A compact and scalable representation of network traffic dynamics using shapes and its applications

The biggest challenge of analysing network traffic dynamics of large-scale networks is its complexity and pattern interpretability. In this work, we present a new computationally efficient method, inspired by human vision, to reduce the dimensions of a large-scale network and describe the traffic conditions with a compact, scalable and...

Metropolitan rail network robustness

In large-scale urban agglomerations, heavy rail in the form of metro and commuter train serves as the backbone of the metropolitan public transport network. The objective of this paper is to investigate whether networks with strikingly different structure and development pattern exhibit different robustness properties in the event of random...

Estimation of metro network passenger delay from individual trajectories

Smart card data enables the estimation of passenger delays throughout the public transit network. However, this delay is measured per passenger trajectory and not per network component. The implication is that it is currently not possible to identify the contribution of individual system components – stations and track segments – to overall...

A data driven method for OD matrix estimation

The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper we propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. We show that with these input data, we do not require an iterative dynamic...

Rail network robustness: The role of rapid development and a polycentric structure in withstanding random and targeted attacks

In large-scale urban agglomerations, heavy rail in the form of metro and commuter train serves as the backbone of the metropolitan public transport network. Transport systems are subject to recurrent disruptions that may result in severe consequences for network performance and society at large. The objective of this paper is to compare the...

Exploring demand patterns of a ride-sourcing service using spatial and temporal clustering

On-demand transport has become a common mode of transport with ride-sourcing companies like Uber, Lyft and Didi transforming the mobility market. Recurrent patterns in prevailing demand patterns can be used by service providers to better anticipate future demand distribution and thus support demand-Anticipatory fleet management strategies. To...

Heuristic Coarsening for Generating Multiscale Transport Networks

Graphs at different scales are essential tools for many transportation applications. Notwithstanding their relevance, these graphs are created and maintained manually for most applications, in both research and practice. In this paper, we develop a heuristic method for automatically generating multiscale graph representations without...

Understanding Network Traffic States using Transfer Learning

Large-scale network traffic analysis is crucial for many transport applications, ranging from estimation and prediction to control and planning. One of the key issues is how to integrate spatial and temporal analyses efficiently. Deep Learning is gaining momentum as a go-to approach for artificial vision, and transfer learning approaches allow...