17,334 research outputs found
Fine-grained traffic state estimation and visualisation
Tools for visualising the current traffic state are used by local authorities for strategic monitoring of the traffic network and by everyday users for planning their journey. Popular visualisations include those provided by Google Maps and by Inrix. Both employ a traffic lights colour-coding system, where roads on a map are coloured green if traffic is flowing normally and red or black if there is congestion. New sensor technology, especially from wireless sources, is allowing resolution down to lane level. A case study is reported in which a traffic micro-simulation test bed is used to generate high-resolution estimates. An interactive visualisation of the fine-grained traffic state is presented. The visualisation is demonstrated using Google Earth and affords the user a detailed three-dimensional view of the traffic state down to lane level in real time
Identification of high-level functional/system requirements for future civil transports
In order to accommodate the rapid growth in commercial aviation throughout the remainder of this century, the Federal Aviation Administration (FAA) is faced with a formidable challenge to upgrade and/or modernize the National Airspace System (NAS) without compromising safety or efficiency. A recurring theme in both the Aviation System Capital Investment Plan (CIP), which has replaced the NAS Plan, and the new FAA Plan for Research, Engineering, and Development (RE&D) rely on the application of new technologies and a greater use of automation. Identifying the high-level functional and system impacts of such modernization efforts on future civil transport operational requirements, particularly in terms of cockpit functionality and information transfer, was the primary objective of this project. The FAA planning documents for the NAS of the 2005 era and beyond were surveyed; major aircraft functional capabilities and system components required for such an operating environment were identified. A hierarchical structured analysis of the information processing and flows emanating from such functional/system components were conducted and the results documented in graphical form depicting the relationships between functions and systems
MobilitApp: Analysing mobility data of citizens in the metropolitan area of Barcelona
MobilitApp is a platform designed to provide smart mobility services in urban
areas. It is designed to help citizens and transport authorities alike.
Citizens will be able to access the MobilitApp mobile application and decide
their optimal transportation strategy by visualising their usual routes, their
carbon footprint, receiving tips, analytics and general mobility information,
such as traffic and incident alerts. Transport authorities and service
providers will be able to access information about the mobility pattern of
citizens to o er their best services, improve costs and planning. The
MobilitApp client runs on Android devices and records synchronously, while
running in the background, periodic location updates from its users. The
information obtained is processed and analysed to understand the mobility
patterns of our users in the city of Barcelona, Spain
Map++: A Crowd-sensing System for Automatic Map Semantics Identification
Digital maps have become a part of our daily life with a number of commercial
and free map services. These services have still a huge potential for
enhancement with rich semantic information to support a large class of mapping
applications. In this paper, we present Map++, a system that leverages standard
cell-phone sensors in a crowdsensing approach to automatically enrich digital
maps with different road semantics like tunnels, bumps, bridges, footbridges,
crosswalks, road capacity, among others. Our analysis shows that cell-phones
sensors with humans in vehicles or walking get affected by the different road
features, which can be mined to extend the features of both free and commercial
mapping services. We present the design and implementation of Map++ and
evaluate it in a large city. Our evaluation shows that we can detect the
different semantics accurately with at most 3% false positive rate and 6% false
negative rate for both vehicle and pedestrian-based features. Moreover, we show
that Map++ has a small energy footprint on the cell-phones, highlighting its
promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on
Sensing, Communication, and Networking (IEEE SECON 2014
How to Improve the Capture of Urban Goods Movement Data?
The surveys specifically focused on the thorough knowledge of urban freight transport appeared about ten years ago. The local problematic of goods transport at local level was partially taken into account by the city planners and by the researchers: until recent years, the integration of goods transport in the total urban flows models was estimated applying a multiplying factor to car traffic. Delivering goods was not considered like a concern.Because of the quick growth of car traffic in the cities, the main stakes changed too: the fight against traffic congestion, the management of the lack of space (shipment consolidation and storage), the attempts to reduce local environmental impacts and global externalities (energy saving, reduction of greenhouse gas emissions), and economic valuation of city centres (under the pressure of a slowed down economic growth).All these changes were taking place in a context in which available rooms for manoeuvre were limited by factors such as congestion, concerns about the quality of urban life and budget restriction. It resulted in a growing unease on the freight transport industry and the city authorities, the latter having little or no data, methods and references in order to elaborate a satisfactory policy framework.surveys on urban freight transport ; urban freight movements ; urban freight data collection ; urban goods data collection ; diversity of measurement units and methods ; state of the art
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
Frequency and timing system for the consolidated DSN and STDN tracking network
The consolidation on the existing Deep Space Network (DSN) and colocated Goddard Spaceflight Tracking and Data Network (STDN) stations into a multiple antenna array is discussed. Each site includes a signal processing center (SPC) centered in an array of four or five antennas each located within approximately 300 to 800 meters of the SPC. A central frequency and timing system (FTS) located in the SPC contains reference frequency, timing and time code generation, and distribution equipment for both the SPC and each antenna with its associated front end antenna control building. The reference frequency distribution and clock equipment are driven by a hydrogen maser as the prime frequency standard with cesium beam frequency standard as the secondary
- …