Suchergebnisse

Light rail transit systems (LRTs) are attractive options for modern communities as they offer high quality, sustainable public transportation services. However, investment costs often may make their application for medium-sized cities prohibitive, particularly if no significant social benefits are achieved. Guided light transit (GLT) has been introduced in the recent years, as a lower cost alternative to LRT, with the additional advantage of being suitable for urban environments with space limitations. In this study, a systematic comparison of LRT and GLT is offered, in the context of a mid-size city in Greece. Results indicate that high investment costs, coupled with low ridership can have a negative impact to the introduction of LRT in a medium-sized city. However, under certain conditions, GLT may be a viable alternative, while its nature and characteristics are not that different to those of LRT.

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a "story" intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models. ; This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government.

By 2050, one in four people will be aged 65 or over. The aging of the population coincides with an increase in older road users since far more elderly will actively participate in traffic. As a result, the road safety situation of the elderly will also change since the normal ageing process makes people more prone to experience functional declines that can make driving a car more difficult. The fatal accident risk for elderly cyclists and pedestrians is also many times higher than for elderly car drivers. In the light of these challenges, this report has explored the road safety risks and main trends for older road user groups (drivers, passengers and pedestrians). Fragility, illnesses and functional limitations, urban roads, walking and medication appear to be the key risk factors of elderly road users. A comprehensive and proactive strategy is required to address these risk factors and meet the safety and mobility needs of elderly road users in the (near) future. This strategy will include all policy levels and needs to apply a "design for all" approach based on a package of interventions in the area of infrastructure, education & training, licensing & enforcement and vehicle & ITS technologies. ; En 2050, une personne sur quatre aura 65 ans ou plus. Le vieillissement de la population est associé avec une augmentation des usagers de la route âgés. Par conséquent, la sécurité routière changera aussi puisque le vieillissement rend les gens plus sensibles aux limitations fonctionnelles qui peuvent rendre la conduite d'une voiture plus difficile. Le risque d'accident mortel pour les cyclistes et les piétons âgés est aussi plus élevé que pour les conducteurs âgés. A la lumière de ces évolutions, ce projet a exploré les risques de la sécurité routière et les tendances principales pour les groupes d'usagers de la route âgés (conducteurs, passagers et piétons). La vulnérabilité, des maladies et des limitations fonctionnelles liées à l'âge, les voies urbaines, le piétonnage et la consommation de médicaments ont été identifiés comme les facteurs de risque principaux. Donc, une stratégie globale et proactive est nécessaire pour répondre à ces facteurs de risque et pour rencontrer les besoins futurs des personnes âgées par rapport à la sécurité routière et la mobilité. Cette stratégie comprendra tous les niveaux politiques et doit appliquer une approche basée sur un ensemble d'interventions dans les domaines de l'infrastructure routière, l'éducation, l'entraînement, la réglementation relative au permis de conduire et la technologie des véhicules.