Harmonization for Omnidirectional Progression in Urban Traffic Control

I describe harmonization as part of my work on distributed, knowledge-based, real-time, traffic-adaptive control of street and highway ramp traffic signals. The control system developed in prior efforts includes a two-stage learning process. The first stage optimizes the control of steady-state traffic at a single intersection and over a network of streets. The second stage of learning, not related yet to harmonization, deals with the predictive/reactive control of dynamic traffic flow in street networks. However, the control regimes at the individual intersections do not consider the time behavior of adjacent intersection control regimes. Optimal traffic flow in street networks must be with reference to signalization, phasing, and harmonization of control at all intersections. Harmonization represents the best approximation to a coordinated omnidirectional progression ("green wave"). This means that the resulting control regime produces a minimum of the sum, over all intersections, of delay times due to red lights and of unused green periods, each contributing term being weighted by the respective traffic flow values. The system has been tested in the laboratory on a range of scenarios (in terms of geometries and traffic flows) and has been found to perform as expected.     

Modeling and Implementation of Adaptive Transit Signal Priority on Actuated Control Systems

Abstract: This article describes the development and implementation of adaptive transit signal priority (TSP) on an actuated dual‐ring traffic signal control system. After providing an overview of architecture design of the adaptive TSP system, the article presents an adaptive TSP optimization model that optimizes green splits for three consecutive cycles to minimize the weighted sum of transit vehicle delay and other traffic delay, considering the safety and other operational constraints under the dual‐ring structure of signal control. The model is illustrated using a numerical example under medium and heavily congested situations. The findings from a field operational test are also reported to validate and demonstrate the developed TSP system. At a congested intersection, it is found that the average bus delay and average traffic delay along the bus movement direction were reduced by approximately 43% and 16%, respectively. Moreover, the average delay of cross‐street traffic was increased by about 12%.     

出口道短车道对信号控制交叉口通行能力影响

研究出口道短车道在不同交通条件和道路几何条件下对道路交通能力的影响,可以进一步完善交叉口通行能力计算理论体系,对工程设计给出理论依据。介绍了相关研究背景,交叉口出口道短车道对信号控制交叉口通行能力的修正。最后从短车道长度、有效绿灯时间、路段与进口道饱和流量差、交通流基本参数关系模型等因素,分析了出口道短车道对通行能力的影响程度。     

Modeling adaptive platoon and reservation-based intersection control for connected and autonomous vehicles employing deep reinforcement learning

As a cutting-edge strategy to reduce travel delay and fuel consumption, platooning of connected and autonomous vehicles (CAVs) at signal-free intersections has become increasingly popular in academia. However, when determining optimal platoon size, few studies have attempted to comprehensively consider the relations between the size of a CAV platoon and traffic conditions around an intersection. To this end, this study develops an adaptive platoon-based autonomous intersection control model, named INTEL-PLT, which adopts deep reinforcement learning technique to realize the optimization of multiple dynamic objectives (e.g., efficiency, fairness, and energy saving). The framework of INTEL-PLT has a two-level structure: The first level employs a reservation-based policy integrated with a nonconflicting lane selection mechanism to determine the lanes’ releasing priorities; and the second level uses a deep Q-network algorithm to identify the optimal platoon size based on real-time traffic conditions (e.g., traffic density, vehicle movement, etc.) of an intersection. The model is validated and examined on the simulator Simulation of Urban Mobility. It is found that the proposed model exhibits superior performances on both travel efficiency and fuel conservation as compared with state-of-the-art methods in three typical traffic conditions. Moreover, several in-depth insights learned from the simulations are provided in this paper, which could better explain the relation between platoon size and traffic condition.     

An Analytical Approach for Estimating the Highway Capacity Manual Signalized Intersection Delay Variability

Abstract: This article developed a generalized model incorporating three stochastic input variables in the Highway Capacity Manual (HCM) delay equation and analyzed the delay variability explicitly considering variations in key input variables including traffic volume, effective green time, and saturation flow rate. An integration method was used for calculations of mean and variance of the HCM delay. Unlike the previous Expectation Function Method, the proposed integration method can be applied for both undersaturated and oversaturated situations. The applicability of the proposed methodology was demonstrated through a hypothetical case study for a lane group at an isolated signalized intersection. The effects of stochastic variables (e.g., traffic volume, saturation flow rate, and effective green time) and correlations among these variables in the HCM delay were examined.     

台北市交通建设管理对重庆的启示

该文简要介绍了台北市概况及交通政策,并通过对台北市公共交通、停车管理、道路交叉口"渠化"与"干道绿波"、占道开挖管理、人行过街设施优化、道路收费政策等方面的研究,对比分析重庆市与台北市的交通承载能力,得出重庆市的道路交通承载能力还有大幅度提升余地的结论,提出当前解决交通拥堵的状况应由修建城市道路转移到改善管理手段上来。最后建议通过大力发展公共交通、增加路网密度、优化交叉口、发展单向交通组织加强智能交通系统建设等方式来提高重庆交通的管理水平与服务水平     

Traffic Prediction and Management via RBF Neural Nets and Semantic Control

The objective of this work has been to develop layers of control and optimization modules for the purpose of urban traffic management. We utilize the semantic control paradigm to model both the macrolevel (traffic control) and the microlevel (vehicle path planning and steering control). A semantic controller consists of three modules for identification, goal selection, and adaptation, respectively. This hierarchical structure has been used successfully at the Center for Optimization and Semantic Control to solve complex, nonlinear, and time-varying problems. In our previous work we have used a judicious combination of artificial intelligence, optimization, and control systems.
The focus of this paper is the identifier module, which performs "system identification," i.e., determines the road network congestion level. Traffic flow can be characterized as a nonlinear stochastic process where linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for data-based modeling and system identification. The radial basis function neural network (RBFNN) is an attractive tool for nonlinear time-series modeling and traffic-flow prediction. The goal selector module that finds the shortest path is also discussed in some detail.
A model of the highway system, based on historical data provided by Missouri Highway and Transportation Department (MoHTD), has been developed. The prediction and planning system is evaluated using the traffic-flow data from nine sensors located on the highway in the St. Louis metropolitan area.     

A meta–reinforcement learning algorithm for traffic signal control to automatically switch different reward functions according to the saturation level of traffic flows

Reinforcement learning (RL) algorithms have been widely applied in solving traffic signal control problems. Traffic environments, however, are intrinsically nonstationary, which creates a convergence problem that RL algorithms struggle to overcome. Basically, as a target problem for an RL algorithm, the Markov decision process (MDP) can be solved only when both the transition and reward functions do not vary. Unfortunately, the environment for traffic signal control is not stationary since the goal of traffic signal control varies according to congestion levels. For unsaturated traffic conditions, the objective of traffic signal control should be to minimize vehicle delay. On the other hand, the objective must be to maximize the throughput when traffic flow is saturated. A multiregime analysis is possible for varying conditions, but classifying the traffic regime creates another complex task. The present study provides a meta-RL algorithm that embeds a latent vector to recognize the different contexts of an environment in order to automatically classify traffic regimes and apply a customized reward for each context. In simulation experiments, the proposed meta-RL algorithm succeeded in differentiating rewards according to the saturation level of traffic conditions.     

Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data

The accurate forecasting of traffic states is an essential application of intelligent transportation system. Due to the periodic signal control at intersections, the traffic flow in an urban road network is often disturbed and expresses intermittent features. This study proposes a forecasting framework named the spatiotemporal gated graph attention network (STGGAT) model to achieve accurate predictions for network-scale traffic flows on urban roads. Based on license plate recognition (LPR) records, the average travel times and volume transition relationships are estimated to construct weighted directed graphs. The proposed STGGAT model integrates a gated recurrent unit layer, a graph attention network layer with edge features, a gated mechanism based on the bidirectional long short-term memory and a residual structure to extract the spatiotemporal dependencies of the approach- and lane-level traffic volumes. Validated on the LPR system in Changsha, China, STGGAT demonstrates superior accuracy and stability to those of the baselines and reveals its inductive learning and fault tolerance capabilities.     

An Integrated Fuzzy Control System for Structural Vibration

Abstract:   Recently, numerous studies of structural control systems of civil structures and infrastructure have been carried out. To develop structural control systems, it is necessary to consider their special features such as complexity, uncertainty, and size. To consider these features, fuzzy theory has been applied to structural control systems. This study proposes an integrated fuzzy active control system based on fuzzy ensemble learning. It combines several fuzzy active control systems and improves structural vibrations caused by earthquakes. The proposed method includes two fuzzy active control systems, a fuzzy ensemble system, and a gating network. In this study, two fuzzy active control systems are constructed by applying particle–swarm optimization. The fuzzy ensemble system assigns a performance grade to each fuzzy active control system according to control effects from input patterns. The gating network determines the final control force based on the weight of their performance grade. By introducing fuzzy ensemble learning, the structural response is reduced more than when the response is controlled by individual fuzzy active control systems.     

A GIS-Based Traffic Signal Coordination and Information Management System

Abstract: This paper discusses research conducted at the Georgia Institute of Technology that investigated the use of geographic information system (GIS) technology as a tool in traffic signal information management and signalized intersection coordination. TRANSYT-7F is the most widely used and respected computer model for optimizing the coordination of traffic signals. Unfortunately, creating an optimal TRANSYT-7F model is very costly. The hypothesis of this research was that using a specialized GIS in conjunction with TRANSYT-7F could enhance the process of coordinating a traffic signal system. The research resulted in the development of a GIS-based traffic signal coordination and information system that operates on a microcomputer. This system is an improvement over existing TRANSYT-7F models because relationships between intersections do not have to be encoded manually. Instead, the system takes advantage of the GIS's topologic data structure, which provides these relationships. The process of analyzing different network optimization scenarios is simplified with this system because the user need only to select intersections to be coordinated from the GIS graphic display rather than cutting and pasting from existing input files. Alternatively, the system can serve as a multipurpose signal information system and play a vital role in decision support. It can provide improved access to signal data and allows for swift identification of intersections that experience excessive delays or unacceptable levels of service.     

Bi-level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization

Abstract:   Although dynamic traffic control and traffic assignment are intimately connected in the framework of Intelligent Transportation Systems (ITS), they have been developed independent of one another by most existing research. Conventional methods of signal timing optimization assume given traffic flow pattern, whereas traffic assignment is performed with the assumption of fixed signal timing. This study develops a bi-level programming formulation and heuristic solution approach (HSA) for dynamic traffic signal optimization in networks with time-dependent demand and stochastic route choice. In the bi-level programming model, the upper level problem represents the decision-making behavior (signal control) of the system manager, while the user travel behavior is represented at the lower level. The HSA consists of a Genetic Algorithm (GA) and a Cell Transmission Simulation (CTS) based Incremental Logit Assignment (ILA) procedure. GA is used to seek the upper level signal control variables. ILA is developed to find user optimal flow pattern at the lower level, and CTS is implemented to propagate traffic and collect real-time traffic information. The performance of the HSA is investigated in numerical applications in a sample network. These applications compare the efficiency and quality of the global optima achieved by Elitist GA and Micro GA. Furthermore, the impact of different frequencies of updating information and different population sizes of GA on system performance is analyzed.     

Signal timing at an isolated intersection under mixed traffic environment with self-organizing connected and automated vehicles

This study provides a signal timing model for isolated intersections under the mixed traffic environment consisting of connected and human-driven vehicles (CHVs) and connected and automated vehicles (CAVs). Different from existing studies, CAVs are not controlled by traffic controllers and conduct trajectory planning themselves, which are called self-organizing CAVs (SOCAVs). The specific trajectory planning strategies of SOCAVs are not accessible to traffic controllers either. The signal optimization problem is formulated as a mixed integer linear programming (MILP) model for total vehicle delay minimization. The states of SOCAVs and CHVs passing the stop bar are predicted without prior information of the trajectory planning strategies of SOCAVs. SOCAVs can lead approaching platoons to pass the intersection effectively, and such “leading effects” of SOCAVs are utilized. Phase sequence and duration are optimized with the “structure-free” phasing scheme. A parallel particle swarm optimization algorithm with a grouping strategy is designed to solve the optimization model at a reduced scale for computational efficiency. Numerical studies validate that (1) the proposed algorithm significantly outperforms the benchmark method, which directly solves the proposed MILP model using the solver Gurobi 9.0, under medium and high traffic demand; and (2) the proposed model significantly outperforms fixed-time and vehicle-actuated signal control in terms of vehicle delay and throughput. Sensitivity analysis shows that the SOCAV penetration rate of 30% is sufficient to guarantee satisfactory performance of the proposed signal timing model.     

Robust Optimal Lane Allocation for Isolated Intersections

Lane allocation including approach and exit lane numbers and lane markings of approach lanes plays an important role in improving the capacity of an intersection. Conventional approaches for optimizing lane allocation often ignore fluctuations in traffic demand (TD). This article presents a stochastic model for robust optimal lane allocation of an isolated intersection under stochastic traffic conditions. This model is built in three steps. In the first step, an enhanced lane‐based model in the form of a binary mixed‐integer nonlinear program is proposed to optimize lane allocation and traffic signals for both vehicles and pedestrians in a unified framework under deterministic traffic conditions. In the second step, a two‐level stochastic model is developed to obtain a robust lane allocation that is less sensitive to traffic flow fluctuations considering the flexibility of traffic signals. In the third step, the two‐level model is further transformed into a TD‐based stochastic model in a two‐phase form to reduce the solution dimension for efficient computation. A TD‐based genetic algorithm procedure is presented for solvability. Numerical studies are conducted to validate the model formulations and solution algorithms.     

Impact and uncertainty of a traffic management intervention: population exposure to polycyclic aromatic hydrocarbons

In urban areas, road traffic is a major source of carcinogenic polycyclic aromatic hydrocarbons (PAH), thus any changes in traffic patterns are expected to affect PAH concentrations in ambient air. Exposure to PAH and other traffic-related air pollutants has often been quantified in a deterministic manner that disregards the various sources of uncertainty in the modelling systems used. In this study, we developed a generic method for handling uncertainty in population exposure models. The method was applied to quantify the uncertainty in population exposure to benzo[a]pyrene (BaP) before and after the implementation of a traffic management intervention. This intervention would affect the movement of vehicles in the studied area and consequently alter traffic emissions, pollutant concentrations and population exposure. Several models, including an emission calculator, a dispersion model and a Geographic Information System were used to quantify the impact of the traffic management intervention. We established four exposure zones defined by distance of residence postcode centroids from major road or intersection. A stochastic method was used to quantify the uncertainty in the population exposure model. The method characterises uncertainty using probability measures and propagates it applying Monte Carlo analysis. The overall model predicted that the traffic management scheme would lead to a minor reduction in mean population exposure to BaP in the studied area. However, the uncertainty associated with the exposure estimates was much larger than this reduction. The proposed method is generic and provides realistic estimates of population exposure to traffic-related pollutants, as well as characterises the uncertainty in these estimates. This method can be used within a decision support tool to evaluate the impact of alternative traffic management policies.     

A spatiotemporal control method at isolated intersections under mixed-autonomy traffic conditions

With the introduction of connected and automated vehicles (CAVs), the integrated control of traffic signals, lane assignments, and vehicle trajectories becomes feasible, offering notable benefits for enhancing intersection operations. However, during the prolonged transition to an entirely CAV environment, how to fully leverage the advantage of CAVs while considering the characteristics of human-driven vehicles remains a huge challenge. To address this challenge, this paper proposes a joint optimization method for spatiotemporal resources at isolated intersections under mixed-autonomy traffic conditions. Initially, the lane assignment optimization problem is modeled as a mixed integer linear program model to maximize the reserve capacity. Subsequently, the signal-vehicle coupled control is formulated as a dynamic programming model with the objective of reducing vehicle travel time. Additionally, criteria are established to assess the need for re-optimizing lane assignments. Simulations validate the superiority of the proposed control method over adaptive control in terms of traffic efficiency and intersection capacity amid substantial traffic demand fluctuations. Sensitivity analyses reveal that the proposed control method can yield higher benefits under medium traffic demand levels. Furthermore, the proposed algorithm exhibits no significant sensitivity to the CAV market adoption rate, suggesting its applicability throughout the CAV adoption process.     

Optimal wide-area hybrid fuzzy controller to enhance the stability in power system

To stabilise power oscillation, power system stabilizer is often used as an effective device to enhance the damping of electromechanical oscillations in power systems. This device is working with small-signal stability, which is often applied as part of excitation control system. Different methods have been proposed to demonstrate the effectiveness of remote signals application in increasing damping of power system. In this paper, we used both local and remote signals control based on fuzzy controller and wide-area control, respectively. Accordingly, Takagi Sugeno controller based an intelligent algorithm and clustering algorithm is optimised. A global signal from the centralised controller is employed in wide-area control scheme to damp out the inter-area mode as well as local mode of oscillations. To demonstrate the capability of proposed strategy, three case studies have been used in this paper. The results obtained demonstrate the validity of the proposed model.     

A graph deep learning method for short‐term traffic forecasting on large road networks

Short‐term traffic flow prediction on a large‐scale road network is challenging due to the complex spatial–temporal dependencies, the directed network topology, and the high computational cost. To address the challenges, this article develops a graph deep learning framework to predict large‐scale network traffic flow with high accuracy and efficiency. Specifically, we model the dynamics of the traffic flow on a road network as an irreducible and aperiodic Markov chain on a directed graph. Based on the representation, a novel spatial–temporal graph inception residual network (STGI‐ResNet) is developed for network‐based traffic prediction. This model integrates multiple spatial–temporal graph convolution (STGC) operators, residual learning, and the inception structure. The proposed STGC operators can adaptively extract spatial–temporal features from multiple traffic periodicities while preserving the topology information of the road network. The proposed STGI‐ResNet inherits the advantages of residual learning and inception structure to improve prediction accuracy, accelerate the model training process, and reduce difficult parameter tuning efforts. The computational complexity is linearly related to the number of road links, which enables citywide short‐term traffic prediction. Experiments using a car‐hailing traffic data set at 10‐, 30‐, and 60‐min intervals for a large road network in a Chinese city shows that the proposed model outperformed various state‐of‐the‐art baselines for short‐term network traffic flow prediction.     

Dynamic headway control for high‐frequency bus line based on speed guidance and intersection signal adjustment

To prevent bus bunching, a dynamic headway control method in the V2I (vehicle to infrastructure) environment for a high‐frequency route with bus lane is developed. Bus operating speed guidance on the mid‐blocks and intersection signal adjustment are two main strategies in the proposed method. A forecasting model of bus travel time under the dynamic control method is developed. The objective function is set up by taking into account differences between actual bus headways and dispatching headways, and the scaling ratios of intersection cycle lengths. The optimization model is solved using genetic algorithm. The proposed method is applied to a real bus route in Meihekou city, China, and compared with the current control plan as well as holding strategy. Results show that the proposed method can reduce bus headway deviations in all investigating periods; negative impacts on cars can be limited by setting reasonable values for the parameters.     

Simulation model based on Monte Carlo method for traffic assignment in local area road network

For a local area road network, the available traffic data of traveling are the flow volumes in the key intersections, not the complete OD matrix. Considering the circumstance characteristic and the data availability of a local area road network, a new model for traffic assignment based on Monte Carlo simulation of intersection turning movement is provided in this paper. For good stability in temporal sequence, turning ratio is adopted as the important parameter of this model. The formulation for local area road network assignment problems is proposed on the assumption of random turning behavior. The traffic assignment model based on the Monte Carlo method has been used in traffic analysis for an actual urban road network. The results comparing surveying traffic flow data and determining flow data by the previous model verify the applicability and validity of the proposed methodology.