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 ?

Monomodal : one transport mode.

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Multimodal : many transport modes.

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.

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This paper presents our contribution to multimodal route guidance problem.

Carlink is European project aiming to develop an intelligent wireless traffic service platform between cars supported by wireless transceivers beside the road(s)..

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-real-time local weather data

-the urban transport traffic management

-the urban information broadcasting for the mobile users

The present work has been done in the context of Carlink

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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 ?

What route calculation mean ?

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’’

The scenario ‘’referred as route planning’’

The scenario ‘’referred as travel monitoring’’

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 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

- CSP 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.

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

- intra components paths

Consider a transfer graph TG=(N, A, GS, TS), let s, t be an origin-destination pairand Gg be a component of TG;

- inter components paths

- full paths

- partial paths

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Assume that for all components Gg in GS we have computed the followingrelevant path sets :

- Full paths

- Relevant Head paths

-Relevant intermediate paths

- Relevant tail path

P*g

s.t the set of best intra component full path within Gg

P*g

s.- the set of all best intra component head paths from s within Gg

P*g

+.t the set of all best intra component tail paths to t within Gg

P*g

+.- 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

Disadvantages :

-Very slow

-Many reputations

The idea

-Compute all the best paths

-Build the relevant graph

-Get the request of the user

-Answer the request user

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Second implementation : an algorithm with database

-Compute all the best paths for all pairs of nodes

-Store the best paths in a database

-Build the relevant graph

The idea

-Answer the request user

-Get the request of the user

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Summary

Future works

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

New decomposition : geographic decomposition

Thank you

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