Cadre méthodologique de co-déploiement des systèmes physique et d’information : contexte de la gestion durable du transport dans les pays en voie de développement

L’expansion de l’activité industrielle conduit généralement à une attraction des populations vers les centres urbains et par voie de conséquence, à une augmentation des besoins de transport. Conjugué à la double croissance économique et démographique, ce phénomène déjà perceptible dans les grandes agglomérations des pays qui aspirent au développement ira en s’amplifiant. Si les modèles de transport (physiques et informationnels) appliqués dans les pays industrialisés permettent de maîtriser cette problématique tout en favorisant une amélioration continue, du fait de leurs spécificités (notamment socio-culturelles), les Pays en Voie de Développement (PEVD) sont confrontés à un dilemme : transposer directement les modèles existants ayant fait leur preuve (avec le risque d’une faible adéquation aux besoins locaux immédiats), ou bien développer de nouveaux modèles qui pourraient manquer de maturité. L’objectif du travail présenté dans ce papier est de proposer un cadre méthodologique de co-déploiement des systèmes physique et d’information (de transport) qui conjugue la maturité des modèles éprouvés et la forte adéquation d’une nouvelle approche. La finalité étant de mettre en œuvre un système d’aide à la décision sous forme de recommandations, basé sur le couplage entre la définition d’un système physique de transport et celle du système d’information approprié

Parallel Ant Colony Algorithm for Shortest Path Problem

During travelling, more and more information must be taken into account, and travelers have to make several complex decisions. In order to support these decisions, IT solutions are unavoidable, and as the computational demand is constantly growing, the examination of state-of-the-art methodologies is necessary. In our research, a parallelized Ant Colony algorithm was investigated, and a parameter study on a real network has been made. The aim was to inspect the sensibility of the method and to demonstrate its applicability in a multi-threaded system (e.g. Cloud-based systems). Based on the research, increased effectiveness can be reached by using more threads. The novelty of the paper is the usage of the processors’ parallel computing capability for routing with the Ant Colony algorithm

Detección de modos de transporte usando datos GPS

The use of mobile devices and GPS technology allow the implementation of systems to analyze the context and typical transport activities of a user, through the analysis of the location data and acceleration sensors. This research includes the processing of data obtained via GPS. This processing is intended to detect the mode of transport of a user in segments of predefined paths. For classification, velocity profiles that identify modes of transport in each segment are used. The software implements a Java programming language and the use of Matlab for analysis and data filters. The software system is developed into two components; the first comprises the filter and transformation of data. These data are plotted from decimal coordinates to cartesian coordinates. The second presents the classification for the detection of transport modes with cartesian coordinates. It also contains the analysis of states of kinematic movements. The tests are performed through a dataset taken from the GeoLife project of Microsoft Asia. The obtained results show a coherent detection on the means of transport that the different users use. These users are compared from predefined speed profiles.Keywords: Transportation mode detection, GPS, multimodal transport.El uso de dispositivos móviles y el aprovechamiento de la tecnología GPS, permiten la implementación de sistemas para analizar el contexto y actividades típicas de transporte de un usuario, a través del análisis de los datos de localización y sensores de aceleración. Este trabajo de investigación comprende el procesamiento de datos obtenidos vía GPS. Con este procesamiento se pretende detectar el modo de transporte de un usuario en segmentos de recorridos predefinidos. Para la clasificación de éstos, se usan perfiles de velocidad que identifican los modos de transporte en cada uno de los segmentos, mediante un sistema software en lenguaje de programación Java y la utilización de Matlab para el análisis y filtros de datos. El sistema software se desarrolla en dos componentes, el primero comprende el filtro y transformación de datos. Estos datos se representan en coordenadas decimales a coordenadas cartesianas. El segundo presenta la clasificación, para la detección de modos de transportes con las coordenadas cartesianas. También contiene el análisis de estados de movimientos cinemáticos. Las pruebas se realizan a través de un dataset tomado del proyecto GeoLife de Microsoft Asia. Los resultados obtenidos muestran una detección coherente sobre los medios de transporte que usan los diferentes usuarios. Estos usuarios se comparan a partir de perfiles de velocidad predefinidos.Palabras Clave: Detección de modos de transporte, GPS, transporte multimodal

Adaptive Travel Mode Choice in the Era of Mobility as a Service (MaaS): Literature Review and the Hypermode Mode Choice Paradigm

Mobility as a Service (MaaS) is becoming a “fashionable” solution to increase transport users’ satisfaction and accessibility, by providing new services obtained by optimally integrating sustainable modes, but also guaranteeing mass transport and less sustainable modes, guaranteeing fast and lean access/egress to the mass transport. In this context, the understanding and prediction of travellers’ mode choices is crucial not only for the effective management of multimodal transport networks, but also successful implementation of new transport schemes. Traditional studies on mode choices typically treat travellers’ decision-making processes as planned behaviour. However, this approach is now challenged by the widely distributed, multi-sourced, and heterogeneous travel information made available in real time through information and communication technologies (ICT), especially in the presence of a variety of available mode options in dense urban areas. Some of the real-time factors that affect mode choices include availability of shared vehicles, real-time passenger information, unexpected disruptions, and weather. These real-time factors are insufficiently captured by existing mode choice models. This chapter aims to propose an introduction to MaaS, a literature review on mode choice paradigms, then it proposes a novel behavioural concept referred to as the hypermode. It will be illustrated a two-level mode choice decision architecture, which captures the influence of real-time events and travellers’ adaptive behaviour. A pilot survey shows the relevance of some real-time factors, and corroborates the hypothesized adaptive mode choice behaviour in both recurrent and occasional trip scenarios