Heliyon
Volume 10, Issue 10, 30 May 2024, e30657
第10卷第10期,2024年5月30日,e30657
第10卷第10期,2024年5月30日,e30657
Research article 研究文章Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm
基于自适应人工鱼群算法的智慧城市交叉口交通信号控制优化方法研究
open access 开放存取
Abstract 摘要
The transportation environment of smart cities is complex and ever-changing, and traffic flow is influenced by various factors. With the increase of traffic flow in smart cities, optimizing traffic intersection signal control has become an important method to improve traffic efficiency and reduce congestion. To this end, a smart city traffic intersection(SCTI) signal control optimization method based on adaptive artificial fish swarm algorithm was studied. Establish the Equation of state of traffic flow at SCTIs to understand the actual traffic flow at SCTIs. On this basis, design SCTI signal control parameters, with the minimum average delay and average number of stops as objective functions, and construct an optimization model for SCTI signal control. By combining chaotic search theory and adaptively improving the artificial fish swarm algorithm, based on the adaptive artificial fish swarm algorithm, the intelligent city traffic intersection signal control optimization model is solved to achieve intelligent city traffic intersection signal control optimization. The experimental results show that the average delay of this method is 7.8 ms, the average number of stops is 2, and the travel time is 68.4 s s. Thus, it is proved that the method in this paper has a good optimization effect of traffic signal control at smart city intersections, which can improve the optimization efficiency of traffic signal control at smart city intersections and reduce traffic congestion at smart city intersections.
智慧城市的交通环境复杂多变,交通流量受多种因素影响。随着智慧城市交通流量的增加,优化交通路口信号控制已成为提高交通效率、减少拥堵的重要方法。为此,研究了一种基于自适应人工鱼群算法的智慧城市交通路口(SCTI)信号控制优化方法。建立 SCTI 交通流状态方程,了解 SCTI 的实际交通流。在此基础上,以最小平均延误和平均停车次数为目标函数,设计 SCTI 信号控制参数,构建 SCTI 信号控制优化模型。结合混沌搜索理论,自适应改进人工鱼群算法,基于自适应人工鱼群算法,求解智能城市交通路口信号控制优化模型,实现智能城市交通路口信号控制优化。实验结果表明,该方法的平均延时为 7.8 ms,平均停车次数为 2 次,行驶时间为 68.4 s s。由此证明,本文方法对智慧城市交叉口交通信号控制具有良好的优化效果,可以提高智慧城市交叉口交通信号控制的优化效率,减少智慧城市交叉口的交通拥堵。
智慧城市的交通环境复杂多变,交通流量受多种因素影响。随着智慧城市交通流量的增加,优化交通路口信号控制已成为提高交通效率、减少拥堵的重要方法。为此,研究了一种基于自适应人工鱼群算法的智慧城市交通路口(SCTI)信号控制优化方法。建立 SCTI 交通流状态方程,了解 SCTI 的实际交通流。在此基础上,以最小平均延误和平均停车次数为目标函数,设计 SCTI 信号控制参数,构建 SCTI 信号控制优化模型。结合混沌搜索理论,自适应改进人工鱼群算法,基于自适应人工鱼群算法,求解智能城市交通路口信号控制优化模型,实现智能城市交通路口信号控制优化。实验结果表明,该方法的平均延时为 7.8 ms,平均停车次数为 2 次,行驶时间为 68.4 s s。由此证明,本文方法对智慧城市交叉口交通信号控制具有良好的优化效果,可以提高智慧城市交叉口交通信号控制的优化效率,减少智慧城市交叉口的交通拥堵。
Keywords 关键词
Adaptive artificial fish school algorithm
Smart city
Traffic intersections
Optimization of signal light control
Chaotic search
自适应人工鱼群算法智能城市交通路口信号灯控制优化恰恰搜索
1. Introduction 1.导言
Smart cities emphasize the rapid development of cities in informatization, digitalization and intelligence. With the progress and innovation of science and technology, smart cities have become an important direction of urban development, aiming at improving the sustainability, quality of life and economic benefits of cities [[1], [2], [3]]. Under the background of smart city, intelligent transportation, as an important part of it, has received more and more attention and application. Intelligent transportation intelligently manages and optimizes the urban transportation system by comprehensively applying information technology, communication technology, big data, artificial intelligence and other means. Intelligent urban transportation system uses big data, sensor networks and other technologies to collect, analyze and utilize traffic data. By monitoring and analyzing real-time traffic conditions, urban managers can make more accurate and effective decisions and respond to traffic problems in time [4]. Intelligent traffic lights, intelligent parking guidance system, intelligent public transportation and other technical means are adopted to optimize traffic flow, reduce traffic congestion and improve traffic efficiency [5,6]. Encourage and support sustainable modes of transportation, such as public transportation, cycling and walking. By providing convenient public transport services, building cycling streets and pedestrian streets, we can reduce the use of private cars, reduce environmental pollution and improve the travel experience of urban residents. In this context, the optimization of signal light control at traffic intersections in smart cities has become a hot issue.
智慧城市强调城市在信息化、数字化和智能化方面的快速发展。随着科学技术的进步和创新,智慧城市已成为城市发展的重要方向,旨在提高城市的可持续性、生活质量和经济效益[[1], [2], [3]]。在智慧城市背景下,智能交通作为其重要组成部分,得到了越来越多的关注和应用。智能交通通过综合运用信息技术、通信技术、大数据、人工智能等手段,对城市交通系统进行智能化管理和优化。城市智能交通系统利用大数据、传感网等技术对交通数据进行采集、分析和利用。通过对实时交通状况的监测和分析,城市管理者可以做出更加准确有效的决策,及时应对交通问题[4]。采用智能交通信号灯、智能停车诱导系统、智能公共交通等技术手段,优化交通流量,减少交通拥堵,提高交通效率[5,6]。鼓励和支持公共交通、自行车和步行等可持续交通方式。通过提供便捷的公共交通服务、建设自行车道和步行街,可以减少私家车的使用,减少环境污染,改善城市居民的出行体验。在此背景下,智慧城市中交通路口信号灯控制的优化已成为一个热点问题。
智慧城市强调城市在信息化、数字化和智能化方面的快速发展。随着科学技术的进步和创新,智慧城市已成为城市发展的重要方向,旨在提高城市的可持续性、生活质量和经济效益[[1], [2], [3]]。在智慧城市背景下,智能交通作为其重要组成部分,得到了越来越多的关注和应用。智能交通通过综合运用信息技术、通信技术、大数据、人工智能等手段,对城市交通系统进行智能化管理和优化。城市智能交通系统利用大数据、传感网等技术对交通数据进行采集、分析和利用。通过对实时交通状况的监测和分析,城市管理者可以做出更加准确有效的决策,及时应对交通问题[4]。采用智能交通信号灯、智能停车诱导系统、智能公共交通等技术手段,优化交通流量,减少交通拥堵,提高交通效率[5,6]。鼓励和支持公共交通、自行车和步行等可持续交通方式。通过提供便捷的公共交通服务、建设自行车道和步行街,可以减少私家车的使用,减少环境污染,改善城市居民的出行体验。在此背景下,智慧城市中交通路口信号灯控制的优化已成为一个热点问题。
Reference [7] proposes dynamic traffic light control methods with different priorities. The dynamic state constructor is used to control the signal combination conversion of traffic lights. By designing duration particles and enhancement particles, the quantum particle swarm optimization algorithm is rebuilt to allocate intersections with different optimization priorities. However, this method has a long travel time. Reference [8] proposes an optimization method for traffic signal control based on deep reinforcement learning. By extending the centralized traffic signal learning and decentralized execution mode of Actor-Critic strategy gradient algorithm, critics use additional information to simplify the traffic signal control process and complete the traffic signal control optimization. However, the average delay time of this method needs to be verified. Reference [9] puts forward an artificial fish swarm algorithm to optimize dynamic fuzzy neural network. Taking the reciprocal of the average delay of vehicles as the food concentration of AFSA, and taking the weights and thresholds of dynamic fuzzy neural network that need to be corrected as the individual state of artificial fish, a set of optimal parameters of dynamic fuzzy neural network are obtained through iterative updating, so as to realize the multi-phase phase phase-change sequence intelligent control of five forks. However, this method has a high average number of stops. Reference [10] puts forward the urban green wave traffic control system. The adaptive mechanism and crossover and mutation operators are introduced into the artificial fish swarm algorithm to adjust the evolutionary population. By setting up bulletin board measures and retaining strategies, the individual state of the optimal artificial fish is recorded, and the green wave traffic control at continuous intersections is completed. However, this method has the problem of poor control effect. Reference [11] proposes artificial fish swarm algorithm, chaotic search and feedback strategy based on signal timing optimization theory. Taking the average of vehicle delay and parking number as the goal, the optimization algorithm is used to improve the intersection timing scheme of the target road. However, this method has the problem of low efficiency. Reference [12] proposes a dynamic traffic signal system based on density. In the intelligent traffic signal, the target detection is processed and transformed, and various features are extracted. According to the calculated threshold, the contour is drawn, the vehicle density and quantity are known, and the signal distribution is completed. However, this method has high computational complexity. Reference [13] proposes an adaptive real-time traffic light control algorithm based on traffic flow. Using neural network and YOLOv3 framework, single image processing is carried out, traffic permission is established at the signal, and traffic flow distribution at traffic intersections is completed. However, the control efficiency of this method for traffic intersection signal lights needs to be verified. Reference [14] puts forward an intelligent traffic monitoring system based on pc, which captures the images of vehicles and pedestrians, helps cities optimize traffic flow and reduce congestion. According to the emergency vehicles in each lane, intelligently decide when to change signals, thus increasing road capacity and traffic flow. However, this method is easy to cause data redundancy.
参考文献[7]提出了具有不同优先级的动态交通灯控制方法。动态状态构造器用于控制交通信号灯的信号组合转换。通过设计持续时间粒子和增强粒子,重建量子粒子群优化算法,分配不同优化优先级的交叉口。然而,这种方法的行程时间较长。参考文献[8]提出了一种基于深度强化学习的交通信号控制优化方法。通过扩展行为批判策略梯度算法的集中交通信号学习和分散执行模式,批判者利用附加信息简化交通信号控制过程,完成交通信号控制优化。但该方法的平均延迟时间有待验证。参考文献[9]提出了一种人工鱼群算法来优化动态模糊神经网络。以车辆平均延误时间的倒数作为人工鱼群的食物浓度,以需要修正的动态模糊神经网络的权值和阈值作为人工鱼群的个体状态,通过迭代更新得到一组动态模糊神经网络的最优参数,从而实现五岔路口的多相位相变序列智能控制。但这种方法的平均停车次数较多。参考文献[10]提出了城市绿波交通控制系统。在人工鱼群算法中引入自适应机制和交叉、变异算子,对进化种群进行调整。 通过设置公告板措施和保留策略,记录最优人工鱼儿的个体状态,完成连续交叉口的绿波交通控制。但该方法存在控制效果不佳的问题。参考文献[11]基于信号配时优化理论,提出了人工鱼群算法、混沌搜索和反馈策略。以车辆延误和停车数量的平均值为目标,采用优化算法改进目标道路的交叉口配时方案。但这种方法存在效率低的问题。参考文献[12]提出了一种基于密度的动态交通信号系统。在智能交通信号中,对目标检测进行处理和变换,提取各种特征。根据计算出的阈值,绘制轮廓线,已知车辆密度和数量,完成信号分布。然而,这种方法的计算复杂度较高。参考文献[13]提出了一种基于交通流量的自适应实时交通灯控制算法。利用神经网络和 YOLOv3 框架,进行单幅图像处理,建立信号灯的交通许可,完成交通路口的交通流分布。然而,该方法对交通路口信号灯的控制效率还有待验证。参考文献[14]提出了一种基于pc的智能交通监控系统,该系统捕捉车辆和行人的图像,帮助城市优化交通流量,减少拥堵。根据各车道上的应急车辆情况,智能决定何时变换信号灯,从而提高道路通行能力和交通流量。 不过,这种方法容易造成数据冗余。
参考文献[7]提出了具有不同优先级的动态交通灯控制方法。动态状态构造器用于控制交通信号灯的信号组合转换。通过设计持续时间粒子和增强粒子,重建量子粒子群优化算法,分配不同优化优先级的交叉口。然而,这种方法的行程时间较长。参考文献[8]提出了一种基于深度强化学习的交通信号控制优化方法。通过扩展行为批判策略梯度算法的集中交通信号学习和分散执行模式,批判者利用附加信息简化交通信号控制过程,完成交通信号控制优化。但该方法的平均延迟时间有待验证。参考文献[9]提出了一种人工鱼群算法来优化动态模糊神经网络。以车辆平均延误时间的倒数作为人工鱼群的食物浓度,以需要修正的动态模糊神经网络的权值和阈值作为人工鱼群的个体状态,通过迭代更新得到一组动态模糊神经网络的最优参数,从而实现五岔路口的多相位相变序列智能控制。但这种方法的平均停车次数较多。参考文献[10]提出了城市绿波交通控制系统。在人工鱼群算法中引入自适应机制和交叉、变异算子,对进化种群进行调整。 通过设置公告板措施和保留策略,记录最优人工鱼儿的个体状态,完成连续交叉口的绿波交通控制。但该方法存在控制效果不佳的问题。参考文献[11]基于信号配时优化理论,提出了人工鱼群算法、混沌搜索和反馈策略。以车辆延误和停车数量的平均值为目标,采用优化算法改进目标道路的交叉口配时方案。但这种方法存在效率低的问题。参考文献[12]提出了一种基于密度的动态交通信号系统。在智能交通信号中,对目标检测进行处理和变换,提取各种特征。根据计算出的阈值,绘制轮廓线,已知车辆密度和数量,完成信号分布。然而,这种方法的计算复杂度较高。参考文献[13]提出了一种基于交通流量的自适应实时交通灯控制算法。利用神经网络和 YOLOv3 框架,进行单幅图像处理,建立信号灯的交通许可,完成交通路口的交通流分布。然而,该方法对交通路口信号灯的控制效率还有待验证。参考文献[14]提出了一种基于pc的智能交通监控系统,该系统捕捉车辆和行人的图像,帮助城市优化交通流量,减少拥堵。根据各车道上的应急车辆情况,智能决定何时变换信号灯,从而提高道路通行能力和交通流量。 不过,这种方法容易造成数据冗余。
In response to the above issues, a SCTI signal control optimization method based on adaptive artificial fish swarm algorithm was studied. By establishing the Equation of state of traffic flow at SCTI, design the control parameters of SCTI signal lights, and build the optimization model of SCTI signal light control. By combining chaotic search theory with adaptive artificial fish swarm algorithm, an optimization model for traffic signal control at intersections in smart cities is solved. The optimization effect of SCTI signal control using this method is good, which can improve the optimization efficiency of SCTI signal control and reduce congestion at SCTI. The contributions of this method are as follows.
针对上述问题,研究了一种基于自适应人工鱼群算法的SCTI信号灯控制优化方法。通过建立SCTI交通流状态方程,设计SCTI信号灯控制参数,建立SCTI信号灯控制优化模型。通过将混沌搜索理论与自适应人工鱼群算法相结合,求解了智慧城市交叉口交通信号控制的优化模型。利用该方法对 SCTI 信号灯控制的优化效果良好,可以提高 SCTI 信号灯控制的优化效率,减少 SCTI 的拥堵。该方法的贡献如下。
针对上述问题,研究了一种基于自适应人工鱼群算法的SCTI信号灯控制优化方法。通过建立SCTI交通流状态方程,设计SCTI信号灯控制参数,建立SCTI信号灯控制优化模型。通过将混沌搜索理论与自适应人工鱼群算法相结合,求解了智慧城市交叉口交通信号控制的优化模型。利用该方法对 SCTI 信号灯控制的优化效果良好,可以提高 SCTI 信号灯控制的优化效率,减少 SCTI 的拥堵。该方法的贡献如下。
- (1)Most of the traditional traffic signal control methods only consider setting a fixed time interval, which can't be flexibly adapted when the actual traffic flow changes greatly. In this paper, the control parameters of traffic lights are dynamically adjusted according to the real-time traffic flow to achieve the goal of minimum average delay and average parking times.
传统的交通信号控制方法大多只考虑设置固定的时间间隔,当实际交通流量发生较大变化时无法灵活调整。本文根据实时交通流量动态调整交通信号灯的控制参数,以实现平均延误时间和平均停车时间最小的目标。 - (2)By establishing the state equation of traffic flow at traffic intersections in smart cities, the actual traffic flow at traffic intersections can be accurately understood, so that the optimization of control parameters of traffic lights is more accurate and effective.
通过建立智慧城市中交通路口的交通流状态方程,可以准确了解交通路口的实际交通流量,从而更加准确有效地优化交通信号灯的控制参数。 - (3)In order to further improve the optimization effect of the algorithm, the chaotic search theory is used to improve the artificial fish swarm algorithm adaptively. Chaos search theory can increase the diversity of algorithms and find the optimal solution more comprehensively in the search space. By combining chaos search theory, this method can better adapt to the dynamic characteristics of traffic flow changes at traffic intersections and improve the effect of signal light control.
为了进一步提高算法的优化效果,利用混沌搜索理论对人工鱼群算法进行自适应改进。混沌搜索理论可以增加算法的多样性,在搜索空间中更全面地找到最优解。结合混沌搜索理论,该方法能更好地适应交通路口交通流变化的动态特性,提高信号灯控制效果。
2. Establish the Equation of state of traffic flow at traffic intersections in smart cities
2.建立智慧城市交通路口的交通流状态方程
The traffic network structure has an important influence on the traffic efficiency of smart cities. In the actual urban traffic system, the traffic network is usually composed of complex road networks, including main roads, secondary roads, branch roads and other roads of different grades. There may be differences in traffic flow, speed and road width of these roads. Therefore, this paper establishes the traffic flow state equation of SCTI, and understands the actual traffic flow of SCTI through this equation.
交通网络结构对智慧城市的交通效率有着重要影响。在实际的城市交通系统中,交通网络通常由复杂的道路网络组成,包括主干道、次干道、支路和其他不同等级的道路。这些道路在交通流量、车速、路面宽度等方面可能存在差异。因此,本文建立了 SCTI 的交通流状态方程,并通过该方程了解 SCTI 的实际交通流。
交通网络结构对智慧城市的交通效率有着重要影响。在实际的城市交通系统中,交通网络通常由复杂的道路网络组成,包括主干道、次干道、支路和其他不同等级的道路。这些道路在交通流量、车速、路面宽度等方面可能存在差异。因此,本文建立了 SCTI 的交通流状态方程,并通过该方程了解 SCTI 的实际交通流。
In a SCTI signal light cycle [[15], [16], [17]], the set of time interval corresponding to the signal light of SCTI is , and the time of green light in time interval in SCTI is . The cycle constraint of traffic signal lights at smart city intersections is:(1)Where, is the number of cycles, is the saturated traffic flow in , and is the loss time of traffic intersections in smart cities.
在一个 SCTI 信号灯周期中[[15]、[16]、[17]],SCTI 信号灯 对应的时间间隔 的集合为 ,SCTI 信号灯 中时间间隔 的绿灯时间为 。智慧城市交叉口交通信号灯的周期约束为 (1) 其中, 为周期数, 为 中的饱和交通流量, 为智慧城市中交通路口的损失时间。
在一个 SCTI 信号灯周期中[[15]、[16]、[17]],SCTI 信号灯 对应的时间间隔 的集合为 ,SCTI 信号灯 中时间间隔 的绿灯时间为 。智慧城市交叉口交通信号灯的周期约束为 (1) 其中, 为周期数, 为 中的饱和交通流量, 为智慧城市中交通路口的损失时间。
Constraints limit the time of green light at SCTI as follows:(2)Where, is the minimum time allowed for the green light at the intersection of smart cities, and is the maximum time allowed for the green light at the intersection of smart cities.
约束条件对 SCTI 绿灯时间的限制如下: (2) 其中, 为智慧城市交叉口绿灯亮起的最短时间, 为智慧城市交叉口绿灯亮起的最长时间。
约束条件对 SCTI 绿灯时间的限制如下: (2) 其中, 为智慧城市交叉口绿灯亮起的最短时间, 为智慧城市交叉口绿灯亮起的最长时间。
The traffic flow of vehicles at the intersection of smart cities is:(3)Where, is the vehicle speed and is the vehicle flow from the connecting section .
智慧城市交叉口的车辆流量为 (3) 其中, 为车辆速度, 为来自连接路段 的车辆流量。
智慧城市交叉口的车辆流量为 (3) 其中, 为车辆速度, 为来自连接路段 的车辆流量。
Set the road width as , the interval between any time periods in the period as [18,19], and the state equation of traffic flow at the intersection of smart cities is:(4)Where, is the allowed flow of vehicles in the continuous section , and is the time when the green light flashes in the signal period of SCTI. Establishing the state equation of traffic flow at traffic intersections in smart cities can provide real-time traffic flow information, predict congestion and provide accurate and timely data support for traffic managers.
设道路宽度为 ,周期 中任意时间段的间隔为 [18,19],则智慧城市交叉口交通流状态方程为 (4) 其中, 为连续段 中允许的车辆流量, 为SCTI信号周期中绿灯闪烁的时间。建立智慧城市交通路口的交通流状态方程,可以提供实时交通流信息,预测拥堵情况,为交通管理者提供准确及时的数据支持。
设道路宽度为 ,周期 中任意时间段的间隔为 [18,19],则智慧城市交叉口交通流状态方程为 (4) 其中, 为连续段 中允许的车辆流量, 为SCTI信号周期中绿灯闪烁的时间。建立智慧城市交通路口的交通流状态方程,可以提供实时交通流信息,预测拥堵情况,为交通管理者提供准确及时的数据支持。
3. Building an optimization model for traffic signal control at intersections in smart cities
3.建立智慧城市交叉口交通信号控制优化模型
SCTI is an important node in the urban traffic network, and its design also has an important impact on the traffic efficiency of smart cities. The design of traffic intersections in smart cities includes the number of lanes, the width of lanes, the timing of signal lights and other parameters, which have a direct impact on the traffic efficiency of vehicles. After establishing the Equation of state of traffic flow at the SCTI, the optimization model of traffic light control at the SCTI is constructed. The Equation of state of traffic flow at the SCTI is taken as the input parameter of the optimization model to determine the optimal optimization strategy of traffic light control.
SCTI 是城市交通网络中的重要节点,其设计对智慧城市的交通效率也有重要影响。智慧城市中交通路口的设计包括车道数量、车道宽度、信号灯配时等参数,这些参数直接影响车辆的通行效率。在建立小城镇交通枢纽的交通流状态方程后,构建了小城镇交通枢纽的交通灯控制优化模型。将小站交通流量状态方程作为优化模型的输入参数,以确定交通灯控制的最优优化策略。
SCTI 是城市交通网络中的重要节点,其设计对智慧城市的交通效率也有重要影响。智慧城市中交通路口的设计包括车道数量、车道宽度、信号灯配时等参数,这些参数直接影响车辆的通行效率。在建立小城镇交通枢纽的交通流状态方程后,构建了小城镇交通枢纽的交通灯控制优化模型。将小站交通流量状态方程作为优化模型的输入参数,以确定交通灯控制的最优优化策略。
In a week, the time required for the change of the phase sequence of the traffic signal lights at SCTI is the signal period [[20], [21], [22]]. The short signal period of SCTI will reduce the traffic capacity of SCTI, while the long signal period of SCTI will increase the average delay time of vehicles. Effective green time refers to the actual effective time when the traffic light is green, and vehicles can use this green time period to pass through the intersection. This time period includes the acceleration of the vehicle starting, the time during driving and the possible waiting time to ensure the vehicle passing through the intersection smoothly. The setting of time parameters of traffic lights at smart city intersections has a direct impact on the control of traffic lights at smart city intersections. The time parameters of traffic lights at smart city intersections set in this paper are shown in Table 1.
一周内,南部通道交汇处交通信号灯相序变化所需的时间即为信号周期[[20]、[21]、[22]]。信号周期过短会降低隧道的通行能力,而信号周期过长则会增加车辆的平均延误时间。有效绿灯时间是指交通信号灯为绿灯时的实际有效时间,车辆可以利用这段时间通过交叉口。这段时间包括车辆起步的加速时间、行驶过程中的时间以及为保证车辆顺利通过交叉路口可能需要的等待时间。智慧城市路口交通灯时间参数的设置直接影响到智慧城市路口交通灯的控制。本文设置的智慧城市交叉口交通灯时间参数如表 1 所示。
一周内,南部通道交汇处交通信号灯相序变化所需的时间即为信号周期[[20]、[21]、[22]]。信号周期过短会降低隧道的通行能力,而信号周期过长则会增加车辆的平均延误时间。有效绿灯时间是指交通信号灯为绿灯时的实际有效时间,车辆可以利用这段时间通过交叉口。这段时间包括车辆起步的加速时间、行驶过程中的时间以及为保证车辆顺利通过交叉路口可能需要的等待时间。智慧城市路口交通灯时间参数的设置直接影响到智慧城市路口交通灯的控制。本文设置的智慧城市交叉口交通灯时间参数如表 1 所示。
Name 名称 | Time 时间 |
---|---|
Green light 绿灯 | Phase green light time ≤70s; 相位绿光时间≤70 秒; Main road green light time ≥15s; 主干道绿灯时间≥15 秒; The green light time on the secondary main road is ≥ 8 s. 二级主干道的绿灯时间≥ 8 秒。 |
Yellow light 黄灯 | Normally set to 3 s 通常设置为 3 秒 |
Red light 红灯 | Red light time under unsaturated traffic conditions ≤120s; 非饱和交通条件下的红灯时间 ≤120s; Under saturated traffic conditions, the red light time is ≤ 150s. 在饱和交通条件下,红灯时间≤ 150 秒。 |
According to the parameters shown in Table 1, set the signal time parameters for SCTIs to ensure that the SCTIs are in a normal and orderly traffic state in real time.
根据表 1 所示参数,设置 SCTI 的信号时间参数,确保 SCTI 实时处于正常有序的交通状态。
根据表 1 所示参数,设置 SCTI 的信号时间参数,确保 SCTI 实时处于正常有序的交通状态。
Improper control of traffic lights at intersections will easily lead to traffic delays and increase the overall traffic congestion. Therefore, minimizing the average delay can effectively reduce the time for vehicles to stay at intersections, improve the efficiency of vehicles passing through intersections, and relieve traffic pressure. In terms of average delay , the calculation is mainly based on the sum of random delay and consistent delay [[23], [24], [25]]. The calculation process of random delay and consistent delay is shown in (5), (6):(5)(6)Where, is the saturation of the inlet in phase , is the actual traffic volume at the inlet in phase , and is the flow ratio at the inlet in phase .
交叉路口红绿灯控制不当,容易造成交通延误,加剧整体交通拥堵。因此,尽量减少平均延误可以有效减少车辆在交叉口的停留时间,提高车辆通过交叉口的效率,缓解交通压力。就平均延误 而言,其计算主要基于随机延误 和一致延误 之和[[23], [24], [25]]。随机延迟 和一致延迟 的计算过程如(5)、(6)所示: (5) (6) 其中, 为 阶段 入口的饱和度, 为 阶段 入口的实际交通量, 为 阶段 入口的流量比。
交叉路口红绿灯控制不当,容易造成交通延误,加剧整体交通拥堵。因此,尽量减少平均延误可以有效减少车辆在交叉口的停留时间,提高车辆通过交叉口的效率,缓解交通压力。就平均延误 而言,其计算主要基于随机延误 和一致延误 之和[[23], [24], [25]]。随机延迟 和一致延迟 的计算过程如(5)、(6)所示: (5) (6) 其中, 为 阶段 入口的饱和度, 为 阶段 入口的实际交通量, 为 阶段 入口的流量比。
The calculation of the average delay required in this article can be obtained by weighting the average delay of different signal phases:(7)Where, is the average delay of the phase, and is the actual traffic flow of the phase.
本文所需的平均延迟 的计算可通过对不同信号阶段的平均延迟进行加权来获得: (7) 其中, 为 阶段的平均延迟, 为 阶段的实际交通流量。
本文所需的平均延迟 的计算可通过对不同信号阶段的平均延迟进行加权来获得: (7) 其中, 为 阶段的平均延迟, 为 阶段的实际交通流量。
Too long signal light period will lead to frequent parking of vehicles, increase the time of vehicle queuing and parking times, and reduce the overall operating efficiency of the transportation system. By minimizing the average number of stops, the number of stops at intersections can be reduced, the delay time of vehicles can be reduced, and the traffic efficiency can be improved. In terms of the average number of stops, in order to ensure normal traffic at smart city intersections, efforts should be made to avoid secondary parking of vehicles and strive to fully release all vehicles within a cycle [[26], [27], [28]]. The calculation process of the average number of stops is shown in formula (8):(8)
信号灯周期过长会导致车辆频繁停放,增加车辆排队时间和停车时间,降低交通系统的整体运行效率。通过尽量减少平均停车次数,可以减少交叉口的停车次数,减少车辆的延误时间,提高交通效率。就平均停车次数而言,为了保证智慧城市交叉口的正常交通,应努力避免车辆的二次停放,力争在一个周期内将所有车辆全部放行[[26], [27], [28]]。平均停车次数 的计算过程如式(8)所示: (8)
信号灯周期过长会导致车辆频繁停放,增加车辆排队时间和停车时间,降低交通系统的整体运行效率。通过尽量减少平均停车次数,可以减少交叉口的停车次数,减少车辆的延误时间,提高交通效率。就平均停车次数而言,为了保证智慧城市交叉口的正常交通,应努力避免车辆的二次停放,力争在一个周期内将所有车辆全部放行[[26], [27], [28]]。平均停车次数 的计算过程如式(8)所示: (8)
The calculation method for the average number of vehicle stops required in this article is obtained through weighted calculation:(9)
本文所需的平均车辆停靠次数 的计算方法是通过加权计算得出的: (9)
本文所需的平均车辆停靠次数 的计算方法是通过加权计算得出的: (9)
By optimizing the control parameters of traffic lights, the delay time of vehicles can be reduced, and the number of vehicle stops can be reduced, thus improving the traffic capacity of intersections and the operating efficiency of the overall traffic system. By taking the average delay index and the average number of stops as the objective function, the intersection traffic can be smoother and more efficient to the greatest extent.
通过优化交通信号灯的控制参数,可以缩短车辆的延误时间,减少车辆的停车次数,从而提高交叉口的通行能力和整个交通系统的运行效率。以平均延误指数和平均停车次数为目标函数,可以最大程度地使交叉口交通更加顺畅和高效。
通过优化交通信号灯的控制参数,可以缩短车辆的延误时间,减少车辆的停车次数,从而提高交叉口的通行能力和整个交通系统的运行效率。以平均延误指数和平均停车次数为目标函数,可以最大程度地使交叉口交通更加顺畅和高效。
The weighting coefficient is introduced, and is set to represent the signal phase of SCTI, and the signal period of SCTI satisfies and less than or equal to 200s. The cycle time consists of green light time and lost time . If the saturation needs to be 0.6–0.9 to maintain good traffic conditions at SCTI, then:(10)
引入加权系数 ,设置 表示 SCTI 的信号相位,SCTI 的信号周期满足 且小于等于 200 秒。周期时间由绿灯时间和丢失时间 组成。如果饱和度 需要达到 0.6-0.9 才能维持 SCTI 的良好交通状况,那么: (10)
引入加权系数 ,设置 表示 SCTI 的信号相位,SCTI 的信号周期满足 且小于等于 200 秒。周期时间由绿灯时间和丢失时间 组成。如果饱和度 需要达到 0.6-0.9 才能维持 SCTI 的良好交通状况,那么: (10)
Establishing the optimization model of traffic signal control at intersections in smart cities:(11)
建立智慧城市交叉口交通信号控制优化模型: (11)
建立智慧城市交叉口交通信号控制优化模型: (11)
4. Optimization model solution based on adaptive artificial fish swarm algorithm
4.基于自适应人工鱼群算法的优化模型解决方案
Based on the establishment of an optimization model for traffic signal control at smart city intersections, an adaptive artificial fish swarm algorithm is used to solve the optimization model for traffic signal control at smart city intersections. The artificial fish swarm algorithm is a population intelligent optimization algorithm. The basic idea is to simulate the foraging behavior, tail chasing behavior, and herd behavior of fish [[29], [30], [31]]. This algorithm can be used to solve various continuous optimization problems and Combinatorial optimization problems.
在建立智慧城市交叉口交通信号控制优化模型的基础上,采用自适应人工鱼群算法求解智慧城市交叉口交通信号控制优化模型。人工鱼群算法是一种群体智能优化算法。其基本思想是模拟鱼类的觅食行为、追尾行为和群居行为[[29], [30], [31]]。该算法可用于解决各种连续优化问题和组合优化问题。
在建立智慧城市交叉口交通信号控制优化模型的基础上,采用自适应人工鱼群算法求解智慧城市交叉口交通信号控制优化模型。人工鱼群算法是一种群体智能优化算法。其基本思想是模拟鱼类的觅食行为、追尾行为和群居行为[[29], [30], [31]]。该算法可用于解决各种连续优化问题和组合优化问题。
Assuming that the current state of an artificial fish is , set its field of view to , and the viewpoint position of the artificial fish at a certain moment is . When the position state of the viewpoint is better than the current artificial fish state, the artificial fish can choose to move one step in that direction to reach state ; If the state of the viewpoint is not better than the current state of the artificial fish, continue to search for other positions within the field of view. As the number of searches increases, artificial fish have a more comprehensive understanding of the surrounding environment, enabling them to have a more comprehensive understanding of the surrounding environment, which helps make corresponding judgments and decisions.
假设人造鱼的当前状态为 ,将其视场设置为 ,人造鱼在某一时刻的视点位置为 。当视点的位置状态优于人工鱼的当前状态时,人工鱼可以选择向该方向移动一步,达到状态 ;如果视点的状态不优于人工鱼的当前状态,则继续搜索视野内的其他位置。随着搜索次数的增加,人工鱼对周围环境有了更全面的了解,从而能够对周围环境有更全面的认识,有助于做出相应的判断和决策。
假设人造鱼的当前状态为 ,将其视场设置为 ,人造鱼在某一时刻的视点位置为 。当视点的位置状态优于人工鱼的当前状态时,人工鱼可以选择向该方向移动一步,达到状态 ;如果视点的状态不优于人工鱼的当前状态,则继续搜索视野内的其他位置。随着搜索次数的增加,人工鱼对周围环境有了更全面的了解,从而能够对周围环境有更全面的认识,有助于做出相应的判断和决策。
Reasonable adjustment of green signal ratio can directly affect the traffic flow in different directions. Priority is given to setting the green light time for straight and left-turning vehicles at peak hours to meet the demand of main traffic flow, thus reducing waiting time and improving traffic efficiency. In the low peak period, set a balanced green signal ratio according to the actual situation to avoid excessive waiting and wasting traffic resources. Among them, state and position , then the process can be represented as follows:(12)Where, the function is used to generate a random number between 0 and 1, and is the step size of the artificial fish movement. Because the number of companions in the environment is limited, artificial fish will perceive the state of their companions in their field of vision and adjust their own state accordingly.
绿灯比例的合理调整会直接影响不同方向的交通流量。高峰时段优先设置直行和左转车辆的绿灯时间,满足主要交通流的需求,从而减少等待时间,提高交通效率。在低峰时段,根据实际情况均衡设置绿灯信号比例,避免过度等待,浪费交通资源。其中,状态 和位置 ,则过程可表示如下: (12) 其中, 函数用于产生一个介于 0 和 1 之间的随机数, 是人工鱼群移动的步长。由于环境中同伴的数量有限,人工鱼会感知视野中同伴的状态,并相应调整自己的状态。
绿灯比例的合理调整会直接影响不同方向的交通流量。高峰时段优先设置直行和左转车辆的绿灯时间,满足主要交通流的需求,从而减少等待时间,提高交通效率。在低峰时段,根据实际情况均衡设置绿灯信号比例,避免过度等待,浪费交通资源。其中,状态 和位置 ,则过程可表示如下: (12) 其中, 函数用于产生一个介于 0 和 1 之间的随机数, 是人工鱼群移动的步长。由于环境中同伴的数量有限,人工鱼会感知视野中同伴的状态,并相应调整自己的状态。
The variable section includes the total number of artificial fish , the state of individual artificial fish , the field of view of the artificial fish, the crowding factor , the maximum step length of the artificial fish movement , the number of attempts , and the distance between individual artificial fish , calculated as follows:(13)
变量部分包括人造鱼总数 、人造鱼个体状态 、人造鱼视野 、拥挤系数 、人造鱼运动最大步长 、尝试次数 和人造鱼个体间距离 ,计算公式如下: (13)
变量部分包括人造鱼总数 、人造鱼个体状态 、人造鱼视野 、拥挤系数 、人造鱼运动最大步长 、尝试次数 和人造鱼个体间距离 ,计算公式如下: (13)
The function section includes the food concentration at the current location of the artificial fish, where is the objective function value. Through encapsulation, the state of artificial fish can be perceived by other companions.
功能部分包括人造鱼当前位置的食物浓度 ,其中 为目标函数值。通过封装,其他同伴可以感知人造鱼的状态。
功能部分包括人造鱼当前位置的食物浓度 ,其中 为目标函数值。通过封装,其他同伴可以感知人造鱼的状态。
Chaotic phenomena have the characteristics of certainty, randomness, and ergodicity. Certainty refers to the fact that chaotic trajectories are usually generated by determined iterative formulas; Randomness refers to the sensitivity of the trajectory of chaotic phenomena to initial values; Ergodicity refers to the ability of chaotic trajectories to experience all states without repetition within a certain range of intervals. In response to the problem of low global optimization ability and easy falling into local optima in the artificial fish swarm algorithm, by combining chaos search theory [[32], [33], [34]], the artificial fish swarm algorithm is adaptively improved to achieve the optimization model solution of traffic intersection signal control in smart cities. Consider an optimization model problem with -dimensional variables, where is the decision variable and the range of the variable is . The implementation process for solving the optimization model based on the adaptive artificial fish school algorithm is as follows:
混沌现象具有确定性、随机性和遍历性等特征。确定性是指混沌轨迹通常由确定的迭代公式产生;随机性是指混沌现象的轨迹对初始值的敏感性;遍历性是指混沌轨迹能够在一定区间范围内经历所有状态而不重复。针对人工鱼群算法全局优化能力低、易陷入局部最优的问题,结合混沌搜索理论[[32], [33], [34]],对人工鱼群算法进行自适应改进,实现了智慧城市交通路口信号控制的优化模型求解。考虑一个具有 维变量的优化模型问题,其中 为 决策变量,变量范围为 。基于自适应人工鱼群算法的优化模型求解实施过程如下:
(17) 在公式 (17) 中, 是控制参数。当 时,公式(17)将处于混沌状态,即除了点 0、0.25、0.50、0.75 和 1 外,其他点都将通过迭代公式产生,且都在 0-1 范围内。
混沌现象具有确定性、随机性和遍历性等特征。确定性是指混沌轨迹通常由确定的迭代公式产生;随机性是指混沌现象的轨迹对初始值的敏感性;遍历性是指混沌轨迹能够在一定区间范围内经历所有状态而不重复。针对人工鱼群算法全局优化能力低、易陷入局部最优的问题,结合混沌搜索理论[[32], [33], [34]],对人工鱼群算法进行自适应改进,实现了智慧城市交通路口信号控制的优化模型求解。考虑一个具有 维变量的优化模型问题,其中 为 决策变量,变量范围为 。基于自适应人工鱼群算法的优化模型求解实施过程如下:
- Step 1: Initially, let map the dimensional decision variable to the chaotic variable using formula (14):
步骤 1:最初,让 利用公式 (14) 将 维决策变量 映射到混沌变量 中:
- Step 2: Based on , use chaotic mapping to generate the next generation of chaotic variable :
步骤 2: 基于 ,使用混沌映射生成下一代混沌变量 :
- Step 3: Use formula (16) to map chaotic variable to decision variable :
步骤 3:使用公式 (16) 将混沌变量 映射到决策变量 :
- Step 4: Evaluate the advantages and disadvantages of the new decision variable ;
步骤 4:评估新决策变量 的优缺点; - Step 5: If the new decision variable is better than , output as the local search result of the chaotic search; otherwise, return to step 2 with another ; There are many mapping methods for the chaotic mapping in step 2, and the chosen one here is the Logistic mapping. The iterative equation for the Logistic mapping is as follows:
步骤 5:如果新的决策变量 优于 ,则输出 作为混沌搜索的局部搜索结果;否则,返回步骤 2,输出另一个 ;步骤 2 中的混沌映射有很多映射方法,这里选择的是 Logistic 映射。Logistic 映射的迭代方程如下:
(17) 在公式 (17) 中, 是控制参数。当 时,公式(17)将处于混沌状态,即除了点 0、0.25、0.50、0.75 和 1 外,其他点都将通过迭代公式产生,且都在 0-1 范围内。
Through the above steps, optimization of traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm is achieved.
通过以上步骤,实现了基于自适应人工鱼群算法的智慧城市交叉口交通信号控制优化。
通过以上步骤,实现了基于自适应人工鱼群算法的智慧城市交叉口交通信号控制优化。
5. Experimental analysis 5.实验分析
In order to verify the effectiveness of the intelligent city traffic intersection signal light control optimization method based on adaptive artificial fish swarm algorithm, this paper takes Xi 'an traffic intersection as an actual case to model and optimize. Choose an intersection in Xi 'an as a traffic scene. There are both east-west traffic and north-south traffic at the intersection. All directions are two-way lanes, including three traffic directions: straight, left and right turns. At this intersection, an intelligent traffic signal control system is set up. By monitoring the traffic flow and collecting sensor data in real time, combined with traffic management strategy, the traffic signal is optimally controlled. 12 detectors are set at the intersection, covering all key positions at the intersection, and comprehensively grasping the flow direction of different vehicles. The specific traffic scene is shown in Fig. 1.
为了验证基于自适应人工鱼群算法的智慧城市交通路口信号灯控制优化方法的有效性,本文以西安交通路口为实际案例进行建模和优化。选择西安市某十字路口作为交通场景。该交叉口有东西向交通和南北向交通。所有方向均为双向车道,包括直行、左转和右转三个交通方向。该路口设置了智能交通信号控制系统。通过实时监测交通流量和收集传感器数据,结合交通管理策略,对交通信号进行优化控制。路口设置了 12 个探测器,覆盖路口所有关键位置,全面掌握不同车辆的流向。具体交通场景如图 1 所示。
为了验证基于自适应人工鱼群算法的智慧城市交通路口信号灯控制优化方法的有效性,本文以西安交通路口为实际案例进行建模和优化。选择西安市某十字路口作为交通场景。该交叉口有东西向交通和南北向交通。所有方向均为双向车道,包括直行、左转和右转三个交通方向。该路口设置了智能交通信号控制系统。通过实时监测交通流量和收集传感器数据,结合交通管理策略,对交通信号进行优化控制。路口设置了 12 个探测器,覆盖路口所有关键位置,全面掌握不同车辆的流向。具体交通场景如图 1 所示。
According to Fig. 1, straight lanes and left-turn lanes are set in the east-west direction, and the number of turning lanes is adjusted according to the traffic flow. Set the corresponding signal light phase, so that straight and left-turning vehicles can reasonably pass alternately. There are also straight lanes and left-turn lanes in the north-south direction, and the number of turning lanes is adjusted according to the traffic flow, and the corresponding signal lights are set to make the straight and left-turn vehicles pass according to reasonable priority. Set up a right-turn lane on the right side of each direction, and set the corresponding right-turn signal phase, so that right-turn vehicles can pass quickly.
根据图 1,东西方向设置直行车道和左转车道,根据车流量调整转弯车道数量。设置相应的信号灯相位,使直行和左转车辆能够合理交替通行。南北方向同样设置直行车道和左转车道,根据交通流量调整掉头车道数量,设置相应的信号灯相位,使直行车和左转车合理优先通行。在每个方向的右侧设置右转车道,并设置相应的右转信号相位,使右转车辆能够快速通过。
根据图 1,东西方向设置直行车道和左转车道,根据车流量调整转弯车道数量。设置相应的信号灯相位,使直行和左转车辆能够合理交替通行。南北方向同样设置直行车道和左转车道,根据交通流量调整掉头车道数量,设置相应的信号灯相位,使直行车和左转车合理优先通行。在每个方向的右侧设置右转车道,并设置相应的右转信号相位,使右转车辆能够快速通过。
In order to effectively carry out the optimization test of traffic signal control at traffic intersections, an experimental test is carried out on VISSIM traffic simulation software. The operating system is Ubuntu 18.04, the CPU is Intel i5-5200U, the main frequency is 2.2 GHz, the physical memory is 64 GB, the programming language is JAVA, and the deep learning framework is TensorFlow 2.0. The parameter setting of the optimization model based on adaptive artificial fish swarm algorithm is shown in Table 2.
为有效开展交通路口交通信号控制优化测试,在VISSIM交通仿真软件上进行了实验测试。操作系统为 Ubuntu 18.04,CPU 为 Intel i5-5200U,主频为 2.2 GHz,物理内存为 64 GB,编程语言为 JAVA,深度学习框架为 TensorFlow 2.0。基于自适应人工鱼群算法的优化模型参数设置如表 2 所示。
为有效开展交通路口交通信号控制优化测试,在VISSIM交通仿真软件上进行了实验测试。操作系统为 Ubuntu 18.04,CPU 为 Intel i5-5200U,主频为 2.2 GHz,物理内存为 64 GB,编程语言为 JAVA,深度学习框架为 TensorFlow 2.0。基于自适应人工鱼群算法的优化模型参数设置如表 2 所示。
Parameter 参数 | Value 价值 |
---|---|
Total number of artificial fish 人工鱼总数 | 1000 |
Visual field of artificial fish 人造鱼的视野 | 2 m × 2 m 2 米 × 2 米 |
Congestion factor 拥堵系数 | 0.5 |
Maximum step length of artificial fish movement 人工鱼群移动的最大步长 | 1 |
Number of attempts 尝试次数 | 30 |
Signal lamp phase 信号灯相位 | 55s |
Lane width 车道宽度 | 3.5 m 3.5 m |
Driving speed 行驶速度 | 40–60 km/h 40-60 公里/小时 |
Steering ratio 转向比 | 20 % |
5.1. Experimental preparation
5.1.实验准备
VISSIM is a professional microscopic traffic simulation software, which is mainly used for the simulation and analysis of urban traffic and highway traffic. It can simulate the driving situation of vehicles in the road network, including vehicle acceleration, deceleration, lane change, parking and other behaviors, and can provide rich traffic flow data and performance indicators. In the VISSIM traffic simulation software, the urban traffic conditions are simulated according to the parameter settings in Table 2, and the corresponding signal light control and management functions are provided. Perform the test according to the test case, record the values such as car flow and parking record, and track the abnormal situation. The specific process is shown in Fig. 2.
VISSIM 是一款专业的微观交通仿真软件,主要用于城市交通和高速公路交通的仿真分析。它可以模拟路网中车辆的行驶状况,包括车辆加速、减速、变道、停车等行为,并能提供丰富的交通流数据和性能指标。在 VISSIM 交通仿真软件中,根据表 2 中的参数设置模拟城市交通状况,并提供相应的信号灯控制和管理功能。根据测试用例进行测试,记录车流量、停车记录等数值,并跟踪异常情况。具体流程如图 2 所示。
VISSIM 是一款专业的微观交通仿真软件,主要用于城市交通和高速公路交通的仿真分析。它可以模拟路网中车辆的行驶状况,包括车辆加速、减速、变道、停车等行为,并能提供丰富的交通流数据和性能指标。在 VISSIM 交通仿真软件中,根据表 2 中的参数设置模拟城市交通状况,并提供相应的信号灯控制和管理功能。根据测试用例进行测试,记录车流量、停车记录等数值,并跟踪异常情况。具体流程如图 2 所示。
5.2. Experimental results
5.2.实验结果
5.2.1. Optimization effect of traffic intersection signal light control in smart cities
5.2.1.智慧城市中交通路口信号灯控制的优化效果
In order to verify the optimization effect of SCTI signal control using the method proposed in this article, the average delay and average number of stops are used as evaluation indicators. The smaller the average delay and average number of stops, the better the optimization effect of SCTI signal control. This article incorporates the methods of this article, the method of reference [7], and the method of reference [8] into VISSIM and conducts simulation experiments. Twelve detectors are set to measure the positions, and the average delay and average stopping times of the three methods are obtained as shown in Fig. 3.
为了验证本文提出的方法对 SCTI 信号控制的优化效果,本文采用平均延误和平均停车次数作为评价指标。平均延误和平均停车次数越小,SCTI 信号控制的优化效果越好。本文将本文方法、参考文献[7]方法和参考文献[8]方法纳入 VISSIM 并进行仿真实验。设置 12 个探测器测量位置,得到三种方法的平均延迟时间和平均停车时间,如图 3 所示。
为了验证本文提出的方法对 SCTI 信号控制的优化效果,本文采用平均延误和平均停车次数作为评价指标。平均延误和平均停车次数越小,SCTI 信号控制的优化效果越好。本文将本文方法、参考文献[7]方法和参考文献[8]方法纳入 VISSIM 并进行仿真实验。设置 12 个探测器测量位置,得到三种方法的平均延迟时间和平均停车时间,如图 3 所示。
From Fig. 3, it can be intuitively seen that the average delay and average stopping times results of the method proposed are significantly smaller than those of the method of reference [7] and the method of reference [8]. In this optimization model, the average delay and average stopping times generated by SCTI are effectively reduced, and the current traffic situation of SCTI is improved. From this, it can be seen that the optimization effect of the intelligent city traffic intersection signal control using the method proposed in this article is good.
从图 3 可以直观地看出,所提方法的平均延误时间和平均停车时间结果明显小于参考文献[7]和参考文献[8]的方法。在该优化模型中,由 SCTI 产生的平均延迟和平均停车时间得到了有效降低,SCTI 的交通现状得到了改善。由此可见,采用本文提出的方法进行智能城市交通交叉口信号控制的优化效果较好。
从图 3 可以直观地看出,所提方法的平均延误时间和平均停车时间结果明显小于参考文献[7]和参考文献[8]的方法。在该优化模型中,由 SCTI 产生的平均延迟和平均停车时间得到了有效降低,SCTI 的交通现状得到了改善。由此可见,采用本文提出的方法进行智能城市交通交叉口信号控制的优化效果较好。
5.2.2. Optimization efficiency of traffic intersection signal light control in smart cities
5.2.2.智慧城市中交通路口信号灯控制的优化效率
Further validate the rationality of the intelligent city traffic intersection signal control optimization method proposed in this article, using travel time as an evaluation indicator. The smaller the travel time, the higher the efficiency of intelligent city traffic intersection signal control optimization. By comparing the method of reference [7] and the method of reference [8], and this article, the travel times of the three methods are shown in Table 3.
以出行时间为评价指标,进一步验证本文提出的城市智能交通交叉口信号控制优化方法的合理性。出行时间越小,智能城市交通交叉口信号控制优化效率越高。通过对比参考文献[7]的方法、参考文献[8]的方法和本文的方法,三种方法的旅行时间如表 3 所示。
以出行时间为评价指标,进一步验证本文提出的城市智能交通交叉口信号控制优化方法的合理性。出行时间越小,智能城市交通交叉口信号控制优化效率越高。通过对比参考文献[7]的方法、参考文献[8]的方法和本文的方法,三种方法的旅行时间如表 3 所示。
According to Table 3, as the traffic flow at smart city intersections increases, the travel time of the three methods also increases. When the traffic flow at smart city intersections reaches 140 veh/h, the travel time of the method of reference [7] and the method of reference [8] is 81.5 s and 76.6 s, respectively, while the travel time of the method proposed is only 68.4 s. From this, it can be seen that compared with the method of reference [7] and the method of reference [8], the travel time of this method is smaller, indicating that through the optimization of traffic signal control in smart city intersections, this method effectively improves the efficiency of traffic signal control optimization in smart city intersections and reduces traffic congestion in smart city intersections.
根据表 3,随着智能城市交叉口交通流量的增加,三种方法的行驶时间也随之增加。当智慧城市交叉口的车流量达到 140 辆/小时时,参考文献[7]和参考文献[8]的方法的行驶时间分别为 81.5 秒和 76.6 秒,而本文提出的方法的行驶时间仅为 68.4 秒。由此可见,与参考文献[7]和参考文献[8]的方法相比,本方法的行驶时间更小,说明通过对智慧城市交叉口交通信号控制的优化,本方法有效提高了智慧城市交叉口交通信号控制优化的效率,减少了智慧城市交叉口的交通拥堵。
根据表 3,随着智能城市交叉口交通流量的增加,三种方法的行驶时间也随之增加。当智慧城市交叉口的车流量达到 140 辆/小时时,参考文献[7]和参考文献[8]的方法的行驶时间分别为 81.5 秒和 76.6 秒,而本文提出的方法的行驶时间仅为 68.4 秒。由此可见,与参考文献[7]和参考文献[8]的方法相比,本方法的行驶时间更小,说明通过对智慧城市交叉口交通信号控制的优化,本方法有效提高了智慧城市交叉口交通信号控制优化的效率,减少了智慧城市交叉口的交通拥堵。
5.3. Discussion 5.3.讨论情况
The method in this paper performs well in the test of optimization effect of traffic signal control at smart city intersections, and its average delay and average parking times are low. This is mainly due to the in-depth understanding of the actual traffic flow when establishing the traffic flow state equation of smart city traffic intersections, and based on this, the control parameters of traffic lights are designed. At the same time, taking the minimum average delay and average parking times as the objective function, an optimization model of traffic signal control at smart city intersections is constructed, which effectively reduces the average delay and average parking times and improves the traffic status of smart city intersections.
本文方法在智慧城市交叉口交通信号控制优化效果测试中表现良好,其平均延误时间和平均停车时间均较低。这主要得益于在建立智慧城市交通路口交通流状态方程时对实际交通流的深入了解,并在此基础上设计交通信号灯的控制参数。同时,以平均延误和平均停车时间最小为目标函数,构建了智慧城市交叉口交通信号控制优化模型,有效降低了平均延误和平均停车时间,改善了智慧城市交叉口的交通状态。
本文方法在智慧城市交叉口交通信号控制优化效果测试中表现良好,其平均延误时间和平均停车时间均较低。这主要得益于在建立智慧城市交通路口交通流状态方程时对实际交通流的深入了解,并在此基础上设计交通信号灯的控制参数。同时,以平均延误和平均停车时间最小为目标函数,构建了智慧城市交叉口交通信号控制优化模型,有效降低了平均延误和平均停车时间,改善了智慧城市交叉口的交通状态。
In the efficiency test of traffic signal light control optimization in smart cities, this method also performs well. With the increase of traffic volume at smart city intersections, the travel time of the three methods will increase. However, when the traffic volume reaches 140veh/h, the travel time of this method is only 68.4 s s. In this paper, the adaptive artificial fish swarm algorithm is selected as the optimization method, which has strong global search ability and diversity. By dynamically adjusting the parameters and behavior rules of artificial fish swarm algorithm, the algorithm can better adapt to different traffic flow changes and find better control parameters of traffic lights. At the same time, the adaptive artificial fish swarm algorithm is improved by combining chaotic search theory. Chaos search theory can increase the diversity of algorithms, and thus search the optimal solution more comprehensively. In this way, this method can better adapt to the dynamic characteristics of traffic flow changes at different traffic intersections and improve the effect of signal light control.
在智慧城市交通信号灯控制优化的效率测试中,该方法也表现良好。随着智慧城市交叉口交通流量的增加,三种方法的通行时间都会增加。本文选用自适应人工鱼群算法作为优化方法,该算法具有较强的全局搜索能力和多样性。通过动态调整人工鱼群算法的参数和行为规则,该算法可以更好地适应不同交通流的变化,找到更好的交通信号灯控制参数。同时,结合混沌搜索理论对自适应人工鱼群算法进行了改进。混沌搜索理论可以增加算法的多样性,从而更全面地搜索最优解。这样,该方法就能更好地适应不同交通路口交通流变化的动态特性,提高信号灯控制的效果。
在智慧城市交通信号灯控制优化的效率测试中,该方法也表现良好。随着智慧城市交叉口交通流量的增加,三种方法的通行时间都会增加。本文选用自适应人工鱼群算法作为优化方法,该算法具有较强的全局搜索能力和多样性。通过动态调整人工鱼群算法的参数和行为规则,该算法可以更好地适应不同交通流的变化,找到更好的交通信号灯控制参数。同时,结合混沌搜索理论对自适应人工鱼群算法进行了改进。混沌搜索理论可以增加算法的多样性,从而更全面地搜索最优解。这样,该方法就能更好地适应不同交通路口交通流变化的动态特性,提高信号灯控制的效果。
To sum up, the method in this paper is excellent in the optimization effect and efficiency of traffic signal control at smart city intersections, which is mainly due to its in-depth understanding of the actual traffic flow, the optimization model design with the minimum average delay and average parking times as the objective function, and the efficient solution of the adaptive improved artificial fish swarm algorithm based on chaotic search theory. These advantages make this method have high application value and popularization prospect in the field of traffic intersection signal light control optimization in smart cities.
综上所述,本文方法在智慧城市交叉口交通信号控制优化效果和效率方面表现优异,这主要得益于其对实际交通流的深入理解,以最小平均延时和平均停车时间为目标函数的优化模型设计,以及基于混沌搜索理论的自适应改进人工鱼群算法的高效求解。这些优点使得该方法在智慧城市交通交叉口信号灯控制优化领域具有较高的应用价值和推广前景。
综上所述,本文方法在智慧城市交叉口交通信号控制优化效果和效率方面表现优异,这主要得益于其对实际交通流的深入理解,以最小平均延时和平均停车时间为目标函数的优化模型设计,以及基于混沌搜索理论的自适应改进人工鱼群算法的高效求解。这些优点使得该方法在智慧城市交通交叉口信号灯控制优化领域具有较高的应用价值和推广前景。
5.4. Control strategy of traffic intersection signal lights in smart cities
5.4.智慧城市中交通路口信号灯的控制策略
In the past 30 years, more than 8000 pedestrians in Australia have died in road traffic accidents [35]. The vulnerability of pedestrians to road collision conflicts with the sustainable traffic goal. In order to improve the safety of these vulnerable road users, machine learning algorithm is used to control the traffic lights at traffic intersections. The results of this study show that the traffic efficiency of traffic intersections in smart cities can be improved and congestion can be reduced by optimizing the control of traffic lights. Therefore, urban management departments should pay attention to the optimization of traffic signal control at traffic intersections as an important means to improve urban traffic conditions. The study of travel mode selection is an important task in forecasting travel demand in transportation planning and policy making [36]. Usually, the data set of pattern selection is unbalanced, and the unbalanced pattern can be dealt with based on support vector machine model and kernel scale adjustment theory. In this study, considering the actual traffic flow, the state equation of traffic flow is established, and the control parameters of traffic lights are designed on this basis. Therefore, when implementing the optimization of signal light control, we should fully understand the actual traffic flow, including the traffic flow during peak hours and off-peak hours, so as to formulate a more reasonable optimization scheme.
在过去 30 年中,澳大利亚有 8000 多名行人死于道路交通事故[35]。行人在道路碰撞中的脆弱性与可持续交通目标相冲突。为了提高这些弱势道路使用者的安全,我们采用了机器学习算法来控制交通路口的红绿灯。研究结果表明,通过优化交通信号灯的控制,可以提高智慧城市交通路口的通行效率,减少拥堵。因此,城市管理部门应重视交通路口交通信号控制的优化,将其作为改善城市交通状况的重要手段。出行模式选择研究是交通规划和政策制定中出行需求预测的一项重要工作[36]。通常情况下,模式选择的数据集是不平衡的,可以基于支持向量机模型和核尺度调整理论来处理不平衡模式。在本研究中,考虑到实际交通流量,建立了交通流状态方程,并在此基础上设计了交通信号灯的控制参数。因此,在实施信号灯控制优化时,应充分了解实际交通流量,包括高峰时段和非高峰时段的交通流量,从而制定出更合理的优化方案。
在过去 30 年中,澳大利亚有 8000 多名行人死于道路交通事故[35]。行人在道路碰撞中的脆弱性与可持续交通目标相冲突。为了提高这些弱势道路使用者的安全,我们采用了机器学习算法来控制交通路口的红绿灯。研究结果表明,通过优化交通信号灯的控制,可以提高智慧城市交通路口的通行效率,减少拥堵。因此,城市管理部门应重视交通路口交通信号控制的优化,将其作为改善城市交通状况的重要手段。出行模式选择研究是交通规划和政策制定中出行需求预测的一项重要工作[36]。通常情况下,模式选择的数据集是不平衡的,可以基于支持向量机模型和核尺度调整理论来处理不平衡模式。在本研究中,考虑到实际交通流量,建立了交通流状态方程,并在此基础上设计了交通信号灯的控制参数。因此,在实施信号灯控制优化时,应充分了解实际交通流量,包括高峰时段和非高峰时段的交通流量,从而制定出更合理的优化方案。
Health and transportation are interrelated in many stages, for example, transportation affects health, and health affects transportation choice, which shows that health is a permanent restriction [37]. Traffic intersections in smart cities usually involve pedestrians and other non-motor vehicles. Therefore, in the process of signal light control optimization, we should consider the needs of pedestrians and non-motor vehicles, arrange the traffic time reasonably, improve the traffic efficiency of intersections, and ensure the safety of pedestrians and non-motor vehicles. Time use interacts with activities, travel participation, built environment and social health [38], and there is a nonlinear relationship between car ownership and social population factors and the quality of built environment [39]. At the same time, bike-sharing is also considered as a sustainable mode of transportation [40]. In this study, the artificial fish swarm algorithm is improved adaptively by combining chaotic search theory, and the efficient control optimization of traffic lights is realized. Therefore, urban management departments can introduce advanced optimization algorithms to further improve the efficiency and accuracy of traffic light control optimization, thus improving the travel behavior of urban cars and bike-sharing and promoting the sustainable development of the built environment.
健康与交通在很多阶段都是相互关联的,例如,交通影响健康,健康影响交通选择,这表明健康是一个永久性的限制因素[37]。智慧城市中的交通路口通常会涉及行人和其他非机动车。因此,在信号灯控制优化过程中,应考虑行人和非机动车的需求,合理安排交通时间,提高交叉口的通行效率,保障行人和非机动车的安全。时间使用与活动、出行参与、建筑环境和社会健康等因素相互作用[38],汽车保有量与社会人口因素和建筑环境质量之间存在非线性关系[39]。同时,共享单车也被认为是一种可持续的交通方式[40]。本研究结合混沌搜索理论,对人工鱼群算法进行了自适应改进,实现了交通信号灯的高效控制优化。因此,城市管理部门可以引入先进的优化算法,进一步提高交通灯控制优化的效率和精度,从而改善城市小汽车和共享单车的出行行为,促进建筑环境的可持续发展。
健康与交通在很多阶段都是相互关联的,例如,交通影响健康,健康影响交通选择,这表明健康是一个永久性的限制因素[37]。智慧城市中的交通路口通常会涉及行人和其他非机动车。因此,在信号灯控制优化过程中,应考虑行人和非机动车的需求,合理安排交通时间,提高交叉口的通行效率,保障行人和非机动车的安全。时间使用与活动、出行参与、建筑环境和社会健康等因素相互作用[38],汽车保有量与社会人口因素和建筑环境质量之间存在非线性关系[39]。同时,共享单车也被认为是一种可持续的交通方式[40]。本研究结合混沌搜索理论,对人工鱼群算法进行了自适应改进,实现了交通信号灯的高效控制优化。因此,城市管理部门可以引入先进的优化算法,进一步提高交通灯控制优化的效率和精度,从而改善城市小汽车和共享单车的出行行为,促进建筑环境的可持续发展。
Travel mode selection prediction is an important part of travel demand prediction, and the negative consequences of automobile use are alleviated by using optimized machine learning methods [41]. The optimization of traffic signal control at traffic intersections in smart cities needs to strengthen the support of traffic planning and management, and urban management departments should rationally plan road networks to improve the utilization rate and traffic capacity of roads. At the same time, strengthen traffic management, including publicity and enforcement of traffic regulations, traffic guidance and flow control, to further alleviate the urban traffic congestion problem.
出行方式选择预测是出行需求预测的重要组成部分,通过使用优化的机器学习方法可以缓解汽车使用带来的负面影响[41]。智慧城市中交通路口交通信号控制的优化需要加强交通规划管理的支撑,城市管理部门应合理规划路网,提高道路的利用率和通行能力。同时,加强交通管理,包括交通法规的宣传与执行、交通诱导与流量控制等,进一步缓解城市交通拥堵问题。
出行方式选择预测是出行需求预测的重要组成部分,通过使用优化的机器学习方法可以缓解汽车使用带来的负面影响[41]。智慧城市中交通路口交通信号控制的优化需要加强交通规划管理的支撑,城市管理部门应合理规划路网,提高道路的利用率和通行能力。同时,加强交通管理,包括交通法规的宣传与执行、交通诱导与流量控制等,进一步缓解城市交通拥堵问题。
6. Conclusion 6.结论
Smart city is an urban model based on intelligent technology and realizing urban management and service through information technology. As an important part of urban traffic system, the smooth and efficient operation of traffic intersections is very important to improve urban traffic efficiency and residents' travel experience. Therefore, an optimization method of traffic intersection signal light control in smart cities based on adaptive artificial fish swarm algorithm is proposed. By establishing the state equation of traffic flow at traffic intersections in smart cities and optimizing the control of traffic lights, traffic delays and parking times can be significantly reduced, thus improving the traffic capacity of traffic intersections, helping to alleviate the problem of urban traffic congestion and making urban traffic more efficient and smooth. Design the control parameters of traffic signal lights at smart city intersections, construct the optimization model of traffic signal lights at smart city intersections, and make intelligent adjustment according to real-time traffic flow, so as to realize intelligent and dynamic traffic signal control. Based on the adaptive artificial fish swarm algorithm, the optimization model of traffic signal control at smart city intersections is solved, which gradually improves the efficiency and operation level of the whole urban traffic system and promotes the wider progress of the traffic system. The experimental results show that this method has good control optimization effect and high control optimization efficiency. The average delay is 7.8 ms, the average number of stops is 2 times, and the travel time is 68.4s.
智慧城市是一种以智能技术为基础,通过信息技术实现城市管理和服务的城市模式。作为城市交通系统的重要组成部分,交通路口的顺畅高效运行对于提高城市交通效率和居民出行体验具有十分重要的意义。因此,本文提出了一种基于自适应人工鱼群算法的智慧城市交通路口信号灯控制优化方法。通过建立智慧城市中交通路口的交通流状态方程,优化交通信号灯的控制,可以显著减少交通延误和停车时间,从而提高交通路口的通行能力,有助于缓解城市交通拥堵问题,使城市交通更加高效顺畅。设计智慧城市路口交通信号灯的控制参数,构建智慧城市路口交通信号灯的优化模型,并根据实时交通流量进行智能调整,实现交通信号控制的智能化和动态化。基于自适应人工鱼群算法,求解了智慧城市路口交通信号控制优化模型,逐步提高了整个城市交通系统的效率和运行水平,促进了交通系统更广泛的进步。实验结果表明,该方法具有良好的控制优化效果和较高的控制优化效率。平均延误为 7.8 ms,平均停车次数为 2 次,行驶时间为 68.4s。
智慧城市是一种以智能技术为基础,通过信息技术实现城市管理和服务的城市模式。作为城市交通系统的重要组成部分,交通路口的顺畅高效运行对于提高城市交通效率和居民出行体验具有十分重要的意义。因此,本文提出了一种基于自适应人工鱼群算法的智慧城市交通路口信号灯控制优化方法。通过建立智慧城市中交通路口的交通流状态方程,优化交通信号灯的控制,可以显著减少交通延误和停车时间,从而提高交通路口的通行能力,有助于缓解城市交通拥堵问题,使城市交通更加高效顺畅。设计智慧城市路口交通信号灯的控制参数,构建智慧城市路口交通信号灯的优化模型,并根据实时交通流量进行智能调整,实现交通信号控制的智能化和动态化。基于自适应人工鱼群算法,求解了智慧城市路口交通信号控制优化模型,逐步提高了整个城市交通系统的效率和运行水平,促进了交通系统更广泛的进步。实验结果表明,该方法具有良好的控制优化效果和较高的控制优化效率。平均延误为 7.8 ms,平均停车次数为 2 次,行驶时间为 68.4s。
However, there are still some limitations in this method. This method only considers the signal control at traffic intersections, and does not consider the influence of other traffic management strategies on traffic efficiency. At the same time, this method mainly pays attention to the traffic efficiency of vehicles, but does not consider the traffic safety and convenience of pedestrians and non-motor vehicles.
然而,这种方法仍存在一些局限性。这种方法只考虑了交通路口的信号控制,没有考虑其他交通管理策略对交通效率的影响。同时,这种方法主要关注车辆的交通效率,而没有考虑行人和非机动车的交通安全和便利。
然而,这种方法仍存在一些局限性。这种方法只考虑了交通路口的信号控制,没有考虑其他交通管理策略对交通效率的影响。同时,这种方法主要关注车辆的交通效率,而没有考虑行人和非机动车的交通安全和便利。
Future research can further expand and improve this method, and comprehensively consider a variety of traffic management strategies, such as roadside parking management, public transport priority, etc., in order to achieve more comprehensive traffic optimization. In addition, we can pay more attention to the traffic safety and convenience of pedestrians and non-motor vehicles in order to realize more humanized traffic management.
未来的研究可以进一步拓展和完善这一方法,综合考虑多种交通管理策略,如路边停车管理、公共交通优先等,以实现更全面的交通优化。此外,我们还可以更加关注行人和非机动车的交通安全和便利,以实现更加人性化的交通管理。
未来的研究可以进一步拓展和完善这一方法,综合考虑多种交通管理策略,如路边停车管理、公共交通优先等,以实现更全面的交通优化。此外,我们还可以更加关注行人和非机动车的交通安全和便利,以实现更加人性化的交通管理。
Data availability statement
数据可用性声明
The data used to support the findings of this study are available from the corresponding author upon request.
用于支持本研究结果的数据可向通讯作者索取。
用于支持本研究结果的数据可向通讯作者索取。
CRediT authorship contribution statement
CRediT 作者贡献声明
Jingya Wei: Writing – review & editing, Writing – original draft, Software, Project administration, Methodology, Formal analysis, Data curation, Conceptualization. Yongfeng Ju: Writing – review & editing, Writing – original draft, Supervision, Software, Resources, Methodology, Formal analysis, Data curation, Conceptualization.
魏静雅写作--审阅和编辑、写作--原稿、软件、项目管理、方法学、形式分析、数据整理、概念化。鞠永峰写作--审阅和编辑、写作--原稿、督导、软件、资源、方法论、形式分析、数据整理、概念化。
魏静雅写作--审阅和编辑、写作--原稿、软件、项目管理、方法学、形式分析、数据整理、概念化。鞠永峰写作--审阅和编辑、写作--原稿、督导、软件、资源、方法论、形式分析、数据整理、概念化。
Declaration of competing interest
利益冲突声明
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
作者声明,他们没有任何可能会影响本文所报告工作的已知经济利益或个人关系。
作者声明,他们没有任何可能会影响本文所报告工作的已知经济利益或个人关系。
References
- [1]Effectiveness of intelligent transportation system: case study of Lahore safe cityTransportation Letters, 14 (8) (2022), pp. 898-908
- [2]An optimal deep learning for cooperative intelligent transportation systemComput. Mater. Continua (CMC), 72 (1) (2022), pp. 19-35
- [3]A systematic literature review of vehicle speed assistance in intelligent transportation systemIET Intell. Transp. Syst., 15 (8) (2021), pp. 973-986
- [4]Deep reinforcement Q-learning for intelligent traffic signal control with partial detectionInternational Journal of Intelligent Transportation Systems Research, 21 (1) (2023), pp. 192-206
- [5]A robust adaptive traffic signal control algorithm using Q-learning under mixed traffic flowSustainability, 14 (10) (2022), pp. 1-16
- [6]Using reinforcement learning with partial vehicle detection for intelligent traffic signal controlIEEE Trans. Intell. Transport. Syst., 22 (1) (2020), pp. 404-415
- [7]Intelligent optimization of dynamic traffic light control via diverse optimization prioritiesInt. J. Intell. Syst., 36 (11) (2021), pp. 6748-6762
- [8]A deep reinforcement learning approach for traffic signal control optimizationTransport. Res. C Emerg. Technol., 104 (2021), pp. 234-248
- [9]Traffic signal optimization control in five-road intersection based on artificial fish swarm algorithmControl Eng. China, 26 (7) (2019), pp. 1284-1290
- [10]Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithmNeural Comput. Appl., 31 (7) (2019), pp. 2073-2083
- [11]An improved artificial fish swarm algorithm for traffic signal controlInternational Journal of Simulation and Process Modelling, 14 (6) (2019), p. 488
- [12]Smart traffic signal control systemInternational Journal of Advanced Research in Science, Communication and Technology, 34 (1) (2023), pp. 337-342
- [13]Machine learning driven intelligent and self adaptive system for traffic management in smart citiesComputing, 104 (5) (2022), pp. 1203-1217
- [14]Intelligent based real time traffic monitoring in smart citiesIrish Interdisciplinary Journal of Science & Research, 7 (2) (2023), pp. 10-15
- [15]A comparative study of algorithms for intelligent traffic signal controlMachine Learning and Autonomous Systems: Proceedings of ICMLAS, 437 (1) (2022), pp. 271-287
- [16]Adaptive traffic light dynamic control based on road traffic signal from google mapsThe 7th International Conference on Engineering & MIS, 11 (5) (2021), pp. 1-9
- [17]Designing traffic signal at an unsignalized intersectionInternational Journal of Scientific Research in Science and Technology, 8 (3) (2021), pp. 90-107
- [18]Smart traffic light control system with Automatic vehicle speed BreakerInternational Research Journal of Computer Science, 10 (4) (2023), pp. 58-68
- [19]Intelligent Slime Mould optimization with deep learning enabled traffic prediction in smart citiesComput. Mater. Continua (CMC), 73 (3) (2022), pp. 6563-6577
- [20]A single-layer approach for joint optimization of traffic signals and cooperative vehicle trajectories at isolated intersectionsTransport. Res. C Emerg. Technol., 134 (2022), Article 103459
- [21]Intersection signal timing Optimisation for an urban street network to Minimise traffic delaysPromet - Traffic & Transp., 33 (4) (2021), pp. 579-592
- [22]Resilience-based adaptive traffic signal strategy against disruption at single intersectionJ. Transport. Eng., Part A: Systems, 148 (5) (2022), Article 04022018
- [23]Using software-Defined networking for data traffic control in smart cities with WiFi coverageSymmetry, 14 (10) (2022), p. 2053
- [24]Smart traffic Scheduling for crowded cities road networks Egyptian InformaticsJournal, 23 (4) (2022), pp. 163-176
- [25]Based on MOPSO algorithm of real-time traffic signal optimization control for intelligent transportation intersectionsJ. Phys. Conf., 2477 (1) (2023), Article 012085
- [26]Traffic flow prediction: an intelligent scheme for forecasting traffic flow using Air pollution data in smart cities with Bagging EnsembleSustainability, 14 (7) (2022), p. 4164
- [27]Traffic management in smart cities using support vector machine for predicting the accuracy during peak traffic conditionsMater. Today: Proc., 62 (1) (2022), pp. 4980-4984
- [28]Secure signaling and traffic exchanges in smart cities: a critical review of the current trendsGlobal Journal of Engineering and Technology Advances, 12 (3) (2022), pp. 26-41
- [29]An adaptive simulated annealing and artificial fish swarm algorithm for the optimization of multi-depot express delivery vehicle routingIntell. Data Anal., 26 (1) (2022), pp. 239-256
- [30]Research on illumination optimization of phototherapy LED based on multi-objective artificial fish swarm algorithmJournal of Applied Optics, 42 (2) (2021), pp. 352-359
- [31]Fuzzy image adaptive enhancement algorithm based on improved artificial fish populationsComput. Simulat., 39 (10) (2022), pp. 229-233
- [32]Chaotic search based equilibrium optimizer for dealing with nonlinear programming and petrochemical applicationProcesses, 9 (2) (2021), pp. 200-210
- [33]Chaotic search algorithm for detection of discontinuities using guided waves and beamforming dataMeas. Sci. Technol., 32 (3) (2020), Article 035105
- [34]Chaotic binary group search optimizer for feature selectionExpert Syst. Appl., 192 (2022), Article 116368
- [35]An advanced machine learning approach to predicting pedestrian Fatality caused by road Crashes: a step toward sustainable pedestrian safetySustainability, 14 (4) (2022), p. 2436
- [36]Classification of Imbalanced travel mode choice to work data using adjustable SVM modelAppl. Sci., 11 (24) (2021), Article 11916
- [37]Travel behaviour and health: Interaction of Activity-travel pattern, travel parameter and physical Intensity. Solid state technologySolid State Technol., 63 (6) (2020), pp. 18-19
- [38]Time-use and Spatio-Temporal variables influence on physical activity Intensity, physical and social health of TravelersSustainability, 13 (21) (2021), Article 12226
- [39]Nonlinear relationships between vehicle ownership and Household travel characteristics and built environment Attributes in the US using the XGBT algorithmSustainability, 14 (6) (2022), p. 3395
- [40]Approaching sustainable bike-sharing development: a systematic review of the influence of built environment features on bike-sharing RidershipSustainability, 14 (10) (2022), p. 5795
- [41]On Hyperparameter optimization of machine learning methods using a Bayesian optimization algorithm to predict work travel mode CIEEE Access, 11 (1) (2023), pp. 19762-19774
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