Smart traffic analytics in the semantic web with STAR-CITY: Scenarios, system and lessons learned in Dublin City
Smart traffic analytics in the semantic web with STAR-CITY: Scenarios, system and lessons learned in Dublin City
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DOI：
10.1016/j.websem.2014.07.002
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发表时间：
2014-08-01
影响因子：
2.5
通讯作者：
Tommasi, Pierpaolo
中科院分区：
文献类型：
--
作者：
Lecue, Freddy;Tallevi-Diotallevi, Simone;Tommasi, Pierpaolo
本文给出了一个高层介绍的STAR-CITY，一个系统支持语义交通分析和推理的城市。STAR-CITY使用各种格式、速度和体积集成了（基于人类和机器的）传感器数据，旨在提供对历史和实时交通状况的洞察，所有这些都支持有效的城市规划。我们的系统演示了如何道路交通拥堵的严重程度可以顺利地分析，诊断，探索和预测使用语义Web技术。我们的原型语义感知的流量分析和推理，说明和实验在都柏林爱尔兰，但也测试了在博洛尼亚意大利，迈阿密美国和里约热内卢巴西的作品和规模有效地与真实的，历史连同实时和异构流数据。本文重点介绍了在都柏林市部署和使用基于语义Web技术的系统的经验教训。(C)2014爱思唯尔有限公司版权所有。
This paper gives a high-level presentation of STAR-CITY, a system supporting semantic traffic analytics and reasoning for city. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time traffic conditions, all supporting efficient urban planning. Our system demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. Our prototype of semantics-aware traffic analytics and reasoning, illustrated and experimented in Dublin Ireland, but also tested in Bologna Italy, Miami USA and Rio Brazil works and scales efficiently with real, historical together with live and heterogeneous stream data. This paper highlights the lessons learned from deploying and using a system in Dublin City based on Semantic Web technologies. (C) 2014 Elsevier B.V. All rights reserved.