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.     

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.     

Cooperative control of a platoon of connected autonomous vehicles and unconnected human-driven vehicles

By using kinematic state information obtained through vehicle-to-vehicle communications, connected autonomous vehicles (CAVs) can drive cooperatively to alleviate shockwave propagation associated with traffic disturbances. However, during the transition to full autonomy, CAVs and human-driven vehicles (HDVs) will coexist on the road, creating mixed-flow traffic. The inherent heterogeneity and randomness in human driving behavior can generate additional disturbances in the traffic flow. Further, HDVs without communication functionality (unconnected HDVs) can cause the control performance of CAVs to degrade by negatively impacting platoon formation. To proactively mitigate the negative impacts of HDVs in mixed-flow traffic, this study proposes a cooperative control strategy with three components for platoons consisting of CAVs and unconnected HDVs: (i) a number estimator for estimating the number of HDVs between two CAVs, (ii) a kinematic state predictor for predicting the kinematic states of HDVs, and (iii) a multi-anticipative car-following controller (i.e., control strategy using kinematic state information of multiple preceding vehicles) for CAVs to maintain string stability and desired time headway. To initialize the proposed strategy, the number estimator is developed using a deep neural network (DNN). Then, a DNN-based kinematic state predictor predicts the kinematic states of HDVs for CAVs to enable multi-anticipative car-following control. The multi-anticipative car-following controller is implemented using an extended intelligent driver model-guided deep deterministic policy gradient algorithm, which ensures safety, string stability, and traffic efficiency. The effectiveness of the proposed control strategy is validated through numerical experiments using NGSIM data. Results indicate that the proposed strategy can produce accurate estimations of the number and the kinematic states of HDVs between CAVs. Further, it can achieve string stability while maintaining smaller time headways, compared to car-following models used for training guidance under different market penetration rates of CAVs, which significantly improves traffic smoothness and mobility.     

A 2D-connected automated vehicle car-following control algorithm

Car-following prevails in daily traffic under various scenarios. However, most connected automated vehicle (CAV) car-following algorithms in the literature only apply to one-dimensional CAV control, which limits the application scenarios. This paper presents a two-dimensional (2D) CAVs car-following strategy on curved roads in the spatial domain with vehicle-to-vehicle and vehicle-to-infrastructure communication to expand the car-following application scenarios. This control strategy constructs a 2D car-following equilibrium policy by extending the simplified Newell's car-following model. Based on that, a constrained optimal CAV car-following control in the spatial domain is constructed to maintain the inter-vehicle spacing and speed difference while regulating the lane deviation and avoiding work zones. As demonstrated by numerical results, our proposed algorithm maintains a high degree of consistency between equilibrium spacing and vehicle speed while duplicating the trajectory of the leading vehicle, implying empirical string stability. The results also suggest the robustness of handling different scenarios.     

道路平面交叉口次路远引几何参数设置及通行效率研究

次要车流的左转和直行对交叉口车辆运行影响较大,特别是主路交通流量较大时尤为明显,通过次要道路车流远引的方式可以减少交叉口车辆冲突,有利于提高交叉口运行效率。分析了平面交叉口次路远引适用条件与车流组织方法,以延误最小为目标,构建了远引回转车流掉头位置、远引车辆掉头排队车道长度、远引掉头处开口长度与宽度等关键几何参数计算模型,提出了次路远引交叉口延误和通行能力等计算方法。结合实例验证了上述方法的可行性,仿真显示,在一定条件下平面交叉口次路远引可有效减少信号相位数和车辆平均延误,表明了平面交叉口次路远引的交通组织方法对优化城市道路交叉口设计有较好的应用价值。     

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%.     

Modeling Vehicle Interactions during Lane‐Changing Behavior on Arterial Streets

Abstract: Lane‐changing algorithms have attracted increased attention during recent years in traffic modeling. However, little has been done to address the competition and cooperation of vehicles when changing lanes on urban streets. The main goal of this study is to quantify the vehicle interactions during a lane‐changing maneuver. Video data collected at a busy arterial street in Gainesville, Florida, were used to distinguish between free, forced, and competitive/cooperative lane changes. Models particularly for competitive/cooperative lane changes were developed, depending on whether the following vehicle cooperates with the subject vehicle or not. By referring to the “TCP/IP” protocol in computer network communications, a sequence of “hand‐shaking” negotiations were designed to handle the competition and cooperation among vehicles. The developed model was implemented and validated in the CORSIM microsimulator package, with the simulation capabilities compared against the original lane‐changing model in CORSIM. The results indicate that the new model better replicates the observed traffic under different levels of congestion.     

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.     

Empirical Innovation of Computational Dual‐Loop Models for Identifying Vehicle Classifications against Varied Traffic Conditions

Clarifying traffic flow phases is a primary requisite for applying length‐based vehicle classifications with dual‐loop data under various traffic conditions. One challenge lies in identifying traffic phases using variables that could be directly calculated from the dual‐loop data. This article presents an innovative approach and associated algorithm for identifying traffic phases through a hybrid method that incorporates level of service method and K‐means clustering method. The “phase representative variables” are identified to represent traffic characteristics in the traffic flow phase identification algorithm. The traffic factors influencing the vehicle classification accuracy under non‐free traffic conditions are successfully identified using video‐based vehicular trajectory data, and the innovative length‐based vehicle classification models are then developed. The result of the concept‐of‐evidence test with use of sample data indicates that compared with the existing model, the accuracy of the estimated vehicle lengths is increased from 42% to 92% under synchronized and stop‐and‐go conditions. The results also foster a better understanding of the traffic stream characteristics and associated theories to lay out a good foundation for further development of relevant microscopic simulation models with other sensing traffic data sources.     

Smart dynamic evacuation planning and online management using vehicular communication system

During disasters, swiftly and efficiently evacuating populations in hazardous situations is crucial to minimize losses. This study proposes a novel framework to address dynamic population evacuation (DPE) problems, which includes planning and online evacuation management phases facilitated by vehicular communication. In the planning phase, a shelter allocation problem (SAP) is solved dynamically for destination choice and a dynamic traffic assignment (DTA) for path choice toward the chosen destinations to obtain an initial evacuation plan. The initial plan is then enhanced by employing a vehicular ad hoc network (VANET) within the vehicular edge computing (VEC) architecture. This enhancement enables communication among evacuees, allowing them to revise their vehicle's route choice and planned destination. These revisions take into account the changing risk and traffic conditions. The proposed online DPE framework is applied to the real evacuation scenario of Mill Valley City, CA. The proposed model is evaluated with different VANET architectures, including vehicular cloud computing (VCC) and VEC. The results show that the VEC framework outperforms other configurations and improves the evacuation process compared to the scenario with an initial plan by more than 30% in network clearance time. Additionally, a performance analysis is carried out for evacuation scenarios with different penetration rates of connected vehicles in VANET.     

基于韦布尔分布的行人过街特性模型

行人过街特性对信号配时设计具有重要影响。为说明行人穿越机动车流的过街特性,基于韦布尔分布函数描述了机动车车头时距分布,并得到行人在一次穿越一条车道、多条车道或一个方向三种穿越方式下的穿越概率、平均等待间隔数、平均等待时间和平均延误等特性。利用长春市典型路段调查数据,验证机动车车头时距分布的韦布尔模型;并对各种穿越方式的行人过街特性进行对比分析。结果表明:韦布尔函数描述的机动车车头时距分布与实测分布接近,检验效果在置信度为95%时非常显著;行人单独穿越内、外侧车道的特性差异不大,而同时穿越内、外侧车道的概率明显偏小,且平均等待间隔数和平均延误明显偏大;行人穿越人行横道时逐车道、逐方向和一次穿越的总延误依次增加。基于韦布尔分布的行人过街特性模型能够很好地解释行人过街行为,可以为城市行人交通设施设计和信号优化提供理论依据。     

Rolling-horizon–based strategy of fully cooperative traffic under signalized intersections

Dedicated left-turn lanes are traditionally used at intersections. This practice may not be optimal where heavy traffic exists from multiple directions. As is well known, the capacity can be increased if vehicles are grouped in the same direction in advance, but the additional infrastructure is usually needed, such as presignal systems. Fortunately, under the development of connected and automated vehicle (CAV) technology, the presignal system can be achieved by coordinating vehicles directly. To this end, we establish a modeling framework and management strategies for pure CAV traffic to achieve direction-based grouping. A rolling time-horizon–based sorting strategy is developed to achieve real-time coordination under uncertain vehicle arrivals. A series of numerical experiments have shown that the improvement of the proposed sorting strategy is significant and robust compared with traditional dedicated left-turn lane intersections without coordination, especially when the traffic is busy.     

Lossless Compression of All Vehicle Trajectories in a Common Roadway Segment

This article describes a method for compressing position and identification data for files containing comprehensive trajectory records of all vehicles traversing the same roadway segment over an arbitrary time period, in such a way that no loss of information content occurs. Such complete trajectory records are important for the study of traffic flow theory and could become increasingly relevant as test data against which to study the behavior of autonomous vehicles in a mixed traffic environment. Compression steps include differential encoding, motion prediction, and parsimonious binary storage, plus steps that are unique to the context of vehicle trajectories, including vehicle ID numbers and lane occupancy. The effectiveness of the compression is due, in part, to the strong correlations between positions (and speeds) of vehicles traveling near each other, as well as recognition that certain trajectory artifacts change with spatial and temporal frequencies much lower than the sampling rates. The algorithm is demonstrated on two sets of publicly available complete trajectory records, and compression performance statistics are given. Compression ratios in the range 10:1 to more than 20:1 are achieved for the sample files.     

Optimizing schedule for improving the traffic impact of work zone on roads

Many different types of construction projects set up work zones on roads. Especially in urban areas, lane closures as a result of work zones have a considerable impact on local traffic. However, for a construction project that consists of several work zones and several work crews, the traffic impact may be improved by appropriate scheduling. Therefore, this paper proposed a scheduling model based on the route-changing behavior of road users. The proposed model calculates the traffic delay of vehicles by microscopic simulation, and applies team ant colony optimization to search for a near-optimal schedule. The project planner then ensures that the contractor executes the activities according to the near-optimal schedule. The proposed model is applied to schedule a sewer system construction project in a city. The results of our study indicate that with our proposed model the total traffic delay is reduced by 11.1% when compared with a schedule proposed by the project planner.     

T型远引左转交叉渠划方案的Vissim仿真分析

针对左转车流影响交叉口通行能力的现象，选取左转视距受限的主干路-次干路“T”型交叉口为研究对象，提出“主路信控左转”、“主路远引左转”两种组织方案，并通过Vissim仿真软件对两种方案中交叉口延误、左转延误、左转运行时间等参数的对比分析，得出“主路远引左转”在T型交叉口的可行性。     

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。     

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.     

信号控制交叉口U-turn设计及延误模型

城市道路信号控制交叉口左转车流是造成交叉口冲突和拥挤,以至于形成交通瓶颈的主要原因之一。通过分析实施U-turn的交通效益和影响因素,利用稳态模型、过渡函数和可插间隙理论建立了基于交叉口车均延误和U-turn行程时间的延误模型。最后利用实例,得到U-turn设计可使饱和度最高下降29%,车均延误最高下降63%,从而使交叉口服务水平有较大提高。     

考虑实际路况的应急救援路径优化算法

着重分析影响当前交通路网中延误到达被救援地点时间的各种因素,如交通流量、道路等级、交通管制、信号管制以及救援车辆作为特种车辆在道路上的通行能力等,利用层次分析法对道路的权重进行综合判定,使用Arcgis Engine平台软件的底层算法,实现考虑实际路口的应急救援路径优化。以河北省保定市城区范围为原型,计算消防安全重点单位发生火灾或抢险救援的实例。通过实际验证,该方法基本满足应急救援情况下考虑实际交通情况的最优路径选取的需求。     

Increasing Signalized Intersection Capacity with Unconventional Use of Special Width Approach Lanes

Heavy traffic volume coupled with insufficient capacity due to limited space cause most of traffic congestion at urban signalized intersections. This article presents an innovative design to increase the capacity of heavily congested intersections by using the special width approach lane (SWAL), which consists of two narrow approach lanes that are dynamically utilized by either two passenger cars or a heavy vehicle (e.g., buses or trucks) depending on the composition of traffic. The impact of the SWAL on the saturation flow rate is quantified and validated, followed by an optimization model for best geometric layout and signal timing design with the presence of the SWAL. The optimization model is formulated as a mixed‐integer‐linear‐program for intersection capacity maximization which can be efficiently solved by the standard branch‐and‐bound technique. Results of extensive numerical analyses and case studies show the effectiveness of SWAL to increase intersection capacity, indicating its promising application at intersections with very limited space that prevents the addition of separate lanes.