EDF2014: Ralf-Peter Schaefer, Head of Traffic Product Unit, TomTom, Germany: Probe Data Analytics and Processing for Traffic Information, Traffic Planning and Traffic Management
Industry Keynote Talk by Ralf-Peter Schaefer, Head of Traffic Product Unit, TomTom, Germany at the European Data Forum 2014, 20 March 2014 in Athens, Greece: Probe Data Analytics and Processing for Traffic Information, Traffic Planning and Traffic Management.
EDF2014: Marta Nagy-Rothengass, Head of Unit Data Value Chain, Directorate Ge...
Similar to EDF2014: Ralf-Peter Schaefer, Head of Traffic Product Unit, TomTom, Germany: Probe Data Analytics and Processing for Traffic Information, Traffic Planning and Traffic Management
Similar to EDF2014: Ralf-Peter Schaefer, Head of Traffic Product Unit, TomTom, Germany: Probe Data Analytics and Processing for Traffic Information, Traffic Planning and Traffic Management (20)
EDF2014: Ralf-Peter Schaefer, Head of Traffic Product Unit, TomTom, Germany: Probe Data Analytics and Processing for Traffic Information, Traffic Planning and Traffic Management
1. Big (Traffic) Data
Probe Data Analytics and Processing for Traffic Information, Traffic
Planning and Traffic Management
Ralf-Peter Schäfer
Fellow & VP Traffic and Travel Information Product Unit
ralf-peter.schaefer@tomtom.com
1
Copyrights: TomTom Internal BV 2014
3. 3
Huge investment and
maintenance costs to
detect traffic information
Typically every 2 km a
loop required to get
precise real-time traffic
infos
Can we do better?
9. From Modeling to Measuring
9
Classical tools for observing traffic flow: Simulation and Data from Loop-Detectors
Simulated elementary traffic jam patterns:
Interpolated and smoothed data from loop detectors:
10. From Modeling to Measuring
10
Direct speeds observations with GPS probe data
• GPS data allows traffic
observation everywhere
• Independent from stationary
devices
• Sampling rate sufficient for
real-time traffic information
11. TomTom Congestion Index Europe (Q3 2013)
Traveltime delays compared to free flow situation at night hours
11
http://www.tomtom.com/congestionindex
12. Speeds Over Time (City, e.g. Berlin)
12
Sun Mon Tue Wed Thu Fri Sat
speed
15. Speeds Over Time (Freeway , e.g. A9 south of Berlin)
15
Sun Mon Tue Wed Thu Fri Sat
speed
16. ORIGIN-DESTINATION ZONE ANALYSIS
16
Data collection of origin-destination
data is difficult
Current techniques include
- Stop cars: road side interviews
- Get address from license plate
and send survey
- Telephone interview
- Panel fills in a diary of their
movements
- Point to point tracking: license
plates (full) or bluetooth
(sample)
17. 17
Selected link: links to links Selected link: zones to zones
A B
A 30 5
B 10 20
OD MatrixRoute choice
18. Detailed junction analysis per path
Speed measurements
over time
Speed measurements
grouped by day of the
week
Speed frequency
distribution, free flow
estimate
Distribution of Travel time
delta
20. Data fusion Send to users
GPS
Probe Data
GSM
Probe Data
Journalistic
info
Historic
Traffic
Map Data
Various input sources
20
REAL-TIME TRAFFIC INFO FROM USERS TO USERS
21. Probe Data Example of Traffic Incident
GPS and GSM input sources and incident output message
21
• Example from Dec 13, 2011
• Near Stuttgart, Germany
• GPS data from floating cars
• Speed data matched to road elements
• GSM data from mobile phone calls
• Sophisticated algorithm
• After fusion and incident detection
• Live incident output to PND
22. JAM TAIL WARNINGS
Detection of jam tails for a safety warning in the navigation unit
• Over 35% of drivers have admitted to experiencing
an accident caused by sudden or unexpected traffic
holdups
• Jam ahead warning messages in traffic output can be
used to create these safety messages with great
accuracy
22
23. Real-time road speed data
Enable traffic information and traffic
management
Measured speed on each road segment
On all important roads
Without the need of road-side equipment
By using Floating Car Data
Updated every minute
23
TOMTOM TRAFFIC
Traffic Flow
Flow conditions (speed) on all roads
24. 24
The TomTom Traffic Manifesto
If 10% of the road drivers use HD Traffic
guided navigation in conurbations there is
a
1. Individual journey time reduction for
informed users by up to 15%
2. A collective journey time reduction for ALL
by up to 5%
http://www.tomtom.com/trafficmanifesto
25. 10% 10%
How to estimate the journey time reduction
claims in the TomTom Manifesto?
Use of traffic modelling and simulation in a simplified road network
Assume a share of equipped navigation users (e.g. traffic guided drivers)
Assume high acceptance rate for detour choices when approaching a traffic jam!
Results from simulation below for medium and high traffic flow
Source: F. Leurent, T. Nguyen, TRB 2010.
Animated, updated GSM, supersized imageRange of different sources, to enable reliable traffic detection on all roads in all situationsQuality of each source is important: precision of location (GPS better than GSM) and update frequency (logged every 2-5s, uploaded ~every 2 minutes)LIVE PND are connected TomTom GPS navigation devicesIn dash navigation currently limited to Europe (Renault, Fiat, Mazda)iPhone data is only from users of TomTom navigation application on the device – and GPS trace data only passed to TomTom if the user subscribes to LIVE services and the device is docked in the TomTom holderBusiness solutions is the TomTom unit focused on connected products for fleet owners (delivery vans, maintenance cars, etc)3rd party GPS data only used in selected countries (USA, South Africa & New Zealand)GSM probes only active in 8 countries (notably not in USA)Incident data = journalistic data describing the cause of the congestion / delay e.g. accident
Animated, updated GSM, supersized imageRange of different sources, to enable reliable traffic detection on all roads in all situationsQuality of each source is important: precision of location (GPS better than GSM) and update frequency (logged every 2-5s, uploaded ~every 2 minutes)LIVE PND are connected TomTom GPS navigation devicesIn dash navigation currently limited to Europe (Renault, Fiat, Mazda)iPhone data is only from users of TomTom navigation application on the device – and GPS trace data only passed to TomTom if the user subscribes to LIVE services and the device is docked in the TomTom holderBusiness solutions is the TomTom unit focused on connected products for fleet owners (delivery vans, maintenance cars, etc)3rd party GPS data only used in selected countries (USA, South Africa & New Zealand)GSM probes only active in 8 countries (notably not in USA)Incident data = journalistic data describing the cause of the congestion / delay e.g. accident
Each yellow dot represents a measurement of a certain speed (vertical axis) at a certain time of the week (horizontal axis).The blue bars mark mean speeds for time bins, and the white line is the speed profile generated from it after filtering etc.What’s interesting is that while most measurements are around 40-50 km/h. there is also a cluster of measurements at around 5-10 km/h.What is it? Let’s take a look…
In this diagram, each pink bar represents the total number of measurements at a certain speed on our road segment.What we see is very typical of urban roads, and we call it bimodal behavior; either traffic flows well, at 43 km/h, or sometimes, the road gets congested, then it’s 6 km/h.We analyze these speed frequencies in 1-hour bins and compute from it a “jam probability” and a “jam speed”, which we may use in routing in the future.
As a second example, let’s take a look at a segment of Autobahn, on the A9 freeway just south of Berlin.
Again, each yellow dot represents a speed measurement at a certain time of the week.Most measurements are around 130, 140 km/h, but there is again a separate cluster at almost exactly 90 km/h.This doesn’t look like jam mode, it’s too fast! What’s going on?