This document discusses the development of routing algorithms for car navigation systems. It covers types of routing including conventional and telematics-based routing. Key challenges are accurately estimating travel time and value of travel time savings. Telematics routing uses real-time traffic and road data from vehicles to improve these estimates. Faster routing algorithms like contraction hierarchies and hub labeling pre-compute supplementary network data to speed up route queries. Future work includes reducing travel time estimates errors and better accounting for individual user preferences.

1. DEVELOPMENT OF ROUTING
FOR CAR NAVIGATION SYSTEMS
Atsushi KOIKE
Sokendai
Sept. 12, 2013
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2. Car navigation systems
Introduction
• Devices that show us routes to our destinations
• Finding good routes is a key feature
http://gadgets.softpedia.com/gadgetsImage/The-AVIC-Z2-In-Dash-GPS-Navigation-System-and-Car-Multimedia-Center-1-13799.html
2
I will explain the development of routing for the systems
• I will only talk about publicly-known technologies

3. Types of routing
Telematics
Routing in telematics
• Navigation services using mobile communications
• With widespread smartphones, it is getting more important
※1 See also Wikipedia: Internavi http://en.wikipedia.org/wiki/Internavi
Honda web site: インターナビ・ルート http://www.honda.co.jp/internavi/service/internaviroute/ (in Japanese)
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Conventional routing
Routing in telematics
Calculation
• Client-side
• Server-side
Memory
• Small amount
• Large amount
Storage
• Small amount
• Static data
• Large amount
• Real-time data※1
• Traffic jams
• Blocked roads
Today’s topic

4. What is routing?
• Routing is a process to find good routes from a starting point
to a destination
http://apptoi.com/archives/10102
What are good routes?
• Save time
• Save money
• Save gasoline consumption etc.※1
※1 See also Honda web site: インターナビ・ルート http://www.honda.co.jp/internavi/service/internaviroute/ (in Japanese)
Good routes save various costs
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5. Developing faster algorithms
Challenges for routing
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• It reduces waiting time for users
• Applications can run more routing queries
Finding better routes
• Users can save time and money on travel by getting better
routes
• Exact estimation of arrival time is sometimes crucial for users
High priority

6. Definition of cost
※1 See also Wikipedia: Generalised cost, http://en.wikipedia.org/wiki/Generalised_cost
竹内 健蔵, 交通経済学 (in Japanese)
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Generalized cost ※1
• Generalized cost C := Monetary cost M + Non-monetary cost N
• N ≈ WT
• Indicates “how much each user pays to save unit time”
• Depends on users
T: travel time
W: value of travel time saving
(conversion factor from time to money)
def
Value of travel time saving

7. Basic formulation of routing
Input
• Road network: directed Graph G = (V, E)
• Set of crossroads: V
• Set of roads: E
• Edge cost function f :
• Vertex cost function h:
Query
• Given a starting point s and a destination t,
find a minimum cost path from s to t
{ }0≥ℜ∈→ xxE
{ }0≥ℜ∈→ xxV
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Red: vertex generalized cost
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Blue: edge generalized cost
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s t5 8
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8. Finding better routes
• It is important to estimate generalized cost correctly
• Monetary cost M can be calculated correctly from the toll fare
table
C = M +WT
C: generalized cost
M: monetary cost
T: travel time
W: value of travel time saving
Monetary cost
Non-monetary cost
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• The challenge is to estimate travel time T and value of
travel time saving W with high accuracy

9. Finding better routes:
estimation of travel time
Why is the estimation difficult?
• Driving speed depends on many parameters such as
• Traffic jams
• Road width/twist
• Traffic lights
• Average speed of each driver
How do we estimate travel time?
• In telematics, we can use a variety of data※1 such as
• Road width/category
• Vehicle Information and Communication System (VICS):
a technology for delivering traffic information to drivers
• Vehicle’s GPS position data
• Congestion prediction data
※1 See also
Aisin web site: Tell me about it! How does the car navigation system work?
http://www.aisin-aw.co.jp/en/products/information/structure/index.html
Wikipedia: Internavi http://en.wikipedia.org/wiki/Internavi
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10. Honda Internavi ※1
An example of estimating travel time
※1 See also Honda web site: インターナビ・ルート http://www.honda.co.jp/internavi/about/result/time/ (in Japanese)
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Error of estimated travel time
Without Internavi
(conventional routing)
With Internavi
(routing in telematics)
The error decreased
by using telematics
30 min
7 min

11. Finding better routes:
estimation of value of travel time saving
A method that exploits user choices※1
• When a user asks for a route, car navigation systems suggest multiple routes
• The system can learn value of travel time saving W from the route the user
has chosen
※1 See also wikipedia: Discrete choice http://en.wikipedia.org/wiki/Discrete_choice
交通工学研究会, やさしい非集計分析 (in Japanese)
s
t
Route 2 (min cost path at W=2500[yen/hour])
40 min, 700 yen
Route 1 (min cost path at W=1500[yen/hour])
60 min, 0 yen
Save money
Save time
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If a user chooses route 2,
the choice indicates W >1500
Save time

12. Logit model ※1
Finding better routes:
estimation of value of travel time saving (cont.)
※1 See also 交通工学研究会, やさしい非集計分析 (in Japanese)
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Each user has
their own value
of travel time
saving
He/she calculates
the cost of each
route according to
the value of travel
time saving
He/she chooses
one route using
the function
below
C1: cost of route 1
C2: cost of route 2
Probability of users choosing route 1
C2 – C1
If C1 is much less than C2,
Users choose route 1 with high probability
If C2 is much less than C1,
Users choose route 2 with high probability
We can estimate the value of travel time saving by
maximum likelihood estimation
Predict the value of
travel time saving from
the user choices

13. Developing faster algorithms
Conventional method
• Dijkstra’s algorithm
• Widely used even today
Main idea for faster computation
• Pre-computing supplementary data that accelerates routing queries
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Suitable for routing in telematics
Pre-computing

14. Developing faster algorithms (cont.)
Recent studies
• Contraction hierarchies [Geisberger et al. 2008]※1
• Supplementary data
• Hierarchical networks
• Advantages
• Small amount of supplementary data
• Features for commercial service
• Hub labeling [Abraham et al. 2011]※2
• Supplementary data
• A set of “hub” (important) vertices for each vertex
• Considerably large amount of supplementary data
⇒ Hub label compression [Delling et al. 2013]※3
• Advantages
• Fastest
※1 R. Geisberger et al. Contraction Hierarchies: Faster and Simpler Hierarchical Routing in Road Networks. (WEA’08)
※2 I.Abraham et al. A Hub-Based Labeling Algorithm for Shortest Paths on Road Networks. (SEA’11)
※3 Delling et al. Hub Label Compression. (SEA’13)
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Road network
Set of hub vertices
…
…
…
Contraction hierarchies
http://algo2.iti.kit.edu/download/
geisberger_badherrenalb08.pdf

15. Developing faster algorithms (cont.)
Andrew V. Goldberg, Shortest Paths in Road Networks, Erice school "Graph Algorithms, Theory, and Applications
http://research.microsoft.com/en-us/people/goldberg/erice.pdf
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16. Conclusion
• The challenges for routing
• To estimate the cost correctly
• To calculate it quickly
• Telematics is an effective means for both of them
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One reason is a lack of data
Issues
Future work
• The error of estimated travel time is still large in some cases
• User preferences (including value of travel time saving) cannot be dealt with
enough.
• Exploit more vehicle’s GPS position data and more user operation logs