混合交通网络设计的双层模型及遗传算法求解

根据混合交通网络设计问题的特点,利用双层规划模型和遗传算法对该问题进行求解。对交通网络中的路段进行分类,通过限定决策变量的取值范围,将混合交通网络离散化。建立混合交通网络设计的双层模型。其中,上层模型以方案总投资额最小为目标函数,以路段负荷度和可行域为约束条件;下层模型为交通流分配的用户均衡模型。根据所建模型的离散特性,研究其遗传算法解法,并给出算法的具体实现步骤。以一个抽象的交通网络为例,给定网络中的路段属性、OD交通量等参数,利用MATLAB软件对模型编程求解,能够获得满意的交通网络设计方案,表明双层模型和遗传算法是一种研究混合交通网络设计问题的有效方法。最后,对该模型存在的不足及改进方向进行了探讨。     

A Genetic Algorithm Approach for Optimizing Traffic Control Signals Considering Routing

Abstract:   It is well known that coordinated, area-wide traffic signal control provides great potential for improvements in delays, safety, and environmental measures. However, an aspect of this problem that is commonly neglected in practice is the potentially confounding effect of drivers re-routing in response to changes in travel times on competing routes, brought about by the changes to the signal timings. This article considers the problem of optimizing signal green and cycle timings over an urban network, in such a way that the optimization anticipates the impact on traffic routing patterns. This is achieved by including a network equilibrium model as a constraint to the optimization. A Genetic Algorithm (GA) is devised for solving the resulting problem, using total travel time across the network as an illustrative fitness function, and with a widely used traffic simulation-assignment model providing the equilibrium flows. The procedure is applied to a case study of the city of Chester in the UK, and the performance of the algorithms is analyzed with respect to the parameters of the GA method. The results show a better performance of the signal timing as optimized by the GA method as compared to a method that does not consider rerouting. This improvement is found to be more significant with a more congested network whereas under a relatively mild congestion situation the improvement is not very clear.     

Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic Algorithms

Abstract:   In this article a dynamic system-optimal traffic assignment model is formulated for a congested urban road network with a number of signalized intersections. A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods.     

环境目标下城市交通综合网络设计的优化模型及求解算法

机动车尾气排放因子(emission factor)、路段行驶时间和交叉口等待时间等都是影响城市交通尾气总排放量的重要因素。而城市交通网络中路段通行能力的改善或者交叉口信号配时方案的调整都会直接影响机动车尾气排放因子和交通出行时间的变化,从而影响城市交通尾气总排放量的大小。为此,首先根据机动车在路段上的行驶状况,引入两个与出行时间关联的尾气排放因子,即路段正常行驶时的尾气排放因子和交叉口排队等待时的尾气排放因子,进而给出适用于城市交通网络设计问题的尾气总排放量计算公式;然后用双层规划方法综合优化信号灯配时方案和道路能力改善方案,以达到最大程度地降低交通尾气总排放量的目的。基于粒子群优化算法,设计了一个求解该双层模型的全局优化算法,该算法操作简单,易于实现,并用一个简单算例验证了本文模型与算法的有效性。     

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.     

A Linear Model for the Continuous Network Design Problem

Abstract:   This article is concerned with the continuous network design problem on traffic networks, assuming system optimum traffic flow conditions and time-dependent demand. A linear programming formulation is introduced based on a dynamic traffic assignment (DTA) model that propagates traffic according to the cell transmission model. The introduced approach is limited to continuous link improvements and does not provide for new link additions. The main contribution of the article is to provide an analytical formulation for network design that accounts for DTA conditions that can be used for further analysis and extensions. The model is tested on a single destination example network, resembling a freeway corridor, for various congestion levels, loading patterns and budget sizes, to demonstrate the simplicity and effectiveness of the approach.     

A Neuro-Fuzzy Logic for ATIS Stand-Alone Control Systems: Structure, Calibration, and Analyses

Abstract:   An Advanced Traveler Information System (ATIS) stand-alone operation scheme is formulated as a bi-level optimization problem. The scheme logic attempts to optimize the network overall travel time by adjusting the path proportions while guessing the signal phase split decisions. An approximate simulation-based optimization algorithm is devised as an example of the logic operating this scheme. The logic is then replicated by a fuzzy-logic control system. Neural nets are utilized to develop the knowledge base of the fuzzy system and to calibrate the fuzzy set parameters. The neural nets utilize data replicates generated by the approximate simulation-based optimization algorithm. The calibration and effectiveness results of the fuzzy control system are presented.     

Time‐Varying Lane‐Based Capacity Reversibility for Traffic Management

Abstract: This article presents a new bi‐level formulation for time‐varying lane‐based capacity reversibility problem for traffic management. The problem is formulated as a bi‐level program where the lower level is the cell‐transmission‐based user‐optimal dynamic traffic assignment (UODTA). Due to its Non‐deterministic Polynomial‐time hard (NP‐hard) complexity, the genetic algorithm (GA) with the simulation‐based UODTA is adopted to solve multiorigin multidestination problems. Four GA variations are proposed. GA1 is a simple GA. GA2, GA3, and GA4 with a jam‐density factor parameter (JDF) employ time‐dependent congestion measures in their decoding procedures. The four algorithms are empirically tested on a grid network and compared based on solution quality, convergence speed, and central processing unit (CPU) time. GA3 with JDF of 0.6 appears best on the three criteria. On the Sioux Falls network, GA3 with JDF of 0.7 performs best. The GA with the appropriate inclusion of problem‐specific knowledge and parameter calibration indeed provides excellent results when compared with the simple GA.     

地下物流节点选址的双层规划模型及算法研究

地下物流系统是正在兴起的物流系统,能够缓解大城市日益严重的地面交通压力,提高交通运输的效率,减少环境污染。物流节点是地下物流系统的重要组成部分,主要负责货物的集散和配送等活动。本文阐述了地下物流节点的意义与职能,根据地下物流节点的特点并分析了双层规划模型的优点,在双层规划模型基础上建立了从决策者角度出发的上层规划模型和从客户角度出发的下层规划模型,分析了模型的解法并在实例中用MATLAB中的fmincon函数求解模型并得到了满意的结果,验证了双层规划模型在地下物流节点选址应用中的实用性和科学性,为以后地下物流系统建设中物流节点的选址问题提供了参考依据。     

Deployment of Fiber-Optic Lane Use Indication Signs Using a Distributed Control Architecture

This article reviews the basic signing technologies available to the traffic engineer for dynamically changing sign displays. An example installation of dynamic lane assignment in Houston, Texas, is reviewed and referenced as the motivation for developing a simple traffic signal cabinet interface for dynamic lane assignment signs. The failure modes of these dynamic lane assignment signs are identified so that suitable control models can be selected. Various candidate control architectures are summarized, and a distributed control model is recommended because of the simple cabinet retrofit and the inherent malfunction management capabilities. The article concludes by describing the distributed computing design comprised of the sign-control interface (SCI) residing in the cabinet and the sign-control modules (SCM) residing in each sign. Photographs of the SCI and SCM prototypes are included.     

环境污染限制及最优信号控制条件下的综合离散网络设计问题

现代城市综合交通网络设计问题研究的主要内容就是通过新建或改善道路网络,用以减少现代交通带来的环境污染、合理设置交通信号等诸方面的问题。将道路环境能力限制、最优交通信号设置问题与城市交通离散网络设计问题结合起来研究,一方面通过修建新的路段使交通需求量达到最大从而满足城市中不断增长的交通需求;另一方面通过道路环境能力限制使交通网络的最大需求量能符合现代城市环境保护的要求。给出了描述上述问题的一个双层规划模型及其启发式求解算法。最后,通过一个简单的算例,说明该算法是可行并且有效的。     

An Implementation of the Mode-Split Traffic-Assignment Method

A software called Optimal Traffic Signal Control System (OTSCS) was developed by us for testing the feasibility of dynamically controlling a traffic signal by finding optimal signal timing to minimize delay at signalized intersections. It also was designed as a research tool to study the learning behavior of artificial neural networks and the properties of heuristic search methods. It consists of a level-of-service evaluation model that is based on an artificial neural network and a heuristic optimization model that interacts with the level-of-service evaluation model. This article discusses the latter model, named the Optimal Traffic Signal Timing Model (OTSTM). The OTSTM was applied to determine optimal signal timing of two-phase traffic signals to evaluate the model's performance. Two search methods were employed: a depth-first search method (an enumeration method) and a direction-search method that the authors developed. It was found that the OTSTM with the direction search resulted in "optimal" signal timings similar to the depth-first search, which would always produce a global optimal timing. Yet the cost of the direction search, as measured by the CPU time of the computer used for analysis, was found to be much less than the cost of obtaining an optimal solution by the depth-first search cases—more than 10 times less. The study showed that once the artificial neural network is properly trained, heuristic optimal signal timing combined with artificial networks can be used as a decision-support tool for dynamic signal control. This article demonstrates how OTSTM can quickly find an optimal signal-timing solution for two-phase traffic signals.     

A Bi‐Level Model for Planning Signalized and Uninterrupted Flow Intersections in an Evacuation Network

Abstract: The problem to be addressed in this paper is the lack of an advanced model in the literature to locate the optimal set of intersections in the evacuation network for implementing uninterrupted flow and signal control strategies, respectively, which can yield the maximum evacuation operational efficiency and the best use of available budgets. An optimization model, proposed in response to such needs, contributes to addressing the following critical questions that have long challenged transportation authorities during emergency planning, namely: given the topology of an evacuation network, evacuation demand distribution, and a limited budget, (1) how many intersections should be implemented with the signals and uninterrupted flow strategies; (2) what are their most appropriate locations; and (3) how should turning restriction plans be properly designed for those uninterrupted flow intersections? The proposed model features a bi‐level framework. The upper level determines the best locations for uninterrupted flow and signalized intersections as well as the corresponding turning restriction plans by minimizing the total evacuation time, while the lower level handles routing assignments of evacuation traffic based on the stochastic user equilibrium (SUE) principle. The proposed model is solved by a genetic algorithm (GA) ‐based heuristic. Extensive analyses under various evacuation demand and budget levels have indicated that the location selection of uninterrupted flow and signalized intersections plays a key role in emergency traffic management. The proposed model substantially outperforms existing practices in prioritizing limited resources to the most appropriate control points by significantly reducing the total evacuation time (up to 39%).     

Hybrid Meta-Heuristic Algorithm for the Simultaneous Optimization of the O–D Trip Matrix Estimation

Abstract:   In the present article, the origin–destination (O–D) trip matrix estimation is formulated as a simultaneous optimization problem and is resolved by employing three different meta-heuristic optimization algorithms. These include a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a hybrid algorithm (GASA) based on the combination of GA and SA. The computational performance of the three algorithms is evaluated and compared by implementing them on a realistic urban road network. The results of the simulation tests demonstrate that SA and GASA produce a more accurate final solution than GA, whereas GASA shows a superior convergence rate, that is, faster improvement from the initial solution, in comparison to SA and GA. In addition, GASA produces a final solution that is more robust and less dependent on the initial demand pattern, in comparison to that obtained from a greedy search algorithm.     

Surrogate‐based toll optimization in a large‐scale heterogeneously congested network

Toll optimization in a large‐scale dynamic traffic network is typically characterized by an expensive‐to‐evaluate objective function. In this paper, we propose two toll‐level problems (TLPs) integrated with a large‐scale simulation‐based dynamic traffic assignment model of Melbourne, Australia. The first TLP aims to control the pricing zone (PZ) through a time‐varying joint distance and delay toll such that the network fundamental diagram (NFD) of the PZ does not enter the congested regime. The second TLP is built upon the first TLP by further considering the minimization of the heterogeneity of congestion distribution in the PZ. To solve the two TLPs, a computationally efficient surrogate‐based optimization method, that is, regressing kriging with expected improvement sampling, is applied to approximate the simulation input–output mapping, which can balance well between local exploitation and global exploration. Results show that the two optimal TLP solutions reduce the average travel time in the PZ (entire network) by 29.5% (1.4%) and 21.6% (2.5%), respectively. Reducing the heterogeneity of congestion distribution achieves higher network flows in the PZ and a lower average travel time or a larger total travel time saving in the entire network.     

Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles

This study presents the Wireless Charging Utility Maximization (WCUM) framework, which aims to maximize the utility of Wireless Charging Units (WCUs) for electric vehicle (EV) charging through the optimal WCU deployment at signalized intersections. Furthermore, the framework aims to minimize the control delay at all signalized intersections of the network. The framework consists of a two‐step optimization formulation, a dynamic traffic assignment model to calculate the user equilibrium, a traffic microsimulator to formulate the objective functions, and a global Mixed Integer Non‐Linear Programming (MINLP) optimization solver. An optimization problem is formulated for each intersection, and another for the entire network. The performance of the WCUM framework is tested using the Sioux Falls network. We perform a comparative study of 12 global MINLP solvers with a case study. Based on solution quality and computation time, we choose the Couenne solver for this framework.     

A Polymorphic Dynamic Network Loading Model

Abstract:   A polymorphic dynamic network loading (PDNL) model is developed and discretized to integrate a variety of macroscopic traffic flow and node models. The polymorphism, realized through a general node-link interface and proper discretization, offers several prominent advantages. First of all, PDNL allows road facilities in the same network to be represented by different traffic flow models based on the tradeoff of efficiency and realism and/or the characteristics of the targeted problem. Second, new macroscopic link/node models can be easily plugged into the framework and compared against existing ones. Third, PDNL decouples links and nodes in network loading, and thus opens the door to parallel computing. Finally, PDNL keeps track of individual vehicular quanta of arbitrary size, which makes it possible to replicate analytical loading results as closely as desired. PDNL, thus, offers an ideal platform for studying both analytical dynamic traffic assignment problems of different kinds and macroscopic traffic simulation.     

An Approach to Dynamical Classification of Daily Traffic Patterns

This article proposes a prototype of an urban traffic control system based on a prediction‐after‐classification approach. In an off‐line phase, a repository of traffic control strategies for a set of (dynamic) traffic patterns is constructed. The core of this stage is the k‐means algorithm for daily traffic pattern identification. The clustering method uses the input attributes flow, speed, and occupancy and it transforms the dynamic traffic data at network level in a pseudo‐covariance matrix, which collects the dynamic correlations between the road links. A desirable number of traffic patterns is provided by Bayesian Information Criterion and the ratio of change in dispersion measurements. In an on‐line phase, the current daily traffic pattern is predicted within the repository and its associated control strategy is implemented in the traffic network. The dynamic prediction scheme is constructed on the basis of an existing static prediction method by accumulating the trials on set of patterns in the repository. This proposal has been assessed in synthetic and real networks testing its effectiveness as a data mining tool for the analysis of traffic patterns. The approach promises to effectively detect the current daily traffic pattern and is open to being used in intelligent traffic management systems.     

AN APPROACH TO DYNAMIC TRAFFIC ASSIGNMENT INCLUDING SOLUTION METHODS

ABSTRACT Most of the previous dynamic traffic assignment models are limited to single destination networks. The reason is that link exit functions of multiple destination flows are not formulated correctly. Since link exit functions of multiple destination flows have been formulated by Liu and Kawakami (1993), the previous dynamic traffic assignment models can be improved from many aspects by the aid of the link exit function. In this paper, the constraint set of the dynamic traffic assignment problem is qualified first. The qualified constraint set can be applied to any dynamic traffic assignment problem. Then a continuous dynamic system optimum model and a discrete dynamic system optimum model are presented. The model can be applied to dynamic traffic assignment for multiple destination networks. Furthermore, an algorithm is developed to solve the discrete model. Test examples show that the algorithm can solve the discrete model efficiently.     

Data–Driven Methodology for Signal Timing Plan Development: A Computational Approach

Traffic signal systems serve as one of the most powerful control tools available to improve the efficiency of surface transportation travel. A large number of signal systems currently operate using the time–of–day (TOD) approach. In TOD systems, a day is segmented into a number of intervals in which a different timing plan is used. Thus, the challenge in operating a TOD system effectively is to (1) identify appropriate TOD intervals, and (2) develop optimal timing plans for each interval. The existing procedures used by traffic engineers to address these challenges are time consuming and use relatively small sets of data. This research effort developed a new timing plan development methodology that takes advantage of the large sets of archived traffic data (volume and occupancy) that modern systems are equipped to compile. Based upon statistical cluster analysis, this methodology (1) automates the identification of TOD intervals using a high–resolution definition of system state, and (2) provides representative volumes for plan optimization based on the set of archived data. The results of a case study reported in this paper demonstrate that the methodology supports the development of a TOD system that provides benefits when considering performance measures such as delay, when compared to currently used techniques.