Intelligent route planning for sustainable mobility

MICHAL JAKOB ET AL. | HTTP://TRANSPORT.FELK.CVUT.CZ
Intelligent route planning for
sustainable mobility
Michal Jakob*
Artificial Intelligence Center
Faculty of Electrical Engineering
Czech Technical University in Prague
http://transport.felk.cvut.cz
*joint work primarily with Jan Hrnčíř, Pavol Žilecký and Jan Nykl

AI for Transport and Mobility
M. JAKOB ET AL. | HTTP://AGENTS4ITS.NET
Data
Common models
Tools
sharing => acceleration
Simulation and
Modelling
Journeyplanning
and routing
Intelligent transport
marketplaces
Transport and
mobility data
analysis
1.Understand transport and mobility
2.Improve transport and mobility

Michal Jakob
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
…

Intermodal
Real-time
context-
aware
Multi-criteria
Personalized
Resource-
aware
Behaviour
changing
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 scale
106 − 107: national scale
107+ : continental scale
Algorithms:
Variants of shortest paths algorithms
Extensions over SP algorithms:
• speed ups
• multi-criteria search
• constrainedshortestpath
Practical responsetimes: 1+ms to <10s

Michal Jakob
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):
𝑐time
time (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.
(Recordedreal 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
CriterionY
𝜖-dominance
Criterion
X
Criterion
Y
1
2
3
4
5
1 2 3 4 5 6
buckets ellipse
pruning

Evaluation
Runtime: Average runtime in ms
Quality: Average distance of the heuristic
Pareto set Π from the optimal Pareto set
Π∗ in the criteria space
𝑑 𝑐 Π∗,Π ≔
1
Π∗
෍
𝜋∗∈Π∗
min
𝜋∈Π
𝑑 𝑐(𝜋, 𝜋∗)
Intuitively, 𝑑 𝑐 = 0.1 ~ 6% diff. in each
criterion (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)

Michal Jakob
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
suggestedplans
planning graph
…
walk
bike
sharing
Detailedinformationaboutallmodes

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
Metaplan
Refinement
suggestedplans
Metaplanning
trip plan query
metaplans
Metagraph
construction
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
Metaplan

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
RefineddetailedplanMetaplan
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 station
ModelConstruction
Planner APIs
Subplanner 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
3
In 23% / 50% of trips a competitive intermodal option(s) added
(all / long trips > 1hr)
Average intermodal speedup of 20% over PT-only trip

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.

Michal Jakob
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

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

Michal Jakob
Engineering Aspects

Engineering Process

Quality Assurance
Preliminary routing
graph
Data importers
and Routing
graph builder
Final routing graph
Anomaly
detection
Constraints
checkingTime
tables
Elevation
Traffic
…
Maps
High-quality
routinggraphs
Grabage in, garbage out
Route
planning
request
generator
Tested
planner
Results
evaluator and
comparator
Detailed planner
performance
statistics
Test
scenario
specs
Overallquality
validation
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

Michal Jakob
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
RawGPStracksMappedtracksSpeedmap

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 routing
and 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

Thank you!

Intelligent route planning for sustainable mobility

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