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Hedi Ayed,
Djamel Khadraoui,
Zineb Habbas,
Pascal Bouvry,
&
Jean Francois Merche
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1- Introduction
2- Problem description and our objectives
3- Existing solutions
4- Our solution : Transfer Graph
5- Conclusion
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-What is multimodal transport problem ?
-What this paper represents ?
-What is Carlink ?
-Route guidance in Carlink ?
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Monomodal : one transport mode.
Multimodal : many transport modes.
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When investigating existing approaches and algorithms on the topic, we observe that
none of them is applicable to multi modal route guidance problems subject to the
following constraints:
i) The multimodal network is assumed to be flat.
ii) Involved unimodal networks may be kept separated and accessed
separately.
iii) If there are multiple network information sources within a single
mode, they may be kept and accessed separately.
This paper presents our contribution to multimodal route guidance
problem.
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The present work has been done in the context of Carlink
Carlink is European project aiming to develop an
intelligent wireless traffic service platform between
cars supported by wireless transceivers beside the
road(s)..
-real-time local weather data
-the urban transport traffic management
-the urban information broadcasting for the mobile users
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Objective of Carlink is the travelling user mobility
management. The main idea behind this objective is to
provide route guidance services to a given traveling
mobile equipped user. The primary challenge of this
work has been to set solid and new basis to address
multimodal route advisory problem under Carlink’s
route guidance requirements.
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What is the traveling mobile user ?
traveling mobile user = ‘’any entity equipped with a mobile technology device which
is planning to move or is already moving from one geographical location to another’’
What route calculation mean ?
The scenario ‘’referred as route planning’’
The user is preparing a travel, route calculation service consist to propose
a set of possible route according to a user defined source and target location.
The scenario ‘’referred as travel monitoring’’
The user is already on his way to the destination, route calculation service
consist to propose a set of alternative routes from user’s current location
to a new destination or to the current destination, in case the user has
revised his choices or when some traffic disturbance.
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-Problem description
- Shortest path problem : In graph theory, the shortest path problem is the
problem of finding a path between two nodes such that the sum of the weights
of its edges is minimized.
- Multi objective and time dependent : In many works on SPP, the path
cost is a single scalar function. However, in the multimodal transport
problem we need to optimize path according to more than one scalar
functions. In this case the problem becomes a multi objective optimization
problem.
-Our objectives
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- Multigraph based approach
The multimodal transportation network is viewed and treated as a multigraph,
In general, a multigraph is simply a graph in which it is allowed to have more
than one arcs between two nodes. So the problem is reduced to the classical
shortest path problem or to one of its variants.
- CSP based approach
Most works consider multimodal route planning from graph theory perspective.
But the problem can also be seen as a constraint satisfaction problem (CSP).
So the problem is viewed as finding a combination of variables value in a search
space which satisfies a set of constraints.
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The problem : keep all existing unimodal transportation networks
separated.
The Solution :
we introduce an unusual graph structure which we call
transfer graph.
Let G=(N, A) denotes a graph
Gs = {G1, G2, ... ,Gq} a set of sub graphs
Each Gg = (Ng, Ag) is called a component
Given two distinct component s Gg , Gg’
› Ng ∩ Ng’ = φ is not mandatory.
› Ag ∩ Ag’ = φ is always hold.
› i in Ng ∩ Ng’ is a transfer point
We denote a transfer graph by TG=(N, A, GS, TS)
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- Shortest path algorithm in transfer graph
Consider a transfer graph TG=(N, A, GS, TS), let s, t be an origin-destination pair
and Gg be a component of TG;
- inter components paths
- intra components paths
- full paths
- partial paths
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- Full paths
- Relevant Head paths
-Relevant intermediate paths
- Relevant tail path
Assume that for all components Gg in GS we have computed the following
relevant path sets :
*g
P
s.t the
*g
P
s.-
the set of all best intra component head paths from s within Gg
+.t
the set of all best intra component tail paths to t within Gg
*g
P
*g
P
set of best intra component full path within Gg
+.-
the set of all best intra component intermediate paths within Gg
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The relevant graph (RG)
RG = (RV, RE)
RV = (URVg Gg in GS) U{s, t}
RE = (UREg Gg in GS)
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First implementation : a basic algorithm
The idea
-Get the request of the user
-Compute all the best paths
-Build the relevant graph
-Answer the request user
Disadvantages :
-Very slow
-Many reputations
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Second implementation : an algorithm with database
The idea
-Compute all the best paths for all pairs of nodes
-Store the best paths in a database
-Get the request of the user
-Build the relevant graph
-Answer the request user
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Summary
This work has been done in context of Carlink
We presented an algorithm to solve the shortest path in multimodal network
Support multi objective and time dependent
Future works
New decomposition : geographic decomposition
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Thank you