Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait

In a conventional traffic signals controller, the lights change at constant cycle time. In many cities, automatic traffic signals are often based on a constant green-to-red cycle. The time period for green light (or red light) to be on is determined based on a stochastic model. The traditional vehicle-actuated control of isolated intersections attempts continuously to adjust green times. The decision to change green light duration involves fuzzy factors that cannot be precisely determined.The main objective of this paper is to develop a fuzzy logic traffic system that considers the two two-way intersections and is able to adjust changes in time intervals of a traffic signal based on traffic situation level.The proposed system has been applied and tested using real data collected from signalized intersection in Hawalli governorate in the State of Kuwait. Twenty-seven iterations have been done; the results show that the proposed fuzzy logic traffic system provides better performance in terms of total waiting time, total moving time, and vehicle queue. Finally, it can be observed from the results that the proposed system can be used to accelerate the cycle time and to give other phases the chance to gain more benefit from the green time lost.     

Designing large‐scale interactive traffic animations for urban modeling

Designing and optimizing traffic behavior and animation is a challenging problem of interest to virtual environment content generation and to urban planning and design. While some traffic simulation methods have appeared in computer graphics, most related systems focus on the design of buildings, roads, or cities but without explicitly considering urban traffic. To our knowledge, our work provides the first interactive approach which enables a designer to specify a desired vehicular traffic behavior (e.g., road occupancy, travel time, emissions, etc.) and the system will automatically compute what realistic 3D urban model (e.g., an interconnected network of roads, parcels, and buildings) yields the specified behavior. Our system both altered and improved traffic behavior in novel procedurally‐generated cities and in road networks of existing cities. Our urban models contain up to 360 km of roads, 300,000 vehicles, and typically cover four hours of simulated peak traffic time. The typical editing session time to “paint” a new traffic pattern and to compute the new/changed urban model is two to five minutes.     

智能交通信号灯控制系统设计与实现

设计的目的是通过传感器监测每个路口的车辆驶入和驶出的流量,利用单片机的计数器来记录传感器产生的车流脉冲,并通过GSM模块通过无线通信的方式,将系统中采集的车流量数据,沿着车辆通行的方向,按照相邻原则进行传递,让信号灯与其邻近的信号灯进行协调,实现车流量对交通信号灯的智能控制。     

基于层次颜色Petri网的全感应控制交通信号灯建模与仿真

针对平面交叉口单点全感应控制信号灯系统实时调度问题,提出一种基于层次颜色Petri网的交叉路口的信号灯动态配时控制模型。首先,运用模糊控制理论对车流量信息进行分类定义。其次,以采集到的车流量信息为控制参数,基于层次颜色Petri网理论建立基本的信号灯轮廓模型,再依据模型中实现的关键功能进行分层细化与逐步求精。此外,将计数器代替时延加入系统模型中,使模型能够直观简洁地反映出红绿灯的循环时间与车流量之间的切换关系。根据所得模型对该交叉口信号灯配时方案的性能指标进行评估与比较。最后,运用CPN Tools对系统模型进行计算仿真,并进行简单的优化性能分析。     

Optimizing urban traffic control using a rational agent

This paper is devoted to developing and evaluating a set of technologies with the objective of designing a methodology for the implementation of sophisticated traffic lights by means of rational agents. These devices would be capable of optimizing the behavior of a junction with multiple traffic signals, reaching a higher level of autonomy without losing reliability, accuracy, or efficiency in the offered services. In particular, each rational agent in a traffic signal will be able to analyze the requirements and constraints of the road, in order to know its level of demand. With such information, the rational agent will adapt its light cycles with the view of accomplishing more fluid traffic patterns and minimizing the pollutant environmental emissions produced by vehicles while they are stopped at a red light, through using a case-based reasoning （CBR） adaptation. This paper also integrates a microscopic simulator developed to run a set of tests in order to compare the presented methodology with traditional traffic control methods. Two study cases are shown to demonstrate the efficiency of the introduced approach, increasing vehicular mobility and reducing harmful activity for the environment. For instance, in the first scenario, taking into account the studied traffic volumes, our approach increases mobility by 23% and reduces emissions by 35%. When the roads are managed by sophisticated traffic lights, a better level of service and considerable environmental benefits are achieved, demonstrating the utility of the presented approach.     

单交叉路口交通灯实时配时算法的研究

研究了城市道路交通信号实时控制系统的问题,重点论述了单交叉路口交通信号灯控制(点控制)模型的实时配时算法.根据城市交通流分布规律,设计出流量序列生成算法来模拟实时交通流;通过建立信号灯动态模型从而获得最佳周期长度和有效绿灯时间,然后采用模糊控制算法对信号灯配时方案进行实时的动态优化调整,并建立基于排队的车辆延误模型来对模糊控制算法进行评价.计算机模拟仿真的结果表明所设计实时配时方案比定时配时方案有显著的改善.     

CATS: An adaptive traffic signal system based on car-to-car communication

Traffic signal controls play an important role in regulating vehicular flow at road intersections. Traditional systems are not capable of adjusting the timing pattern in accordance with vehicular demand. This results in excessive delays for road users. Hence it is necessary to develop dynamic systems that can adjust the timing patterns according to traffic demand. In this paper, the design and implementation of an adaptive traffic signal control system based on car-to-car communication is presented. Also, a clustering algorithm is defined which will assist in estimating the density of vehicles approaching an intersection. The cycle time, which is calculated using the estimated density of vehicular traffic, helps in reducing both the waiting time for vehicles at intersections and queue length. It is also shown that the proposed solution is collision free at intersections. The proposed system is compared with a classic pre-timed system and an adaptive fuzzy logic system. The simulations also show that the data convergence time and the communication delay between vehicles and traffic signals do not compromise the efficiency of the system.     

交通信号灯智能控制算法研究

针对当前城市交通信号灯控制的技术缺陷,即国内大多数城市交通信号灯控制方法仍然停留在时间程序控制的技术层面,或者采用感应线圈等设备来获取道路交通信息等技术现状,提出一种交通信号灯智能控制算法。该系统算法由图像边缘检测、道路行车类型切割、纵向坐标投影和车流长度分析等核心技术组成,能够充分、有效和实时地获取交通流量信息,使城市交通信号灯的开／关时间能够根据道路上车流量的实际大小实施准确控制,因此使交通信号灯的控制达到智能化的技术水平,为最终实现城市智能交通提供科学的信息基础。     

基于自组织竞争神经网络的智能交通信号灯控制

为了解决拥挤的城市交通问题,针对交通灯监控系统中可变的交通状况,提出一种基于自组织神经网络算法的监控系统。应用优化模型参实现交通灯的控制,使道路通畅。对监控系统进行分析,合理选择优化的模型参数,根据动态的车流量,通过模式分类有效分配当前车道的通行时间,并全面考虑同时通行的各种车道组合。该监控系统提高了车辆通行效率,使道路更为通畅。与传统的固定配时系统相比,它更适于当前纷繁复杂的交通状况。     

Offloading mobile data traffic for QoS-aware service provision in vehicular cyber-physical systems

Owing to the increasing number of vehicles in vehicular cyber-physical systems (VCPSs) and the growing popularity of various services or applications for vehicles, cellular networks are being severely overloaded. Offloading mobile data traffic through Wi-Fi or a vehicular ad hoc network (VANET) is a promising solution for partially solving this problem because it involves almost no monetary cost. We propose combination optimization to facilitate mobile data traffic offloading in emerging VCPSs to reduce the amount of mobile data traffic for the QoS-aware service provision. We investigate mobile data traffic offloading models for Wi-Fi and VANET. In particular, we model mobile data traffic offloading as a multi-objective optimization problem for the simultaneous minimization of mobile data traffic and QoS-aware service provision; we use mixed-integer programming to obtain the optimal solutions with the global QoS guarantee. Our simulation results confirm that our scheme can offload mobile data traffic by up to 84.3% while satisfying the global QoS guarantee by more than 70% for cellular networks in VCPSs.     

Experience classification for transfer learning in traffic signal control

In recent years, due to the drastic rise in the number of vehicles and the lack of sufficient infrastructure, traffic jams, air pollution, and fuel consumption have increased in cities. The optimization of timing for traffic lights is one of the solutions for the mentioned problems. Many methods have been introduced to deal with these problems, including reinforcement learning. Although a great number of learning-based methods have been used in traffic signal control, they suffer from poor performance and slow learning convergence. In this paper, a transfer learning-based method for traffic signal control has been proposed. Multi-agent system has also been used for modelling the traffic network and transfer learning has been used to make reinforcement learning agents transfer their experience to each other. Furthermore, a classifier has been utilized to classify the transferred experiences. The results show that using the proposed method leads to a significant improvement on average delay time and convergence time of the learning process.

     

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.     

模糊控制在智能交通灯监控系统中的应用

为了解决拥挤的城市交通问题,针对交通灯监控系统中可变的交通状况,提出一种基于模糊算法的监控系统。应用模糊算法,通过模糊控制实现交通灯的控制,使道路通畅。对监控系统进行分析,合理选择模糊控制器的结构,根据动态的车流量,通过模糊算法分配当前车道的通行时间,并全面考虑同时通行的各种车道组合。该监控系统提高了车辆通行效率,使道路更为通畅。与传统的固定配时系统相比,它更适于当前纷繁复杂的交通状况。     

Gamma deployment problem in grids: hardness and new integer linear programming formulation

Vehicular networks are mobile networks designed for the domain of vehicles and pedestrians. These networks are an essential component of intelligent transportation systems and have the potential to ease traffic management, lower accident rates, and offer other solutions to smart cities. One of the most challenging aspects in the design of a vehicular network is the distribution of its infrastructure units, which are called roadside units (RSUs). In this work, we tackle the gamma deployment problem that consists of deploying the minimum number of RSUs in a vehicular network in accordance with a quality of service metric called gamma deployment. This metric defines a vehicle as covered if it connects to some RSUs at least once in a given time interval during its whole trip. Then, the metric parameterizes the minimum percentage of covered vehicles necessary to make a deployment acceptable or feasible. In this paper, we prove that the decision version of the gamma deployment problem in grids is NP‐complete. Moreover, we correct the multiflow integer linear programming formulation present in the literature and introduce a new formulation based on set covering that is at least as strong as the multiflow formulation. In experiments with a commercial solver, the set covering formulation widely outperforms the multiflow formulation with respect to running time and linear programming relaxation gap.     

Multirate numerical scheme for large-scale vehicle traffic simulation

In modeling vehicular traffic on large scales in large cities, the number of vehicles could reach tens of thousands, which, for the microscopic approach, corresponds to the solution of ordinary differential equations (ODEs) with large dimensions. The speed of changes in the size of the components of such systems usually lies in a wide range, as the dynamics and behavior of the vehicles can strongly differ. In this paper we introduce a multirate numerical scheme with a self-adjusting time stepping strategy. Instead of using a single step size for the whole system, the step size for each component is determined by estimating its own local variation. The stability analysis for the developed scheme is performed and the stability conditions are obtained. The presented multirate scheme provides a significant speed-up in the CPU times compared to the corresponding single-rate one. The use of multiple time steps allows parallel computing.     

交通灯的一种新型智能控制系统

文章设计了一种新型交通灯智能控制系统,突破了传统固定配时模式,可依据实时交通流量灵活运行,而且全面考虑了同时通行的各种车道组合,大大提高了车辆通行效率,具有实际应用前景.具体描述了系统控制方案、硬件选择及单片机程序设计.     

Angular and Metric Distance in Road Network Analysis: A nationwide correlation study

There is an epistemological divide in the field of road network analysis, concerning the way network distance should be conceptualized. On one hand, the generality of studies in the field adopt metric distance as a self-evident choice. On the other, space syntax studies adopt a different distance concept, namely that of angular distance, which ignores actual physical lengths. Theoretically, these two distance concepts imply quite different assumptions. Analytically, they produce also different results.In this paper we assess the differences between these two network distance concepts, using a model of the UK's complete road network (2,031,971 nodes) and a very large dataset of vehicular movement counts (20,752 locations). We describe the statistical associations between observed vehicular flows and the betweenness centrality of the road-network nodes where such flows were measured, the latter calculated with metric and angular distance functions, across a number of increasing radii. Relations to road capacity are also discussed in principal roads where this is known.The geographical comprehensiveness of our model and the size of our movement sample allow us to state, with unprecedented statistical validity, the clear outperformance of angular distance over metric distance, in what concerns the strength of the studied correlations. This is true for all types of roads (urban and non-urban) and for all motorized vehicles, representing 99.05% of the total traffic; the remaining 0.95% correspond to pedal cycles, which yielded inconclusive results. We also demonstrate the existence of two distinct regimes of association between movement and centrality, conspicuous in cities but altogether absent in non-urban areas, which represent new evidence supporting space syntax's dual model of urban form.     

Reliable simulation-optimization of traffic lights in a real-world city

In smart cities, when the real-time control of traffic lights is not possible, the global optimization of traffic-light programs (TLPs) requires the simulation of a traffic scenario (traffic flows across the whole city) that is estimated after collecting data from sensors at the street level. However, the highly dynamic traffic of a city means that no single traffic scenario is a precise representation of the real system, and the fitness of any candidate solution (traffic-light program) will vary when deployed on the city. Thus, ideal TLPs should not only have an optimized fitness, but also a high reliability, i.e., low fitness variance, against the uncertainties of the real-world. Earlier traffic-light optimization methods, e.g., based on genetic algorithms, often simulate a single traffic scenario, which neglects variance in the real-world, leading to TLPs not optimized for reliability.Our main contributions in this work are the following: (a) the analysis of the importance of reliable solutions for TLP optimization, even when all traffic scenarios are consistent with the real-world data and highly correlated; (b) the adaptation of irace, an iterated racing algorithm that is able to dynamically adjust the number of traffic scenarios required to evaluate the fitness of TLPs and their reliability; (c) the use of a large real-world case study for which real-time control is not possible and where data was obtained from sensors at the street level; and (d) a thorough analysis of solutions generated by means of irace, a Genetic Algorithm, a Differential Evolution, a Particle Swarm Optimization and a Random Search. This analysis shows that simple strategies that simulate multiple traffic scenarios are able to obtain optimized solutions with improved reliability; however, the best results are obtained by irace, among the algorithms evaluated.     

基于机器视觉的交通拥堵评估系统

传统的交通灯采用固定配时模式,缺乏灵活性、智能性.针对上述问题,提出一种基于机器视觉的交通拥堵评估系统对采集到的视频进行智能分析处理.首先,利用提取的梯度直方图特征和AdaBoost级联分类器实现对车辆的检测,并辅以RFID来实现车辆计数;进而通过Spark大数据分析平台而评估出当前的交通拥堵情况.实验表明本系统能根据当前的实际交通情况智能调整交通灯的变换时间,达到动态缓解交通压力的目的.     

Virtualized Traffic: reconstructing traffic flows from discrete spatiotemporal data

We present a novel concept, Virtualized Traffic, to reconstruct and visualize continuous traffic flows from discrete spatiotemporal data provided by traffic sensors or generated artificially to enhance a sense of immersion in a dynamic virtual world. Given the positions of each car at two recorded locations on a highway and the corresponding time instances, our approach can reconstruct the traffic flows (i.e., the dynamic motions of multiple cars over time) between the two locations along the highway for immersive visualization of virtual cities or other environments. Our algorithm is applicable to high-density traffic on highways with an arbitrary number of lanes and takes into account the geometric, kinematic, and dynamic constraints on the cars. Our method reconstructs the car motion that automatically minimizes the number of lane changes, respects safety distance to other cars, and computes the acceleration necessary to obtain a smooth traffic flow subject to the given constraints. Furthermore, our framework can process a continuous stream of input data in real time, enabling the users to view virtualized traffic events in a virtual world as they occur. We demonstrate our reconstruction technique with both synthetic and real-world input.