Headway Control Research Articles

Gain-scheduled model predictive controller for vehicle-following trajectory generation for autonomous vehicles

As the development of autonomous vehicles accelerates, the need to enhance the comfort characteristics for those vehicles has become important. In the present article, an enhanced vehicle-following motion planner algorithm is presented. The aim of the algorithm is to smoothen the repetitive braking and acceleration behaviour during vehicle following in traffic jam situations. The algorithm uses the information gathered from Lidar sensor, cameras and vehicle-embedded sensors in real time to construct the range vs. range-rate diagram, and it computes the desired velocity trajectory for the speed controller. The algorithm is based on the Gain-Scheduled Model Predictive Controller (MPC), where at least one MPC controller is designed to handle one of the three vehicle-following operating conditions: speed control, headway control and emergency brake control. The algorithm allows the designer to manipulate two vehicle following variables: standstill distance between lead vehicle and ego vehicle, and the headway time gap. The algorithm is experimentally validated on a full-size passenger vehicle.

Maximin headway control of automated vehicles for system optimal dynamic traffic assignment in general networks

This study develops the headway control framework in a fully automated road network, as we believe headway of Automated Vehicles (AVs) is another influencing factor to traffic dynamics in addition to conventional vehicle behaviors (e.g. route and departure time choices). Specifically, we aim to search for the optimal time headway between AVs on each link that achieves the network-wide system optimal dynamic traffic assignment (SO-DTA). To this end, the headway-dependent fundamental diagram (HFD) and headway-dependent double queue model (HDQ) are developed to model the effect of dynamic headway on roads, and a dynamic network model is built. It is rigorously proved that the minimum headway could always achieve SO-DTA, yet the optimal headway is non-unique. Motivated by these two findings, this study defines a novel concept of maximin headway, which is the largest headway that still achieves SO-DTA in the network. Mathematical properties regarding maximin headway are analyzed and an efficient solution algorithm is developed. Numerical experiments on both a small and large network verify the effectiveness of the maximin headway control framework as well as the properties of maximin headway. This study sheds light on deriving the desired solution among the non-unique solutions in SO-DTA and provides implications regarding the safety margin of AVs under SO-DTA.

A Time Headway Control Scheme for Virtually Coupled Heavy Haul Freight Trains

Abstract Virtual coupling of railway trains is an emerging technology that has the potential to significantly increase railway operational efficiency by reducing the train following distance from absolute braking distances to relative braking distances. Current research in this topic is mainly focused on passenger trains and uses distance-based headways. This paper studied virtual coupling for heavy haul freight trains and demonstrated that the distance headway scheme was challenging and sometimes impractical for heavy haul trains to achieve virtual coupling. A time-based headway scheme was then proposed to set the follower train to be a certain time behind the schedule of the leader train rather than a distance headway. The time-based headway required the follower train to reproduce the leader train's operational status at the same track location. This also allowed the follower train to copy any optimized train driving strategies from the leader train. Demonstrative simulations were carried out without the consideration of communication errors and train localization errors. The results show that a conventional distance headway simulation had maximum distance and speed errors of 716 m (36%, reference 2 km) and 24 km/h (66%, reference 36 km/h), respectively. A time-based headway simulation reduced the maximum distance and speed errors to 0.07 m (0%, reference 2 km) and 0.1 km/h (9%, reference 1.18 km/h), respectively.

Freeway congestion management on multiple consecutive bottlenecks with RL‐based headway control of autonomous vehicles

AbstractAdaptive cruise control (ACC) is the core building block of future full autonomous driving. Numerous recent research demonstrated that Autonomous Vehicles (AVs) adopting shorter headways generally increase road capacity and may relieve congestion at bottlenecks for moderate demand scenarios. However, with high demand scenarios, bottlenecks can still be activated causing capacity breakdown. Therefore, extra control measures as dynamic traffic control near bottlenecks is necessary. The challenge is harder on urban freeways with consecutive bottlenecks which affect each other. This paper aims to improve the performance of ACC systems in a high demand scenario. A multi‐bottleneck dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts headways to optimize traffic flow and minimize delay is proposed. The controller dynamically assigns an optimal headway for each controlled section, based on state measurement representing the current traffic conditions. The case study is a freeway stretch with three consecutive bottlenecks which is then extended to include eight bottlenecks. Three different RL agent configurations are presented and compared. It is quantitatively demonstrated that the proposed control strategy improves traffic and enhances the system delay by up to 22.30%, and 18.87% compared to shortest headway setting for the three‐bottleneck and the eight‐bottleneck networks, respectively.

Longitudinal Control for Cruise Control Features in Vehicle Automation

This research focuses on longitudinal control methods implemented in Cruise Control (CC) and Adaptive Cruise Control (ACC) systems, as well as the mechanism for switching between these two modes. Two primary longitudinal control methods are examined: Constant Space Headway Control, which maintains a constant distance between vehicles, and Constant Time Headway Control, which maintains a constant time gap between vehicles.
 The noteworthy feature of this research focuses on incorporating a switching mechanism between Cruise Control and Adaptive Cruise Control through the utilization of the virtual target method.

Research on V2V Time Headway Strategy Considering Multi-Front Vehicles Based on Artificial Potential Field

Within the Internet of Vehicles, the driver’s conduct is more affected by the information about the vehicles in front of them. To meet the driver’s demand for the design of the safety vehicle distance of the adaptive cruise control system in the Vehicle-to-Vehicle (V2V) environment, this paper employs the artificial potential field (APF) theory to enhance the vehicle time headway strategy and constructs a mixed force function in the global force field with three front vehicles as the center of the three potential fields. Taking into account the inadequate control effect of conventional APF in a dynamic environment, this paper describes the relative kinematic characteristics of the vehicles in front and the host vehicle as parameters and obtains the improved APF. The improved strategy based on APF enhances the forward-looking and anti-interference ability of the distance strategy. Also, the strategy presented in this paper can achieve time headway control under all operating conditions and prevent acceleration fluctuations due to switching between various control strategies. For collaborative simulation, this research uses MATLAB/Simulink and Carsim software. The simulation is conducted under three common traffic scenarios, and it is determined that the improved control method results in smoother changes in vehicle speed, acceleration, and car-following distance, hence improving driving and riding comfort.

Decentralized signal control for multi-modal traffic network: A deep reinforcement learning approach

Managing traffic flow at intersections in a large-scale network remains challenging. Multi-modal signalized intersections integrate various objectives, including minimizing the queue length and maintaining constant bus headway. Inefficient traffic signals and bus headway control strategies may cause severe traffic jams, high delays for bus passengers, and bus bunching that harms bus line operations. To simultaneously improve the level of service for car traffic and the bus system in a multi-modal network, this paper integrates bus priority and holding with traffic signal control via decentralized controllers based on Reinforcement Learning (RL). The controller agents act and learn from a synthetic traffic environment built with the microscopic traffic simulator SUMO. Action information is shared among agents to achieve cooperation, forming a Multi-Agent Reinforcement Learning (MARL) framework. The agents simultaneously aim to minimize vehicles' total stopping time and homogenize the forward and backward space headways for buses approaching intersections at each decision step. The Deep Q-Network (DQN) algorithm is applied to manage the continuity of the state space. The tradeoff between the bus transit and car traffic objectives is discussed using various numerical experiments. The introduced method is tested in scenarios with distinct bus lane layouts and bus line deployments. The proposed controller outperforms model-based adaptive control methods and the centralized RL method regarding global traffic efficiency and bus transit stability. Furthermore, the remarkable scalability and transferability of trained models are demonstrated by applying them to several different test networks without retraining.

Improving the performance of headway control tools by using individual driving speed data

Improving the performance of headway control tools by using individual driving speed data

Adaptive Headway Control Algorithm for Mixed-Traffic Stabilization and Optimization with Automated Cars and Trucks

Automated vehicle (AV) technology provides an opportunity to increase capacity and reduce congestion while maintaining stability and flow rate by allowing AVs to control their movements using references such as vehicle headways. Studies have been conducted on advancing the control algorithm of automated cars, and recent research has focused on different AV market penetration ratios. However, a straightforward way to control AVs using model parameters considering different AV types (i.e., cars and trucks) has seldom been both investigated and implemented. A method is developed to show the feasibility of controlling AVs using the desired headway as a variable to ensure stability and satisfy demand. The proposed adaptive headway control method for automated vehicles can stabilize flow and optimize flow rate in a mixed-traffic environment with four vehicle types: (1) AV cars, (2) non-AV cars, (3) AV trucks, and (4) non-AV trucks. At various AV market penetration ratios and truck percentages, the relationship between AV headways and stability is investigated. The model is implemented and tested in a simulation environment based on real-time interactions of the External Driver Model and VISSIM. Numerical analyses demonstrate that if the truck percentage is 80% or less, traffic is stable with the proposed control algorithm. The proposed algorithm is evaluated in microscopic simulation. Compared with the baseline case with no headway control, the proposed adaptive headway control algorithm can lead to less oscillation in traffic flow and can also reduce the delay by 23.19% while increasing the average travel speed up to 9.09%.

Freeway Congestion Management With Reinforcement Learning Headway Control of Connected and Autonomous Vehicles

Adaptive cruise control (ACC) systems are increasingly offered in new vehicles in the market today, and they form a core building block for future full autonomous driving. ACC systems allow vehicles to maintain a desired headway to a leading vehicle automatically. Recent research demonstrates that (1) shorter headways lead to higher throughput, and (2) the effective use of ACC can improve traffic flow by adapting the desired time headway in response to changing traffic conditions. In this paper we show that, although shorter headways result in higher capacity, flow breakdown still occurs if traffic densities at bottlenecks are allowed to exceed the critical density. Therefore, dynamic traffic control near bottlenecks is still necessary to avoid bottleneck activation and capacity loss. We propose an adaptive reinforcement learning (RL) headway controller that uses ACC headways to optimize traffic flow and minimize delay. Based on state measurements, the controller dynamically assigns an optimal headway value for each freeway section within a control cycle. In a freeway simulation example, we first demonstrate that different nondynamic headway assignment strategies failed to avoid congestion and traffic breakdown. We then present a dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts the desired headway according to the changing traffic conditions on both the freeway and the ramp to effectively maximize traffic flow and minimize system delay. We quantitatively demonstrate that our DRL dynamic headway control strategy improved traffic and reduced system delay by up to 57% compared with the examined nondynamic headways.

Research on headway control of a guided transport system based on intersection condition evaluation

Abstract In order to ensure the safety and efficiency of the section tracking operation of a guided transport system, a safety headway control method of section tracking based on intersection conditions is proposed in this paper. Considering the difference of signal phase, the evaluation model of road conditions was established based on a fuzzy comprehensive evaluation method, FAGT. Based on the artificial potential field method, the time-varying hybrid artificial potential field (TH-APF) method was proposed, and the tracking headway control algorithm was designed to realize the dynamic control of the tracking headway of the guide transport vehicle. The simulation results verified the effectiveness and applicability of the evaluation model of intersection road conditions; the tracking headway can be maintained at about 120 s. The tracking headway control algorithm of guided transport vehicles can respond to the road conditions and avoid the local optimum of the artificial potential field method, thus improving the operating efficiency.

Understanding what elements influence a bus driver to use headway regularity tools: case study of Santiago public transit system

ABSTRACT The effectiveness of the control strategies applied in real-time to maintain regular headways between buses, especially those operating without schedules, requires the driver to execute the instruction from central control. This work aims to understand how drivers approach the tools that seek to control the regularity of the headways. Our study is conducted on the Transantiago, the public transport system of Santiago in Chile. Buses operating two routes of this service provider have been equipped with a headway control tool that provides drivers instructions to improve headway regularity. Our results show experienced drivers (over 50 years) perceive being faster and maintaining more regular headways than younger ones. They also appear to be more reluctant to use onboard headway control tools. Less experienced drivers recognize the accuracy of the information delivered and assure that it has improved their driving performance.

A Method of Optimal Control for Class 8 Vehicle Platoons Over Hilly Terrain

Abstract This work develops and implements a nonlinear model predictive control (NMPC) control system to facilitate fuel-optimal platooning of class 8 vehicles over challenging terrain. Prior research has shown that cooperative adaptive cruise control (CACC), which allows multiple class 8 vehicles to follow in close succession, can save between 3% and 8% in overall fuel consumption on flat terrain. However, on more challenging terrain, e.g., rolling hills, platooning vehicles can experience diminished fuel savings, and, in some cases, an increase in fuel consumption relative to individual vehicle operation. This research explores the use of NMPC with predefined route grade profiles to allow platooning vehicles to generate an optimal velocity trajectory with respect to fuel consumption. In order to successfully implement the NMPC system, a model relating vehicle velocity to fuel consumption was generated and validated using experimental data. Additionally, the predefined route grade profiles were created by using the vehicle's GPS velocity over the desired terrain. The real-time NMPC system was then implemented on a two-truck platoon operating over challenging terrain, with a reference vehicle running individually. The results from NMPC platooning are compared against fuel results from a classical proportional-integral-derivative (PID) headway control method. This comparison yields the comparative fuel savings and energy efficiency benefit of the NMPC system. In the final analysis, significant fuel savings of greater than 14 and 20% were seen for the lead and following vehicles relative to their respective traditional cruise control and platooning architectures.

Load-sensitive bus headway control for reducing onboard passenger waiting time

Regularity in the bus schedule on low frequency lines or the maintenance of headways on high frequency lines are desired in the operation of public transportation. Bus holding is a technique used for this purpose, but it may incur in delays for onboard passengers. Previous work based on a predictive control method identified a better performance when the holding actions were concentrated in stations where the buses were emptier. Based on these results, the forward headway and the two-way headway feedback control methods, both operated with static gains and mathematically and computationally simpler than predictive control, are modified to operate with variable gains according to the bus load using two different techniques. A variable slack, dependent on the bus load, is also incorporated into the controllers. Microsimulations of a BRT corridor with concentrated and distributed demand patterns have shown that the proposed techniques improve the system performance when compared to the corresponding methods with fixed gain.

Optimal holding time calculation algorithm to improve the reliability of high frequency bus route considering the bus capacity constraint

The self-equalizing bus headway control method, as a type of headway-based control method, has been proved to resist bus bunching effectively. This control method is suitable for use in real-time transit operations owing to its simplicity in the formulation and solution. However, previous studies paid more attention on the demonstration that the bus headways can converge under variable conditions, without considering the bus capacity constraint. In addition, when the bus capacity constraint is considered, the optimal holding time calculation becomes a nonlinear problem instead of a simple linear problem. Therefore, in this study, a more precise and comprehensive model is proposed to describe the bus operation process. An optimal holding time calculation algorithm is proposed to solve the nonlinear optimization problem by transforming it into an one-dimensional line search problem. The proposed model was validated through numerical tests. In the traditional analyses, bus bunching will worsen if no additional control method is applied to the bus system. However, the present study indicates that bus bunching will be mitigated to some extent by the public transit system itself without external control at relatively high traffic demand level.

Generalized Auto-Sequencing Bus Headway Control Formulation

In a BRT system, for an efficient planning and management of fleets and drivers, precision of operations is required at the intervals provided. However, sometimes actions are taken that change the order of buses on the circuit, such as overtaking. Despite their impact on operations, these changes are not fully addressed by main headway control algorithms found in the literature and, when considered, they are dealt with enumerating processes to decide on the reordering of buses. To mitigate this issue, this paper presents a generalized formulation for auto-sequencing buses in headway control strategies. The proposed methodology allows the modeling of overtaking, injection, and removal of buses, without resorting to enumerating methods for bus ordering. The obtained results demonstrate that the proposed formulation can handle the events of change of order in a BRT circuit while, at the same time, controlling the headway between the buses and reducing the waiting time for passengers. Thus, the generalized auto-sequencing method is a framework that can be used to augment other control strategies already present in the literature.

A Novel Graph and Safety Potential Field Theory-Based Vehicle Platoon Formation and Optimization Method

Platooning is considered to be a very effective method for improving traffic efficiency, traffic safety and fuel economy under the connected and automated environment. The prerequisite for realizing these advantages is how to form a platoon without any collisions and how to maintain and optimize the car-following behavior after platoon formation. However, most of the existing studies focus on the platoon configuration and information transmission method, while only a few attempt to address the issue of platoon formation and optimization methods. To this end, this study proposes a novel platoon formation and optimization model combining graph theory and safety potential field (G-SPF) theory for connected and automated vehicles (CAVs) under different vehicle distributions. Compared to previous studies, we innovatively incorporate the concept of the safety potential field to better describe the actual driving risk of vehicles and ensure their absolute safety. A four-step platoon formation and optimization strategy is developed to achieve platoon preliminary formation and platoon driving optimization control. Three traffic scenarios with different CAVs distributions are designed to verify the effectiveness of our proposed platoon formation method based on G-SPF theory, and the simulation results indicate that a collision-free platoon can be formed in a short time. Additionally, the G-SPF-based platoon driving optimization control method is demonstrated by comparing it with two typical control strategies. Compared with the constant spacing and constant time headway control strategies, the simulation results show that our proposed method can improve the traffic capacity by approximately 48.8% and 26.6%, respectively.

Bus Trajectory Optimization With Holding, Speed and Traffic Signal Actuation in Controlled Transit Systems

We propose an optimal actuation of operational commands for regularizing bus headways. Assuming that a transit headway control system manages the bus operation by issuing reference arrival times at the next station for a bus, the problem is how to implement the control decisions for the bus in terms of three nonexclusive alternative actions: holding the bus at stops; varying bus speeds; and controlling traffic lights. Mathematical programming provides the basis of the approach. The constraints specify the bus trajectory model and the operator objectives are formulated as a multi-objective cost function solved by a lexicographic method. Results for a bus run on a single segment between two stops highlight the properties of the solution. A simulation study of an entire transit corridor in Quebec City, Canada, shows the advantage of the method over both headway control based on pure holding at stops and on one that combines holding and absolute transit signal priority.

Coordinated Headway-Based Control Method to Improve Public Transit Reliability considering Control Points Layout

The headway-based control method is usually used to regulate the bus headways and improve reliability of public transit. In general, the holding control strategy is applied at the control point, because enough space for dwell longer at the control point is required, while the stop-skipping control strategy can be used at any bus stop. However, in the headway-based control method, too much stop-skipping will bring longer waiting time and make the passengers impatient. The number and distribution of control points for stop-skipping are not considered in previous self-equalizing bus headway control works. Therefore, in this paper, the control points selection rules for stop-skipping involving their number and distribution on the bus route are discussed. A second by second discrete system is formulated to describe the bus operation. In the proposed control method, the threshold value for activating stop-skipping strategy is raised, avoiding provoking much additional waiting time because of boarding rejected. In the numerical analysis, a set of cases are conducted to evaluate the performance of control method under different number and distribution of control points for stop-skipping. The numerical results show that distribution of control points for stop-skipping has a greater influence on the public transit than the number.

The impact of bus fleet size on performance of self-equalise bus headway control method

Headway fluctuation and bus bunching are common in transit operations, while holding control is a proven strategy for the reduction of bus bunching. Recently, benefited from the advances in technology and tools, adaptive control method for equalising bus headways on the bus route gradually has become successful. Although this method performs well in regulating the bus operation, it does not consider the impact of the number of buses on the bus route. The control method may fail to regulate bus headways due to an insufficient number of buses, despite the control method itself being effective. Therefore, this paper explores the relationship between the number of buses on the bus route and the performance of the self-equalise bus headway control method. A model is proposed to search the suitable number of buses on the bus route by analysing the changes of the bus headway standard deviations with increasing the number of buses. Furthermore, a set of numerical tests were conducted. According to the results of the analysis, under high demand, the performance of the self-equalise bus headway control method is more sensitive to changes of bus number on the bus route.