Traffic Control Strategies Research Articles

Cooperative multi-agent actor–critic control of traffic network flow based on edge computing

Most of the existing traffic signal control strategies are hard to satisfy the real-time requirements of traffic big data analysis, knowledge reasoning and decision making for sophisticated traffic dynamics and heterogeneous intersection structures in the context of Internet of Vehicles (IoV). In this paper, we attempt to propose a cooperative multi-agent actor–critic (CMAC) deep reinforcement learning (DRL) approach with value decomposition based on edge computing architecture. The intuition behind CMAC is to decompose the global actor–critic learning tasks into several local actor–critic​ sub-problems with respect to each intersection. Each agent searches the local optimal decision by actor–critic network that takes the discrete state encoding about several consecutive frames of image-like traffic states as the inputs of the network. Among them, the green ratio output strategy considering multiple constraints is formulated in the output layer of the actor network, so that the continuous control of traffic signals using multi-agent DRL (MADRL) can be realized. Furthermore, a cooperative mechanism that considers contribution weight distributions of local agents to the global traffic pattern is proposed to coordinate multiple local agents to evolve toward global optimization. Especially, some parallel training tasks of CMAC with a large number of computing loads are deployed on the cloud side in the edge computing architecture to accelerate learning and reconstructing knowledge. The well-trained multi-agent model is downloaded from the cloud side into the edge side for real-time decision making of traffic network flow adaptive control. Simulation results with regard to a realistic traffic network demonstrate that the proposed CMAC approach under edge computing architecture outperforms the value-decomposition based multi-agent actor–critic (VMAC), independent multi-agent actor–critic (IMAC), and the fixed timing control (FTC) in terms of alleviating traffic congestion.

Solving logistics on the basis of fuzzy package

The article discusses an approach to solving a logistic problem based on fuzzy set theory. The classic fuzzy control module is developed at the node of the transport network, as well as when solving the problem of modeling the traffic control strategy in the network. An ambiguous rule base was developed for defining traffic control parameters at a transport network node, and rules were proposed for mapping a control parameter to a specific class and fuzzy rule base.

Allocating exclusive and intermittent transit lanes in dynamic traffic networks with connected and automated vehicles

Exclusive bus lanes (XBLs) have been widely discussed and implemented worldwide in recent years. An improved and more flexible transit lane management strategy—intermittent bus lanes (IBLs)—prove to be potentially more efficient and car-friendly than XBLs. The common benefit of XBLs and IBLs arises from the fact that they separate the bus and car traffic and hence eliminate the impacts of slowly moving buses on the car traffic. Compared to XBLs, the application of IBLs has not been broadly adopted in the real world so far except very few cases, because managing an urban IBL segment or area typically involves extra traffic signals and message signs and the switch between its regular and protected statuses could potentially cause unexpected traffic chaos and safety issues. The recent emergence of connected and automated vehicle (CAV) technologies, however, favors relieving these concerns and bring the dawn to a future application of this theoretically sound traffic control strategy. This paper is dedicated to evaluating the network performance of XBLs and IBLs and optimizing the networkwide configurations of these two strategies for future urban traffic networks where CAVs prevail. For this purpose, we formulate a system-optimal dynamic traffic assignment (SO-DTA) problem with car-exclusive lane segments, on the basis of a cell transmission model for separate car and bus traffic (CTM-SCB). By limiting its applicable context to morning commute networks and emergency evacuation networks, this model is set with only one real or virtual destination, which greatly eases its modeling and solution complexity. On the basis of the single-destination SO-DTA model, an XBL-based network design problem and an IBL-based network design problem are then both formulated into mixed integer programming models and solved by a discrete optimization solver, where the former problem statically configures bus lanes while the later one allows a dynamic allocation of bus lanes. Insightful findings obtained by applying the models and solver to a synthetic corridor network and a real commute network illustrate the great promise of these two lane-based strategies in mixed car and bus traffic management.

An Optimal Dynamic Lane Reversal and Traffic Control Strategy for Autonomous Vehicles

This paper studies an optimal dynamic lane reversal and traffic control (DLRTC) strategy in the presence of autonomous vehicles (AVs). A centralized controller is set to change lane directions dynamically and regulate traffic flow on a motorway network. Through vehicle to infrastructure (V2I) communication, the roadside sensors can send lane reversal information and flow control actions to the AVs which can perform lane-changing behaviors and adjust travel speed. To model the traffic dynamics under DLRTC, we propose a novel multi-lane cell transmission model (CTM). A logit model is used to characterize the lane-changing behaviors under uncontrolled cases. A mixed integer linear programming model (MILP) is formulated for DLRTC, and optimal control actions are implemented in a framework of model predictive control (MPC). The numerical experiments based on the Ayer Rajah Expressway (AYE) in Singapore are conducted to demonstrate the effectiveness of the proposed methods. The results show that the DLRTC strategy can effectively reduce road congestion and achieve better system performance compared to the benchmark method.

Implementing an Urban Bicycle Highway on Pedestrian Traffic - Finding a Traffic Control Strategy with Optimal Coordination for the City Center of Munich

Bicycle highways are specially designed infrastructures allowing cyclists to travel at a considerable speed through different environments. These elements in densely built urban areas are difficult to understand, since many requirements and standards need to be met. Serving all modes of traffic with acceptable efficiency and implementing a bicycle highway design is a challenge and therefore the aim of this study is to define a strategy for bicycle road traffic control in an urban area, the city center of Munich, which will improve its traffic efficiency. The current situation of the studied area was developed in PTV VISSIM to achieve the objective. Firstly, the base model was calibrated and validated to match the present state and synchronized separately for bicycles and cars. Five alternative models were developed based on coordinating and developing the bicycle highway infrastructure. Attempts were made to compare the models on efficiency measurements and the models were evaluated to analyze traffic safety parameters. In this assessment, interaction between pedestrians and bicycles was given priority. The research result shows improvement of the traffic efficiency of bicycle highways within the alternatives proposed. The loop pause time decreased up to 7%, the number of stops decreased by 28%, bicycle travel time has been reduced up to 7%. The results of this research show improvement of the traffic efficiency of bicycle highways within the alternatives proposed.

Data‐driven urban traffic model‐free adaptive iterative learning control with traffic data dropout compensation

In this paper, to fully utilize the urban traffic flow characteristics of similarity and repeatability without using a mathematical traffic model, a data-driven urban traffic control strategy based on model-free adaptive iterative learning control (MFAILC) scheme is put forward. Firstly, by dynamically linearizing the urban traffic dynamics along the iteration axis, the traffic network system is transformed into a MFAILC data model with the help of repetitive pattern of urban traffic flow. Then, the traffic controller is designed based on the derived MFAILC data model only using the I/O data of the traffic network. Finally, a traffic data compensation method is proposed to deal with data dropout problem. Simulation study verifies the feasibility and effectiveness of the proposed control method.

A platoon regulation algorithm to improve the traffic performance of highway work zones

AbstractThis paper presents a cooperative traffic control strategy to increase the capacity of nonrecurrent bottlenecks such as work zones by making full use of the spatial resources upstream of work zones. The upstream area is divided into two zones: the regulation and the merging areas. The basic logic is that a large gap is more efficient in accommodating merging vehicles than several small and scattered gaps with the same total length. In the regulation area, a nonlinear programming model is developed to balance both traffic capacity improvements and safety risks. A two‐step solving algorithm is proposed for finding optimal solutions. In the merging area, the sorting algorithm is used to design lane‐changing trajectories based on the regulated platoons. A case study is conducted, and the results indicate that the proposed model is able to significantly improve work zone capacity with minor disturbances to the traffic.

A hierarchical MPC and sliding mode based two-level control for freeway traffic systems with partial demand information

In this paper a hierarchical two-level control scheme is proposed in order to solve a ramp metering problem in freeway traffic systems. The proposal combines a Model Predictive Control (MPC) based supervisor with decentralized low-level Sliding Mode Controllers (SMCs). The considered freeway METANET model describes the traffic system, which is naturally affected by uncertainties since the mainstream inflow, the traffic demands on the on-ramps, and the flows exiting the off-ramps are not a priori known. In the proposed scheme, two control loops are present. A centralized MPC computes the optimal control inputs by minimizing the total time spent by the vehicles in the traffic system on the basis of a nominal model of the considered freeway stretch. The computed optimal inputs are used to generate the corresponding density references for the low-level on-ramp controllers, which rely on a saturated version of the Suboptimal Second-Order Sliding Mode (SSOSM) control algorithm. SSOSM controllers allow to regulate the density error to zero in a finite time, thus reducing the mismatch between the controlled traffic system dynamics and the model used by the MPC supervisor during the optimization. A simulation study encompassing several realistic scenarios is reported to assess the validity of the proposed approach also in comparison with other classical traffic control strategies. As confirmed by simulation, in spite of the uncertainties, the proposed control scheme produces satisfactory performance in terms of both total time spent minimization and capacity drop alleviation.

Self-Detecting Traffic Interference Control for Multi-Zone Services under 5G-Based Cellular Networks.

In this paper, we propose a multi-zone service control scheme to maximize the performance of each service zone when a large number of cellular service zones and Device-to-Device (D2D) service zones are composed into the 5G cellular network. This paper also improves performance of service zone by dividing traffic into real-time traffic and non-real-time traffic in order to minimize traffic interference. Real-time traffic and non-real-time traffic have a significant impact on communication performance. We propose a new self-detection traffic interference control technique to improve the Quality of Service (QoS) and throughput of D2D and Cellular-to-Device (C2D) communication in a cellular network, Self-detecting Traffic Interference Control Scheme (STICS). The proposed STICS mechanism distinguishes between short-term traffic congestion process and long-term traffic congestion process according to traffic characteristics to detect and control traffic. When the proposed scheme is applied to the 5G-based cellular network environment, it is expected that the traffic type will be efficiently classified by self-detecting the traffic according to the flow. Such classified traffic is less sensitive to communication between the D2D and C2D links, thereby reducing traffic overload. We evaluate the performance of the proposed scheme through simulation and show that the proposed scheme is more efficient than other comparison schemes.

Development of a conflict-free unsignalized intersection organization method for multiple connected and autonomous vehicles.

An effective traffic control strategy will improve travel reliability in urban transportation networks. Lack of coordination between vehicles, however, exacerbates congestion due mainly to frequent stops at unsignalized intersections. It is beneficial to develop a conflict-free cooperation method that collects basic safety message from multiple approaching Connected and Autonomous Vehicles (for short, CAVs) and guarantees efficient unsignalized intersection operations with safe and incident free vehicle maneuvers. This paper proposes an interspersed traffic organization method under controlled constraints. Firstly, relied on shared location technology and considered the operating characteristics of CAVs at unsignalized intersections to detect and analyze traffic conflicts to establish a right-of-way judgment model for CAVs. In order to further ensure the safety and operating efficiency of the vehicle, based on the judgment results of right-of-way judgment model, a vehicle speed guidance model is established for different traffic conditions. Taking the real city standard intersection as the experimental analysis object, through data collection and simulation experiment, the signal control method and the organization method proposed in this paper are compared and analyzed. The results showed that the traffic organization method proposed in this paper improves the operational efficiency of 46%, the average travel time is reduced by 6.54s, which is not only better than the signal control method, but also supports the development of car networking technology.

A System Optimal Speed Advisory Framework for a Network of Connected and Autonomous Vehicles

The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network.

Green and Safety School Regional Program to Sustainable Development Using Limited Traffic Zone

The traffic around the schools is one of the worst events for sustainability. The effects are heavy for pollution, for safety deriving from multiplied walk-car cross, for autonomy reduced for student. This paper discusses on Green and Safety School program that realizes Limited Traffic Zones (LTZ) around the schools. Calabria Region has made political and technical decisions oriented towards sustainable development, according to international, European and national targets: by Regional Transportation Plan (RTP), the Region has implemented several ways for realization of the planned measures relative to the urban centres defined by Action 2 Urban areas; by School Plan and program the Region has invested to reduce the vulnerability of school building. The aim of Green and Safety School program is offering a new and never developed field for social, economic and environmental sustainability in urban areas and to propose a strategy for traffic control near schools from basic considerations regarding a problem common of all cities around the world. This program derives from the results obtained with two different previously implemented programs: one relative to the rebuild of school who reduce risk (Scuola Sicura) and another one to the City logistics (leaving from RTP). From an advanced synthesis of these 2 programs born the very new use of LTZ. Green and Safety School is oriented to guarantee green areas and to reduce risk outside school. Even if the program leaves before COVID-19, it assumes significant importance to guarantee social distancing.

A new medium access control mechanism for energy optimization in WSN: traffic control and data priority scheme

Over the last few years, energy optimization in wireless sensor networks (WSNs) has drawn the attention of both the research community and actual users. Sensor nodes are powered by attached batteries that are considered as a critical aspect of sensor nodes design. Besides, the constraint of the limited battery capacity is associated with the concern on how to reduce the energy consumption of nodes to extend the network lifetime. In this context, the purpose of this study is to implement an adaptive medium access control (MAC) for energy saving and traffic control enhancement. This program was designed to arrange nodes into two priority groups according to their traffic rate and data transmission packet delay. This fuzzy algorithm depends on their queue length where it is implemented into the carrier sense multiple access with collision avoidance (CSMA/CA) algorithm. However, other types of nodes should send their data during the contention-free period with a GTS reallocation scheme. Those nodes are classified as low priority access to the medium, and their data transmission is scheduled using time division multiple access methods. Moreover, this proposed scheme dynamically adjusts the contention access period length to ensure that nodes can complete their data transmission during the same super-frame. Simulation results are done using the network simulator tool (NS-2), and it has improved efficiency regarding the IEEE-802.15.4 standard.

The Role of Trip Lengths Calibration in Model-Based Perimeter Control Strategies

Since the introduction of the Macroscopic Fundamental Diagram (MFD), many traffic control strategies and algorithms have been developed to implement MFD-based perimeter control over a specific urban region. A model-based controller consists of two components: a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">plant model</i> that represents reality; and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prediction model</i> used to determine optimal control actions. In most studies, the authors assume a constant average trip length for all drivers traveling within the same region, for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prediction model</i> . In these studies about perimeter control and MFD traffic models, the controllers show a good performance because accumulations, i.e. traffic states, from the plant are used to reflect the initial state of the prediction model with a high frequency (about a few seconds). However, this average trip length changes over time as it depends on the Origin-Destination flow decomposition, playing an important role in real applications. The main contributions of this paper are twofold. First, we show that the assumption about constant trip lengths used in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prediction model</i> deteriorates the controller’s performance for low frequency updates of the optimal control actions. Second, we propose a methodological framework based on the Unscented Kalman Filter (UKF) for dynamically adjusting the average trip lengths and accumulations. Our test results on a real city network show that applying this methodological framework significantly improves the controller’s performance.

Accelerated construction of urban intersections with Portland Cement Concrete Pavement (PCCP)

The frequent maintenance required on asphalt concrete (AC) pavement sections has made reconstruction with portland cement concrete pavement (PCCP) a feasible alternative. However, many constructability issues need to be addressed in order to realize the full potential of this alternative. Accelerated paving encompasses three classes of activities: methods to accelerate the rate of strength gain, methods to minimize the construction time, and traffic control strategies to minimize user delay. In this paper a case study will be presented in which an AC intersection was reconstructed with portland cement concrete pavement. The entire reconstruction of the intersection, including demolition of the AC pavement and its replacement with PCCP, took place over a period of three days, starting on Thursday evening and opening the intersection to the traffic on Sunday afternoon. This paper documents this effort in order to provide practitioners additional options for rapid reconstruction of urban intersections and includes documentation of the construction process, traffic management strategies, and an analysis of the costs. The results of this investigation can be used to educate pavement construction professionals and the academic community on the use of PCCP for accelerated reconstruction of major urban intersections with minimal user and traffic disruption, using innovative construction techniques and traffic management optimization principles. This investigation produced valuable information to demonstrate that concrete pavements can be constructed efficiently and quickly.

A sustainable road pricing oriented bilevel optimization approach under multiple environmental uncertainties

The rapidly-increasing urban transportation contributes to a number of externalities such as congestion and pollution; moreover, environmental uncertainties widely exist and bring challenges to creating traffic emission control strategies. To address these problems, this paper proposes an inexact bilevel programming approach under stochastic and fuzzy uncertainties (BLP-SF) for a toll scheme design with the considerations of the externalities of vehicular emission and road pricing policy. The BLP-SF approach can deal with multiple environmental uncertainties by (1) specifying the uncertainties as probability distributions and/or fuzzy sets, and (2) integrating chance-constrained programming and fuzzy possibility programming into the bilevel programming framework. A road pricing problem is exemplified under various policy scenarios to demonstrate the applicability of the BLP-SF approach. It is shown that the proposed BLP-SF approach can achieve optimal charging schemes with improvements in total emission reduction and congestion alleviation compared with traditional models. This study contributes to urban transportation management associated with congestion and emission.

An optimization model and traffic light control scheme for heterogeneous traffic systems

In this paper, one type of heterogeneous traffic network, which consists of signalized intersections and non-signalized ones, is analyzed and modelled with a macroscopic model and logic constraints, which involves the evolution of the number of vehicles on each link and the leaving flow dynamics at each intersection. We propose a novel model describing the dynamic behaviors of such a system and the proposed model is validated via road tests in Singapore traffic network and simulations in VISSIM. A piecewise linear model is adopted to describe the traffic flow dynamics at each signalized intersection, while a platooning-like first-in-first-out (FIFO) model is proposed to depict the dynamics at each non-signalized intersection. A traffic light control problem for a heterogeneous traffic network is formulated based on the proposed macroscopic model as a mixed integer programming problem. Simulations indicate that the proposed optimal traffic control scheme could outperform the fixed-time traffic light control strategy and conventional actuated traffic light control scheme. Besides, we demonstrate potentials in developing a systematic planning approach on deciding which traffic intersections require signal control to ensure good traffic control performance.

Pedestrian-Safety-Aware Traffic Light Control Strategy for Urban Traffic Congestion Alleviation

Conflicts between pedestrians and vehicles are one of the common safety issues at signalized intersections. Pedestrian Flashing GREEN (FG), a time interval for pedestrians on crosswalks to safely finish crossing before the next phase occurs, may fail to clear the crosswalk in the allotted time, due to significant pedestrian non-compliant behavior. In this manner, probability of pedestrian-vehicle exposures increases when non-compatible vehicle flows are released at the next immediate phase. This paper seeks to address this issue by presenting a traffic signal control strategy for urban traffic networks that aims to minimize vehicle traveling delay (increase efficiency) as well as pedestrian crossing risk (increase safety). First, a macroscopic model for pedestrian-vehicle mixed-flow networks is proposed. Considering the high-incidence rate of pedestrian violations during FG, an additional Dynamic All RED (DAR) phase is introduced at the end of each FG period, whose duration is adaptively adjusted according to the number of non-compliant pedestrians. With computational complexity being a concern for our model, an evolutionary algorithm with repairing mechanism (EARM), is proposed to solve our problem. Case studies are provided to illustrate the potential impact of the pedestrian movement to the vehicle traffic networks when pedestrian safety is considered in the system, as well as the efficacy of our traffic light control strategy for pedestrians and vehicles on risk reduction.

Home Network Traffic Control Scheme Based on Two-Level Bargaining Game Model

This article presents a new smart home network (SHN) traffic control scheme to adaptively handle different quality of service (QoS) traffic services. According to their special characteristics, we categorize the SHN traffic services into three classes - class I, II and III data services - to share the limited SHN bandwidth resource. To effectively share the limited resource, we adopt the bridging and iterated egalitarian bargaining solutions, and formulate a two-level cooperative bargaining game. At the over bargaining process, the idea of bridging bargaining solution is used to compromise the conflicting views of real-time and non real-time data services. At the under bargaining process, class I and class II data services share the assigned bandwidth based on the concept of iterated egalitarian bargaining solution. These both processes are implemented to take full advantages of collaborative SHN traffic services. This interactive coordinated paradigm explores the mutual benefits under dynamically changing SHN environments. The most important novelties of our two-level bargaining approach is to compromise diverse QoS requirements while leveraging a reciprocal consensus among different traffic services. In a coordinated manner, we can get a globally desirable solution to challenge the relatively fair-efficient gains. Finally, simulation testbed is constructed and the numerical analysis is conducted to demonstrate the performance improvement of our proposed method.

KSUTraffic: A Microscopic Traffic Simulator for Traffic Planning in Smart Cities

Simulation is a powerful tool for improving, evaluating and analyzing the performance of new and existing systems. Traffic simulators provide tools for studying transportation systems in smart cities as they describe the evolution of traffic to the highest level of detail. There are many types of traffic simulators that allow simulating traffic in modern cities. The most popular traffic simulation approach is the microscopic traffic simulation because of its ability to model traffic in a realistic manner. In many cities of Saudi Arabia, traffic management represents a major challenge as a result of expansion in traffic demands and increasing number of incidents. Unfortunately, employing simulation to provide effective traffic management for local scenarios in Saudi Arabia is limited to a number of commercial products in both public and private sectors. Commercial simulators are usually expensive, closed source and inflexible as they allow limited functionalities. In this project, we developed a local traffic simulator “KSUtraffic” for traffic modeling, planning and analysis with respect to different traffic control strategies and considerations. We modeled information specified by GIS and real traffic data. Furthermore, we designed experiments that manipulate simulation parameters and the underlying area. KSUTraffic visualizes traffic and provides statistical results on the simulated traffic which would help to improve traffic management and efficiency.