An Automatic Matcher and Linker for Transportation Datasets

Ali Masri,Karine Zeitouni,Bertrand Leroy,Zoubida Kedad

doi:10.3390/ijgi6010029

Abstract

Multimodality requires the integration of heterogeneous transportation data to construct a broad view of the transportation network. Many new transportation services are emerging while being isolated from previously-existing networks. This leads them to publish their data sources to the web, according to linked data principles, in order to gain visibility. Our interest is to use these data to construct an extended transportation network that links these new services to existing ones. The main problems we tackle in this article fall in the categories of automatic schema matching and data interlinking. We propose an approach that uses web services as mediators to help in automatically detecting geospatial properties and mapping them between two different schemas. On the other hand, we propose a new interlinking approach that enables the user to define rich semantic links between datasets in a flexible and customizable way.

Highlights

Multimodality requires the integration of heterogeneous transportation data to construct a broad view of the transportation network
Some approaches have moved into creating a public repository to integrate public transportation data (Google Transit, Syndicat des transports d’ile-de-France (STIF)); they still do not take into consideration highly-evolving datasets, such as car sharing, bike sharing, car pooling, etc
We have implemented our approach, and an executable version of the system can be found online via the link https://github.com/alimasri/link-plus-plus.git; in addition to a video tutorial on: https://youtu.be/u2gr7Wa4eT4. We evaluate both of our two approaches using two datasets representing transportation companies in the Paris area, SNCF and Autolib, a railway company and a car sharing service, respectively

Summary

Introduction

Multimodality requires the integration of heterogeneous transportation data to construct a broad view of the transportation network. This raises the need for integrating multiple transportation data in order to provide a global view of the network Enabling such a solution for each company requires identifying the nearby services and finding ways to integrate them, which is a repetitive and tedious task, especially when done manually. Some approaches have moved into creating a public repository to integrate public transportation data (Google Transit (http://maps.google.com/landing/transit/index.html), Syndicat des transports d’ile-de-France (STIF) (http://www.stif.info)); they still do not take into consideration highly-evolving datasets, such as car sharing, bike sharing, car pooling, etc. Such services are highly dynamic and do not always have the notion of a fixed transportation stop

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Results

Conclusion