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Abstract
During the last 60 years, incessant efforts have been made to improve the efficiency of traffic control systems to meet ever-increasing traffic demands. Some recent works attempt to enhance traffic efficiency via vehicle-to-vehicle communications. In this paper, we aim to give a survey of some research frontiers in this trend, identifying early-stage key technologies and discussing potential benefits that will be gained. Our survey focuses on the control side and aims to highlight that the design philosophy for traffic control systems is undergoing a transition from feedback character to feedforward character. Moreover, we discuss some contrasting preferences in the design of traffic control systems and their relations to vehicular communications. The first pair of contrasting preferences are model-based predictive control versus simulation-based predictive control. The second pair are global planning-based control versus local self-organization-based control. The third pair are control using rich information that may be highly redundant versus control using concise information that is necessary. Both the potentials and drawbacks of these control strategies are explained. We hope these comparisons can shed some interesting light on future traffic control studies.
... The research on boosting traffic efficiency with CAVs can be broadly categorized into two evolutionary threads. The first evolutionary thread begins with isolated single intersections, then gradually expands to multiple intersections and the ultimate goal, i.e., road networks [16]. Research on single intersections has received a lot of attention and accumulation. ...
... Pei et al. [29] design a distributed strategy for the coordination of adjacent intersections. However, due to the complexity of the road network planning problem and the severe coupling between sub-problems, there are still gaps in network-wide approaches [15], [16]. ...
... Second, and more prominently, there are still gaps in network-wide planning problems due to the complexity of the problem. The complexity of the network-wide planning problem increases dramatically with the number of intersections and vehicles, while there are causal loops that are difficult to solve directly [16], [29]. To address the above deficiencies, we decompose the network-level cooperative driving problem into two subproblems and accordingly propose a bi-level network-wide cooperative driving approach, as illustrated in Fig. 1. ...
Cooperative driving of connected and automated vehicles (CAVs) has attracted extensive attention and researchers have proposed various approaches. However, existing approaches are limited to small-scale isolated scenarios and gaps remain in network-wide cooperative driving, especially in routing. In this paper, we decompose the network-level cooperative driving problem into two dominant sub-problems and accordingly propose a bi-level network-wide cooperative driving approach. The dynamic routing problem is considered in the upper level and we propose a multi-agent deep reinforcement learning (DRL) based routing model. The model can promote the equilibrium of network-wide traffic through distributed self-organized routing collaboration among vehicles, thereby improving efficiency for both individual vehicles and global traffic systems. In the lower level, we focus on the right-of-way assignment problem at signal-free intersections and propose an adaptive cooperative driving algorithm. The algorithm can adaptively evaluate priorities of different lanes, and then uses the lane priorities to guide the Monte Carlo tree search (MCTS) for better right-of-way assignments. Essentially, the upper level determines which conflict areas the vehicles will pass through, and the lower level addresses how the vehicles use the limited road resources more efficiently in each conflict area. The experimental results show that the upper and lower levels complement each other and work together to significantly improve the network-wide traffic efficiency and reduce the travel time of individual vehicles. Moreover, the results demonstrate that microscopic and mesoscopic cooperative driving behaviors of vehicles can significantly benefit the macroscopic traffic system.
... [2,[18][19][20][21]. Several signal-based control systems assured the efficient management of intersections and aided in the alleviation of traffic congestion [21,22]. Thanks to the new emergent vehicular communication technologies, a large range of unsignalised intersection management approaches are also introduced in recent literature [23,24]. ...
... For concerned readers, refs. [22,29,30] provide an overview of traffic control, and refs. [21,22,31] discuss the link between traffic control and vehicle connectivity. ...
... [22,29,30] provide an overview of traffic control, and refs. [21,22,31] discuss the link between traffic control and vehicle connectivity. The existing CAVs-based control algorithm is mainly concerned with single intersection. ...
Advanced intersection control systems have been created to alleviate traffic congestion. CAVs may benefit from cooperative navigation in order to address the regular traffic issue. Due to whole uncertainty in transportation network, the conventional motion planning for local areas may lead to undesirable effects in long run. In prior works, a micro–macro flow control (MiMaFC) strategy is used to investigate CAVs' navigation at unsignalised intersections by taking flow velocity and vehicle passing priority into account. To get a better understanding of motion control and how it can be utilised to impact traffic flow behaviour, this study expands the intersection navigation protocol and develops a velocity planning methodology based on the proposed MiMaFC technology. Correspondingly, cooperative navigation protocol used in the addressed architecture is specifically developed for CAVs that continually cross intersections. Further, spatio‐temporal velocity adaption mechanism is presented in this work. Depending on the vehicles' location and speed, CAVs might use either the MiMaFC‐based or self‐interested velocity strategy. Simulation results, which include a congested traffic network, are shown to demonstrate the proposed method's potential. The study found that the suggested motion planning framework may increase urban network mobility over a non‐supervised CAVs system.
... The emergence of Connected and Automated Vehicles (CAVs) along with new traffic infrastructure technologies [1], [2] over the past decade have brought the promise of resolving long-lasting problems in transportation networks such as accidents, congestion, and unsustainable energy consumption along with environmental pollution [3], [4], [5]. Meeting this goal heavily depends on effective traffic management, specifically at the bottleneck points of a transportation network such as intersections, roundabouts, and merging roadways [6]. ...
... Our goal is to determine a control law achieving objectives 1-2 subject to constraints 1-3 for each i ∈ F (t ) governed by the dynamics (1). Choosing L i (u i (t )) = 1 2 u 2 i (t ) and normalizing travel time and 1 2 u 2 i (t ), we use the weight α ∈ [0, 1] to construct a convex combination as follows: ...
... To derive the CBFs that ensure the constraints (3), (4), and (5) are always satisfied, we use the vehicle dynamics (1) ...
We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints. It has been shown that such problems can be solved through a combination of tractable optimal control problem formulations and the use of Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved on-line over discrete time steps. However, the feasibility of each such QP cannot be guaranteed over every time step. To overcome this limitation, we develop both an event-triggered approach and a self-triggered approach such that the next QP is triggered by properly defined events. We show that both approaches, each in a different way, eliminate infeasible cases due to time-driven inter-sampling effects, thus also eliminating the need for selecting the size of time steps. Simulation examples are included to compare the two new schemes and to illustrate how overall infeasibilities can be significantly reduced while at the same time reducing the need for communication among CAVs without compromising performance.
... The objectives of coordinating and controlling vehicles in such conflict areas include reducing congestion and energy consumption while also ensuring passenger comfort and guaranteeing safety [2], [3]. The emergence of Connected and Automated Vehicles (CAVs) [1] and the development of new traffic infrastructure technologies [4] provide promising new solutions to this problem through better information utilization and more precise vehicle trajectory design. ...
... Similarly, the safe-merging CBF constraint (19) applied to (4) is ...
We consider the problem of scaling up optimal and safe controllers for Connected and Automated Vehicles (CAVs) from a single Control Zone (CZ) around a traffic conflict area to an entire network. The goal is to jointly minimize travel time and energy consumption for all CAVs, while providing speed-dependent safety guarantees within a CZ and satisfying velocity and acceleration constraints. A hierarchical modular CZ framework is developed consisting of a lower level where decentralized controllers are used that combine Optimal control and Control Barrier Functions (OCBF) and a higher level where a feedback flow controller is proposed to coordinate adjacent CZs. The flow controller is parameterized by a terminal velocity constraint that serves as the interface between CZs. Simulation results show that the proposed modular control zone framework outperforms a direct extension of the OCBF framework to multiple CZs without any flow control.
... The emergence of Connected and Automated Vehicles (CAVs) has the potential to drastically impact transportation systems in terms of increased safety, as well as reducing congestion, energy consumption, and air and noise pollution [8]. In particular, CAVs enable intelligent traffic management at conflict areas, such as intersections, roundabouts, and merging roadways, through cooperation using connectivity, all of which critically affect the performance of a traffic network [15]. ...
... For any i ∈ s 0 , let us identify the vehicles that i must merge ahead and behind of. These are similar to i + and i − in (6), (8) and, for the SDF sequence, are denoted byî + andî − . Letting ind[s 0 (i )] denote the index of the vehicle at position s 0 (i ) in s 0 : (19) As an example, if s 0 = {1, 2, 3, 4, 5, 6, 7} in Fig. 1, then4 − = 5. ...
We address the problem of merging traffic from two roadways consisting of both Connected Autonomous Vehicles (CAVs) and Human Driven Vehicles (HDVs). Guaranteeing safe merging in such mixed traffic settings is challenging due to the unpredictability of possibly uncooperative HDVs. We develop a hierarchical controller where at each discrete time step first a coordinator determines the best possible Safe Sequence (SS) which can be realized without any knowledge of human driving behavior. Then, a lower-level decentralized motion controller for each CAV jointly minimizes travel time and energy over a prediction horizon, subject to hard safety constraints dependent on the given safe sequence. This is accomplished using a Model Predictive Controller (MPC) subject to constraints based on Control Barrier Functions (CBFs) which render it computationally efficient. Extensive simulation results are included showing that this hierarchical controller outperforms the commonly adopted Shortest Distance First (SDF) passing sequence over the full range of CAV penetration rates, while also providing safe merging guarantees.
... First, the planning and dynamical modeling of vehicles in traditional simulators are based on a feedback mode, which is incompatible with the feed-forward decision-making and planning for CAVs. Here, feedback mode refers to specifying control commands in response to the current values of traffic state variables; feedforward mode refers to taking preemptive control commands to optimize the traffic performance (e.g., efficiency, safety, fuel consumption, etc.) based on the measurement and effective prediction of the traffic state [17], [18]. For example, traditional simulators generally plan the motion control of the next step according to the state of the last step. ...
... the solution space of passing order consisting of groups and find the passing order with the shortest delay.17 return The passing order. ...
Connected and automated vehicles (CAVs) are expected to play a vital role in the emerging intelligent transportation system. In recent years, researchers have proposed various cooperative driving methods for CAVs, and there is an urgent need for a generic and unified traffic simulator to simulate and evaluate these methods. However, traditional traffic simulators have two critical deficiencies for CAV simulation needs: 1) the planning and dynamical modeling of vehicles in traditional simulators are based on a feedback mode, which is incompatible with the feed-forward decision and planning that CAVs commonly adopt; 2) the traditional simulators cannot provide typical traffic scenarios and corresponding standardized algorithms for multi-CAV cooperative driving. In this paper, we introduce CAVSim, a novel microscopic traffic simulator for CAVs, to address these deficiencies. CAVSim is developed modularly according to the emerging technology of the CAV environment, emphasizes feed-forward decision and planning for CAVs, and highlights the cooperative decision and planning components in the CAV environment. CAVSim incorporates rich and typical traffic scenarios and provides standardized cooperative driving algorithms and comparable performance metrics for multi-CAV cooperative driving. With CAVSim, researchers can conveniently deploy decision, planning, and control methods for CAVs at different levels, evaluate their performance, compare them with the standardized algorithms incorporated in CAVSim, and even further explore their impact on traffic flow. As a unified platform for CAVs, CAVSim can facilitate the studies on CAVs and promote the advancement of methods and techniques for CAVs.
... With the development of Connected and Automated Vehicles (CAVs), researchers are committed to proposing many cooperative driving strategies applied at on-ramps and intersections. It is pointed out in [2], [6] that determining the passing order of vehicles is the core issue in the cooperative driving problem. As summarized in [7], [8], existing studies of planning the passing order can be classified into two categories, i.e., optimization-based and heuristics-based strategies. ...
... The first assumption aims to ensure real-time information sharing between vehicles so that all vehicles within the control area can be scheduled to maximize the performance of cooperative driving in improving traffic safety and efficiency. For the second assumption, as presented in [6], [14], [22], [23], overtaking is usually prohibited when vehicles approach the merging zone for safety considerations. ...
This paper focuses on cooperative driving strategies at on-ramps and comprehensively compares the performance of five representative strategies. The simulation results show that the dynamic programming (DP)-based, the grouping-based, and the rule-based strategies perform well in computation time and traffic efficiency, so all three strategies are recommended for practical use. We also show that improving traffic efficiency is usually at the expense of fairness, but a better trade-off between them can be realized in the modified DP-based and rule-based strategies. All cooperative driving strategies compared in this study will be integrated into CAVSim (a simulation platform dedicated to CAVs) for the convenience of researchers and the community.
... To the best of our knowledge, there do not seem to be many surveys and systematic literature reviews (SLRs) examining the various approaches autonomous vehicles use to control traffic at junctions. Even though different AV-and CV-related topics [11][12][13][14][15][16][17] have been the subject of several research studies, our study is distinct from those in terms of techniques, content, and research interests. Articles [15][16][17] summarized the methods used in traffic management systems to assess various traffic flow patterns and/or different types of crossings. ...
... Even though different AV-and CV-related topics [11][12][13][14][15][16][17] have been the subject of several research studies, our study is distinct from those in terms of techniques, content, and research interests. Articles [15][16][17] summarized the methods used in traffic management systems to assess various traffic flow patterns and/or different types of crossings. Recently, a study document from 2022 primarily concerned with driverless vehicles' environmental effects was published [18]. ...
The emergence of autonomous vehicles and the advancement of technology over the past several decades has increased the demand for intelligent intersection management systems. Since there has been increased interest in researching how autonomous vehicles manage traffic at junctions, a thorough literature analysis is urgently needed. This study discovered peer-reviewed publications published between 2012 and 2022 in the most prestigious libraries to address this problem. After that, 100 primary studies were identified, and the chosen literature was subjected to systematic analysis. According to the findings, there are four primary categories of approaches, i.e., rule-based, optimization, hybrid, and machine learning procedures, which are used to achieve diverse driving objectives, including efficacy, safety, ecological, and passenger ease. The analyses illustrate the many attributes, limits, and views of the current solutions. This analysis enables the provision of potential future difficulties and directions in this study area.
... A S one key part of smart city, intelligent transportation systems have attracted a lot of attention by virtue of the advanced management and monitoring systems with smart sensors and excellent information transformation based on Vehicle-to-X (or V2X) technology [1]- [3]. Such emerging techniques provide new means to reduce traffic congestion, fuel consumption, and environmental footprints and enhance safety. ...
... T 2 )ε(t) for t ∈ [T k,q , T k,q k ). For simplify, we only consider the optimal profile below ∆ =δ (t)(Θ ⊗ (P −1 T k,q T 1 T 2 + (T 1 T 2 ) P −1 T k,q ))δ(t) − 2cδ (t)(ΘL 1 ...
The unknown sharp changes of vehicle acceleration rates, also called the unknown jerk dynamics, may significantly affect the driving performance of the leader vehicle in a platoon, resulting in more drastic car-following movements in platooning tracking control, which could cause safety and traffic capacity concerns. To address these issues, in this paper, we investigate cooperative platooning tracking control and intermittent optimization problems for connected automated vehicles (CAVs) with a nonlinear car-following model. We assume that the external inputs of the leader CAV contain unknown but bounded jerk parameters, and the acceleration signals of the leader CAV are known only to a few neighboring follower CAVs in a free-design but directed communication network. To solve these problems, a distributed observer law is developed to provide a reference signal expressed as an estimated unknown jerk dynamic of the leader CAV and implemented by each follower CAV. Then, a novel distributed platooning tracking control protocol is proposed to construct the cooperative tracking controllers under identical inter-vehicle constraints, which can ensure a desired safety distance among the CAVs and allow each follower CAV to track their leader CAV by using only local information interaction. We also present a novel intermittent sampling condition and a robust intermittent optimization design that can ensure optimally scheduled feedback gains for the cooperative platooning tracking controllers to minimize the control cost under nonidentical inter-vehicle constraints and unknown jerk dynamics. Simulation case studies are carried out to illustrate the effectiveness of the proposed approaches
... Incident detection algorithms are also categorized based on the source of data. Fixedsensor algorithms can be classified based on their operating principles into [7]: comparative algorithms, comparing input data against threshold(s) [8,9]; statistical algorithms, using statistical models to estimate and forecast incidents [10]; time-series algorithms, recognizing patterns over time to forecast incidents [11]; filtering/smoothing algorithms, eliminating short-term noise or traffic data inhomogeneity [12]; traffic modeling algorithms, using traffic flow theory and estimation [13,14]; image processing algorithms, via image and video recordings [15]; and artificial intelligence algorithms, including fuzzy logic and artificial neural networks (ANNs) [16,17]. ...
Urban traffic congestion and vehicle/passenger port recurring delays are major obstacles of coastal urban area sustainability. Most research in coastal urban road management has focused on congestion detection without the effective integration of the dynamic interactions with port queueing systems. For securing coastal city environmental, social and economic efficiency, this paper develops and tests a dynamic urban coastal traffic and port management system. The integrated system controls traffic and port gates’ operations based on ITS/C-ITS methodologies. The system integrates dynamic models for congestion detection, using ANN and a parameterized model, on a coastal urban road network that leads to a city port and identifies optimal solutions for road traffic and port queuing gate control. The system communicates with users via connected vehicles and VMS. The system was tested in a coastal urban road leading to Patras Southern Port, Greece, and at port control gates. Field and simulation data were used to assess system performance and social–environmental impacts. The results reveal that the system’s application offers benefits to the individual driver moving towards the Port to board a ship (gaining at least 7 min and consuming 0.306 L less fuel) as well as to society (39.72% increase in traffic safety) and environment (1,445,132 g CO2 emission reduction).
... C urrently, there is an increase in the need for the design and implementation of intelligent transport systems (ITS) for a "smart city" due to the ever-increasing traffic, causing the formation of multiple traffic jams. At the same time, one of the most promising directions of the ITS evolutionary development is the use of "smart traffic lights" that analyze the dynamics and structure of traffic and pedestrian flows [1]. ...
This article presents a new simulation model of an intelligent transportation system (ITS) for the “smart city” with adaptive traffic light control. The proposed transportation model, implemented in the AnyLogic, allows us to study the behavior of interacting agents: vehicles (V) and pedestrians (P) within the framework of a multi-agent ITS of the “Manhattan Lattice” type. The spatial dynamics of agents in such an ITS is described using the systems of finite-difference equations with the variable structure, considering the controlling impact of the “smart traffic lights.” Various methods of traffic light control aimed at maximizing the total traffic of the ITS output flow have been studied, in particular, by forming the required duration phases with the use of a genetic optimization algorithm, with a local (“weakly adaptive”) switching control and based on the proposed fuzzy clustering algorithm. The possibilities of optimizing the characteristics of systems for individual control of the behavior of traffic lights under various scenarios, in particular, for the ITS with spatially homogeneous and periodic characteristics, are investigated. To determine the best values of individual parameters of traffic light control systems, such as the phases’ durations, the radius of observation of traffic and pedestrian flows, threshold coefficients, the number of clusters, etc., the previously proposed parallel real-coded genetic optimization algorithm (RCGA type) is used. The proposed method of adaptive control of traffic lights based on fuzzy clustering demonstrates greater efficiency in comparison with the known methods of collective impact and local (“weakly adaptive”) control. The results of the work can be considered a component of the decision-making system in the management of urban services.
... Connected and Automated Vehicles (CAVs) are expected to revolutionize road traffic because we can collect useful information via vehicle-to-everything (V2X) communication and plan their movements to fully utilize limited road resources. If we can successfully implement cooperative driving in practice, then we will undoubtedly improve traffic safety and efficiency [1][2][3][4] . ...
Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion. In recent years, several cooperative driving approaches for idealized traffic scenarios (i.e., uniform vehicle arrivals, lengths, and speeds) have been proposed. However, theoretical analyses and comparisons of these approaches are lacking. In this study, we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance. We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles. The obtained conclusions shed light on future cooperative driving studies.
... With the rapid development of wireless communication technologies, vehicles can now communicate with other vehicles, infrastructure, and other traffic participants and are referred to as connected vehicles (CVs) (Mahmassani, 2016;Mahmoud et al., 2022). CVs can be used as a new sensing platform that enables intersections to acquire richer and more accurate data than infrastructure-based vehicle detection (Li et al., 2014a;Massaro et al., 2017) and conventional probe vehicle-based information collection systems (Cheng et al., 2012;Ou et al., 2011). ...
The advent of connected vehicles (CVs) has enabled the availability of richer and more accurate data for more flexible and sophisticated traffic signal control. However, the complexity of optimization models, especially when individual vehicle trajectories are considered, makes solving existing CV-based signal control methods difficult. This study proposes a computationally efficient and refined signal control method for isolated intersections in a CV environment. A mixed-integer nonlinear program model, which minimizes the total vehicle travel time, is developed by employing a simplified car-following model to predict individual vehicle trajectories. To address the computational concerns, the signal optimization model is reformulated by clustering incoming vehicles into platoons and analyzing the platoon features based on the interactions between vehicles as well as between vehicles and traffic signals. Simulation studies are conducted to compare the proposed method with the adaptive signal and vehicle-actuated control methods. The results show significant reductions in vehicle travel time and fuel consumption compared to benchmark methods under different demand levels. Furthermore, the proposed control method has high computational efficiency with a solving time of less than 0.05 s, validating its potential for practical applications.
... Numerous applications, including autonomous cars [129][130][131], traffic control [132,133], and road safety [134], rely on vehicular networks for communication services. Nowadays, users in vehicular environments place a higher priority on entertainment, which significantly increases consumers' demands for multimedia. ...
... To address these issues, trajectory planning is introduced to smooth vehicle trajectories and at the same time optimize certain objectives including safety [16], fuel efficiency [17], travel time [18], etc. However, most of these studies optimize traffic signals and vehicle trajectories separately [4], [19], [20], [21]. As traffic signals and vehicle trajectories are always intertwined, the integrated optimization of traffic signals and vehicle trajectories is expected to further improve the intersection operations, which is called the signal-vehicle coupled control (SVCC). ...
There is a growing number of studies on the traffic control strategies of signal timings and vehicle trajectories at signalized intersections, while lane assignments are widely pre-specified and fixed. Meanwhile, existing strategies generally require a fully connected and automated vehicles (CAVs) environment. To fill up the gaps, this study contributes to a two-dimensional (spatiotemporal) control strategy by jointly optimizing traffic signals, lane settings, and vehicle trajectories at isolated signalized intersections under the mixed traffic of connected automated and human-driven vehicles. Specifically, based on the pseudo-platoons, signal timing plans and settings of approach lanes are jointly optimized by a piece-wise linear programming model. Then, vehicle trajectory control is integrated into the collaborative control framework to smooth vehicle trajectories. Three groups of numerical experiments are conducted to verify the effectiveness and efficiency of the proposed control method. Results show that the proposed algorithm outperforms the actuated control in terms of vehicle travel time under both under-saturated and over-saturated traffic conditions.
... The rise of connected and automated vehicles (CAVs) and advancements in traffic infrastructure [1] promise to offer solutions to transportation issues like accidents, congestion, energy consumption, and pollution [2]- [4]. To achieve these benefits, efficient traffic management is crucial, particularly at bottleneck locations such as intersections, roundabouts, and merging roadways [5]. ...
Security is crucial for cyber-physical systems, such as a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a road network safely. In this paper, we tackle the security of a cooperating network of CAVs in conflict areas by identifying the critical adversarial objectives from the point of view of uncooperative/malicious agents from our preliminary study, which are (i) safety violations resulting in collisions, and (ii) traffic jams. We utilize a trust framework (and our work doesn't depend on the specific choice of trust/reputation framework) to propose a resilient control and coordination framework that mitigates the effects of such agents and guarantees safe coordination. A class of attacks that can be used to achieve the adversarial objectives is Sybil attacks, which we use to validate our proposed framework through simulation studies. Besides that, we propose an attack detection and mitigation scheme using the trust framework. The simulation results demonstrate that our proposed scheme can detect fake CAVs during a Sybil attack, guarantee safe coordination, and mitigate their effects.
... Autonomous vehicles (AVs) can alleviate traffic congestion and enhance safety in various driving scenarios, including highway merging. A survey of different studies that consider the impact of AVs on improving traffic congestion in highway merging scenarios can be found in [5] and [6]. Apart from being a source of congestion, highway merging is also a challenging driving scenario, as the driver in the merge lane needs to negotiate with other drivers in the main lane and make several key decisions with hard constraints on time and road geometry [7]. ...
This paper discusses the limitations of existing microscopic traffic models in accounting for the potential impacts of on-ramp vehicles on the car-following behavior of main-lane vehicles on highways. We first surveyed U.S. on-ramps to choose a representative set of on-ramps and then collected real-world observational data from the merging vehicle's perspective in various traffic conditions ranging from free-flowing to rush-hour traffic jams. Next, as our core contribution, we introduce a novel car-following model, called MR-IDM, for highway driving that reacts to merging vehicles in a realistic way. This proposed driving model can either be used in traffic simulators to generate realistic highway driving behavior or integrated into a prediction module for autonomous vehicles attempting to merge onto the highway. We quantitatively evaluated the effectiveness of our model and compared it against several other methods. We show that MR-IDM has the least error in mimicking the real-world data, while having features such as smoothness, stability, and lateral awareness.
... With complex powertrains involving multiple operation modes and/or power plants, the energy-saving performance of EVs relies on the energy management strategy (EMS) that is sensitive to the variations in the driving environment [3], [4]. However, the emergence of connected vehicle technologies enables vehicles to actively plan their speed trajectories, with perceiving the driving environment through Vehicle-to-Vehicle (V2V) and Vehicle-to -Infrastructure (V2I) communications [5], [6]. Hence, the Manuscript integration of speed planning and powertrain EMS is expected to further exploit the energy benefits of EVs while adapting to the variations in the driving environment [7]. ...
Active co-optimization of future speed profiles together with powertrain control is the optimal solution to further exploiting the energy benefit of electric vehicles (EVs) in real-world operation. However, with uncertainties in driving conditions and concerns about driving safety, speed planning results are cautious and with frequent speed variations, which deteriorates the energy economy of EVs in turn. To comprehensively optimize the energy economy and driving safety of EVs in a stochastic driving environment, this paper develops a chance constraint model predictive control (CC-MPC) for co-optimizing the speed planning and powertrain control, which forms an advanced energy management method. To handle the instantaneous disturbance, a coordinated hierarchical method (CHM) is engineered for solving the CC-MPC. As suggested by simulation, the driving safety (measured by success rate) can be increased to 81% with the CC-MPC, which realizes a 62% improvement compared with situations without CC-MPC. Moreover, the proposed CC-MPC significantly mitigates the conflict between driving safety and the energy economy, and the worst deterioration of the energy economy is only 9.3%. Sacrificing merely 2.1% sub-optimality, CHM removes 86% computation loads, and the median of CPU time is merely 0.58s at each computation step (control interval 1s), which makes the CC-MPC promising for online implementation.
... With the advent of connected and automated vehicles (CAVs) equipped with advanced autonomous driving and networking capabilities, they can substantially reduce intersection traffic accidents and reduce congestion [2]. Currently, research on intersection management systems for CAV vehicles can be broadly categorized into two categories: (1) CAV vehicles integrated with traditional traffic signal control systems, which utilize high-precision traffic data acquired by CAV vehicles to adjust signal control system phase and phase sequence schemes, catering to the needs of intelligent vehicles [3,4]; and (2) signal-free intersection management modes designed for fully automated driving vehicle scheduling, abandoning traditional signal control methods. ...
Current autonomous intersection control strategies are facing issues, such as lack of foresight, frequent occurrence of deadlock, and low control system efficiency. To address these issues, a vehicle–road cooperative autonomous intersection control strategy based on reducing vehicle conflict relationships is proposed in this study. First, a conflict relationship graph that can describe the driving conflict relationship between vehicles is constructed. Second, the complement of the maximum clique in the conflict relationship graph is solved to determine the set of accepted vehicle reservation requests, enabling more vehicle reservation requests to be successfully processed in unit time while ensuring safe driving at the intersection and improving intersection throughput efficiency. Third, based on the maximum clique method, a taboo search method is used to search the neighborhood, thus improving the quality of the final solution with a smaller search cost. Simulation results show that compared to other control strategies, such as the FCFS (First Come First Served) strategy, the traffic signal control strategy (Traffic-Light), and the control strategy based on greedy algorithm search (Batch-Light), the proposed strategy can considerably reduce the average vehicle waiting time by 42%, 19%, and 10%, respectively, as well as increasing the number of vehicles passing through the intersection per unit of time by 35%, 20%, and 12%, respectively. These results demonstrate the effectiveness of the proposed strategy in improving the throughput of the intersection and reducing the average vehicle waiting time.
... In [17], design and implementation issues of model-based traffic control, especially model predictive control for traffic regulation, were discussed in detail. However, for model predictive control, the traffic flow dynamic is often described with different equations, which might not accurately characterize the time-varying stochastic traffic flow dynamics [18]. Conversely, the newly emerging method called parallel transportation management system [19,20] uses artificial system for computational experiment and parallel execution. ...
Traffic signal control is critical for traffic efficiency optimization but is usually constrained by traffic detection methods. The emerging V2I (Vehicle to Infrastructure) technology is capable of providing rich information for traffic detection, thus becoming promising for traffic signal control. Based on parallel simulation, this paper presents a new traffic signal optimization method in a V2I environment. In the proposed method, a predictive optimization problem is formulated, and a cellular automata model is employed as traffic flow model. By using genetic algorithm, the predictive optimization problem is solved online to implement receding horizon control. Simulation results show that the proposed method can improve traffic efficiency in the sense of reducing average delay and number of stops. Meanwhile, simulation also shows that greater communication range brings better performance for reducing the average number of stops. Simulation results show that the proposed V2I-based signal control method can improve traffic efficiency, especially when the traffic volume is relatively high. The proposed algorithm can be applied to traffic signal control to improve traffic efficiency.
... A more detailed survey of the traditional methods of adaptive and nonadaptive traffic signal control can be found in reviews [23,34,35]. ...
Cooperative control of vehicle trajectories and traffic signal phases is a promising approach to improving the efficiency and safety of transportation systems. This type of traffic flow control refers to the coordination and optimization of vehicle trajectories and traffic signal phases to reduce congestion, travel time, and fuel consumption. In this paper, we propose a cooperative control method that combines a model predictive control algorithm for adaptive traffic signal control and a trajectory construction algorithm. For traffic signal phase selection, the proposed modification of the adaptive traffic signal control algorithm combines the travel time obtained using either the vehicle trajectory or a deep neural network model and stop delays. The vehicle trajectory construction algorithm takes into account the predicted traffic signal phase to achieve cooperative control. To evaluate the method performance, numerical experiments have been conducted for three real-world scenarios in the SUMO simulation package. The experimental results show that the proposed cooperative control method can reduce the average fuel consumption by 1% to 4.2%, the average travel time by 1% to 5.3%, and the average stop delays to 27% for different simulation scenarios compared to the baseline methods.
... The third category is tree search algorithms that describe the passing order as a sequence of symbols (CAVs) and formulate the problem as a tree search problem [19], [20], [21]. The detailed tree formulations are diverse and usually dependent on the search algorithms. ...
Connected and automated vehicles (CAVs) have the potential to significantly improve the safety and efficiency of traffic. One revolutionary CAV’s impact on transportation system is cooperative driving that turns signalized intersections to be signal-free and boosts traffic efficiency by better organizing the passing order of CAVs. However, how to get the optimal passing order is an NP-hard problem (specifically, enumerating based algorithm takes days to find the optimal solution to a 20-CAV scenario). Here, we introduce a novel cooperative driving algorithm (AlphaOrder) that combines offline deep learning and online tree searching to find a near-optimal passing order in real-time. AlphaOrder builds a pointer network model from solved scenarios and generates near-optimal passing orders instantaneously for new scenarios. For the scenarios with 40 CAVs, AlphaOrder reduces the travel delay by more than
$20\%$
on average compared to the best-so-far MCTS based algorithm. Moreover, our algorithm provides a general approach to managing preemptive resource sharing between multi-agents (e.g., scheduling multiple automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at conflicting areas).
... MILP is easy to handle when the traffic demand is low, but the computation time increases exponentially with the number of vehicles [21]. In addition, some studies formulated the scheduling of vehicle passing order as a tree search problem [5], [22]- [24]. All possible passing orders are generated as leaf nodes of the solution tree, and tree search methods are used to select a satisfactory solution [22]. ...
Autonomous intersection management (AIM) guides vehicles to pass through signal-free intersections individually. Since vehicles are permitted to turn in any direction from any lane in AIM, it is desired to erase lane changes on the entry lane to achieve high traffic efficiency. However, erasing lane changes exposes intersections to complex conflicts. To study the trade-off between lane changes on the entry lanes and conflicts inside the intersection, this paper makes a comprehensive comparison of two kinds of intersection systems. First, all-direction turn lanes (ADTL) and specific-direction turn lanes (SDTL) are designed to distinguish lane change behaviors. Second, a two-stage method is proposed to manage the oncoming vehicles at the intersection. The first stage is timing schedule optimization and the second stage is trajectory optimization. Then, a method based on Monte Carlo Tree Search (MCTS) is designed to solve the timing schedule optimization model at high traffic demands. Finally, two parts of simulation experiments are conducted in this paper. The first part verifies the performance of the MCTS-based method. The second part compares ADTL and SDTL in multiple scenarios. The results show that ADTL outperforms SDTL in the efficient system, but the opposite conclusion appears in the fault-tolerant system. Besides, combining ADTL with lane changes may bring out the full value of ADTL.
... The authors in [22] include TLS information as a soft constraint within the optimization problem, which is then solved by using deterministic dynamic programming (DDP). The problem can further benefit from the presence of vehicleto-everything (V2X) (more specifically, vehicle-to-infrastructure (V2I)) communication [23]. ...
Recent advancements in automated driving technology and vehicle connectivity are associated with the development of advanced predictive control systems for improved performance, energy efficiency, safety, and comfort. This paper designs and compares different linear and nonlinear model predictive control strategies for a typical scenario of urban driving, in which the vehicle is approaching a traffic light crossing. In the linear model predictive control (MPC) case, the vehicle acceleration is optimized at every time instant on a prediction horizon to minimize the root-mean-square error of velocity tracking and RMS acceleration as a comfort metric, thus resulting in a quadratic program (QP). To tackle the vehicle-distance-related traffic light constraint, a linear time-varying MPC approach is used. The nonlinear MPC formulation is based on the first-order lag description of the vehicle velocity profile on the prediction horizon, where only two parameters are optimized: the time constant and the target velocity. To improve the computational efficiency of the nonlinear MPC formulation, multiple linear MPCs, i.e., a parallel MPC, are designed for different fixed-lag time constants, which can efficiently be solved by fast QP solvers. The performance of the three MPC approaches is compared in terms of vehicle velocity tracking error, root-mean-square acceleration, traveled distance, and computational time. The proposed control systems can readily be implemented in future automated driving systems, as well as within advanced driver assist systems such as adaptive cruise control or automated emergency braking systems.
... The emergence of connected and automated vehicles (CAVs) along with new traffic infrastructure technologies [1], [2] over the past decade have brought the promise of resolving long-lasting problems in transportation networks such as accidents, congestion, and unsustainable energy consumption along with environmental pollution [3]- [5]. Meeting this goal heavily depends on effective traffic management, specifically at the bottleneck points of a transportation network such as intersections, roundabouts, and merging roadways [6]. ...
In this paper we analyze the effect of cyberattacks on cooperative control of connected and autonomous vehicles (CAVs) at traffic bottleneck points. We focus on three types of such bottleneck points including merging roadways, intersections and roundabouts. The coordination amongst CAVs in the network is achieved in a decentralized manner whereby each CAV formulates its own optimal control problem and solves it onboard in real time. A roadside unit is introduced to act as the coordinator that communicates and exchanges relevant data with the CAVs through wireless V2X communication. We show that this CAV setup is vulnerable to various cyberattacks such as Sybil attack, jamming attack and false data injection attack. Results from our simulation experiments call attention to the extent to which such attacks may jeopardize the coordination performance and the safety of the CAVs.
... The recent developments in connected and automated vehicle (CAV) technologies enable real-time data access and sharing with other vehicles and infrastructure via vehiclevehicle (V2V), infra-vehicle (I2V), and vehicle-infra (V2I) communications [6,7]. When such necessary information is available, such as states (position, velocity, and acceleration) of other vehicles, destination, and speed limit, it is possible to precisely control the movement and trajectories of individual vehicles to enhance traffic flow efficiency, fuel economy, and driving safety via a connected vehicle environment (CVE) [8,9]. ...
Uncoordinated driving behavior is one of the main reasons for bottlenecks on freeways. This paper presents a novel cyber-physical framework for optimal coordination of connected and automated vehicles (CAVs) on multi-lane freeways. We consider that all vehicles are connected to a cloud-based computing framework, where a traffic coordination system optimizes the target trajectories of individual vehicles for smooth and safe lane changing or merging. In the proposed framework, the vehicles are coordinated into groups or platoons, and their trajectories are successively optimized in a receding horizon control (RHC) approach. Optimization of the traffic coordination system aims to provide sufficient gaps when a lane change is necessary while minimizing the speed deviation and acceleration of all vehicles. The coordination information is then provided to individual vehicles equipped with local controllers, and each vehicle decides its control acceleration to follow the target trajectories while ensuring a safe distance. Our proposed method guarantees fast optimization and can be used in real-time. The proposed coordination system was evaluated using microscopic traffic simulations and benchmarked with the traditional driving (human-based) system. The results show significant improvement in fuel economy, average velocity, and travel time for various traffic volumes.
... CAVs are expected to improve the characteristics of traditional traffic flow from the micro vehicle level, and then provide an effective way to solve the problems of traffic congestion, traffic efficiency, and traffic pollution. Scholars have also carried out some research to demonstrate the great potential benefits of CAVs [1][2][3]. However, with the help of diverse and advanced communication technology, the "intelligent" information exchange between vehicles and the surrounding environment/world is realized all the time. ...
Connected and automated vehicles (CAVs) present significant potential for improving road safety and mitigating traffic congestion for the future mobility system. However, cooperative driving vehicles are more vulnerable to cyberattacks when communicating with each other, which will introduce a new threat to the transportation system. In order to guarantee safety aspects, it is also necessary to ensure a high level of information quality for CAV. To the best of our knowledge, this is the first investigation on the impacts of cyberattacks on CAV in mixed traffic (large vehicles, medium vehicles, and small vehicles) from the perspective of vehicle dynamics. The paper aims to explore the influence of cyberattacks on the evolution of CAV mixed traffic flow and propose a resilient and robust control strategy (RRCS) to alleviate the threat of cyberattacks. First, we propose a CAV mixed traffic car-following model considering cyberattacks based on the Intelligent Driver Model (IDM). Furthermore, a RRCS for cyberattacks is developed by setting the acceleration control switch and its impacts on the mixed traffic flow are explored in different cyberattack types. Finally, sensitivity analyses are conducted in different platoon compositions, vehicle distributions, and cyberattack intensities. The results show that the proposed RRCS of cyberattacks is robust and can resist the negative threats of cyberattacks on the CAV platoon, thereby providing a theoretical basis for restoring the stability and improving the safety of the CAV.
... Tis category of optimization focuses on the ramp vehicles and proposes signal control methods and optimal algorithms to organize the ramp vehicles' inserting sequences and driving trajectories. Carlson et al. [5][6][7] adopted a ramp signal control method to optimize the infow rate of ramp vehicles and evaluate the safe infow into the main road. Lim et al. [8] proposed a signal control scheme to reduce the overall travel cost and improve vehicles' merging speed, simultaneously setting the minimum delay on the ramp and the outfow on the main road as optimization objectives. ...
Traffic flow optimization and trajectory guidance in merging zones have significant implications for improving capacity and reducing time consumption. The development of V2X communication provides new insights to solve this problem by tackling the information and releasing trajectories schemes. Therefore, this paper aims to discuss the trajectory management in the merging zone for ACC vehicles. A vehicle dispatching and car-following model is proposed to generate steady traffic flow first. An algorithm framework for consecutive traffic flow is presented with the idea of FIFO rules. Then, a two-step method for an individual vehicle is discussed in detail to compute a trajectory. The first step is to select and determine the priority of the optional gaps. The next step is to verify the options’ feasibility, decide on the target gap, and output the trajectories to merge successfully. Numerical experiments validate that the proposed method guarantees safe driving and provides relatively smooth trajectories to the vehicles. Furthermore, increased capacity and higher velocity are observed in a comparative experiment. The cooperative optimization algorithm could be applied efficiently in practice and benefit from its rapid response and low computation complexity.
... To address this limitation, an alternative is the microscopic-level approach which allows to include the safety requirements in the design of RM to optimize the freeway performance (Rios-Torres and Malikopoulos (2016)). In addition, it allows to naturally incorporate vehicle automation, which can compensate for human errors, or Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, which can provide accurate traffic measurements, in the RM design (Sugiyama et al. (2008); Stern et al. (2018); Zheng et al. (2020); Pooladsanj et al. (2020Pooladsanj et al. ( , 2022; Rios-Torres and Malikopoulos (2016); Li et al. (2013); Lioris et al. (2017)). ...
... The third category is tree search algorithms that describe the passing order as a sequence of symbols (CAVs) and formulate the problem as a tree search problem [19]- [21]. The detailed tree formulations are diverse and usually dependent on the search algorithms. ...
Connected and automated vehicles (CAVs) are viewed as a special kind of robots that have the potential to significantly improve the safety and efficiency of traffic. In contrast to many swarm robotics studies that are demonstrated in labs by employing a small number of robots, CAV studies aims to achieve cooperative driving of unceasing robot swarm flows. However, how to get the optimal passing order of such robot swarm flows even for a signal-free intersection is an NP-hard problem (specifically, enumerating based algorithm takes days to find the optimal solution to a 20-CAV scenario). Here, we introduce a novel cooperative driving algorithm (AlphaOrder) that combines offline deep learning and online tree searching to find a near-optimal passing order in real-time. AlphaOrder builds a pointer network model from solved scenarios and generates near-optimal passing orders instantaneously for new scenarios. Furthermore, our approach provides a general approach to managing preemptive resource sharing between swarm robotics (e.g., scheduling multiple automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at conflicting areas
... The emergence of Connected and Automated Vehicles (CAVs) has the potential to drastically impact transportation systems in terms of increased safety, as well as reducing congestion, energy consumption, and air and noise pollution [1]. While infrastructure improvements typically offer only short-term solutions, research to date has shown that CAVs can provide long-term solutions to these problems through better information utilization and more precise trajectory design, especially in conflict areas such as intersections, roundabouts, and merging roadways all of which critically affect the performance of a traffic network [2]. ...
We consider the problem of a single Autonomous Vehicle (AV) merging into traffic consisting only of Human Driven Vehicles (HDVs) with the goal of minimizing both the travel time and energy consumption of the entire group of vehicles involved in the merging process. This is done by controlling only the AV and determining both the optimal merging sequence and the optimal AV trajectory associated with it. We derive an optimal index policy which prescribes the merging position of the AV within the group of HDVs. We also specify conditions under which the optimal index corresponds to the AV merging before all HDVs or after all HDVs, in which case no interaction of the AV with the HDVs is required. Simulation results are included to validate the optimal index policy and demonstrate cases where optimal merging can be achieved without requiring any explicit assumptions regarding human driving behavior.
... Owing to the rapid development of vehicle-to-infrastructure communication (V2I) and vehicle-to-vehicle (V2V) communication technology [9], the eco-driving technology for vehicles has attracted great attention from researchers, and many ecodriving algorithms have been achieved, which can be classified into two types, i.e., the highway-based eco-driving [10] and the city-based one [11]. On highways, the road conditions are not complex, and the vehicles are often in cruise and adaptive cruise scenarios [8], [12], [13], [14]. ...
In this paper, an ecological driving (eco-driving) algorithm considering queue effects is proposed for connected and automated vehicles (CAVs) at unsaturated intersections in order to reduce fuel consumption and travel time. Firstly, after the traffic flow parameters are obtained using vehicle-to-infrastructure communication technology, the kinematic shockwave model is used to predict the vehicle queue length at the saturated intersections. Secondly, to decrease fuel consumption, a fuel-saving optimization problem is formulated using the estimated queue length, and for real-time control, the formulated optimization problem is decomposed into two subproblems depending on whether the CAV will stop to queue. Then, to reduce fuel consumption and travel time simultaneously, the eco-driving algorithm is designed, where the trajectory re-optimization process is implemented in order not to block the upstream vehicles. Finally, extensive simulations are carried out on VISSIM to demonstrate the control performance of the proposed eco-driving algorithm on a single CAV and on the entire traffic flow. Simulation results show that the proposed eco-driving algorithm can significantly decrease the fuel consumption and travel time of both CAVs and the traffic flow, and higher market penetration rate of CAVs can result in better control performance.
... The main goal is to determine traffic signals for each intersection individually with a predetermined cycle length. The phases of a traffic signal plan are based on a fixed cycle-based sequence split into phases [17][18][19][20][21]. The fuzzy logic control Sugeno technique was used to design an adaptive traffic light controller to determine the length of green time at an intersection. ...
Traffic congestion is a significant issue in many countries today. The suggested method is a novel control method based on multiple intersections considering the kind of traffic light and the duration of the green phase to determine the optimal balance at intersections by using fuzzy logic control, for which the balance should be adaptable to the unchanging behavior of time. It should reduce traffic volume in transport, average waits for each vehicle, and collisions between cars by controlling this balance in response to the typical behavior of time and randomness in traffic conditions. The proposed method is investigated at intersections using a sampling multi-agent system to set traffic light timings appropriately. The program is provided with many intersections, each of which is an independent entity exchanging information with the others. The stability per entity is proven separately. Simulation results show that Takagi–Sugeno (TS) fuzzy modeling performs better than Takagi–Sugeno (TS) fixed-time scheduling in decreasing the length of queueing times for vehicles.
... Based on the advanced communication capabilities and accurate detection technologies embedded in CVs, varying traffic signal controllers can be developed, such as improved signal arterial coordination, TSP, or signalvehicle coupled control [49,41,86,12,32,68]. ...
For many decades, urban traffic management systems have been vehicle-dominated. That is not only because of a lack of attention to users, but conventional data collection tools are powerless to collect individual vehicle data as well as vehicle users data. Connected vehicles (CV), as an emerging technology, can collect and transmit real-time vehicle and its users data. This ability facilitates the development of user-centred traffic management strategies in urban transport networks. However, there are some challenges yet to be addressed to convert raw CV data to efficient input for traffic controls. Moreover, achieving a fully connected environment is not possible in near future due to various limitations.Accordingly, this dissertation aims at developing a traffic management strategy based on CV data that improves user-related performance measures at signalized intersections. Furthermore, this dissertation assesses the effect of CV data accuracy on traffic controllers and presents a method to compensate lack of CVs in the urban environment to deploy in traffic management strategies.
In this dissertation, we research two vital aspects of traffic signal control which are signal timing optimization and data. For signal timing optimization, First, using CV data, We develop a user-based signal timing optimization strategy where the objective of the controller is to maximize the user throughput of a signalized intersection. Second, We present a user-based Transit signal priority strategy where the objective of the controller is to reduce users average delay and bus scheduled delay by providing priority for buses that are behind the schedule and with a higher number of passengers on board. Moreover, secondary effects of the current transit priority systems and the proposed transit signal priority are compared, by considering the concept of total social cost. In the data section, first, the impact of CV data accuracy on the performance of signal controllers is investigated. Second, We develop a data-driven vehicle estimation method to make limited CV data usable for a signal controller.
The results of this dissertation show that implementing proper signal timing optimisation-based CVs data improves user and vehicle performance measures at signalized intersections. Moreover, a CV-based transit signal propriety that considers users of buses as well as other motorists can improve current Transit signal priority strategies while the delay of other motorists would not be increased. Moreover, the proposed transit signal priority strategy can reduce other social costs such as emission and fuel consumption. According to this dissertation's findings, data collection tools' accuracy can affect signal timing performance in some circumstances. Furthermore, the potential of a data-driven method to compensate lack of CVs has been presented in this dissertation.
Diverse transport demands have resulted in the wide existence of heterogeneous vehicle automation systems. While these systems have demonstrated effectiveness, they also pose challenges in terms of the share of technological advancements among different organizations and lead to poor generalization ability of individual systems. This paper proposes a Transformer-based unified framework, VistaGPT, to address these challenges. VistaGPT, composed of Modular Federations of Vehicular Transformers (M-FoV) and Automated Composing of Autonomous Driving Systems (AutoAuto), aims to overcome the information barriers due to system-level and module-level heterogeneity. M-FoV collects and organizes Transformer-based models in a modular fashion to facilitate system integration by providing diversity and versatility. AutoAuto utilizes large language models (LLMs) to automatically compose end-to-end autonomous driving systems with a “Dividing and Recombining” strategy. Besides, we deploy Scenario Engineering systems to evaluate the composed systems and provide systematic feedback for the optimization of AutoAuto, and Federated intelligence to contribute to diverse training samples and applications. With its capacity, scalability, and diversity, VistaGPT provides a new paradigm of LLM-aided system development for transport automation, which promotes virtual-real interactive parallel driving and advances progress toward “6S” objectives.
This research paper explores the utilization of predictive modelling and drone-captured image analysis to enhance urban traffic management in the context of smart traffic lights. The study focuses on employing advanced machine learning techniques, including LSTM and GRU architectures, to predict traffic flow patterns. Comparative analysis is conducted by evaluating the performance of these deep learning models against traditional algorithms such as Linear Regression, Gradient Boosting Regressor, and Random Forest Regressor. Metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared values are utilized to quantify the predictive accuracy of these models. Experimental results reveal that the LSTM model achieves an MAE of 6.32 and an RMSE of 12.76, while the GRU model yields an MAE of 6.50 and an RMSE of 13.12. These values outperform traditional algorithms, emphasizing the effectiveness of the proposed models in improving traffic flow predictions. The dataset comprises drone-captured images of urban traffic scenes, enabling the extraction of relevant features for accurate predictions. Findings underscore the potential of the proposed models in advancing intelligent traffic management systems.
With the development of internet of vehicles and automated driving, individual-based trajectory control at intersections becomes possible. Trajectory planning and coordination for connected and automated vehicles (CAVs) have been studied at isolated “signal-free” intersections and in “signal-free” corridors under the fully CAV environment in the literature. Most existing studies are based on the definition of approaching and exit lanes. The route a vehicle takes to pass through an intersection is determined by its movement. That is, only the origin and destination arms are included. This study proposes a mixed-integer linear programming (MILP) model to optimize vehicle trajectories at an isolated “signal-free” intersection without lane allocation, denoted as “lane-allocation-free” (LAF) control. Each lane can be used as both approaching and exit lanes for all vehicle movements including left-turn, through, and right-turn. A vehicle can take a flexible route by way of multiple arms to pass through the intersection. In this way, the spatial–temporal resources are expected to be fully utilized. The interactions between vehicle trajectories are modeled explicitly at the microscopic level. Vehicle routes and trajectories (i.e., car-following and lane-changing behaviors) at the intersection are optimized in one unified framework for system optimality in terms of total vehicle delay. Considering varying traffic conditions, the planning horizon is adaptively adjusted in the implementation of the proposed model to make a balance between solution feasibility and computational burden. Numerical studies validate the advantages of the LAF control in terms of both vehicle delay and throughput with different demand structures and temporal safety gaps.
The crossing behaviors modeling of non-networked road users can improve Connected and Autonomous Vehicles (CAVs)’s awareness of the imminent hazards in shared space while planning routes. In this study, an agent-based microsimulation model is utilized to simulate the crossing behavior of e-bikes which is difficult to model due to their greater maneuverability. To deeply understand the crossing mechanism of e-bikes, a customized neural network-based nonlinear reward function is developed from five dimensions, including travel efficiency, travel direction, travel destination, risk avoidance, and travel location. A Deep Maximum Entropy Inverse Reinforcement Learning(Deep MEIRL), which can recover the nonlinear reward function, is introduced to predict trajectories of e-bikes from the real drone-based video dataset, collected at the intersection of Jixiangcun, Xi’an (China). The results reveal that the Deep MEIRL can simulate the e-bike crossing trajectory more precisely, particularly in the microscopic behaviors of riders. Comparing Deep MEIRL with the baseline model MEIRL, it can be found that the nonlinear reward function designed in this paper is more advantageous in terms of continuous space modeling, with an improvement of 19.77%. Notably, Deep MEIRL outperforms MEIRL in modeling distance to potentially risk target states for left-and right-turn crossing behavior, improving Mean Absolute Error (MAE) by 71% and 30%, respectively. It means that the Deep MEIRL with a designed neural network is of great value to provide a logical result in modeling e-bike interaction with other road users. Therefore, the research is conducive to CAVs to understand e-bike behaviors in complex traffic scenarios, thereby assisting CAVs in making decisions efficiently.
We consider the problem of controlling Connected and Automated Vehicles (CAVs) traveling through a roundabout so as to jointly minimize their travel time, energy consumption, and centrifugal discomfort while providing speed-dependent and lateral roll-over safety guarantees, as well as satisfying velocity and acceleration constraints. We first develop a systematic approach to determine the safety constraints for each CAV dynamically, as it moves through different merging points in the roundabout. We then derive the unconstrained optimal control solution which is subsequently optimally tracked by a real-time controller while guaranteeing that all constraints are always satisfied. Simulation experiments are performed to compare the controller we develop to a baseline of human-driven vehicles, showing its effectiveness under symmetric and asymmetric roundabout configurations, balanced and imbalanced traffic rates, and different sequencing rules for CAVs.
. Forced traffic flow management using automated controls, such as traffic lights, remains relevant and often the only one. Due to the oversaturation of transport traffic, which many cities of the world are facing today, special attention is paid to effective management and the development of new optimal management methods in order to allow vehicles to pass through a regulated section of the road network as much as possible. One of the alternative options that can increase the efficiency of management by up to 20% on average is the introduction of coordinated management. The presented material provides an analysis of various approaches to the calculation of coordination plans.
Traffic disturbance in urban cities challenges the most advanced traffic signal control systems (TSCS). The challenge is mainly related to the capability of TSCS to ensure a quick detection and to suggest suitable decisions. Neural network has shown great potential in predicting traffic disturbance. In addition, smart clustering could be beneficial to ensure fast disturbance reaction while TSCS are providing control decisions. Moreover, the immune network approach has succeeded in controlling interrupted intersections. Motivated by these assumptions, we propose in this paper a disturbance mining approach based on the occurrence of traffic disturbances to ensure optimal signals control that minimizes traffic delay. Initially, the queue delay is calculated based on mutual information of different traffic scenarios. At that point, within the maximum traffic delay constraint, the feedforward neural network is considered to find the optimal traffic delay and maximize traffic fluidity. As a result, disturbances and related control decisions are clustered based on the calculated traffic delay. Our approach helped the immune network control system (INCS) by prompting it with faster reaction and lower traffic delay compared to its classical version.
In this paper, we take a self-scheduling approach to solving the traffic signal control problem, where each intersection is controlled by a self-interested agent operating with a limited (fixed horizon) view of incoming traffic. Central to the approach is a representation that aggregates incoming vehicles into critical clusters, based on the non-uniformly distributed nature of road traffic flows. Starting from a recently developed signal timing strategy based on clearing anticipated queues, we propose extended real-time decision policies that also incorporate look-ahead of approaching vehicle platoons, and thus focus attention more on keeping vehicles moving than on simply clearing queues. We present simulation results that demonstrate the benefit of our approach over simple queue clearing, both in promoting the establishment of “green waves” where vehicles move through the road network without stopping and in improving overall traffic flows.
As computing technologies develop, there is a trend in traffic simulation research in which the focus is moving from macro- and meso-simulation to micro-simulation since microsimulation can provide more detailed quantitative results. Moreover, the success of the Artificial societies-Computational experiments-Parallel execution (ACP) approach indicates that integrating other metropolitan systems such as logistic, infrastructure, legal and regulatory, and weather and environmental systems to build an Artificial Transportation System (ATS) can be helpful in solving Intelligent Transportation Systems (ITS) problems. However, the computational burden is very heavy as there are many agents interacting in parallel in the ATS. Therefore, a parallel computing tool is desirable. We think that we can employ a Graphics Processing Unit (GPU), which has been applied in many areas. In this paper, we use a GPU-adapted Parallel Genetic Algorithm (PGA) to solve the problem of generating daily activity plans for individual and household agents in the ATS, which is important as the activity plans determine the traffic demand in the ATS. Previous research has shown that GA is effective but that the computational burden is heavy. We extend the work to GPU and test our method on an NVIDIA Tesla C2050 GPU for two scenarios of generating plans for 1000 individual agents and 1000 three-person household agents. Speedup factors of 23 and 32 are obtained compared with implementations on a mainstream CPU.
Recent analysis of empirical data from cities showed that a macroscopic fundamental diagram (MFD) of urban traffic provides for homogenous network regions a unimodal low-scatter relationship between network vehicle density and network space-mean flow. In this paper, the optimal perimeter control for two-region urban cities is formulated with the use of MFDs. The controllers operate on the border between the two regions and manipulate the percentages of flows that transfer between the two regions such that the number of trips that reach their destinations is maximized. The optimal perimeter control problem is solved by model predictive control, where the prediction model and the plant (reality) are formulated by MFDs. Examples are presented for different levels of congestion in the regions of the city and the robustness of the controller is tested for different sizes of error in the MFDs and different levels of noise in the traffic demand. Moreover, two methods for smoothing the control sequences are presented. Comparison results show that the performances of the model predictive control are significantly better than a “greedy” feedback control. The results in this paper can be extended to develop efficient hierarchical control strategies for heterogeneously congested cities.
Field data are important for convenient daily travel of urban residents, reducing traffic congestion and accidents, pursuing a low-carbon environment-friendly sustainable development strategy, and meeting the extra peak traffic demand of large sporting events or large business activities, etc. To meet the field data demand during the 2010 Asian (Para) Games held in Guangzhou, China, based on the novel Artificial systems, Computational experiments, and Parallel execution (ACP) approach, the Parallel Traffic Management System (PtMS) was developed. It successfully helps to achieve smoothness, safety, efficiency, and reliability of public transport management during the two games, supports public traffic management and decision making, and helps enhance the public traffic management level from experience-based policy formulation and manual implementation to scientific computing-based policy formulation and implementation. The PtMS represents another new milestone in solving the management difficulty of real-world complex systems.
Model Predictive Control is applied to control and coordinate large-scale urban traffic networks. However, due to the large scale or the nonlinear, non-convex nature of the on-line optimization problems solved, the MPC controllers become real-time infeasible in practice, even though the problem is solvable in theory. In this thesis, we mainly focus on the solutions for increasing the real-time feasibility of the MPC controllers for large-scale urban traffic networks.
A unified platoon-based mathematical formulation called PAMSCOD is presented to perform arterial (network) traffic signal control while considering multiple travel modes in a vehicle-to-infrastructure communications environment. First, a headway-based platoon recognition algorithm is developed to identify pseudo-platoons given probe vehicles’ online information. It is assumed that passenger vehicles constitute a significant majority of the vehicles in the network. This algorithm identifies existing queues and significant platoons approaching each intersection. Second, a mixed-integer linear program (MILP) is solved to determine future optimal signal plans based on the current traffic controller status, online platoon data and priority requests from special vehicles, such as transit buses. Deviating from the traditional common network cycle length, PAMSCOD aims to provide multi-modal dynamical progression (MDP) on the arterial based on the probe information. Microscopic simulation using VISSIM shows that PAMSCOD can easily handle two common traffic modes, transit buses and automobiles, and significantly reduce delays for both modes under both non-saturated and oversaturated traffic conditions as compared to traditional state-of-practice coordinated-actuated signal control with timings optimized by SYNCHRO.
The ability to monitor vehicle queue length accurately at metered on-ramps can improve ramp meter performance and help to create improved ramp metering algorithms. The queue length distribution can be considered as a continuous analog signal, which consists of both deterministic and stochastic components. Three types of methods for the estimation of on-ramp queue length are discussed: Kalman filter, linear occupancy, and Highway Capacity Manual (HCM) back of queue. Queue data estimated with these methods are compared with field-observed queue data and random number samples. The comparisons indicate that the Kalman filter and linear occupancy methods are usable for real-world operations, but both of them have limitations. The HCM back-of-queue method does not produce reliable estimates for on-ramp queue length.
In this paper, we attempt to summarize the impact of technologies, especially Intelligent Transportation System (ITS) technologies, on transportation research during the last several decades and provide perspectives on how future transportation research may be affected by the availability and development of new ITS technologies. The intended audience of the paper includes young transportation researchers and professionals. Current transportation models are divided into ‘generations’ based on their technological and practical background. Based on the trends in the past and the potential technologies to be implemented in the future, general characteristics of the next generations of transportation models are proposed and discussed to provide a vision regarding expected future achievements in transportation research. This paper is intended to be a working document, in the sense that it will be updated periodically.
Today's strongly connected, global networks have produced highly interdependent systems that we do not understand and cannot control well. These systems are vulnerable to failure at all scales, posing serious threats to society, even when external shocks are absent. As the complexity and interaction strengths in our networked world increase, man-made systems can become unstable, creating uncontrollable situations even when decision-makers are well-skilled, have all data and technology at their disposal, and do their best. To make these systems manageable, a fundamental redesign is needed. A 'Global Systems Science' might create the required knowledge and paradigm shift in thinking.
Autonomous intersection management (AIM) is an innovative concept for directing vehicles through the intersections. AIM assumes that the vehicles negotiate the right-of-way. This assumption makes the problem of the intersection management significantly different from the usually studied ones such as the optimization of the cycle time, splits, and offsets. The main difficulty is to define a strategy that improves the traffic efficiency. Indeed, due to the fact that each vehicle is considered individually, AIM faces a combinatorial optimization problem that needs quick and efficient solutions for a real time application. This paper proposes a strategy that evacuates vehicles as soon as possible for each sequence of vehicle arrivals. The dynamic programming (DP) that gives the optimal solution is shown to be greedy. A combinatorial explosion is observed if the number of lanes rises. After evaluating the time complexity of the DP, the paper proposes an ant colony system (ACS) to solve the control problem for large number of vehicles and lanes. The complete investigation shows that the proposed ACS algorithm is robust and efficient. Experimental results obtained by the simulation of different traffic scenarios show that the AIM based on ACS outperforms the traditional traffic lights and other recent traffic control strategies.
This paper examines the effects of two traffic management measures, speed limit reduction and coordinated traffic lights, in an area of Antwerp, Belgium. An integrated model is deployed that combines the microscopic traffic simulation model Paramics with the CO(2) and NO emission model VERSIT+. On the one hand, reductions in CO(2) and NO emissions of about 25% were found if speed limits are lowered from 50 to 30 km/h in the residential part of the case study area. On the other hand, reductions in the order of 10% can be expected from the implementation of a green wave signal coordination scheme along an urban arterial road.
Probe vehicle data are increasingly becoming more attractive for real-time system state estimation in transportation networks. This paper presents analytical models for the real-time estimation of queue lengths at traffic signals using the fundamental information (i.e., location and time) that probe vehicles provide. For a single queue with Poisson arrivals, analytical models are developed to evaluate how error changes in queue length estimation as the percentage of probe vehicles in the traffic stream varies. When the overflow queue is ignored, a closed-form solution is obtained for the variance of the estimation error. For the more general case with the overflow queue, a formulation for the error variance is presented, which requires the marginal probability distribution of the overflow queue as the input. In addition, an approximate model is presented for the latter case, which yields results that are comparable with the exact solution. Overall, the formulations presented here can be used to assess the error in queue length estimation from probe data without conducting simulation runs for various scenarios of probe vehicle market-penetration rates and congestion levels.
Traffic merging in urban environments is one of the main causes of traffic congestion. From the driver's point of view, the difficulty arises along the on-ramp where the merging vehicle's driver has to discern whether he should accelerate or decelerate to enter the main road. In parallel, the drivers of the vehicles already on the major road may have to modify their speeds to permit the entrance of the merging vehicle, thus affecting the traffic flow. This paper presents an approach to merging from a minor to a major road in congested traffic situations. An automated merging system that was developed with two principal goals, i.e., to permit the merging vehicle to sufficiently fluidly enter the major road to avoid congestion on the minor road and to modify the speed of the vehicles already on the main road to minimize the effect on that already congested main road, is described. A fuzzy controller is developed to act on the vehicles' longitudinal control - throttle and brake pedals - following the references set by a decision algorithm. Data from other vehicles are acquired using wireless vehicle-to-infrastructure (V2I) communication. A system installed in the infrastructure that is capable of assessing road traffic conditions in real time is responsible for transmitting the data of the vehicles in the surrounding area. Three production vehicles were used in the experimental phase to validate the proposed system at the facilities of the Centro de Automática y Robótica with encouraging results.
A system is described that measures the vehicle count and travel time in the links of a road network. The measurements require matching vehicle signatures recorded by a wireless magnetic sensor network. The matching algorithm is based on a statistical model of the signatures. The model itself is estimated from the data. The approach is first discussed for a single-lane road and extended to multiple-lane roads. The algorithm yields a correct matching rate of 75% for a false matching rate of 5% and reliably estimates the number of vehicles on each link and its travel-time distribution. The system is tested on a 0.9-mi-long segment of San Pablo Avenue, Albany, CA.
Broadcast transmission is an effective way to disseminate safety-related information for cooperative driving in intervehicle communication (IVC). However, it is fraught with fundamental challenges such as message redundancy, link unreliability, hidden terminals, and broadcast storms, which greatly degrade network performance. In this paper, we introduce a cross-layer approach to design an efficient and reliable broadcast protocol for emergency message dissemination in IVC systems. We first propose a novel composite relaying metric for relay selection by jointly considering geographical locations, physical-layer channel conditions, and moving velocities of vehicles. Based on the relaying metric, a distributed relay-selection scheme is proposed to assure that a unique relay is selected to reliably forward the emergency message in the desired propagation direction. We further apply IEEE 802.11e enhanced distributed coordination access (EDCA) medium-access control (MAC) to guarantee quality-of-service (QoS) provisioning to safety-related services. In addition, an analytical model is developed to study the performance of the proposed cross-layer broadcast protocol (CLBP) in terms of the relay-selection delay and the emergency message access delay. Network Simulator (NS-2) simulation results are given to validate our analysis. It is shown that the CLBP not only can minimize the broadcast message redundancy but can quickly and reliably deliver emergency messages in IVC as well.
A major research topic in intelligent transportation systems (ITSs) is the development of systems that will be capable of controlling the flow of vehicular traffic through crossroads, particularly in urban environments. This could significantly reduce traffic jams, since autonomous vehicles would be capable of calculating the optimal speed to maximize the number of cars driving through the intersection. We describe the use of vehicle-to-vehicle (V2V) communications to determine the position and speed of the vehicles in an environment around a crossroad. These data are used to estimate the intersection point, and a fuzzy controller then modifies the speed of the cars without right of way according to the speed of the car with right of way. Experimental tests conducted with two mass-produced cars on a real circuit at the facilities of the Instituto de Automa??tica Industrial, Consejo Superior de Investigaciones Cienti??ficas, Madrid, Spain, gave excellent results.
The continuously increasing daily traffic congestions on motorway networks call for innovative control measures that would drastically improve the current traffic conditions. Mainstream traffic flow control (MTFC) is proposed as a novel motorway traffic management tool. Its possible implementation and principal impact on traffic flow efficiency is analysed. Variable speed limits, suitably operated and enforced, is considered as one (out of several possible) way(s) for MTFC realisation, either as a stand-alone measure or in combination with ramp metering. A previously developed software tool for optimal integrated motorway network traffic control including MTFC is applied to a large-scale motorway ring-road. It is demonstrated via several investigated control scenarios that traffic flow can be substantially improved via MTFC with or without integration with coordinated ramp metering actions.
Since 1973 the UK Transport and Road Research Laboratory has been researching a vehicle responsive method of signal control called SCOOT (Split, Cycle and Offset Optimisation Technique). Research was carried out in Glasgow. The success of the research phase led to a development project between the Departments of Transport and of Industry and the three traffic companies. This paper describes the SCOOT system and the results of the trials which compared SCOOT with up-to-date fixed-time systems. -from Authors
Signal retiming is one of the most cost effective ways to improve traffic flow along a corridor. It can significantly reduce delays and stops experienced by motorists, which can improve safety and reduce fuel consumption and emissions. Typically, the benefit to cost ratio for signal retiming is about 40:1.
The use of traffic control systems can potentially improve the traffic flows on traffic networks. However, for the implementation of such control systems both in simulation and in practice many steps should be taken, and many choices are to be made. In this paper a list of considerations is provided for developing model-based traffic control systems in general, with a more detailed discussion on the use of model-predictive control for traffic regulation. A case study of designing a traffic controller is provided for the Dutch A12 freeway.
Connected Vehicle (CV) technology, formerly known as IntelliDrive, has emerged and is expected to provide unprecedented improvements in mobility. A recent study developed a cooperative vehicle intersection control (CVIC) algorithm for an urban intersection that does not require a stop-and-go style traffic signal and demonstrated significant mobility improvements over an actuated traffic signal control. This paper expanded the algorithm and implemented it to a corridor consisting of multiple intersections. In addition, this paper investigated sustainability aspects of the CVIC system for an urban traffic control system by applying surrogate safety assessment model (SSAM) and VT-Micro model to measure safety and environmental impacts, respectively. A simulation-based case study was performed on a hypothetical arterial consisting of four intersections with eight traffic congestion cases covering low to high volume conditions. When compared to the coordinated actuated control, the CVIC system dramatically reduced the total delay times for the volume cases considered (i.e., from 82% to 100% delay time savings observed). The CVIC system also reduced the number of rear-end crash events by 30-87% for the volume cases considered, indicating that safer driving conditions would be achieved with the CVIC system. Finally, the CVIC system contributed to improving the air quality (i.e., 12-36% CO2 emission reduction) and saving fuel consumptions (11-37% of gas saving).
: This article describes a coordinated ramp metering algorithm for systematically mitigating freeway congestion. A preemptive hierarchical control scheme with a three-priority-layer structure is employed in this algorithm. Ramp metering is formulated as a multiobjective optimization problem to enhance system performance. These optimization objectives include promptly tackling freeway congestion, sufficiently utilizing on-ramp storage capacities, and preventing on-ramp vehicles from overflowing to local streets, balancing on-ramp vehicle equity, and maximizing traffic throughputs for the entire system. Instead of relying heavily on accurate estimates of freeway traffic flow evolvement, this new approach models ramp meter control as a linear program and uses real-time traffic sensor measurements for minimizing the indeterminate impacts from the mainstream flow capacities. VISSIM-based simulation experiments are performed to examine its practicality and effectiveness using geometric and traffic demand data from one real-world freeway segment. The simulation test results show that the proposed ramp metering approach performed well in optimizing overall freeway system operations under various traffic conditions. The system-wide optimal control performance can be achieved to quickly mitigate freeway congestion, prevent traffic from overflowing to local streets, and maximize overall traffic throughputs. The proposed ramp metering approach can dynamically assemble relevant ramp meters to work together and effectively coordinate the individual meter rates to leverage their response strengths for minimizing time to clear the congestion. This study demonstrates that utilization of existing freeway infrastructure can be optimized through the proposed algorithm.
In this paper, we propose to use vehicular ad hoc networks (VANETs) to collect and aggregate real-time speed and position information on individual vehicles to optimize signal control at traffic intersections. We first formulate the vehicular traffic signal control problem as a job scheduling problem on processors, with jobs corresponding to platoons of vehicles. Under the assumption that all jobs are of equal size, we give an online algorithm, referred to as the oldest job first (OJF) algorithm, to minimize the delay across the intersection. We prove that the OJF algorithm is 2-competitive, implying that the delay is less than or equal to twice the delay of an optimal offline schedule with perfect knowledge of the arrivals. We then show how a VANET can be used to group vehicles into approximately equal-sized platoons, which can then be scheduled using OJF. We call this the two-phase approach, where we first group the vehicular traffic into platoons and then apply the OJF algorithm, i.e., the oldest arrival first (OAF) algorithm. Our simulation results show that, under light and medium traffic loads, the OAF algorithm reduces the delays experienced by vehicles as they pass through the intersection, as compared with vehicle-actuated methods, Webster's method, and pretimed signal control methods. Under heavy vehicular traffic load, the OAF algorithm performs the same as the vehicle-actuated traffic method but still produces lower delays, as when compared with Webster's method and the pretimed signal control method.
We present a freeway-traffic control strategy that continuously adapts traffic control measures to prevailing traffic conditions and features faster computation speed than conventional model-based predictive control (MPC). The control approach is based on the principles of state feedback control and MPC. Instead of computing the control input sequence, the proposed controller optimizes the parameters of control laws that parametrize the control input sequences. This way, the computational burden of the controller is substantially reduced. We demonstrate the proposed control approach on a calibrated model of part of the Dutch A12 freeway using variable speed limits and ramp-metering rate.
Traffic management, especially in intersections, is an important part of intelligent transportation systems (ITS). In this paper, a new traffic controller is proposed which removes traffic lights in intersections. The heart of the proposed controller is a simple integer linear program (ILP) code. This program manages safe driving patterns crossing a junction while it tries to maximize number of passed vehicles across the intersection. The demanded data are prepared by VANET. The proposed safe driving pattern assures the safety of moving vehicles and avoiding any accident. Simulation results show that the proposed controller performs efficiently. The average queue lengths and also tolerated delay by vehicles are far better than traditional approaches. Also, for different probabilities of output selection, the proposed approach remains stable yet.
Model-based intersection optimization strategies have been widely investigated for distributed traffic signal control in road networks. Due to the form of “black-box” optimization that is typically assumed, a basic challenge faced by these strategies is the combinatorial nature of the problem that must be solved. The underlying state space is exponential in the number of time steps in the look-ahead optimization horizon at a given time resolution. In this paper, we present a schedule-driven intersection control strategy, called SchIC, which addresses this challenge by exploiting the structural information in non-uniformly distributed traffic flow. Central to our method is an alternative formulation of intersection control optimization as a scheduling problem, which effectively reduces the state space through use of an aggregate representation on traffic flow data in the prediction horizon. A forward recursive algorithm is proposed for solving the scheduling problem, which makes use of a dominance condition to efficiently eliminate most states at early stages. SchIC thus achieves near optimal solutions with a polynomial complexity in the prediction horizon, and is insensitive to the granularity of time resolution that is assumed. The performance of SchIC with respect to both intersection control and implicit coordination between intersections is evaluated empirically on two ideal scenarios and a real-world urban traffic network. Some characteristics and possible real-world extensions of SchIC are also discussed.
Traffic congestion has become a stringent issue in urban areas. Traffic control systems are designed to make a better use of the existing traffic infrastructures in order to improve traffic conditions. Along with the fast development of the transportation infrastructures, traffic networks become larger and more complex. Therefore, network-wide traffic control systems that can coordinate the whole network and improve the utilization of the entire traffic infrastructure, are highly required. In this paper, a structured network-wide traffic controller is presented based on Model Predictive Control (MPC) theory. Two macroscopic models are proposed to be the prediction model of the MPC controller. One is more accurate, but correspondingly requires more computation time; the other sacrifices a certain amount of accuracy, but is computationally much more efficient. Based on these two models, MPC controllers are developed. Simulation results show that the MPC controllers are capable of coordinating an urban traffic network, especially in the situations that the traffic flow is not spread evenly through the network. Through reducing the prediction model, the corresponding MPC controller exhibits less on-line computational burden, and thus becomes more applicable in practice. Therefore, it becomes possible for the control system to deal with complex urban road networks more efficiently.
We present an integrated traffic management and control approach for automated highway systems (AHS). The AHS consist of interacting roadside controllers and intelligent vehicles that are organized in platoons with short intraplatoon distances and larger distances between platoons. All vehicles are assumed to be fully automated, i.e., throttle, braking, and steering commands are determined by an automated onboard controller. The proposed control approach is based on a hierarchical traffic control architecture for AHS, and it also takes the connection and transition between the nonautomated part of the road network and the AHS into account. In particular, we combine dynamic speed limits and lane allocation for the platoons on the AHS highways with access control for the on-ramps using ramp metering, and we propose a model-based predictive control approach to determine optimal speed limits and lane allocations, as well as optimal release times for the platoons at the on-ramps. To illustrate the potential of the proposed traffic control method, we apply it to a simple simulation example.
Automated cooperative collision avoidance of mul- tiple vehicles is a promising approach to increase road safety in the future. This approach requires a real-time motion planner which computes cooperative maneuvers of multiple cognitive vehicles. As motion planning is a task of high computational complexity, computing times of the planner have to be traded off against solution quality. This contribution compares several cooperative motion planning algorithms with respect to these criteria. The considered algorithms are a tree search algorithm relying on precomputed lower bounds, the elastic band method, mixed-integer linear programming, and a priority-based ap- proach. Success rates and computing times on various simulated scenarios are reported. I. INTRODUCTION the algorithms have been designed specifically for multi- vehicle planning and explore combinations of individual ac- tions, unlike the decoupling approaches. The algorithms are examined by simulating several scenarios. Both computing times and success rates are compared. It is shown that the suitability of the algorithms varies with the scenario. B. Paper Outline The paper is structured as follows. In Section II, the cooperative motion planning problem is formalized. Sec- tion III presents the four algorithms considered: a tree search algorithm, mixed-integer linear programming, the elastic band method, and a prioritized approach. Results of the comparison are reported in Section IV, and Section V presents some conclusions.
This paper presents measurements of CO2 emission and efficiency of stop/start technology on a diesel vehicle in urban traffic. Two four-wheel-drive diesel vehicles with on-board exhaust emission and vehicle activity measurement systems were tested in two urban driving circuits representative of downtown Madrid. The vehicles had similar turbocharged and intercooled diesel engines fulfilling the same Euro 4 emissions regulation; but one had an improved engine incorporating stop/start technology. CO2 emission reduction of more than 20% for the car equipped with the stop/start system was obtained. Regardless of the variability in driving style, the grade and type of streets, traffic congestion, and the engine operating temperature, the car equipped with the stop/start system has intrinsically a lower CO2 emission factor.
An approach for the real-time recognition of vehicle platoons based on the time epochs of vehicles passing over a point (detector) on a link is introduced. Since platoons on a link depend on its traffic volume and the distribution of the vehicle headways, the algorithm first computes second-by-second vehicle density (vehicles per second) for each network link. To identify platoon beginnings and endings, this link density is multiplied by two fixed factors, one greater than 1 and the other less than 1, to obtain upper link density and lower link density, respectively; these are subsequently utilized in the platoon-recognition process. Using detector data from microsimulations of traffic scenarios, the platoon-recognition method is evaluated with respect to the following measures of effectiveness: percentage of vehicles captured in platoons, platoon size distribution, platoon densities, number of platoons versus percentage of free time (defined as the time between platoons), model robustness, and computational time.
The problem of minimizing the total tardiness for a set of independent jobs on one machine is considered. Lawler has given a pseudo-polynomial-time algorithm to solve this problem. In spite of extensive research efforts for more than a decade, the question of whether it can be solved in polynomial time or it is NP-hard in the ordinary sense remained open. In this paper the problem is shown to be NP-hard in the ordinary sense.
In this paper, we propose a new approach for controlling the traffic at isolated intersections. We assume that all vehicles are equipped with on-board units (ITS station) that make them able to wirelessly negotiate the “right of way” according to the measurements done by the positioning system during their travel. A vehicle is allowed to cross the intersection if the green color is displayed to the driver in an on-board screen. The control aims to smooth the traffic through the sequence of vehicles authorized to traverse the intersection. The main challenge raised with the assumption is that the sequence must be dynamically formed by a real time application. The dynamic behavior of the traffic is considered discrete, in order to determine the switching rule according to the instantly observed events. We propose a model based on Timed Petri Nets with Multipliers (TPNM) which allows us to propose the control policy through the structural analysis. The resulting switching rules are very simplistic and efficient for isolated intersections. Indeed, microscopic simulations show that they perform as well as the optimal sequence based on the detection of vehicles at the entrance of the intersection. Moreover, the proposed approach has been tested through a real intersection.