Outcome Assessment of Peer-to-Peer Adaptive Control Adjacent to a National Park

Author(s):  
Lucy M. Richardson ◽  
Matthew D. Luker ◽  
Christopher M. Day ◽  
Mark Taylor ◽  
Darcy M. Bullock

In the town of Moab, Utah, a combination of seasonal tourist traffic, heavy truck traffic, and high pedestrian volumes creates a unique traffic management challenge; Moab’s remote location adds additional challenges for real-time traffic monitoring and maintaining of signal timing plans. The Main Street corridor is a strong candidate for an adaptive traffic control system (ATCS). Peer-to-peer (P2P) communication and user-definable control logic were used to develop and implement a cost-effective ATCS called “P2P adaptive control” that used only the existing local controllers and detection. The adaptive control logic adjusts green time along the mainline in response to detector inputs while keeping the side streets at the minimum time needed for pedestrian service. System performance was evaluated by comparing performance measures generated from high-resolution signal controller data before and after implementation of P2P adaptive control. The P2P adaptive control increased the through bandwidth of the corridor and reduced the number of split failures (i.e., the number of phase occurrences with insufficient green). Future work will include adjusting the algorithm to improve service on side streets and expanding P2P adaptive control to additional signals expected to be constructed in the area.

Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>


Transport ◽  
2011 ◽  
Vol 26 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Yong-Gang Wang ◽  
Kuan-Min Chen ◽  
Yu-Long Pei ◽  
Ying Wang

Many studies focused on the analysis of effect factors contributing to the crashes and development of crash prediction models have resulted in aggregate researches to quantify the safety effects of geometric and traffic variables and environmental concerns on the expected outcome of fatal, injury and/or property damage losses at specific locations. Crash insight regarding different locations, however, has rarely been performed. Such investigations are useful for at least two reasons. First, there is a priori need to identify high risk sites with respect to crash. Second, it is generally believed that different crash types (e.g. rear-end, angle etc.) are associated with road geometry, the environment and traffic condition, and as a result justifying the inside causes of such crashes helps with understanding and improving the specific ability to make effective countermeasures. Therefore, the objectives of this paper are to (1) demonstrate that different crash types are associated to intersections in different ways and (2) reveal that the statistics of intersection crashes may lead to greater insights considering crash occurrence and countermeasure effectiveness. This paper first divides crashes into 5 categories or types: pedestrian-involved, rear-end, head-on, angle and sideswipe crash types. Based on 3208 crashes collected on the intersections in the city of Harbin during the period of 1992–2008, distribution, overall count and the occurrence of rate features are estimated resulting in two models. The performed analysis reveals that safety improvement factors such as the presence of a signal light set, a traffic monitoring device and ITS measure have a positive association with intersection crash in different ways, suggesting that different traffic control and management aspects may be helpful in identifying specific countermeasures in the overall safety improvement project. Santrauka Daugelis tyrimų, nagrinėjančių efektyvumo veiksnius, padedančius nustatyti susidūrimų prognozavimo modelius, paskatino įvairius tyrimus įvertinti eismo aplinkos sudedamųjų dalių ir jų parametrų įtaką mirties ir (arba) turtinės žalos atvejams konkrečiose vietose. Iki šiol tai buvo retas reiškinys. Tokie tyrimai yra svarbūs bent jau dėl dviejų priežasčių. Pirma, reikia nustatyti padidintos rizikos vietas atsižvelgiant į eismo įvykį. Antra, manoma, kad skirtingos susidūrimo rūšys yra susijusios su kelio geometrija, aplinkos ir eismo sąlygomis ir kaip pasekmė, pateisinanti vidines tokių susidūrimų priežastis, padeda suprasti ir pagerinti konkrečias galimybes imtis atsakomųjų veiksmų. Todėl šio darbo tikslas—parodyti, kad skirtingos susidūrimo rūšys įvairiai susijusios su eismu sankryžose, kad susidūrimų sankryžose statistika gali lemti didesnę įžvalgą, atsižvelgiant į įvykusį eismo įvykį ir atsakomųjų priemonių veiksmingumą. Šiame darbe susidūrimai skirstomi į 5 rūšis: susidūrimai su pėsčiaisiais, įvažiavimas į galinę transporto priemonės dalį, susidūrimas priekinėmis transporto priemonės dalimis, kampinis smūgis ir šoninis smūgis. Pasiskirstymas, bendras skaičius ir susidūrimų dažnis apskaičiuojami pagal du modelius remiantis informacija, surinkta iš 3028 eismo įvykių, įvykusių 1992–2008 m. Charbino miesto (Kinija) sankryžose. Atlikta analizė parodė, kad saugaus gerinimo veiksniai, t. y. šviesoforas, eismo stebėjimo prietaisai ir t.t., turi teigiamą įtaką. Skirtingi eismo kontrolės ir valdymo aspektai gali padėti rasti konkrečias atsakomąsias priemones, įgyvendinant visą saugaus eismo gerinimo projektą. Резюме Проблема безопасности дорожного движения актуальна во всех городах мира. Не является исключением и китайский город Харбин. Авторы исследуют влияние совокупности факторов, возникающих до и после дорожно-транспортного происшествия, для оценки эффективности проекта по безопасному движению на перекрестках в упомянутом городе. Исследуются места увеличенного риска возникновения дорожно-транспортного происшествия. Также принимается во внимание, что различные типы дорожно-транспортных происшествий тесно связаны с геометрическими параметрами дороги, условиями окружающей среды, условиями самого движения и т. д. Целью исследования было показать связь и влияние различных типов дорожно-транспортных происшествий на конкретные ситуации движения на перекрестках. Это необходимо для того, чтобы понять необходимость ответных мер по обеспечению безопасности дорожного движения в потенциально опасных местах. Все дорожно-транспортные происшествия разделены на 5 типов. Далее на основании информации о 3028 дорожно-транспортных происшествий, зарегистрированных в период с 1992 по 2008 гг. на перекрестках города Харбина, представлены статистические результаты исследования. Проведенное исследование показало, что меры по увеличению безопасности дорожного движения (например, светофор, видео наблюдение за движением и т. д.) имели положительное влияние. Различные аспекты контроля за дорожным движением и управления им помогают найти ответные меры по претворению в жизнь проекта по обеспечению безопасности дорожного движения.


2018 ◽  
Vol 7 (2.18) ◽  
pp. 7 ◽  
Author(s):  
Venkata Ramana N ◽  
Seravana Kumar P. V. M ◽  
Puvvada Nagesh

Big data is a term that describes the large volume of data – both structured and unstructuredthat includes a business on a day-to-day basis including Intelligent Transportation Systems (ITS). The emerging connected technologies created around ubiquitous digital devices have opened unique opportunities to enhance the performance of the ITS. However, magnitude and heterogeneity of the Big Data are beyond the capabilities of the existing approaches in ITS. Therefore, there is a crucial need to develop new tools and systems to keep pace with the Big Data proliferation. In this paper, we propose a comprehensive and flexible architecture based on distributed computing platform for real-time traffic control. The architecture is based on systematic analysis of the requirements of the existing traffic control systems. In it, the Big Data analytics engine informs the control logic. We have partly realized the architecture in a prototype platform that employs Kafka, a state-of-the-art Big Data tool for building data pipelines and stream processing. We demonstrate our approach on a case study of controlling the opening and closing of a freeway hard shoulder lane in microscopic traffic simulation. 


2008 ◽  
Vol 35 (4) ◽  
pp. 370-378 ◽  
Author(s):  
Jin-Tae Kim ◽  
Jeongyoon Lee ◽  
Myungsoon Chang

Adaptive traffic control systems (ATCS) are designed to calculate traffic signal timings in real time to accommodate current traffic demand changes. A conventional off-line computer-based design procedure that uses iterative evaluations to select alternatives may not be appropriate for ATCS due to its unstable searching time. Search-free analytical procedures that directly find solutions have been noted for ATCS for this reason. This paper demonstrates (i) the shortcomings of an analytical cycle-length design model, specifically COSMOS, in its ability to generate satisfactory solutions at various saturation levels and (ii) an artificial neural network (ANN) based model that can overcome these shortcomings. The ANN-based model consistently yielded cycle lengths that ensure a proper operational target volume to capacity (v/c) ratio, whereas the use of the analytical model resulted in unstable target v/c ratios that might promote congestion.


Author(s):  
G. Kalyan

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.


Author(s):  
K. R. SHRUTHI ◽  
K. VINODHA

Vehicular traffic is continuously increasing around the world, especially in large urban areas. The resulting congestion has become a major concern to transportation specialists and decision makers. The existing methods for traffic management, surveillance and control are not adequately efficient in terms of performance, cost, maintenance, and support. In this paper, the design of a system that utilizes and efficiently manages traffic light controllers is presented. In particular, we present an adaptive traffic control system based on a new traffic infrastructure using Wireless Sensor Network (WSN). These techniques are dynamically adaptive to traffic conditions on both single and multiple intersections. An intelligent traffic light controller system with a new method of vehicle detection and dynamic traffic signal time manipulation is used in the project. The project is also designed to control traffic over multiple intersections and follows international standards for traffic light operations. A central monitoring station is designed to monitor all access nodes..


2000 ◽  
Vol 1727 (1) ◽  
pp. 95-100 ◽  
Author(s):  
David E. Lucas ◽  
Pitu B. Mirchandani ◽  
K. Larry Head

Simulation is a valuable tool for evaluating the effects of various changes in a transportation system. This is especially true in the case of real-time traffic-adaptive control systems, which must undergo extensive testing in a laboratory setting before being implemented in a field environment. Various types of simulation environments are available, from software-only to hardware-in-the-loop simulations, each of which has a role to play in the implementation of a traffic control system. The RHODES (real-time hierarchical optimized distributed effective system) real-time traffic-adaptive control system was followed as it progressed from a laboratory project toward actual field implementation. The traditional software-only simulation environment and extensions to a hardware-in-the-loop simulation are presented in describing the migration of RHODES onto the traffic controller hardware itself. In addition, a new enhancement to the standard software-only simulation that allows remote access is described. The enhancement removes the requirement that both the simulation and the traffic control scheme reside locally. This architecture is capable of supporting any traffic simulation package that satisfies specific input-output data requirements. This remote simulation environment was tested with several different types of networks and was found to perform in the same manner as its local counterpart. Remote simulation has all of the advantages of its local counterpart, such as control and flexibility, with the added benefit of distribution. This remote environment could be used in many different ways and by different groups or individuals, including state or local transportation agencies interested in performing their own evaluations of alternative traffic control systems.


Author(s):  
Michael L. Pack ◽  
Phillip Weisberg ◽  
Sujal Bista

This research developed a system for visualizing four-dimensional (4-D), real-time transportation data for the major road networks of Washington, D.C., Northern Virginia, and the entire state of Maryland. The effort employed a combination of OpenGL and other modeling techniques to develop a scalable, highly interactive 4-D model using available geographic information system (GIS) and transportation infrastructure data in conjunction with real-time traffic management center data. The prototype system interacts with real-time traffic databases to show animations of real-time traffic data (volume and speed) along with incident data (accident locations, lane closures, responding agencies, etc.). A user can “fly” or “drive” through the region to inspect conditions at an infinite number of angles and distances. The program also allows users to monitor the status of and interact with traffic control devices such as dynamic message signs, closed-circuit television feeds, and traffic sensors and even view the location of emergency response vehicles equipped with Global Positioning System transceivers. Because the system uses standard GIS data and relatively standard transportation databases to derive traffic measures, it can be scaled to incorporate other states and agencies.


Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.


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