Transfer Graph Approach for Multimodal Transport Problems MCO'08 Transfer Graph Approach for Multimodal Transport Problems Hedi Ayed, Djamel Khadraoui, Zineb Habbas, Pascal Bouvry, & Jean Francois Merche

Plan MCO'08 4- Our solution : Transfer Graph 1- Introduction 2- Problem description and our objectives 3- Existing solutions 4- Our solution : Transfer Graph 5- Conclusion

-What is multimodal transport problem ? MCO'08 Introduction -What is multimodal transport problem ? -What this paper represents ? -What is Carlink ? -Route guidance in Carlink ?

What is multimodal transport problem ? MCO'08 What is multimodal transport problem ? Monomodal : one transport mode. Multimodal : many transport modes.

What this paper represents ? MCO'08 What this paper represents ? 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.

MCO'08 What is Carlink ? 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

The Objective of Carlink MCO'08 The Objective of Carlink 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.

Route guidance in Carlink MCO'08 Route guidance in Carlink 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.

Problem description and our objectives MCO'08 Problem description and our objectives -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

Main existing solutions MCO'08 Main existing solutions - 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.

Our solution : Transfer Graph MCO'08 Our solution : Transfer Graph 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)

Our solution : Transfer Graph MCO'08 Our solution : Transfer Graph - 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

Our solution : Transfer Graph MCO'08 Our solution : Transfer Graph - 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 : 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

Our solution : Transfer Graph MCO'08 Our solution : Transfer Graph The relevant graph (RG) RG = (RV, RE) RV = (URVg Gg in GS) U{s, t} RE = (UREg Gg in GS)

Implementations and results MCO'08 Implementations and results 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

Implementations and results MCO'08 Implementations and results 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

Conclusion MCO'08 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 Future works New decomposition : geographic decomposition

MCO'08 Thank you