Traffic Control Strategies Research Articles

Analysis of Trams’ Consumption Depending on the Type of Traffic Light Used

In the XXI century, when environmental awareness is growing and the impact of human activity on the planet is more and more noticeable, striving to minimize energy consumption seems to be a necessary direction in the development of technology. This development cannot take place without an initial understanding and describing the relationships influencing specific technologies. It also needs empirical verification of assumed theories. Modern trams play an important role in the functioning of urban transport. Being one of the oldest modes of environmentally friendly transport, in European capitals they are currently perceived as one of the most convenient means of transport. This is due, among other things, to the high velocity of transport along the route. The energy consumed by trams indirectly depends on the driving characteristics, i. e. speed, acceleration and stops on the route, which are also caused by stopping at traffic light controlled junctions. This paper presents the results of an experiment showing the change in the level of electric energy consumption depending on the applied method of traffic light control. This article presents the conditions influencing the power consumption in trams, describes the possible strategies of traffic lights control and their consequences for other traffic participants. The research was carried out in real conditions in everyday traffic, measuring the level of electricity consumption in case of both fixed-time and actuated signaling with full priority for trams. On the examined section there were both modern asynchronous-drive as well as traditional resistor-drive vehicles. The conclusions drawn from the survey confirm the validity of introducing modern solutions and may be useful for estimating investment costs.

Intelligent Traffic Control System Based on Cloud Computing and Big Data Mining

This article aims at discussing problems such as complex object types, large amount of data collection, high demand for transmission and calculation, and weak real-time scheduling and control ability in the construction of modern intelligent traffic information physical fusion network, cloud-based control system theory, modern intelligent traffic control network as the research object, and the physical design of the intelligent transportation information fusion cloud control system scheme. The scheme includes intelligent transportation edge control technology and intelligent transportation network virtualization technology. Based on intelligent traffic flow data, in the center of the cloud control management server using deep learning and overrun learning machine intelligence study methods, such as the forecast of traffic flow data for training, to predict urban road short-term traffic flow and congestion. Further up in the air by using intelligent optimization scheduling algorithm for real-time traffic flow control strategy, the simulation results show the effectiveness of the proposed method.

An equitable traffic signal control scheme at isolated signalized intersections using Connected Vehicle technology

This paper extends a real-time, Connected Vehicle-based traffic signal control algorithm at isolated intersections to balance between two competing intersection objectives: efficiency and equity. In this approach, a central controller is used to collect real-time locations of connected vehicles at regular intervals, which can then be used to also identify the existence of some of the non-connected vehicles. The control algorithm then aims to optimize the discharge sequence of naturally occurring platoons of vehicles based on their proximity. Specifically, the strategy selects the platoon discharge sequence—and associated signal phase and timing plan—that minimizes average vehicle delay (measure of efficiency) while limiting the maximum delay any individual vehicle may experience (measure of equity). The latter objective is only possible with detailed vehicle-level information available from connected vehicles. The results show that without the threshold on maximum individual vehicle delay, average delay is often minimized at the expense of very large delays imposed onto some vehicles. By implementing a threshold, both the maximum vehicle delay and the distribution of individual vehicle delays—as measured by the standard deviation and a common measure of population inequity, the Gini coefficient—can be improved, often with only negligible impacts to intersection efficiency. However, the tradeoff between equity and efficiency becomes more significant as the maximum vehicle delay threshold decreases. Sensitivity tests show that this control algorithm works well for different total traffic demands and different demand patterns. The proposed algorithm is also effective under imperfect connected vehicle penetration rates when the connected vehicles make up more than 40% of the traffic stream. The results suggest that the proposed strategy can help significantly reduce long delays and inequitable treatment of vehicles at an intersection when vehicle-level information is available to a signal controller.

A traffic control framework for urban networks based on within-day dynamic traffic flow models

ABSTRACTThe paper aims to provide a further development of traffic control strategies, to propose an enhanced version of two traffic flow models and to integrate these models within an urban traffic control framework. Concerning the traffic control method, the synchronisation approach is adopted and three objective functions are considered and compared: two are mono-criterion and the third is multi-criteria. Simulated annealing and multi-objective simulated annealing are adopted as a solution algorithm. In terms of traffic flow representation the approaches analysed are macroscopic cell-based and mesoscopic link-based, both able to model path choice behaviours and vehicle dispersion phenomena. Furthermore, traffic flow prediction is pursued through a Kalman filter and a rolling horizon approach is adopted as a forecasting framework for the optimisation procedure. In order to test the framework and to compare two traffic flow models, a 15-node grid network was considered, including different levels of congestion and demand profiles.

Environmentally friendly traffic control strategy - A case study in Xi’an city

Given the increasing painful repercussions of air pollution caused by massive vehicular emissions and the unsustainable development of transport sectors, the integration of traffic models with emission estimation models has been investigated in depth to evaluate the performance of the pre-determined environment-friendly transportation policies. Nevertheless, there is a lack of the optimization approach to design the traffic control strategy combining the coupled models. Therefore, an optimization framework is developed to optimize the traffic control strategies to mitigate the vehicular emissions in the considered network. The proposed framework integrates a macroscopic analytical traffic model with a macroscopic emissions estimation model to account for the vehicle emissions, and then genetic algorithm (GA) is employed to minimize vehicle emissions. A congested urban area in Xi’an city that consists of two signalized intersections is selected for a case study. The performance of the signal plans derived by the proposed framework is compared with the currently in use signal plans via the microscopic simulator AIMSUN. It indicates that the proposed traffic control scheme reduces expected vehicle trip travel time and vehicle emissions of four sorts of pollutants for the considered area. This optimization framework helps traffic operators to design environment-friendly traffic signal control strategy.

Intellectual Green Corridor for Crisis Wellbeing Transference

Traffic is a major concern for most of the metropolitan cities of the world. The design proposes a notion for Traffic Control System which is more imaginativethan currently existing schemes.The system automatically affords a distinctive lane in which entirely the red signal indication will be turned spontaneously to green intended for the ambulance. Subsequently this assists the ambulance instantly in reaching its destination within stint.In accumulation to the Traffic Control Scheme, wellbeing specialist care Scheme displays the patient long-suffering state of affairs resembling heartbeat, blood pressure. This system comprises of dedicated intellectual smart ambulance with GPS, GSM and smart mobile solicitation beside with Internet of Things (IoT).The patient’s state of affairs will be directed to the medical wingover cloud.The information of the patient is sent to the hospital via GSM module and blood bank gets information only if the condition is chosen as serious. The projected effort stays targeted to plan and progress an operative traffic control scheme for smart ambulance. The outcomes of the recommended traffic control model transports upright decline of time by clearing the traffic very fast and protect the patient’s lifespan at the most primitive.

Traffic Control for RDMA-Enabled Data Center Networks: A Survey

Data centers, the infrastructure of cloud computing, have been widely deployed around the world to accommodate the increasing cloud computing demands. A data center network (DCN) connects tens or hundreds of thousands of servers in the data center and uses a traffic control scheme to enable the data transmission among servers. Various new applications in cloud present new requirements on traffic control of DCNs, such as low latency and high throughput. Existing traffic control schemes in DCNs suffer from the complicated kernel processing and cannot satisfy the requirements. Remote direct memory access (RDMA), which bypasses the kernel processing to enable fast memory moving across a network, is recognized as a promising solution. In this article, we present a survey of traffic control schemes for traditional RDMA, traditional DCNs, and RDMA-enabled DCNs and explain their limitations. We also differentiate the existing schemes from congestion control, performance, and components. In order to encourage future research, we point out some potential research directions of this research.

Using Traffic Control Scheme In Intelligent Transportation System

Using Traffic Control Scheme In Intelligent Transportation System

Multiclass information flow propagation control under vehicle-to-vehicle communication environments

Most existing models for information flow propagation in a vehicle-to-vehicle (V2V) communications environment are descriptive. They lack capabilities to control information flow, which may preclude their ability to meet application needs, including the need to propagate different information types simultaneously to different target locations within corresponding time delay bounds. This study proposes a queuing-based modeling approach to control the propagation of information flow of multiple classes. Two control parameters associated with a vehicle, the number of communication servers and the mean communication service rate, are leveraged to control the propagation performance of different information classes. A two-layer model is developed to characterize the information flow propagation wave (IFPW) under the designed queuing strategy. The upper layer is formulated as integro-differential equations to characterize the spatiotemporal information dissemination due to V2V communication. The lower layer characterizes the traffic flow dynamics using the Lighthill–Whitham–Richards model. The analytical solution of the asymptotic density of informed vehicles and the necessary condition for existence of the IFPW are derived for homogeneous traffic conditions. Numerical experiments provide insights on the impact of the mean communication service rate on information spread and its spatial coverage. Further, a numerical solution method is developed to solve the two-layer model, which aids in estimating the impacts of the control parameters in the queuing strategy on the IFPW speed under homogenous and heterogeneous conditions. The proposed modeling approach enables controlling the propagation of information of different information classes to meet application needs, which can assist traffic managers to design effective and efficient traffic management and control strategies under V2V communications.

Solving Traffic Signal Scheduling Problems in Heterogeneous Traffic Network by Using Meta-Heuristics

This paper addresses a traffic signal scheduling (TSS) problem in a heterogeneous traffic network with signalized and non-signalized intersections. The objective is to minimize the total network-wise delay time of all vehicles within a given finite-time window. First, a novel model is proposed to describe a heterogeneous traffic network with signalized and non-signalized intersections. Second, five meta-heuristics are implemented to solve the TSS problem. Based on the problem characteristics, three local search operators and their ensemble are proposed. Then, five meta-heuristics with such an ensemble are proposed to solve the TSS problem. Third, experiments are carried out based on the real traffic data in the Jurong area of Singapore. The performance of the ensemble of local search operators is verified. Ten algorithms, including five meta-heuristics with and without the ensemble, are evaluated by solving 18 cases with different scales. Finally, the algorithm with the best performance is compared against the currently used traffic signal control strategies. The comparisons and discussions show the competitiveness of the proposed model and meta-heuristics.

Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data.

Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, which is a key element in RL-based traffic signal control, plays a vital role. However, the data used for state definition in the literature are either coarse or difficult to measure directly using the prevailing detection systems for signal control. This paper proposes a deep reinforcement learning-based traffic signal control method which uses high-resolution event-based data, aiming to achieve cost-effective and efficient adaptive traffic signal control. High-resolution event-based data, which records the time when each vehicle-detector actuation/de-actuation event occurs, is informative and can be collected directly from vehicle-actuated detectors (e.g., inductive loops) with current technologies. Given the event-based data, deep learning techniques are employed to automatically extract useful features for traffic signal control. The proposed method is benchmarked with two commonly used traffic signal control strategies, i.e., the fixed-time control strategy and the actuated control strategy, and experimental results reveal that the proposed method significantly outperforms the commonly used control strategies.

Designing maps with fictional and real road intersections for the evaluation of countdown timer displays for bicyclists

Abstract. Urban environments are more or less characterized by active travel modes, including the bicycle usage. Present transport infrastructure for bicyclists can often not serve the high traffic volumes, especially in rush hours. Therefore, introducing novel and specific traffic control strategies for bicyclists might serve as supportive element for improving bicycle traffic efficiency and safety. We evaluate a traffic control strategy implying countdown timer displays for bicyclists with one device being permanently installed in Munich (Germany) by (1) extracting cyclist trajectories from video observations before and after the installations, and, (2) introducing a bicycle simulator scenario via a map design approach. The latter includes a sequence of fictional and real road intersections and goes beyond evaluating the present countdown timers in VR, since different other countdown timer display designs are being tested. The whole bicycle simulator study consists of test rides and subsequent questionnaires with test subjects. We explain the design of the first simulator study and show selected results, which imply significant new insights resulting from the first 30 test rides and questionnaires.

Traffic Management using Convolution Neural Network

Traffic is one of the major problems in most of the metropolitan cities. Classifying the traffic conditions are important for determining traffic control strategies and management. Traffic congestions have negative impact on society, as a lot of time is wasted in it and controlling the congestions is necessary. By classification we can get to know which lane has traffic, from which we can further check the reasons for traffic and to take appropriate decisions to improve the performance. Video on traffic data is suitable source for traffic analysis. In this paper, video surveillance data is used for classification of road traffic using Convolution Neural Network. Convolution Neural Network requires minimal preprocessing when compared to other classification algorithms and is known for its accuracy. The video is classified based on rating of the traffic of its content. The Convolution Neural Network is first trained and then it is evaluated and updated using validation set. Once the model is completely trained it is tested with the testing set. This trained model is capable of processing the live streaming video and classifies each of the frames and gives the rating of the traffic for each lane, which can be helpful for traffic management.

Self-organisation in traffic signal control algorithms under light traffic conditions

Fixed-time control and vehicle-actuated control are two distinct types of traffic signal control. The latter control method involves switching traffic signals based on detected traffic flows and thus offers more flexibility (appropriate for lighter traffic conditions) than the former, which relies solely on cyclic, predetermined signal phases that are better suited for heavier traffic conditions. The notion of self-organisation has relatively recently been proposed as an alternative approach towards improving traffic signal control, particularly under light traffic conditions, due to its flexible nature and its potential to result in emergent behaviour. The effectiveness of five existing self-organising traffic signal control strategies from the literature and a fixed-control strategy are compared in this paper within a newly designed agent-based, microscopic traffic simulation model. Various shortcomings of three of these algorithms are identified and algorithmic improvements are suggested to remedy these deficiencies. The relative performance improvements resulting from these algorithmic modifications are then quantified by their implementation in the aforementioned traffic simulation model. Finally, a new self-organising algorithm is proposed that is particularly effective under lighter traffic conditions.

Effects of traffic control regulation on Network Macroscopic Fundamental Diagram: A statistical analysis of real data

This research aims to verify the influence of traffic control strategies using signal plans at junctions on observed Network Macroscopic Fundamental Diagram (NMFD), as a variable to describe the aggregate traffic conditions of an urban road network.After establishing the theoretical basis of its existence, the research on NMFDs has been finalized for applications connected to traffic congestion control and management. The degree NMFD depends on traffic control strategies has not been fully demonstrated, nor has its relationship with different congestion levels. In the opinion of the authors, experimental NMFDs based on observed traffic data in the presence of different control strategies are key elements in supporting the understanding of this relationship.The paper has been structured into two parts. The first part presents the formulation of an optimization model for the design of signal plans at junctions which incorporates NMFD variables. The more extensive second part examines the effects of traffic control strategies on observed NMFDs, built with real traffic data. The investigation follows a procedure where observed traffic data are analysed to find variables, functions and diagrams at link and network levels (NMFD). The procedure is applied in two adjacent urban zones located in the central area of Santander (Spain). These zones were identified according to traffic characteristics and land uses. The junctions on the road sub-networks in the two zones have different control regulations, but they are used by the same road users (with the same behavioural strategy). Observed traffic data (flows, densities) from loop sensors have been collected and correlated with the signal control plans at different periods of a working day.What emerges from the analysis is that junction regulation affects traffic flow variables at a link and network level. The NMFDs are statistically different when comparing junction regulations at two homogenous adjacent zones. The results apply only to the studied area and should be confirmed in other real transport systems.

A bilevel approach to enhance prefixed traffic signal optimization

The segmentation of multivariate temporal series has been studied in a wide range of applications. This study investigates a challenging segmentation problem on traffic engineering, namely, identification of time-of-day breakpoints for pre-fixed traffic signal timing plans. A large number of urban centres have traffic control strategies based on time-of-day intervals. We propose a bilevel optimization model to address simultaneously the segmentation problems and the traffic control problems over these time intervals.Efficient memetic algorithms have been developed for the bilevel model based on the hybridization of the particle swarm optimization, genetic algorithms or simulated annealing with the Nelder–Mead method. Numerically the effectiveness of the algorithms using real and synthetic data sets is demonstrated.We address the problem of automatically estimating the number of time-of-day segments that can be reliably discovered. We adapt the Bayesian Information Criterion, the PETE algorithm and a novel oriented-problem approach. The experiments show that this last method gives interpretable results about the number of reliably necessary segments from the traffic-engineering perspective.The experimental results show that the proposed methodology provides an automatic method to determine the time-of-day segments and timing plans simultaneously.

Traffic Flow Variables Estimation: An Automated Procedure Based on Moving Observer Method. Potential Application for Autonomous Vehicles

Abstract The estimation of traffic flow variables (flow, space mean speed and density) plays a fundamental role in highways planning and designing, as well as in traffic control strategies. Moving Observer Method (MOM) allows traffic surveys in a road, or in a road network. This paper proposes a novel automated procedure, called MOM-AP based on Moving Observer Method and Digital Image Processing (DIP) Technique able to automatically detect (without human observers) and calculate flow q, space mean speed vs and density k in case of stationary and homogeneous traffic conditions. In order to evaluate how reliable is the MOM-AP, an experiment has been carried out in a segment of one two-lane single carriageway road, in Italy. 30 datasets for the segment have been collected (in total 30 round trips). A comparative analysis between MOM-AP and traditional MOM has been carried out. First results show that the current MOM-AP algorithms underestimate the local mean flow variable values of around 10%. Nowadays MOM-AP may be implemented in smartphone apps. Instead, in the near future, it is realistic expecting the increase in the use of automated procedures for calculating the traffic flow variables (based on the “moving observer method”), due to the amount of sensors and digital cameras employed in the new autonomous vehicles (AVs). Considering such technical advances, the MOM-AP is a feasible model for real-time traffic analyses of road networks.

Coordinated Traffic Light Control in Cooperative Green Vehicle Routing for Pheromone-based Multi-Agent Systems

Green transportation has been increasingly gaining attention in recent years. Existing pheromone-based traffic management frameworks were developed to reduce urban congestion by fusing traffic lights control strategies and vehicle routing schemes. Despite a significant reduction in traffic congestions, the greener aspects of transportation were not well investigated. In view of this, a Pheromone-based Green Transportation System (PGTS) is proposed to reduce Greenhouse Gas emissions and urban congestion in a three-step approach. First, traffic congestions are predicted based on the transport pheromone intensity of the target and adjacent upstream roads through an online epsilon-Support Vector Regression model. Second, a Coordinated Traffic Light Control (CTLC) strategy generates green wave scenario, dispersing heavy traffic on congested roads to the coordinated downstream paths. Third, a Cooperative Green Vehicle Routing (CGVR) takes a further leap by probabilistically rerouting upstream vehicles from entering the congested road, preventing the accumulation of vehicles that can lead to upstream congestion. Intuitively, the integration of CTLC and CGVR increases the chances that vehicles traversing multiple intersections with fewer frequencies of acceleration, effectively marking down fuel consumption. The proposed PGTS can be realized through a Pheromone-based Hierarchical Multi-Agent System (PHMAS). Based on Singapore traffic data, experimental results from a microscopic simulation SUMO show that the proposed PGTS outperforms other six approaches in reducing carbon dioxide emissions by 37.7%, fuel consumption by 37.6%, mean travel time by 47.5%, mean waiting time by 57.3%, and increasing number of arrived vehicles at designated destinations by 62.6%.

Microscopic Estimation of Road Impedance by Decomposing Traffic Delay into Individual Road Segments: An Analytical Approach

Urban road dynamic impedance calculation is the important basis of dynamic traffic assignment and real-time traffic management and control scheme. The current impedance calculation is mostly based on BPR function, and the BPR function is derived from macroscopic statistical laws, in which the microscopic characteristics of traffic flow are insufficiently described. In order to more accurately express the change laws of traffic impedance at the microscopic level, a stochastic dynamic traffic assignment algorithm based on road impedance function is designed to analyze the time impedance of congested roads under random dynamic traffic assignment under different conditions of road network saturation. Compared with the current model, the comprehensiveness and portability are greatly improved. The results show that the impedance calculation error of this method is less than 10% when the load degree of the road is lower than 0.85, which proves that the method has good precision under unsaturated flow conditions.

과포화 확산 방지를 위한 네트워크 단위 앞막힘 예방 제어 전략 개발

서울을 비롯한 국내 대도시에서는 신호교차로에서 하루에도 수없이 과포화상황을 경험하고 있는데, 이때 적절한 조치가 없으면 대기행렬이 제어할 수 없을 정도로 증가해 상류 교차로까지 도달하는 교차로 앞막힘이 발생한다. 현재 국내에서 개발되어 운영 중인 신신호시스템(COSMOS)은 이와같은 과포화상황에 대응하기 위해 앞막힘 예방제어전략을 수행하고 있다. 그러나 신신호시스템의 과포화 전략은 주변 네트워크의 교통상황을 고려하지 않고 해당 교차로 앞막힘 상황만 고려해 앞막힘 발생방향 유입 현시의 녹색시간을 조기종결 함으로써, 오히려 전체 네트워크적인 측면에서 혼잡이 악화될 수 있다. 본 연구에서는 기존 앞막힘 예방제어 전략의 한계를 극복하기 위해 기존 신신호 시스템의 앞막힘 검지체계를 이용한 네트워크 단위 앞막힘 예방 제어전략을 개발하였다. 네트워크 단위 앞막힘 예방 제어전략은 교차로 앞막힘 발생에 즉각적으로 대응할 수 있고 상하류 네트워크 교통상황을 고려하여 현시를 조정할 수 있는 전략이다. 또한 실제 현장에 적용할 수 있도록 제어전략을 간략화 하여 단계별로 규칙화한 신호제어 전략을 제시하였다. VISSIM Com-interface를 이용한 모의실험 결과 기존 TOD (Time of Day) 신호운영에 비해 차량당 평균제어지체가 30초 정도 줄어들었으며 차량당 정지 횟수 역시 2.5회 정도 줄어든 것으로 나타나, 네트워크 단위 앞막힘 예방 제어전략이 과포화상황 개선에 효과적인 것으로 분석되었다.