Abstract

International audience; We study the problem of robust routing in urban public transportation networks. In order to propose solutions that are robust for typical delays, we assume that we have past observations of real traffic situations available. In particular, we assume that we have "daily records" containing the observed travel times in the whole network for a few past days. We introduce a new concept to express a solution that is feasible in any record of a given public transportation network. We adapt the method of Buhmann et al. [Buhmann et al., ITCS 2013] for optimization under uncertainty, and develop algorithms that allow its application for finding a robust journey from a given source to a given destination. The performance of the algorithms and the quality of the predicted journey are evaluated in a preliminary experimental study. We furthermore introduce a measure of reliability of a given journey, and develop algorithms for its computation. The robust routing concepts presented in this work are suited specially for public transportation networks of large cities that lack clear hierarchical structure and contain services that run with high frequencies.

Abstract

We present an application of Integer Programming to the design of arrival routes for aircraft in a Terminal Maneuvering Area (TMA). We generate operationally feasible merge trees of curvature-constrained routes, using two optimization criteria: (1) total length of the tree, and (2) distance flown along the tree paths. The output routes guarantee that the overall traffic pattern in the TMA can be monitored by air traffic controllers; in particular, we keep merge points for arriving aircraft well separated, and we exclude conflicts between arriving and departing aircraft. We demonstrate the feasibility of our method by experimenting with arrival routes for a runway at Arlanda airport in the Stockholm TMA. Our approach can easily be extended in several ways, e.g., to ensure that the routes avoid no-fly zones.

Abstract

PUBLISHED Public transport vehicles often share a road network with other road users making their journeys susceptive to changing road conditions and especially to congestion. Travelers using such public transport increasingly depend on real-time information to plan their journeys. While such information can be provided by Automatic Vehicle Location (AVL) systems, AVLs depend heavily on large-scale deployment of designated sensory equipment, which may prevent their pervasive adoption. This paper presents a system for estimating vehicle location based on information generated by data sources typically integrated within existing ITS platforms. This enables location estimation for public transport vehicles without the need for deploying a designated sensor infrastructure in each vehicle, thereby reducing deployment and maintenance cost significantly. A prototypical vehicle location estimation system has been realized as part of and using data provided by the iTransIT ITS framework. Initial evaluation results show that such a system is feasible in a distributed manner and that estimated results are within 20% compared to empirical data. The work described in this paper was partly supported by Lero - the Irish Software Engineering Research Centre. SFI)

Abstract

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework. Comment: KDD 2018. The first two authors share equal contribution

Abstract

International audience; Transfer pattern routing is a state-of-the-art speed-up technique for finding optimal paths which minimize multiple cost criteria in public transportation networks. It precomputes sequences of transfer stations along optimal paths. At query time, the optimal paths are searched among the stored transfer patterns, which allows for very fast response times even on very large networks. On the other hand, even a minor change to the timetables may affect many optimal paths, so that, in principle, a new computation of all optimal transfer patterns becomes necessary. In this paper, we examine the robustness of transfer pattern routing towards delay, which is the most common source of such updates. The intuition is that the deviating paths caused by typical updates are already covered by original transfer patterns. We perform experiments which show that the transfer patterns are remarkably robust even to large and many delays, which underlines the applicability and reliability of transfer pattern routing in realistic routing applications.

Abstract

Passengers on metro platforms can board a train only when the train has surplus capacity and the dwell time is sufficient, while the latter condition is omitted in previous studies. Taking into account the impacts of train capacity and dwell time on passengers boarding, this study develops a model on optimizing metro timetable to reduce passenger travel time and metro operating cost, through regulating trains’ inter-station run-time, dwell time and headway. The NSGA-II algorithm is employed to obtain the near-optimal Pareto Frontier of the proposed model. To address insufficient dwell time scheduled in the timetable, three operating strategies are proposed and compared: a. sticking to nominal timetable; b. extending dwell time only; c. extending dwell time and recovering delay as soon as possible by compressing train inter-station run-time. Case studies on real-life metro line prove that some passengers cannot board the train during peak hours due to insufficient dwell time. In this context, strategy a brings low-quality service because passengers are stranded at platform even though the train has surplus capacity. In contrast, more passengers can board the train with strategies b and c because dwell time is extended for passengers’ boarding when train has surplus capacity. Compared to strategy b, strategy c reduces the average in-vehicle time of passengers by 2.5% through compressing inter-station run-time to recover the delay. The timetable optimized based on strategy c saves total travel time of passengers by 3.1% without increasing operating cost when compared to the practical timetable. Document type: Article

Abstract

The final publication is available at Springer via http://dx.doi.org/10.1007/s13437-018-0143-6 This contribution investigates the economic benefits of using weather ship routing on Short Sea Shipping (SSS) activities. The investigation is supported with the development of a ship routing system based on pathfinding algorithm, the parametrization of the wave effect on navigation, and the use of high-resolution meteo-oceanographic predictions. The optimal ship routing analysis is investigated in a European SSS system: the link between Spanish and Italian ports. The results show the economic benefits using ship routing in SSS during energetic wave episodes. The rate of cost savings may reach 18% of the total costs under particular bad weather conditions in the navigation area. The work establishes the basis of further developments in optimal route applied in relatively short distances and its systematic use in the SSS maritime industry. Peer Reviewed Document type: Article

Abstract

fter the emergence of the H1N1 influenza in 2009, some countries responded with travel-related controls during the early stage of the outbreak in an attempt to contain or slow down its international spread. These controls along with self-imposed travel limitations contributed to a decline of about 40% in international air traffic to/from Mexico following the international alert. However, no containment was achieved by such restrictions and the virus was able to reach pandemic proportions in a short time. When gauging the value and efficacy of mobility and travel restrictions it is crucial to rely on epidemic models that integrate the wide range of features characterizing human mobility and the many options available to public health organizations for responding to a pandemic. Here we present a comprehensive computational and theoretical study of the role of travel restrictions in halting and delaying pandemics by using a model that explicitly integrates air travel and short-range mobility data with high-resolution demographic data across the world and that is validated by the accumulation of data from the 2009 H1N1 pandemic. We explore alternative scenarios for the 2009 H1N1 pandemic by assessing the potential impact of mobility restrictions that vary with respect to their magnitude and their position in the pandemic timeline. We provide a quantitative discussion of the delay obtained by different mobility restrictions and the likelihood of containing outbreaks of infectious diseases at their source, confirming the limited value and feasibility of international travel restrictions. These results are rationalized in the theoretical framework characterizing the invasion dynamics of the epidemics at the metapopulation level. This work has been partially funded by the National Institutes of Health R21-DA024259 award, the Lilly Endowment grant 2008 1639-000 and the DTRA-1-0910039 award to AV; the EC-ICT contract no. 231807 (EPIWORK) to AV and VC; the EC-FET contract no. 233847 (DYNANETS) to AV, VC, and JJR; the ERC Ideas contract n.ERC-2007-Stg204863 (EPIFOR) to VC, PB, CP, and MT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Peer reviewed Document type: Article

Abstract

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operator-dependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully-automated, learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation, 2) inter-slice motion detection, 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method - integrating both regression and structured classification models - to extract landmarks as well as probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank as well as on 100 cases from the UK Digital Heart Project, and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition. Document type: Article

Abstract

The current Internet has been founded on the architectural premise of a simple network service used to interconnect relatively intelligent end systems. While this simplicity allowed it to reach an impressive scale, the predictive manner in which ISP networks are currently planned and configured through external management systems and the uniform treatment of all traffic are hampering its use as a unifying multi-service network. The future Internet will need to be more intelligent and adaptive, optimizing continuously the use of its resources and recovering from transient problems, faults and attacks without any impact on the demanding services and applications running over it. This article describes an architecture that allows intelligence to be introduced within the network to support sophisticated self-management functionality in a coordinated and controllable manner. The presented approach, based on intelligent substrates, can potentially make the Internet more adaptable, agile, sustainable, and dependable given the requirements of emerging services with highly demanding traffic and rapidly changing locations. We discuss how the proposed framework can be applied to three representative emerging scenarios: dynamic traffic engineering (load balancing across multiple paths); energy efficiency in ISP network infrastructures; and cache management in content-centric networks.