Cyclist-aware traffic lights through distributed smartphone sensing

Cycling in smart cities can be safer if enhanced with a smart traffic lights infrastructure. A distributed smartphone-based sensing approach is a cost-effective infrastructure to enable cyclist-aware traffic lights system. In this article, we treat cyclist movement on a trajectory with a Boundary model able to reduce GPS sensor power consumption, while performing time-of-arrival estimation to the nearest light. A global quantitative metric of model efficiency is proposed for assessing the overall behavior of the model, and a false-positives rating qualitative metric is used to assess the recall of the model. We evaluated the model with confined yet realistic cycling experiments and verify the precision of our model using an Android application installed in participants’ smartphones. We compared our model with previous literature, achieving a promising model for in-the-wild cycling scenarios.     

A multi-modal fusion YoLo network for traffic detection

Traffic detection (including lane detection and traffic sign detection) is one of the key technologies to realize driving assistance system and auto drive system. However, most of the existing detection methods are designed based on single-modal visible light data, when there are dramatic changes in lighting in the scene (such as insufficient lighting in night), it is difficult for these methods to obtain good detection results. In view of multi-modal data can provide complementary discriminative information, based on the YoLoV5 model, this paper proposes a multi-modal fusion YoLoV5 network, which consists of three key components: the dual stream feature extraction module, the correlation feature extraction module, and the self-attention fusion module. Specifically, the dual stream feature extraction module is used to extract the features of each of the two modalities. Secondly, input the features learned from the dual stream feature extraction module into the correlation feature extraction module to learn the features with maximum correlation. Then, the extracted maximum correlation features are used to achieve information exchange between modalities through a self-attention mechanism, and thus obtain fused features. Finally, the fused features are inputted into the detection layer to obtain the final detection result. Experimental results on different traffic detection tasks can demonstrate the superiority of the proposed method.     

Acquisition of multi-modal expression of slip through pick-up experiences

To realize adaptive and robust manipulation, a robot should have several sensing modalities and coordinate their outputs to achieve the given task based on underlying constraints in the real environment. This paper discusses acquisition of multi-modal expression of slip consisting of vibration, pressure and vision sensations through pick-up experiences. A sensor network is proposed to acquire the expression, whose learning ability is demonstrated by a real experiment. The applicability of the learned network is also demonstrated by experiments to realize robust and adaptive picking.     

MEMS-based multi-modal vibration energy harvesters for ultra-low power autonomous remote and distributed sensing

Microsystem Technologies - In this contribution, we discuss the implementation of a novel microelectromechanical-systems (MEMS)-based energy harvester (EH) concept within the technology platform...     

From single- to multi-modal remote sensing imagery interpretation: a survey and taxonomy

Modality is a source or form of information. Through various modal information, humans can perceive the world from multiple perspectives. Simultaneously, the observation of remote sensing(RS) is multimodal. We observe the world macroscopically through panchromatic, Lidar, and other modal sensors.Multimodal observation of remote sensing has become an active area, which is beneficial for urban planning,monitoring, and other applications. Despite numerous advancements in this area, there has still ...     

基于SA-FQL算法的区域交通控制方法

将模拟退火算法的Metropolis准则用于平衡模糊Q学习中探索和扩张之间的关系,提出基于Metropolis准则的模糊Q学习算法Simulated Annealing Fuzzy Q-learning（SA-FQL）。利用SA-FQL算法优化区域的公共周期,在给定周期的基础上再用SA-FQL算法优化区域中各干线相邻两路口的相位差,最后根据交通流量确定各路口的绿信比。TSIS仿真结果表明,相比基于Q学习和模糊Q学习的控制方法,该方法能显著提高学习速度和交通效率。     

基于蓝牙技术的城市交通系统

提出了基于蓝牙技术的智能交通系统在城市中运用的系统方案。详细介绍了最新的蓝牙规范,结合现代数字无线通信技术,通过蓝牙通信收发模块,实现实时数据采集、监控查询、事故应急和交通控制以及与系统现有各种网络信息之间的数据融合。系统的蓝牙应用具有低成本、低功耗、高稳定性、高灵活性和可扩展性等特点,可极大地提高城市交通运行的效率,有效解决了城市交通系统信息化面临的众多难题。     

Urban traffic congestion propagation and bottleneck identification

Bottlenecks in urban traffic network are sticking points in restricting network collectivity traffic efficiency. To identify network bottlenecks effectively is a foundational work for improving network traffic condition and preventing traffic congestion. In this paper, a congestion propagation model of urban network traffic is proposed based on the cell transmission model （CTM）. The proposed model includes a link model, which describes flow propagation on links, and a node model, which represents link-to-link flow propagation. A new method of estimating average journey velocity （AJV） of both link and network is developed to identify network congestion bottlenecks. A numerical example is studied in Sioux Falls urban traffic network. The proposed model is employed in simulating network traffic propagation and congestion bottleneck identification under different traffic demands. The simulation results show that continual increase of traffic demand is an immediate factor in network congestion bottleneck emergence and increase as well as reducing network collectivity capability. Whether a particular link will become a bottleneck is mainly determined by its position in network, its traffic flow （attributed to different OD pairs） component, and network traffic demand.     

On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines

In the last years, the interest in methods and techniques for circumventing the security of the available digital video broadcasting systems is continuously increasing. Digital TV providers are struggling to restrict access to their video contents only to authorized users, by deploying more and more sophisticated conditional access systems. At the state-of-the-art, the most significant menace is the card-sharing activity which exploits a known weakness allowing an authorized subscriber to provide access to digital contents to a potentially large group of unauthorized ones connected over a communication network. This is usually realized by using ad hoc customized devices. Detecting the presence of these illegal systems on a network, by recognizing their related traffic is an issue of primary importance. Unfortunately, to avoid the identification of such traffic, payload obfuscation strategies based on encryption are often used, hindering packet inspection techniques.This paper presents a strategy for the detection of card-sharing traffic, empowered by machine-learning-driven traffic classification techniques and based on the natural capability of wavelet analysis to decompose a traffic time series into several component series associated with particular time and frequency scales and hence allowing its observation at different frequency component levels and with different resolutions. These ideas have been used for the proof-of-concept implementation of an SVM-based binary classification scheme that relies only on time regularities of the traffic and not on the packet contents and hence is immune to payload obfuscation techniques.     

Discovering traffic congestion through traffic flow patterns generated by moving object trajectories

The discovery of moving object trajectory patterns representing high traffic density has been covered in various works using diverse approaches. These models are useful in areas such as transportation planning, traffic monitoring, and advertising on public roads. However, though studies tend to recognize the importance of these types of patterns in utility, they usually do not consider traffic congestion as a particular condition of high traffic. In this work, we present a model for the discovery of high traffic flow patterns in relation to traffic congestion. This relationship is represented in terms of traffic that is shared between different sectors of the pattern, making it possible to identify traffic flow situations causing congestion. We also complement this model by discovering alternative paths for the severe traffic depicted in these patterns. These alternative paths depend on traffic level and location inside the road network. Depending on the traffic conditions, alternative paths are commonly sought by drivers when they are approaching a traffic jam, in order to mitigate the effects of traffic congestion. We compare these models with related work from similar areas and validate them by conducting experiments using real data. We describe discovered patterns related to the main elements of the road network in the dataset and show their advantages in comparison to related models. Based on the displayed metrics, the algorithms’ implementation offers good performance execution for the given dataset volume. The results presented confirm the usefulness of the proposed patterns as a tool that helps to improve traffic, allowing the identification of problems and possible alternatives.     

A framework of enroute air traffic conflict detection and resolution through complex network analysis

The scalability of increasingly complex air transportation requires better automation support to prevent accidents and assure safety. Collaborative control mechanisms for error and conflict detection and prevention are an essential part of this support. Analysis of the network structure of proposed design for automated conflict detection and resolution for the next generation air transportation system was conducted. This analysis provides insight into the tradeoffs of alternative concepts of collaborative operations, including tradeoffs between expected conflicts, communications requirements, and vulnerability to targeted attack. The resulting design framework provides a structure for the application of network architecture methods to these, and other, techniques for air traffic control, enabling in-depth analysis and evaluation of the resulting system.     

动态视角下的城市道路交通网络失效评价模型

利用可靠性相关理论,在对交通网络状态变化规律分析的基础上,给出了了城市道路交通单元及交通网络的‘失效—非失效’态的表达方法;分析了路段单元及交通网络的状态从观察起始点t0到首次演变到‘失效态’的时间分布规律,并以此为基础推导了城市道路单元及交通网络的失效概率、平均失效时长、失效率以及条件失效时长等失效评价模型,给出了模型标定的方法。以国内某城市核心城区5条道路单元组成的路网为实例,对首次失效时间进行了分布拟合及检验,对模型进行了标定,并将模型计算结果与实际观察结果进行了对比。结果表明,利用对数正态分布来拟合首次失效时间较为理想,而且模型的计算结果与实际观察结果也非常接近,表明模型具有良好的实用性。     

Interactive multi-modal suturing

We present a mechanics-based interactive multi-modal environment designed as part of a serious gaming platform. The specific objectives are to teach basic suturing and knotting techniques for simple skin or soft tissue wound closure. The pre-wound suturing target, skin, or deformable tissue is modeled as a modified mass-spring system. The suturing material is designed as a mechanics-based deformable linear object. Tools involved in a typical suturing procedures are also simulated. Collision management modules between the soft tissue and the needle, the soft tissue and the suture are analyzed. In addition to modeling the interactive environment of a typical suturing procedure, basics of the modeling approaches on the evaluation of a stitch formed by the user are also discussed. For example, if needle insertion points are too close from each other or to the edge of the wound, when the suture is pulled, the suture will tear the soft tissue instead of suturing the incision together. Experiment results show that our simulator can run on a standard personal computer and allow users to perform different suturing patterns with smooth graphics and haptic feedback.     

Predicting separation errors of air traffic controllers through integrated sequence analysis of multimodal behaviour indicators

Predicting separation errors in the daily tasks of air traffic controllers (ATCOs) is essential for the timely implementation of mitigation strategies before performance declines and the prevention of loss of separation and aircraft collisions. However, three challenges impede accurate separation errors forecasting: 1) compounding relationships between many human factors and control processes require sufficient operation process data to capture how separation errors occur and propagate within controller-in-the-loop processes; 2) previous human factor measurement approaches are disruptive to controllers’ daily operations because they use invasive sensors, such as electroencephalography (EEG) and electrocardiography (ECG), 3) errors accumulated in using the tasks and human behaviors for estimating system dynamics challenge accurate separation error predictions with sufficient leading time for proactive control actions. This study proposed a separation error prediction framework with a long leading time (>50 s) to address the above challenges, including 1) a multi-factorial model that characterizes the inter-relationships between task complexity, behavioral activity, cognitive load, and operational performance; 2) a multimodal data analytics approach to non-intrusively extract the task features (i.e., traffic density) from high-fidelity simulation systems and visual behavioral features (i.e., head pose, eyelid movements, and facial expressions) from ATCOs’ facial videos; 3) an encoder-decoder Long Short-Term Memory (LSTM) network to predict long-time-ahead separation errors by integrating multimodal features for reducing accumulated errors. A user study with six experienced ATCOs tested the proposed framework using the Phoenix Terminal Radar Approach Control (TRACON) simulator. The authors evaluated the model performance through two types of metrics: 1) point-level metrics, including precision, recall, and F1-score, and 2) sequence-level metrics, including alignment accuracy and sequence similarity. The results showed that 1) the model using the task and visual behavioral features significantly improved the prediction performance compared to the model using one single feature (eyelid movements), with an improvement of up to 26.95% in alignment accuracy for 10s-ahead prediction; 2) the model that combined task and visual behavioral features had a higher or comparable performance to models with different hybrid features, achieving an alignment accuracy of 82.38% for 50s-ahead error prediction; and (3) the proposed method outperformed three baseline models – Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and classic LSTM – by 8.21%, 3.47%, and 3.14% in alignment accuracy, respectively, for predicting 50s-ahead separation errors. These results suggest that the proposed model can effectively predict separation errors in air traffic control.     

基于WPD-PSO-ESN的城市交通感应信号控制系统设计

传统城市交通感应信号控制系统缺少对交通车流量的预测,导致信号控制效果较差。为此,设计基于WPD-PSO-ESN的城市交通感应信号控制系统。在系统硬件设计中,将?PC作为上位机,?PLC作为下位机。使用S7-226型号PLC控制器连接EM221数字输入模块与EM223组合输入/继电器,移除?I/O终端;选择AT89S51型号采集器,将其接收到的指令向S7-226PLC控制器发送数据信息;安装单环自愈RS-485多机通信接收发送器,自动修复单回路。在软件设计中,设计手动、闪光控制模块子程序流程,实现信号控制系统状态检测。建立WPD-PSO-ESN交通流量预测模型,结合PSO算法优化参数,以交通流量预测结果为依据,分析交叉口的通行能力,确定不同信号相位绿灯时间,设计感应信号控制流程。由实验结果可知,该系统车流量共计为310,与交叉口历史平均交通流量统计结果一致,说明系统信号感应较为精准,对实现实时交通控制具有现实意义。     

基于深度强化学习的城市交通信号控制算法

针对城市交通信号控制中如何有效利用相关信息优化交通控制并保证控制算法的适应性和鲁棒性的问题，提出一种基于深度强化学习的交通信号控制算法，利用深度学习网络构造一个智能体来控制整个区域交通。首先通过连续感知交通环境的状态来选择当前状态下可能的最优控制策略，环境的状态由位置矩阵和速度矩阵抽象表示，矩阵表示法有效地抽象出环境中的主要信息并减少了冗余信息；然后智能体以在有限时间内最大化车辆通行全局速度为目标，根据所选策略对交通环境的影响，利用强化学习算法不断修正其内部参数；最后，通过多次迭代，智能体学会如何有效地控制交通。在微观交通仿真软件Vissim中进行的实验表明，对比其他基于深度强化学习的算法，所提算法在全局平均速度、平均等待队长以及算法稳定性方面展现出更好的结果。其中，与基线相比，平均速度提高9%，平均等待队长降低约13.4%。实验结果证明该方法能够适应动态变化的复杂的交通环境。     

Urban parking space reservation through bottom-up information provision: An agent-based analysis

The aim of this paper is to study the impacts of a reservation system for on-street parking. Such a system provides drivers looking for on-street parking with information on available parking spaces, thereby possibly reducing the need to cruise for parking and the accompanying negative externalities. The performance of the proposed system is studied using a highly detailed spatial agent-based simulation. The results of the simulations show that users of a reservation system benefit in terms of reduced search time and reduced walking distance under virtually all simulated circumstances. However, societal benefits are not as clear-cut. The benefit in search time for the users of the system comes at a cost to the regular drivers, which see a nearly identical increase in search time. In contrast, the positive impact on walking distance hardly influences walking distance for regular drivers. Hence, we conclude that the introduction of a reservation system for on-street parking results in a more efficient distribution of available parking spaces among drivers searching for parking.     

BNs-OLS-SARIMA对城市短时交通流的预测*

通过在目标路口构建贝叶斯交通网（BNs）,并对与此交通网相关的交通流建立非平稳季节（SARIMA）模型,采用最小二乘法（OLS）取得相应模型的最佳权重组合,对缺失数据下的城市道路短时交通流进行预测。使用重庆市某路口的交通流数据对模型进行检测,通过多种预测指标对结果进行对比分析,结果表明BNs-OLS-SARIMA把交通流的网络结构与其周期性结合在一起,对短时交通流有良好的预测效果。     

面向城市道路交通状态估计的数据融合研究

实时道路交通状态估计是ATMS和ATIS的重要内容。布设于城市道路网络中的各类检测器提供了丰富实时的动态信息。针对目前我国各检测器间相互独立形成信息孤岛、数据参数多样、结构迥异、采样周期和精度不一等现状,采用贝叶斯估计、模糊逻辑等数据融合方法建立多源异构交通信息三层次融合体系,得到精度更高、可靠性更强的交通信息。实例证明,数据融合适用于城市道路交通状态估计。