Towns and cities are currently equipping themselves with a host of connected
devices, with a view to transforming themselves into ''smart cities''. To
manage this mass of connected objects, autonomous software entities, known as
agents, can be attached to them to cooperate and use these devices to offer
personalized services. However, this object infrastructure needs to be
semantically structured in order to be exploited. This is why the proposal of
this article is an ontology, formatted in OWL, describing the object
infrastructures, their links with the organization of the multi-agent system
and the services to be delivered according to the users of the system. The
ontology is applied to smart mobility for people with reduced mobility, and
could be adapted to other smart city axes.

对于智能城市的发展，本文提出了一个以 OWL 格式的本体论描述物体基础设施、多智能体系统的组织以及根据系统用户需求提供个性化服务的关联，该本体论在解决残疾人士智能出行问题上得以应用，并可用于其他智能城市领域。

智能城市中环境多 agent 系统的本体论

Une ontologie pour les syst{è}mes multi-agents ambiants dans les  villes intelligentes

The notion of smart cities is being adapted globally to provide a better
quality of living. A smart city's smart mobility component focuses on providing
smooth and safe commuting for its residents and promotes eco-friendly and
sustainable alternatives such as public transit (bus). Among several smart
applications, a system that provides up-to-the-minute information like bus
arrival, travel duration, schedule, etc., improves the reliability of public
transit services. Still, this application needs live information on traffic
flow, accidents, events, and the location of the buses. Most cities lack the
infrastructure to provide these data. In this context, a bus arrival prediction
model is proposed for forecasting the arrival time using limited data sets. The
location data of public transit buses and spatial characteristics are used for
the study. One of the routes of Tumakuru city service, Tumakuru, India, is
selected and divided into two spatial patterns: sections with intersections and
sections without intersections. The machine learning model XGBoost is modeled
for both spatial patterns individually. A model to dynamically predict bus
arrival time is developed using the preceding trip information and the machine
learning model to estimate the arrival time at a downstream bus stop. The
performance of models is compared based on the R-squared values of the
predictions made, and the proposed model established superior results. It is
suggested to predict bus arrival in the study area. The proposed model can also
be extended to other similar cities with limited traffic-related
infrastructure.

本文提出了一种基于 XGBoost 机器学习算法的公共交通到站时间预测模型，能够利用公交车的位置信息和空间特征预测其到站时间，针对有交叉口和无交叉口两种空间情境下的公交路线进行预测，结果表明该模型比其他模型表现更优，可在以及其他交通基础设施有限的类似城市中推广。

基于空间模式的公交到站时间估计动态模型（机器学习）

A Dynamic Model for Bus Arrival Time Estimation based on Spatial Patterns using Machine Learning

Recent developments in intelligent transport systems (ITS) based on smart
mobility significantly improves safety and security over roads and highways.
ITS networks are comprised of the Internet-connected vehicles (mobile nodes),
roadside units (RSU), cellular base stations and conventional core network
routers to create a complete data transmission platform that provides real-time
traffic information and enable prediction of future traffic conditions.
However, the heterogeneity and complexity of the underlying ITS networks raise
new challenges in intrusion prevention of mobile network nodes and detection of
security attacks due to such highly vulnerable mobile nodes. In this paper, we
consider a new type of security attack referred to as crossfire attack, which
involves a large number of compromised nodes that generate low-intensity
traffic in a temporally coordinated fashion such that target links or hosts
(victims) are disconnected from the rest of the network. Detection of such
attacks is challenging since the attacking traffic flows are indistinguishable
from the legitimate flows. With the support of software-defined networking that
enables dynamic network monitoring and traffic characteristic extraction, we
develop a machine learning model that can learn the temporal correlation among
traffic flows traversing in the ITS network, thus differentiating legitimate
flows from coordinated attacking flows. We use different deep learning
algorithms to train the model and study the performance using Mininet-WiFi
emulation platform. The results show that our approach achieves a detection
accuracy of at least 80%.

通过软件定义网络监控和流量特征提取，本文采用不同的深度学习算法和 Mininet-WiFi 仿真平台，开发了一种可学习 ITS 网络中流量流的时间相关性的机器学习模型，从而区分合法流与协调攻击流，以提高安全性，并在至少 80％的准确率下检测跨境攻击。