Steven Logghe & Pieter Audenaert - Mobiroute

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This document summarizes the MobiRoute project, which developed a multimodal route planner with predictions based on historical traffic information. The project combined data from trains and cars to plan routes using multiple transportation methods. It included clustering historical traffic data to predict congestion and developed applications to showcase route planning capabilities.

Business Economy & Finance

MobiRoute
@ Steven Logghe
iMinds Pieter Audenaert

Projectleiders MobiRoute

18/10/2011 1

Motivation:
 Car routeplanner with predictions did not exist.
 Combination train – car did not exist.

Goal :
Development of a multimodal routeplanner
with predictions based on historical traffic information

Customized projectconsortium
Industrial partners :
Duo : train routeplanning
Be-Mobile : traffic information

Research groups :
UGent IBCN
KULeuven CIB
KULeuven ICRI

Go-Mobile birth during the project

Project scheme
car train
Car traffic Historical
Clustering Predictions Real-time Schedules
observations data

Route – engine

Applications

Web app Mobile app API

Project scheme
car train
Car traffic
observations
Historical
data
Data
ClusteringPredictions Real-time Schedules

Route – engine

Applications

Web app Mobile app API

Navigation
Floating
Car Data

Mobile
SmartMove
Drivers &
Operators Traffic
Platform Media

Road
Sensors Traffic
Mgt

data

Thursday 13th of January 2011
‘A rainy morning between Ghent and Brussel

Wetteren

Erpe-Mere E19
Aalst
Affligem
E40 R0
Ternat

Groot-
Bijgaarden

data

Groot- Thursday Jan 13th 2011
Bijgaarden
E40 Ghent -> Brussels
Ternat Between Wetteren and
Groot-Bijgaarden

Affligem Rainy morning
Incident in Aalst
and structural congestion

Aalst

Erpe-Mere

Wetteren
5:00 7:00 8:00 9:00 10:00 11:00

Floating Car Data

The principle
1. Retrieve real-time GPS positions of millions of vehicles (taxis, trucks, vans…)
2. Calculate the routes and travel times between these positions
3. Detect the traffic jams

Floating
Car Data

Clustering data to predict Traffic
Traffic fluctuates
on Thursday 13 jan 2011 on Tuesday 6 sept 2011

data

Clustering data to predict Traffic
Traffic fluctuates
on Thursday 13 jan 2011 on Monday 19 sept 2011

data

Route Engine

 Routeplanner which is

• Time-dependent

• Stochastic

• Multimodal

Route – engine

MobiRoute = a time-dependent planner
• Travel-times fluctuate throughout the day
• Picture of actually measured travel-time
traveltime (s)

8u00 12u00 18u00 hour of day

Route – engine

MobiRoute = a stochastic planner
Origin: Gaston Crommenlaan 8, Gent
Destination: Opera, Luik
Start: 9h30m0s
10% arrival before 10h59m56s
30% arrival before 11h06m17s
50% arrival before 11h12m22s
70% arrival before 11h18m10s
90% arrival before 11h22m12s
Traveltime during the day
50% certainty 90% certainty
Travel-time

10% certainty

Hour

Route – engine

MobiRoute = a multimodal planner

• Car & train, can be extended to e.g. bus
• Each mode of transportation keeps its own network
• Trans-shipment links between nodes of different modes

bus
trans-shipment links
train
car

Route – engine

MobiRoute = a multimodal planner
• Links are distinguished by their properties
• Time-dependent or time-independent
• No timetable vs. timetable
No timetable
No timetable

Time-
Time-independent Time-
Time-dependent
independent dependent

Timetable
timetable

Route – engine

demo web application
Tielt > Heist-op-den-Berg

08:00 - 10:11 | 1 train
app

demo web application
Tielt > Heist-op-den-Berg

08:00 - 09:53 | car reference
app

demo web application
Tielt > Heist-op-den-Berg

08:16 - 10:55 | 2 trains via Brussels
app

demo web application
Tielt > Heist-op-den-Berg

09:03 - 11:28 | 2 trains via Antwerp
app

demo web application
Charleroi > Spa

app

demo web application
Lier > Waremme

app

test iPad application

http://mobiroute.devel1.duo.be
app

MobiRoute
@ Steven Logghe
iMinds Pieter Audenaert

Projectleiders MobiRoute

18/10/2011 29

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