

Ove

rall

qu

alit

y va

lida

tio

n

Existing planners

Existing planners

Real-world routes

Pseudocodes algorithms do not reveal the full story

Choose your data structures wisely memory requirements (for storing routing

graphs)

time complexity (of search control structures)

Testing on real-world problem instances critical

Java-specific Examples: Using arrays and bit operations instead of

maps and sets => 84% reduction of storage space

Binomial heap provides better results than Fibonacci heap in Dijsktra’s / A* search control

Counter-example object recycling did not noticeably increase

performance ( garbage collection in modern JVMs pretty efficient)

Further lower-level optimizations possible memory access to maximize cache hits


Implementation matters


Conclusions and Outlook



Automated high-quality routing graph fusion from heterogeneous data sources

Route planning with real-world trajectories

Integrated route planning and resource allocation (=> multi-agent trip planning) booking / ticketing

electric vehicle charging

Trip planning for mobility as a service

Indoor routing


Further Research Topics

Ra

w G

PS

tra

ck

sM

ap

pe

d t

rac

ks

Sp

ee

d m

ap

Conclusions

Despite the decades of research, journey and route planning remains thriving research area with many open problems.

Recent contributions in the area multi-critera bicycle routingand metaplanning-based trip planner integration.

Our approach aims to consider both algorithmic and software engineering aspects

Our robust experimentation stack linked with real-world applications accelerates further research and boosts its practical relevance



http://transport.felk.cvut.cz

Thank you!

Documents

Intelligent route planning for sustainable mobility