scholarly journals Extending the learning and communicating shout-ahead architecture with user-defined exception rules -- a case study for traffic light controls

2021 ◽  
Vol 2 (1) ◽  
pp. 46-67
Author(s):  
Christian Roatis ◽  
Jorg Denzinger
Keyword(s):  
Traffic Flow ◽  
Hybrid Learning ◽  
Learning Approach ◽  
Application Area ◽  
Agent Architecture ◽  
Traffic Light ◽  
Traffic Lights ◽  
Learning Time

We present an extension of the shout-ahead agent architecture that allows for adding human user-defined exception rules to the rules created by the hybrid learning approach for this architecture. The user-defined rules can be added after learning as reaction to weaknesses of the learned rules or learning can be performed with the user-defined rules already in place. We applied the extended shout-ahead architecture and the associated learning to a new application area, cooperating controllers for the traffic lights of intersections. In our experimental evaluations, adding user-defined exception rules to the learned rules for several traffic flow instances increased the efficiency of the resulting controllers substantially compared to just using the learned rules. Performing learning with user-defined exception rules already in place decreased the learning time substantially for all flows, but had mixed results with respect to efficiency. We also evaluated user-defined exception rules for a variant of the architecture that is not using communication and saw similar effects as for the variant with communication. For the communicating version, both variants of adding user-defined exception rules create controllers that are much more flexible than what using the original shout-ahead architecture with its learning is able to create as indicated by experiments with variations of flows.

scholarly journals An Analysis of A Three-Phase Versus A Four-Phase Traffic Lights Regulation for A Four-Leg Intersection

2020 ◽  
Vol 26 (2) ◽  
pp. 192-201
Author(s):  
Sri Redjeki Pudjaprasetya ◽  
Dear Michiko Noor
Keyword(s):  
Traffic Management ◽  
Traffic Density ◽  
Traffic Light ◽  
Traffic Lights ◽  
Characteristic Lines ◽  
Three Phase ◽  
Light Management ◽  
Phase Regulation ◽  
Parameter Values

Traffic management of intersections is an important factor that can determine traffic density at the intersection, as well as its surrounding. Long traffic queues we encounter in daily life, were often caused by ineffectiveness of traffic lights management of the cross sections.In this article, an analytic study of traffic light management of a four-leg intersection, based on the kinematic LWR model, was presented. Comparison was based on observing the end of queues over three cycles of red-green lights, under the assumption of a constant traffic flux. On every leg of the intersection, the end of the queues were obtained from characteristic lines of the shock waves.From these observations, the three phase regulation was preferred over the four-phase one. Finally, a case study of Taman Sari - Baltos intersection located in Bandung City, Indonesia, was discussed. Parameter values used in these simulations were obtained from direct observation. 

scholarly journals PROFIL FIJAI WARGA BELAJAR PROGRAM KESETARAAN PAKET A SANGGAR KEGIATAN BELAJAR (SKB)

2020 ◽  
Vol 2 (2) ◽  
pp. 111-115
Author(s):  
Putri Mardayani ◽  
Rufran Zulkarnain ◽  
Suardi Jasma
Keyword(s):  
Data Collection ◽  
Qualitative Method ◽  
Family Background ◽  
Traffic Light ◽  
Time Data ◽  
Light Traffic ◽  
Data Presentation ◽  
Traffic Lights ◽  
Data Validity

This study was aimed at describing the profile of Fijai, a student of program kesetaraan paket A Sanggar Kegiatan Belajar of Bengkulu municipality(case study on Fijai who is also a clown). This study used qualitative method. Data collection was done by interview , documentation, and observation. The subjects of this study included Fijai's parents and Fijai's tutor at program kesetaraan paket A Sanggar Kegiatan Belajar of Bengkulu municipality. Data validity was examined using triangulation of subject, technique and time. Data were analyzed through three steps which were data reduction, data presentation, and conclusion drawing. From the results, Fijai's profile was as follows. Firstly, how was Fijai's family background? His parents did not have a job except to help him work as a clown. Fijai got Rp 2,500,000/month from his job as a clown. Secondly, how was his study at program kesetaraan paket A Sanggar Kegiatan Belajar of Bengkulu municipality? He always attended the class once a week. Thirdly,how did he do his job as a clown? He started his job from 10 A.M. until 5 P.M. He moved around while doing his job, sometimes at Suka Merindu traffic light, traffic light near the roundabout, and other traffic lights. Fourthly,how was his parents' response on his education and his job as a clown? His parents agreed with his education at SKB and his job as a clown did not disturb his education. Fively,how was his tutor's response on his education and his job as a clown? According to his tutor he had highly enthusiasm in studying, he always attended the class once a week. Sixly, how was his achievement in package A program? His achievement was increasing per semester. Keywords : The profile of Fijai, student, package A equivalence program

scholarly journals Characteristics of Traffic Flows at Kuda Mati non-traffic light Intersection in Workhour and Holidays

2018 ◽  
Vol 73 ◽  
pp. 08030
Author(s):  
F. Betaubun Herbin
Keyword(s):  
Traffic Flow ◽  
Movement Pattern ◽  
Survey Method ◽  
Traffic Flows ◽  
Traffic Light ◽  
City Center ◽  
Work Time ◽  
Traffic Lights ◽  
Economic Support ◽  
The City

Characteristics of traffic flow needs to be revealed to describe the traffic flow that occurred at the research location. One of the patterns of traffic flow movement of Merauke Regency that is important enough to be observed is the movement pattern that occurs at Kuda Mati Non-traffic lights Intersection. This intersection is one of the access for economic support of Merauke Regency. The intersection connects the city center to the production centers and is used by the community to perform activities in meeting their needs such as working and meeting the needs of clothing, food and shelter. This fulfillment activity is usually differentiated according to work time and holiday time. The method used is survey method to describe the characteristics of traffic flow at the intersection. Data analysis applied MKJI 1997. The results show that peak hour traffic flow occurs at 17.00 - 18.00 on holiday 803 smp / hour, while for working time the traffic flow is evenly distributed with maximum vehicle volume occur at 12:00 to 13:00 which amounted to 471 smp / hour.

scholarly journals Optimization of Traffic Flow On KS Tubun Street using Greenberg Model

2019 ◽  
Vol 8 (2S7) ◽  
pp. 83-86
Keyword(s):  
Traffic Flow ◽  
Red Light ◽  
Green Light ◽  
Light Cycle ◽  
Traffic Light ◽  
Traffic Jam ◽  
Traffic Lights ◽  
The Road ◽  
Road Users ◽  
Time Parameters

KS Tubun Street is a street in Bogor, which has a fairly high vehicle volume and become one of a high-traffic jam area. This is caused by KS Tubun Street is the main road for road users from Jakarta and Bogor. Traffic jam problem that occurs due to the confluence interchange of traffic flow and traffic lights settings that are not proportional to the volume of vehicles across the road. Optimization of traffic flow at KS Tubun Street performed by the stages of forming a model of traffic flow, determining the density and velocity of the vehicle is based on the Greenberg model, and determining the length of the traffic lights to avoid a buildup of vehicles. The result is a traffic flow model with distance and time parameters. The density of vehicles that occurs on the streets of KS. Tubun street based on the Greenberg model between 180 to 240 unit car of passanger (ucp) with the average velocity of vehicles 15 to 19.5 km per hour. The density of vehicles on KS. Tubun street can be break down by increasing time. Traffic light cycle time can be reduced for 8 seconds with the red light glowing time is 80 seconds and the green light glowing time is 62 seconds.

Proposed Traffic Light Control Mechanism Based on Multi-Agent Coordination

2016 ◽  
Vol 20 (5) ◽  
pp. 803-812
Author(s):  
Satoshi Kurihara ◽  
◽  
Ryo Ogawa ◽  
Kosuke Shinoda ◽  
Hirohiko Suwa ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Urban Areas ◽  
Transport Systems ◽  
Light Control ◽  
Traffic Light ◽  
Agent Based ◽  
Traffic Lights ◽  
Control Framework ◽  
Traffic Light Control ◽  
Multi Agent

Traffic congestion is a serious problem for people living in urban areas, causing social problems such as time loss, economical loss, and environmental pollution. Therefore, we propose a multi-agent-based traffic light control framework for intelligent transport systems. Achieving consistent traffic flow necessitates the real-time adaptive coordination of traffic lights; however, many conventional approaches are of the centralized control type and do not have this feature. Our multi-agent-based control framework combines both indirect and direct coordination. Reaction to dynamic traffic flow is attained by indirect coordination, whereas green-wave formation, which is a systematic traffic flow control strategy involving several traffic lights, is attained by direct coordination. We present the detailed mechanism of our framework and verify its effectiveness using simulation to carry out a comparative evaluation.

scholarly journals Device and Algorithm for Vehicle Detection and Traffic Intensity Analysis

2021 ◽  
Vol 17 (1) ◽  
pp. 83-92
Author(s):  
Mikhail Gorobetz ◽  
Andrey Potapov ◽  
Aleksandr Korneyev ◽  
Ivars Alps
Keyword(s):  
Traffic Flow ◽  
Internal Combustion Engines ◽  
Radio Channel ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Traffic Intensity ◽  
Traffic Light ◽  
Traffic Lights ◽  
Advantages And Disadvantages ◽  
Sensor Module

Abstract To effectively manage the traffic flow in order to reduce traffic congestion, it is necessary to know the volumes and quantitative indicators of this flow. Various detection methods are known for detecting a vehicle in a lane, which, in turn, have their own advantages and disadvantages. To detect vehicles and analyse traffic intensity, the authors use a pulse coherent radar (PCR) sensor module. Testing of various modes of operation of the radar sensor was carried out to select the optimal mode for detecting vehicles. The paper describes a method for fixing vehicles of different sizes, filtering and separating the vehicle from the traffic flow. The developed vehicle detection device works in conjunction with signal traffic lights, through which traffic control takes place. The signal traffic lights, which have their own sensors and control units, communicate with each other via a radio channel; there is no need for cable laying. The system is designed to work on road maintenance sites. The paper describes the experimental data when testing on a separate section of the road. The experiment showed the advantage of traffic lights (cars passed the regulated traffic light faster) from the point of view of calculating the traffic flow over the normal traffic light operation. Reducing downtime in traffic jams, in turn, has a beneficial effect on the environmental situation, since at the moment internal combustion engines prevail in vehicles.

scholarly journals Recent development of smart traffic lights

2021 ◽  
Vol 10 (1) ◽  
pp. 224
Author(s):  
A’isya Nur Aulia Yusuf ◽  
Ajib Setyo Arifin ◽  
Fitri Yuli Zulkifli
Keyword(s):  
Traffic Flow ◽  
Time Allocation ◽  
Traffic Density ◽  
Light Cycle ◽  
Traffic Light ◽  
Large Cities ◽  
Traffic Lights ◽  
Smart Traffic ◽  
Very High ◽  
Signal Time

<span id="docs-internal-guid-288f4dcc-7fff-1e8c-0350-5032593b6e4f"><span>Increased traffic flow causes congestion, especially in large cities. Even though congestion is not unusual, traffic jams still result in very high economic and social losses. Several factors cause congestion, one of which is traffic lights. Therefore, a mechanism is needed so that traffic lights can intelligently and adaptively manage signal time allocation according to traffic flow conditions. A traffic light with this type of mechanism is known as a smart traffic light. Smart traffic light cycle settings can be grouped based on the traffic density, scenarios for emergency vehicles, and the interests of pedestrians. This paper analyzes the methods and technologies used in the development of smart traffic light technology from the perspective of these three situations as well as the development of smart traffic light technology in the future.</span></span>

scholarly journals Testing an Adaptive Cruise Controller with coupled traffic and driving simulations

10.29007/84rc ◽  
2019 ◽  
Author(s):  
Mirko Barthauer ◽  
Alexander Hafner
Keyword(s):  
Traffic Flow ◽  
Driving Simulator ◽  
Traffic Simulation ◽  
Traffic Signals ◽  
Simulation Environment ◽  
Cruise Control ◽  
Traffic Light ◽  
Traffic Lights ◽  
Subjective Judgement ◽  
Traffic Simulations

In many cases, driving simulator studies target how test persons interact with surround- ing traffic and with traffic signals. Traffic simulations like SUMO specialize in modeling traffic flow, which includes signal control. Consequently, driving and traffic simulation are coupled to benefit from the advantages of both. This means that all except the driven (ego) vehicle are controlled by the traffic simulation. Essential vehicle dynamics data are exchanged and applied frequently to make the test person interact with SUMO-generated traffic. Additionally, traffic lights are controlled by SUMO and transferred to the driving simulation. The system is used to evaluate an Adaptive Cruise Control (ACC) system, which considers current and future traffic light states. Measures include objective terms like traffic flow as well as the subjective judgement of the signal program, the ACC and the simulation environment.

scholarly journals CELLULAR AUTOMATA SIMULATION OF TRAFFIC LIGHT STRATEGIES IN OPTIMIZING THE TRAFFIC FLOW

2005 ◽  
Vol 4 (2) ◽  
pp. 13
Author(s):  
E. H. NUGRAHANI ◽  
R. RAMDHANI
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Control Strategies ◽  
Velocity Model ◽  
Flow Density ◽  
Traffic Light ◽  
Cellular Automata Model ◽  
Optimal Velocity ◽  
Traffic Lights ◽  
Traffic System

The behavior of traffic system controlled by traffic lights is presented under assumptions of city traffic on a single lane based on the optimal velocity model.  The effect of differrent traffic light control strategies on the traffic flow is discussed using three different strategies, i.e. the synchronized, green wave, and random offset strategies. The flow-density diagrams are analyzed using these strategies.   The numerical investigation is carried out using cellular automata model to get a better understanding of the microscopic behavior of the system under the three different traffic light strategies.

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