Regional Coordinated Traffic Signal Control Based on Improved Chaotic Particle Swarm Optimization

This paper proposes a dynamic vehicle count estimation method for signalized links using license plate recognition (LPR) data considering the recognition errors. The framework contains three sub-components. First, travel time probability density function is estimated based on the kernel density model using the LPR data of matched vehicles; Second, the expected travel time is estimated for unmatched vehicles based on the travel time probability distribution; Third, with the complete arrival and departure information, the cumulative arrival-departure curve of the link is reconstructed, and the link dynamic vehicle count (LDVC) can be calculated correspondingly. To explore how different recognition errors affect LDVC estimation performance, the proposed model is validated through an empirical case of a real LPR dataset and a simulation case with different levels of matched rate. The results show that the model has a high level of estimation accuracy in different levels of matched rate of LPR data.

The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public's responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels; whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public's vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal.

To partition an urban network into several subareas (i.e., subarea partition) is a vital step for regional coordinated signal control. The correlation between intersections must be analyzed for achieving reliable subarea partition results. However, because of the incompleteness of spatial–temporal information in traffic data, previous studies merely explored the relationship between any intersections. Subarea partition considering the correlation of any pair of intersections remains a challenge in a large-scale network. This paper proposes a subarea partition method that integrates a novel correlation-degree model and the Newman fast algorithm with an edge-elimination strategy using automatic license plate recognition (ALPR) data. First, vehicle trips are extracted and a correlation-degree model is developed for measuring the relationship of any pair of intersections. Second, an edge-elimination strategy is proposed to generate candidate subarea partition solutions under conditions with different proportions of correlated edges. Finally, an optimal solution of subarea partition is identified by the ratios of ideal connected intersections to total intersections of different partition solutions’ correlation index (CI). The proposed method was implemented in a real-world urban network in Kunshan, China. The results show that the optimal partition solution can be obtained when the top 33% of correlated edges are maintained, and the ratio of ideal connected intersections’ CI is 72.35% with most of the intersections being connected, which demonstrates the rationality of the proposed partition method in large-scale urban networks.

Signal phase and timing (SPaT) information is a necessary input for traffic performance evaluation. However, current SPaT estimation studies mainly focus on estimation of cycle length or green time of a certain movement, and are realized mostly by floating car data whose data quality significantly affects the estimation accuracy. As license plate recognition (LPR) systems are becoming a widely implemented and reliable data source in China, in this study, a SPaT estimation method is proposed using the LPR data for fixed-time controlled intersections. The SPaT estimation problem is formulated as a bi-level programming model to find the optimal match between the phase boundaries and the LPR passing time series in the study period. Evaluation is done with an empirical case and compared with an existing method, results show that the estimation accuracies of the phase duration can reach 90.0%, outperforming the existing method and demonstrating great potential for practical application.

Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic community and industry society throughout the world. To improve the performance of particle swarm optimization (PSO), we herein propose a chaotic proportional integral derivative (PID) controlling PSO algorithm by the hybridization of chaotic logistic dynamics and hierarchical inertia weight. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles’ search space. Moreover, the chaotic logistic map is not only used in the substitution of the two random parameters affecting the convergence behavior, but also used in the chaotic local search for the global best position so as to easily avoid the particles’ premature behaviors via the whole search space. Thereafter, the convergent analysis of chaotic PID controlling PSO is under deep investigation. Empirical simulation results demonstrate that compared with other several chaotic PSO algorithms like chaotic PSO with the logistic map, chaotic PSO with the tent map and chaotic catfish PSO with the logistic map, chaotic PID controlling PSO exhibits much better search efficiency and quality when solving the optimization problems. Additionally, the parameter estimation of a nonlinear dynamic system also further clarifies its superiority to chaotic catfish PSO, genetic algorithm (GA) and PSO.

The phase diversity (PD) algorithm is an image-based co-phasing error detection method for segmented mirror synthetic aperture optical systems. Particle swarm optimization (PSO) is suitable for PD for its fast convergence speed and simple structure. However, with the increase of the numbers of subapertures, optimization of cost function formed by the PD algorithm becomes a high-dimension non-linear optimization problem, which would lead PSO to result in a premature solution. Regarding the problem above, this paper presented a modified PSO in the PD algorithm to co-phase the segmented primary mirror, which overcame drawbacks of the traditional PSO, such as premature convergence and deactivation of particles, and enhanced the dig ability of the algorithm. Vast simulation results show that the modified PSO in the PD algorithm can effectively restore the segmented primary mirror, reducing the peak-to-valley (PV) value to 0.0012λ and the root mean square error to 0.0007λ, and raising the Strehl ratio to over 0.999. A comparison was also conducted to show that the modified PSO has advantages over two existing algorithms in higher accuracy and faster convergence speed.

In this paper, we take the urban road network as the object, a further study on the method of traffic signal self organization under the condition of traffic state identification and local congestion. In the urban road traffic state identification study, according to the need of traffic signal control, respectively from qualitative and quantitative point of view, the urban road traffic state is defined. In order to be able to obtain real-time traffic data as the foundation, design urban road traffic state evaluation index system. Based on the current mainstream micro traffic simulation software VB and VISSIM as the tool to build the experimental platform, and set up the UTC-CI (Urban Traffic Control for Congested Intersections) experimental environment, the above method is verified by simulation. The results show that the traffic state identification method and the related signal control method have the expected effect in reducing congestion duration.

Multimode electric propulsion system is widely used in deep space exploration. Its thrust is output in stages with the change of input power, which also means that continuous power changes lead to discontinuous thrust changes. In the design and optimization of low-thrust trajectory, the discontinuity of thrust is often neglected and regarded as a continuous parameter. The optimal design of low-thrust transfer trajectory constrained by multimode electric propulsion is studied, and the influence of thrust grading characteristics on the trajectory is analyzed in this paper. A specific optimization strategy is proposed, including three main steps: model discretization based on direct method, fast generation of initial value based on particle swarm optimization (PSO), and optimization based on sequential quadratic programming (SQP). The dynamic model and optimization model of low-thrust transfer trajectory is established. Then, the optimization model is discretized based on the direct method and the original optimal control problem is transformed into a parameter optimization problem in the form of nonlinear programming (NLP). A hybrid optimization algorithm based on PSO and SQP is proposed to solve the new NLP problem. PSO is used to search the initial value efficiently and guarantee the global performance of the initial value and SQP is used for local optimization based on initial value, which can give full play to the excellent global optimization performance of PSO and the outstanding local optimization performance of SQP. The effectiveness of the proposed optimization strategy is verified by a simulation example of the main belt asteroid Metis (1974 QU2) rendezvous mission with once Mars gravity assist. The results show that the optimal control curve obtained is in accordance with the optimal control form of bang-bang control derived from the theory, and the thruster has just two situations at any time: shutdown or startup with maximum thrust. In the working power range, increasing the number of thrust stages can improve the rate of energy utilization, thereby improving the efficiency of thruster, shortening the startup time and reducing fuel consumption. Meanwhile, ineffective grading should be avoided in the non-working power range.

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling.

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

Probabilistic travel time progression and its application to automatic vehicle identification data

This paper presents an environment perception method using for mobile Robots based on Occupancy Grid Maps, which is based on ultrasonic probability grid map feature points extraction and matching. Low-cost ultrasonic sensors as the design scheme of distance measuring is adopted. Aiming to obtain the probability of grid map update effectively, an improved Bayesian formula is proposed. To realize synchronous positioning and map construction, dynamic random objects are related to the map with edge detection algorithm. Then the motion of the next step of robot is planned by improved particle swarm algorithm. The result of the numerical simulation shows that the novel particle swarm optimization is effective and can find the more optimal global solutions with high efficiency compared to the basic particle swarm optimization (PSO). The validity and reliability of this method are tested via the simulation. The result of the simulation shows that the method can solve the SLAM problem of robot in complex environment and realize real-time dynamic collision avoidance planning.

To prevent premature convergence frequently appeared in the particle swarm optimization (PSO) for the complex and highly nonlinear relationship between the system inputs and output(s), an improved particle swarm optimization was proposed, named particle swarm optimization with invasive weed (IW-PSO). In IW-PSO, PSO and IWO were integrated in parallel form based on the powerful local search ability of the invasive weed optimization (IWO), and after some iterations, the more diversiform and adaptive invasive weeds were introduced in particle swarm for improving the diversity, the worse particles comparing the fitness of the best previous position were partly mutated for assistantly local searching in invasive weeds. Simulation results show that the method has better searching ability and stability for some complex multimodal functions. Then, IW-PSO was introduced into low Nox combustion optimization, comparison results indicate that the proposed method is a fast and effective one to reduce NOx emissions.

Multidisciplinary design optimization (MDO) was performed on a helicopter rotor blade. The blade was modeled as a rigid flapping blade for dynamics; Blade Element Theory (BET) was the analysis approach to model the aerodynamic loading, and a simple linearly elastic hollowed rectangular section was the main structural component. MATLAB was used to solve the flapping differential equations and its Sequential Quadratic Programming (SQP) and Genetic Algorithm (GA) were used for the optimization. A Particle Swarm Optimization (PSO) routine was also tested. The optimization process consisted of three cases. The first case was a simple cantilever beam under centrifugal and an assumed bending loads. The optimization was performed using the SQP, GA, and PSO algorithms. The SQP resulted in the superior design with 75.45 compared to the GA's 87.1 and the PSO's 79.2, but a local minimum was present. The second case was an expansion of the first case by turning it into multidisciplinary problem. Aerodynamics was included in the design variables and objective function. Only the SQP algorithm was used and there was a reduction in hub vertical shear by 33.6%. The blade mass increased by 36.84%. The last case was an improvement to the second by creating a multiobjective problem by including the hub radial shear and the results were improved significantly by reducing the hub vertical shear by 34.06% and radial shear by 17.87% with a reduction of blade mass by 23.86%.

Customer loyalty means that customers identify with products and services and have the performance of repeated purchase, which is presented through behavior and emotion. To achieve higher customer loyalty, specific analysis of the factors affecting customer loyalty is needed. Currently, most scholars believe that the decisive influencing factors on customer loyalty are: customer satisfaction, customer conversion cost, trust, service quality, and corporate image. In this paper, the [Formula: see text]-nearest neighbors (KNN) clustering evaluation model optimized by improved particle swarm algorithm is used for customer loyalty evaluation. The paper particleizes the eigenvalues and thresholds of the factors influencing loyalty, realizes the dynamic change of inertia weights by introducing particle swarm evolution degree and particle swarm aggregation degree, and utilizes the global searchable feature particles and threshold updates of particle swarm algorithm to enhance the algorithm’s ability to deal with nonlinear problems and accelerate the model. The results show that our model can evaluate customer loyalty using multiple parameters, which is more comprehensive, reasonable, and accurate than other evaluation methods, and can consider all parameters and their interrelationships, so as to obtain more reasonable evaluation conclusions. Experimental results demonstrate a 3.35% average error, surpassing KNN (5.28%) in loyalty prediction accuracy.