Kamaldeep Singh Oberoi

Groupe Cesi · Department of Information Technology

Spatio-temporal (ST) graphs have been used in many application domains to model evolving ST phenomenon. Such models represent the underlying structure of the phenomenon in terms of its entities and different types of spatial interactions between them. The reason behind using graph-based models to represent ST phenomenon is due to the existing well-...

Graphs have been used in different fields of research for performing structural analysis of various systems. In order to compare the structure of two systems, the correspondence between their graphs has to be verified. The problem of graph matching, especially subgraph isomorphism (SI), has been well studied in case of static graphs. However, many...

Over the past few years, Mobility-as-a-Service (MaaS) has become an alternative to owning a private car, integrating various public and shared travel modes in a single platform. Despite its attractiveness in meeting everyday travel needs, it has not seen high penetration rates within the urban population. A solution to increase its acceptability is...

Spatio-temporal (ST) models are often used for analyzing ST phenomena. One such analysis technique is to detect patterns in the phenomenon to understand its evolution and model the behaviour of its entities over space-time. In this paper, we focus on using a dynamic graph-based representation for modeling ST phenomena, within which structural patte...

For past several decades, researchers have been interested in understanding traffic evolution, hence, have proposed various traffic models to identify bottleneck locations where traffic congestion occurs, to detect traffic patterns, to predict traffic states etc. Most of the existing models consider traffic as many-particle system, describe it usin...

For past several decades, researchers have been interested in understanding traffic evolution, hence, have proposed various traffic models to identify bottleneck locations where traffic congestion occurs, to detect traffic patterns, to predict traffic states etc. Most of the existing models consider traffic as many-particle system, describe it usin...

To model dynamic road traffic environment, it is imperative to integrate spatial and temporal knowledge about its evolution into a single model. This paper introduces temporal dimension which provides a method to reason about time-varying spatial information in a spatio-temporal graph-based model. Two types of evolution, topological and attributed,...

In this paper, we present a qualitative model, based on graph theory, which will help to understand the spatial evolution of urban road traffic. Various real world objects which affect the flow of traffic, and the spatial relations between them, are included in the model definition. Heterogeneous data, at microscopic and macroscopic levels, will be...

This paper presents preliminary steps towards the development of a general spatial model, based on graph theory, to visualize and reason about the road traffic in an urban environment. This model includes qualitative, in addition to quantitative, data which improves its computation and makes it robust to quantitative errors. The paper also describe...