Real-time Traffic Control System Research Articles

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization.

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection.

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

AUTOMATED REAL-TIME TRAFFIC FORECASTING SYSTEM

Based on the analysis of the vehicles total number growth rates, which exceed the rates of expansion and optimization of the transport infrastructure, the need for the introduction of real-time traffic forecasting and control systems is shown. The factors that make it possible to detect the probability of potentially dangerous situations on the road, such as traffic jams, accidents and lack of parking spaces, respectively, in certain urban areas, based on the data of sensor networks and surveillance cameras combined within the global system of the Internet of Things, have been determined. It is proposed to build a sensor network based on magnetic sensors, which allows for high-precision geolocation with refinement of the received data by using ultrasonic sensors and optical monitoring tools, while identification is carried out by reading RFID tags. It is shown that the task of optimal organization of the relay system includes the determination of the features of the city infrastructure and statistical indicators of the city's traffic flows, and for a multi-level communication system, protocols are determined depending on the distance between nodes, requirements for the level of data protection, data transmission speed, minimum radio signal amplitude, as well as restrictions on the power supply of a separate node. The presented topology of the relay network includes the organization of sensor nodes into clusters, transmission from the main node of the cluster to the gateway node, and from the gateway nodes to the base station. On the basis of the specified model, a scheme for building cluster self-organization algorithms can be presented by forming clusters in real time according to the topology of the cluster tree, which allows simplifying the data transfer subsystem and reducing the time of processing input data. The developed scheme for analyzing the traffic flow at the intersection and the availability of parking spaces can be used in the development of methodological recommendations for the implementation of the "Smart City" concept and the creation of software applications that provide drivers with information about the state of traffic and predicted changes within a certain time interval. Keywords: automaticregulation of auto-traffic, controlled intersection, sensor network, Internet of Things, geolocation, identification, self-organization of clusters.

Quantifying the Extent to Which Connected and Autonomous Vehicles Reduce Accidents at Railroad Grade Crossings: A Machine Learning Approach

This paper quantifies the safety benefits of a proposed near real-time traffic control system for highway–rail grade crossing (HRGC) utilizing emerging safety technologies in connected and autonomous vehicles (CAV). The connected-vehicle technologies that have applications at a railroad crossing include vehicle-based technologies (railroad crossing violation warning, automated or semi-automated braking system, drowsiness/distracted driver alert) and technologies that require cooperation from the railroad industry (advanced warnings to trains about an occupied crossing). This paper provides a methodology to quantify the reduction in crashes as safety technologies become prevalent. It first identifies crash characteristics that enable the classification of crashes as preventable crashes or not. This classification is used to train machine learning models to estimate the likelihood of a potential crash being preventable. The machine learning model is used along with the zero inflated negative binomial with empirical Bayes system (ZINEBS) model to estimate the expected accident count at a crossing when a percentage of vehicles in the traffic stream is CAV. This paper presents case studies for three crossings to show the reduction in crashes with the increase in the percentage of connected vehicles in the traffic stream. It also presents the general trend in the reduction expected by analyzing 50 crossings of each warning device type.

Multi-attribute decision-making method for prioritizing autonomous vehicles in real-time traffic management: towards active sustainable transport

In order to alleviate traffic congestion, traffic control systems are an important tool. These systems strive to boost the efficiency of road systems to optimize traffic flow on individual road segments. The benefits of real-time traffic control systems might be increased by integrating new telecommunication and autonomous car technology. There is six real-time traffic advancing sustainable development examined in this study: variable message signs and ramp meters, traffic diversion, and the integration of driverless vehicles into other traffic management systems, with four key criteria: economics, community and social, ecologic and traffic security, as well as 13 sub-criteria using MCDM. To do this, we offer unique additions to the WASPAS technique.

Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set

Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still working on novel approaches. This study aims to develop an algorithm that dynamically updates the training set of models in order to make more accurate predictions. For this purpose, an algorithm called Periodic Clustering and Prediction (PCP) has been developed for use in short-term traffic forecasting. In this study, PCP was used to improve Artificial Neural Networks (ANN) predictive performance by improving the training set of ANN to predict short-term traffic flow using selected clusters. A large amount of traffic data collected from the US and UK motorways was used to determine the PCP ability to increase the ANN performance. The robustness of the proposed approach was determined by the performance measures used in the literature and the mean prediction errors of PCP were significantly below other approaches. In addition, the studies showed that the percentage errors of PCP predictions decreased in response to increasing traffic flow values. Considering the obtained positive results, this method can be used in real-time traffic control systems and in different areas needed.

A New Way to Resolve Real Time Traffic Control System by Using Machine Learning Approach

A New Way to Resolve Real Time Traffic Control System by Using Machine Learning Approach

Integrated Ambulance Service with Advanced Real Time Traffic Control Systems

Viable and proficient open supplier conveyance like medicinal services contributions are basic in nowadays' general public. Congested driving conditions is one of the significant issues in India in view of which rescue vehicle contributions get influenced on huge amount. Due to defer in its contributions, patients can likewise lose their ways of life and scope of those projections are getting improved each day. "Green Corridor" is the thought by methods for which patient will get required cure on schedule. Envision being in a clinical crisis and hauling out your cellphone, opening an application and mentioning the nearest inactive emergency vehicle with appropriate gadget come select you up. As answer for the current and unattractive requesting circumstances went up against by means of different sufferers in gaining admittance to required therapeutic offices, we've structured and executed a cell-based absolutely clinical crisis rescue vehicle device this is incorporated with a Location based thoroughly Service (LBS) which is out there through the Internet and wi-fi network on cell apparatus. This cell application furthermore offers an on-request, non-crisis logical transportation. The application not best associates clients with the close by inactive therapeutic transportation vehicles, anyway likewise with in transit medicinal teams through literary substance, phone and video calls, to hold them state-of-the-art on patterns. In this way, incorporating cell correspondence into the medicinal services device can adjust social insurance to an additional stature. Our proposed model incorporates both the propelled functionalities required for a Smart rescue vehicle just as organizing different Non-crisis logical conveyance frameworks at the equivalent time.

On-field experiment of the traffic-responsive co-ordinated control strategy CRONOS-2 for under- and over-saturated traffic

Recurrent road traffic congestion in large cities is a blight that wastes a great deal of time and increases greenhouse gas emissions, fuel consumption, noise and stress. One way of combating such congestion is traffic control by means of traffic signals. The role of traffic signals is to temporally separate the different traffic flows at each intersection and alternately distribute the right of way with appropriate time durations. The real-time traffic control systems that have been developed during the three last decades include CRONOS-2 which has several advantages under congested situations, in particular, its capacity to control several intersections in a centralized way. A real-site trial has been performed on a zone consisting of five adjacent intersections. The results show significant benefits for total delay in the zone during congestion compared to the usual control strategy for this zone. These results vary for each intersection considered on its own. This trial has demonstrated the effectiveness of this system in complex and constrained traffic situations.

The Real-time Traffic Signal Control System for the Minimum Emission using Reinforcement Learning in V2X Environment

As the population and vehicle ownership increase, emission of pollutants is also increasing. The percentage of GHG emission by transportation sector is about 21 % in 2015 (OECD), and this may be caused by frequent stop-and-go phenomenon or delay time of vehicles in signalized intersection. Generally, these could be minimised by driving in constant speed or decreasing the delay times with an efficient traffic signal control. On the other hand, researches try to decrease vehicles’ delay time and to exclude the unnecessary stop-and-go phenomenon in an urban signalized intersection with an advent of V2X (Vehicle-to-Everything) technology development. Especially, in traditional pre-timed traffic signal control situation, even the autonomous vehicles would be impossible to exhibit their own maximum performance. Thus, the development of the traffic signal control system could have effects not only on the traffic flow but also on environmental aspects, which optimizes the signalized traffic flow based on the real-time vehicle information. In this research, on the premise of V2X environment, changes in traffic flow and the emission are analysed based on microscopic traffic information. In specific, the reinforcement learning model is constructed based on Deep Learning which learns the real-time traffic information and displays the optimal traffic signal. The performance of the system was analysed through microscopic traffic simulator - Vissim. The proposed system is expected to contribute on analysing the traffic flow and the environmental effects. Also, it is expected to contribute on constructing the green smart cities with an advent of autonomous vehicle operation in future V2X environment.

Method for Imputing Missing Data using Online Calibration for Urban Freeway Control

Real-time traffic control systems are widely implemented on roadways around the world as a measure to improve freeway mobility. However, the systems, which rely on data from road-side and on-road sensors and other electronic equipment, continue to suffer from issues related to missing and erroneous data. While many data imputation methods are documented in the related literature, traffic control systems still lack an imputation method that is applicable in practice, accurate in imputation, and simple in computation. In response, this paper puts forth a linear imputation model that considers both temporal traffic trend and spatial detector correlations. To adapt the model to dynamic traffic variations, the imputation method was equipped with an online calibration module. The proposed imputation method was evaluated with field data from two stations on the Whitemud Drive, a busy urban freeway in Edmonton, Alberta, Canada. The proposed model benefited from its time-of-day temporal trend and outperforms the previous model that considers only spatial correlations. Moreover, the online calibration module was effective in improving imputation accuracy. Finally, the sensitivity of imputation performance was analyzed. The results show that the imputation with online calibration is more sensitive to missing data ratios than that with offline calibration. The sensitivity analysis revealed that imputation with online calibration is more suitable for online imputation in traffic control implementations.

An Intelligent Distributed Embedded System Architecture for Real-Time traffic control system

An Intelligent Distributed Embedded System Architecture for Real-Time traffic control system

Modulating Traffic Signal Phases to Realize Real-Time Traffic Control System

This paper proposes innovations to address challenges emanating from road traffic congestion. Improving economies create more car owners resulting in increased automobile manufacturing, increased vehicle population giving rise to higher emission of CO2 resulting in traffic congestion. Congested traffic has idling vehicles which emit higher CO2 and pollution. Besides, traffic congestion increases turnaround time, delivery time, commuting time and related logistical aspects. Commuting time negatively impacts working hours. Unless the traffic congestion is mitigated, the economy will take a beating creating a vicious ecology cycle. Building new roads, bridges or reconditioning of infrastructure is not always the best possible solutions. Efficient traffic management is a key to country’s economic growth. Various analytical models are employed to study, appreciate traffic congestion. The paper studies these models to infer that real time approach is the only solution. Several approaches are being worked on and few commercial systems too are available. These systems provide traffic information for course correction. However, it has latency and hence deviates from real time environment. Traffic congestion being highly dynamic in nature, it necessitates real time solution with real time inputs. It is proposed to integrate Real time traffic data with the traffic signal thus modulating the cycle timings at every junction. Deviation from static asymmetric cycle timing is implemented by assigning green phases based on density of vehicles. With minimalistic infrastructure and negligible incremental cost, the paper not only proposes to address traffic congestion but also paves the way for capturing traffic offenses, vehicle tracking and toll collection. The research is imminently realizable and makes a strong case for a PPP (Public Private Partnership) project.

Agent Based Intelligent Traffic Management System for Smart Cities

The performance of traffic systems is greatly dependent on their ability to react to changing traffic patterns and different situations. On traditional traffic systems, the lights run green for fixed intervals of time no matter what the density of the traffic is. Here, we implement an intelligent-agent traffic model that controls the amount of time a light runs green for, based on the number of cars (density) standing in the light. With an increase in the pace at which we lead our lives, time is of the essence. One place where time is wasted is on the road being stuck in traffic jams due to mismanagement of traffic. The effectiveness of (urban) traffic control systems greatly depends on its ability to react upon changes in traffic patterns, however, conventional traffic control systems, being unintelligent are unable to do this. Be the traffic density, high or low, the signals are timed and run according to those times only. This results in increased congestion along the roads which also results in significant air pollution, an increased safety risk and also has a negative effect on the populace's mental wellbeing. It is only when the ability to react becomes an integral part of the traffic control unit, its ability to react to changes in traffic conditions improves. The responsive control system should have sufficient knowledge of the situation to be able to handle unforeseen changes in traffic flow, such as accidents or car break-down. Intelligent signal control systems must have the capability to optimize the traffic flow by adjusting the traffic lights and co- ordinate operation between each signal in order to maximize the person and vehicular throughput and minimize delay. For intelligent urban traffic control, we ideally need a fully pro-active, real-time traffic control system; anticipating what will happen within the near future. 2. Need for Smart City A smart city (also smarter city) uses digital technologies or information and communication technologies (ICT) to enhance quality and performance of urban services, to reduce costs and resource consumption, and to engage more effectively and actively with its citizens. Smart city applications are developed with the goal of improving the management of urban flows and allowing for real time responses to challenges (18).A

A combined toolset for the verification of real-time distributed systems

Checking the correctness of distributed systems is one of the most difficult and urgent problems in software engineering. A combined toolset for the verification of real-time distributed systems (RTDS) is described. RTDSs are specified as statecharts in the Universal Modeling Language (UML). The semantics of statecharts is defined by means of hierarchical timed automata. The combined toolset consists of a UML statechart editor, a verification tool for model checking networks of real-time automata in UPPAAL, and a translator of UML statecharts into networks of timed automata. The focus is on the translation algorithm from UML statecharts into networks of hierarchical timed automata. To illustrate the proposed approach to the verification of RTDSs, a toy example of a real-time crossroad traffic control system is analyzed.

Real-Time Dynamic Transit Signal Priority Optimization for Coordinated Traffic Networks Using Genetic Algorithms and Artificial Neural Networks

Transit signal priority (TSP) has gained popularity in providing public transportation buses with preferential treatment at signalized intersections. Many studies have addressed its implementation in prompting enhanced public transportation service, such as reducing person delay and reducing transit travel time. However, most TSP implementations are done at the intersection level. Only a few studies have addressed the problem of integrating signal priority in coordinated real-time traffic signal control systems. A particular problem in this case is the uncertainty of predicting transit movements when considering the variability of dwell times at service stops. This study presents the development of a real-time traffic signal control integrating traffic signal timing optimization and TSP control using genetic algorithms (GA) and artificial neural networks (ANN) modeling. The GA is used to find near-optimal signal timings. Six different signal control systems were evaluated: fixed-time control with and without standard TSP, actuated signal control with and without standard TSP, real-time GA-based control without TSP, and real-time GA-based with advanced TSP logic. The standard TSP is implemented at the intersection level, by providing either early green (red truncation) or green extension strategies whenever a bus exists. A traffic signal control system that incorporates GA to optimize the fitness function and ANN for transit travel time prediction is developed. A microscopic simulation environment using VISSIM 4.3 simulation environment is used to test the previously mentioned six traffic control systems. The simulation results show that the proposed control system can reduce transit vehicle delay and improve schedule adherence. The reductions in delay and schedule adherence are statistically significant.

Vehicle actuation based short-term traffic flow prediction model for signalized intersections

Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks. By exploring available detector and signal controller information from neighboring intersections, a dynamic data-driven flow prediction model was developed. The model consists of two prediction components based on the signal states (red or green) for each movement at an upstream intersection. The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted. With an online turning proportion estimation method, along with the predicted travel times, the anticipated vehicle arrivals can be forecasted at the downstream intersection. The model performance was tested at a set of two signalized intersections located in the city of Gainesville, Florida, USA, using the CORSIM microscopic simulation package. Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%–20%, and show a normal distribution. It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.

Optimizing Traffic Signal Timing through Network Decomposition

A signal control system named Real-Time/Internal Metering Policy to Optimize Signal Timing (RT/IMPOST) has been under development for several years. It is designed to compute signal timing plans for the entire range of operating conditions from under- to oversaturation for control systems ranging from first generation (GEN 1) to highly responsive advanced traffic management systems. The different flow regimes of urban traffic control are reviewed. Then the focus is on the treatment to develop cycle-based signal timing plans for inclusion in the data libraries referenced by GEN 1 real-time traffic control systems for the undersaturated-flow regime. This approach decomposes a grid network into its constituent arterial subsystems in a way that is responsive to user-specified priorities. The user may define and rank the arterial systems subsumed within a grid network so as to satisfy strategic objectives responsive to traffic demand patterns and local needs. The procedure computes optimal signal timing plans for all these arterial subsystems in a priority-ranked sequence and integrates these arterial-based timing plans to form a networkwide signal timing plan. Field and simulation test results are presented. The field tests confirm that the IMPOST timing plans improved the operational performance of traffic along an arterial system in New York State relative to a fine-tuned existing control. The simulation studies compared the performance of traffic responding to IMPOST signal timing plans with that responding to Synchro 6.0 signal timing plans. The benefits of network decomposition and of arterial priority ranking are also documented.

Optimizing Traffic Signal Timing Through Network Decomposition

A signal control system named Real-Time/Internal Metering Policy to Optimize Signal Timing (RT/IMPOST) has been under development for several years. It is designed to compute signal timing plans for the entire range of operating conditions from under- to oversaturation for control systems ranging from first generation (GEN 1) to highly responsive advanced traffic management systems. The different flow regimes of urban traffic control are reviewed. Then the focus is on the treatment to develop cycle-based signal timing plans for inclusion in the data libraries referenced by GEN 1 real-time traffic control systems for the undersaturated-flow regime. This approach decomposes a grid network into its constituent arterial subsystems in a way that is responsive to user-specified priorities. The user may define and rank the arterial systems subsumed within a grid network so as to satisfy strategic objectives responsive to traffic demand patterns and local needs. The procedure computes optimal signal timing plans ...

IEC1131-3 표준언어 처리를 위한 지능적 소프트웨어 PLC 개발

IEC1131-3의 PLC(Programmable Logic Controller) 프로그래밍 언어는 프로그래밍이 복잡하여 디버깅이 어려우며 범용성이 없을 뿐 아니라 국내는 PC기반 소프트웨어 PLC 연구개발이 매우 미약하다. 따라서 본 논문은 국제 PLC 표준언어로 제정된 5가지 언어 중 국내에서 90%이상을 사용하고 있는 PLC 언어인 LD언어에 대한 표준규격을 연구하고, LD를 기존 상용화된 편집기(Visual C++)에서 활용 가능한 C코드로 변환하여 LD에 익숙한 사용자나 고급언어에 익숙한 사용자 모두 사용할 수 있는 지능적 에이전트 기반의 통합 시스템 ISPLC(Intelligent Agent System based Software Programmable Logic Controller)를 개발하였다. ISPLC에서는 LD에서보다 C에서 논리오류 검출기능이 훨씬 효율적이며, GUI 기반 인터페이스를 제공하며 에이전트에 의한 프로그래밍 코드를 제공한다. ISPLC는 초보자는 물론 PLC에 익숙한 사용자들에게도 효율적인 프로그래밍 플랫폼을 제공한다. 이러한 LD에서 IL로, IL에서 C로의 코드변환체제에 관한 연구는 국내외적으로 처음 시도되는 연구이다. ISPLC를 실제 실시간 교통량 제어 시스템에 적용하여 시뮬레이션한 결과 ISPLC가 오류검색 뿐 아니라 프로그래밍 시간을 기존 소프트에어 PLC에 비해 단축시켜줌을 알 수 있었다. The PLC programming by IEC1131-3 is hard to handle to ordinary users as well as professionals. Also it has not a generality, so that it couldn't be debugging some logic errors easily. In order to be adapted for such environment, In this paper, I have developed the ISPLC(Intelligent Agent System based Software Programmable Logic Controller). In ISPLC system, LD programmed by a user is converted to the C code which can be used in a commercial editor such as Visual C++. The detection of logical errors in C code is more effective than PLC programming itself. ISPLC provides the GUI-based interface in web environment and an easy programming platform to such beginners as well as professionals. The study of code conversion of LD to IL as well as IL to C is firstly tried in the world as well as KOREA. To show the effectiveness of the developed system, I applied it to a practical case, a real time traffic control system. ISPLC is minimized the error debugging and programming time owing to be supported by windows application programs.