23,791 research outputs found
A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks
One of the key ideas to make Intelligent Transportation Systems (ITS) work
effectively is to deploy advanced communication and cooperative control
technologies among the vehicles and road infrastructures. In this spirit, we
propose a consensus-based distributed speed advisory system that optimally
determines a recommended common speed for a given area in order that the group
emissions, or group battery consumptions, are minimised. Our algorithms achieve
this in a privacy-aware manner; namely, individual vehicles do not reveal
in-vehicle information to other vehicles or to infrastructure. A mobility
simulator is used to illustrate the efficacy of the algorithm, and
hardware-in-the-loop tests involving a real vehicle are given to illustrate
user acceptability and ease of the deployment.Comment: This is a journal paper based on the conference paper "Highway speed
limits, optimised consensus, and intelligent speed advisory systems"
presented at the 3rd International Conference on Connected Vehicles and Expo
(ICCVE 2014) in November 2014. This is the revised version of the paper
recently submitted to the IEEE Transactions on Intelligent Transportation
Systems for publicatio
Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting
Modern urbanization is demanding smarter technologies to improve a variety of
applications in intelligent transportation systems to relieve the increasing
amount of vehicular traffic congestion and incidents. Existing incident
detection techniques are limited to the use of sensors in the transportation
network and hang on human-inputs. Despite of its data abundance, social media
is not well-exploited in such context. In this paper, we develop an automated
traffic alert system based on Natural Language Processing (NLP) that filters
this flood of information and extract important traffic-related bullets. To
this end, we employ the fine-tuning Bidirectional Encoder Representations from
Transformers (BERT) language embedding model to filter the related traffic
information from social media. Then, we apply a question-answering model to
extract necessary information characterizing the report event such as its exact
location, occurrence time, and nature of the events. We demonstrate the adopted
NLP approaches outperform other existing approach and, after effectively
training them, we focus on real-world situation and show how the developed
approach can, in real-time, extract traffic-related information and
automatically convert them into alerts for navigation assistance applications
such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering
Management Society International Conference (TEMSCON'20), Metro Detroit,
Michigan (USA
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