Adaptive Traffic Light Control Based on Actual Condition Using Google Map API

Traffic congestion is one of the main problems in transportation sector and it causes a lot of drawbacks to public. The traffic light system is used to reduce the level of occurring traffic congestion. Generally, the available traffic light systems use a fixed time setting. This old traffic control system is no longer able to manage the ever-changing traffic conditions effectively and efficiently, causing a long queue of vehicles. To overcome this problem, a traffic light control system that can adapt to actual conditions of road density and can run automatically is offered. This system utilizes Google Map API as a road density data source. The result of this study is a traffic control system that can adjust the green light time duration based on the obtained density values and density trends, simulation of this adaptive system as well as simulation results analysis. A prototype of this adaptive control system was also produced in this study

BEAGLEBOARD EMBEDDED SYSTEM FOR ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM WITH CAMERA SENSOR

Traffic is one of the most important aspects in human daily life because traffic affects smoothness of capital flows, logistics, and other community activities. Without appropriate traffic light control system, possibility of traffic congestion will be very high and hinder people’s life in urban areas. Adaptive traffic light control system can be used to solve traffic congestions in an intersection because it can adaptively change the durations of green light each lane in an intersection depend on traffic density. The proposed adaptive traffic light control system prototype uses Beagleboard-xM, CCTV camera, and AVR microcontrollers. We use computer vision technique to obtain information on traffic density combining Viola-Jones method with Kalman Filter method. To calculate traffic light time of each traffic light in intersection, we use Distributed Constraint Satisfaction Problem (DCSP). From implementations and experiments results, we conclude that BeagleBoard-xM can be used as main engine of adaptive traffic light control system with 91.735% average counting rate. Lalu intas adalah salah satu aspek yang paling penting dalam kehidupan sehari-hari manusia karena lalu lintas memengaruhi kelancaran arus modal, logistik, dan kegiatan masyarakat lainnya. Tanpa sistem kontrol lampu lalu lintas yang memadai, kemungkinan kemacetan lalu lintas akan sangat tinggi dan menghambat kehidupan masyarakat di perkotaan. Sistem kontrol lampu lalu lintas adaptif dapat digunakan untuk memecahkan kemacetan lalu lintas di persimpangan karena dapat mengubah durasi lampu hijau di setiap persimpangan jalan tergantung pada kepadatan lalu lintas. Prototipe sistem kontrol lampu lalu lintas menggunakan BeagleBoard-XM, kamera CCTV, dan mikrokontroler AVR. Peneliti menggunakan teknik computer vision untuk mendapatkan informasi tentang kepadatan lalu lintas dengan menggabungkan metode Viola-Jones dan metode Filter Kalman. Untuk menghitung waktu setiap lampu lalu lintas di persimpangan, peneliti menggunakan Distributed Constraint Satisfaction Problem (DCSP). Dari hasil implementasi dan percobaan dapat disimpulkan bahwa BeagleBoard-XM dapat digunakan sebagai mesin utama sistem kontrol lampu lalu lintas adaptif dengan tingkat akurasi penghitungan rata-rata sebesar 91.735%

Congestion adaptive traffic light control and notification architecture using Google maps APIs

Mishra, S., Bhattacharya, D., & Gupta, A. (2018). Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs. Data, 3(4), [67]. DOI: 10.3390/data3040067Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has become relatively easy for developers to create systems for controlling signals and informative systems. Hence, for enhancing the power of Intelligent Transport Systems in automotive telematics, in this study, we used crowdsourced traffic congestion data from Google to adjust traffic light cycle times with a system that is adaptable to congestion. One aim of the system proposed here is to inform drivers about the status of the upcoming traffic light on their route. Since crowdsourced data are used, the system does not entail the high infrastructure cost associated with sensing networks. A full system module-level analysis is presented for implementation. The system proposed is fail-safe against temporal communication failure. Along with a case study for examining congestion levels, generic information processing for the cycle time decision and status delivery system was tested and confirmed to be viable and quick for a restricted prototype model. The information required was delivered correctly over sustained trials, with an average time delay of 1.5 s and a maximum of 3 s.publishersversionpublishe

An adaptive traffic light control system using reinforcement learning

Traffic signal control (TSC) is a challenging issue in managing an urban transportation system. A fixed time TSC is easy to implement but has drawbacks in such measures as flow rate, waiting time, and traffic density. The situation gets worse when the arrival rates of vehicles periodically change over time, which is usual in most urban cities. We propose adaptive reinforcement learning (RL) to manage TSC with varying vehicle arrival rates. Our objectives are to improve the averages of flow rate and waiting time and reduce the wasteful green light problem by considering the vehicle densities of the current lane and the downstream directions. Experiments were conducted by Simulation of Urban MObility (SUMO) under three traffic layouts and various vehicle arrival rates. The proposed method not only reduced on average traffic density, waiting time, and queue length, but also increased the average flow rate and average speed, relative to the other algorithms tested

Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning

Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spaces. Inspired by these successes, in this paper, we build two kinds of reinforcement learning algorithms: deep policy-gradient and value-function based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The policy-gradient based agent maps its observation directly to the control signal, however the value-function based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Our methods show promising results in a traffic network simulated in the SUMO traffic simulator, without suffering from instability issues during the training process

A Review: Implementation of Advance Adaptive Traffic Light Control System Using DIP and Embedded

As we know the population of city and number of cars is increasing day by day. With increasing urban population and hence the number of cars, need of controlling streets, highways and roads is vital. In this paper, a system that estimates the size of traffic in highways by using image processing has been proposed and as a result a message is shown to inform the number of cars in highway. This project has been implemented by using the Matlab software and it aims to prevent heavy traffic in highways. Moreover, for implementing this project following steps must be considered: 1) image acquisition 2) RGB to grayscale transformation 3) image enhancement and 4) morphological operations. At first, film of highway is captured by a camera has been installed in highway. Then, the film comes in the form of consecutive frames and each frame is compared with the first frame. After that, the number of cars in highways is specified. At the end, if the number of cars is more than a threshold, a message is shown to inform the traffic status. By this message we can predict the need to reduce the size of traffic carried. Experiments show that the algorithm will work properly