scholarly journals Raspberry Pi as an Inexpensive Platform for Real-Time Traffic Jam Analysis on the Road

2018 ◽  
Author(s):  
Robert Baumgartl ◽  
Dirk Mueller
Keyword(s):  
Real Time ◽  
Raspberry Pi ◽  
Traffic Jam ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

scholarly journals Design and Development of Traffic Control System using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3247-3252
Author(s):  
G. Kalyan
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Density ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

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.

scholarly journals Real-Time Driver Alert System Using Raspberry Pi

2019 ◽  
Vol 17 (2) ◽  
pp. 193-203 ◽  
Author(s):  
Jie Yi Wong ◽  
Phooi Yee Lau
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Economic Losses ◽  
Raspberry Pi ◽  
Alert System ◽  
Driving Experience ◽  
The Road ◽  
Car Accidents ◽  
Passenger Safety ◽  
On The Road

Malaysia has been ranked as one of the country in the world with deadliest road. Based on the statistic, there are around 7000 to 8000 people in the country died on the road among the population of 31 million Malaysians every year. In general, Advances Driver Assistance System (ADAS) aims to improve not only the driving experience but also consider the overall passenger safety. In recent years, driver drowsiness has been one of the major causes of road accidents, which can lead to severe physical injuries, deaths and significant economic losses. In this paper, a vison-based real-time driver alert system aimed mainly to monitor the driver’s drowsiness level and distraction level is proposed. This alert system could reduce the fatalities of car accidents by detecting driver’s face, detecting eyes region using facial landmark and calculating the rate of eyes closure in order to monitor the drowsiness level of the driver. Later, the system is embedded into the Raspberry Pi, with a Raspberry Pi camera and a speaker buzzer, and is used to alert the driver in real-time, by providing a beeping sound. Experimental results show that proposed system is practical and low-cost which could (1) embed the drowsiness detection module, and (2) provide alert notification to the driver when the driver is inattentive, using a medium loud beeping sound, in real-time.

scholarly journals Real-time traffic sign detection and recognition using Raspberry Pi

2022 ◽  
Vol 12 (1) ◽  
pp. 331
Author(s):  
Ida Syafiza Binti Md Isa ◽  
Choy Ja Yeong ◽  
Nur Latif Azyze bin Mohd Shaari Azyze
Keyword(s):  
Real Time ◽  
Video Recording ◽  
Recognition System ◽  
Road Accident ◽  
Raspberry Pi ◽  
Traffic Sign ◽  
Traffic Signs ◽  
The Road ◽  
Real Time Traffic ◽  
Driving Assistance

Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay.

Intelligent Traffic Signal Monitoring System Using Image Processing

2021 ◽  
pp. 173-195
Author(s):  
SureshKumar M. ◽  
Anu Valliammai R.
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Signal ◽  
Traffic Light ◽  
The Road ◽  
Real Time Traffic ◽  
Traffic Intersection ◽  
Signal Monitoring ◽  
On The Road

This project aims at making an intelligent traffic signal monitoring system that makes decisions based on real-time traffic situations. The choices will be such that the traditional red, green, or amber lighting scheme is focused on the actual number of cars on the road and the arrival of emergency services rather than using pure timing circuits to control car traffic by using what the traffic appears like via smart cameras to capture real-time traffic movement pictures of each direction. The control system will modify the traffic light control parameters dynamically in various directions due to changes in traffic flow, thus increasing the traffic intersection efficiency and ensuring improved traffic management. This work involves performing a traffic management study of the city.

scholarly journals Ant Colony Optimization for Real Time Traffic Lights Control on a Single Intersection

2020 ◽  
Vol 14 (02) ◽  
pp. 196 ◽  
Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Aberrahim Hasbi
Keyword(s):  
Road Traffic ◽  
Green Light ◽  
Optimal Solution ◽  
Ant Colony ◽  
Traffic Data ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

In this paper, we present a new scheme to intelligently control the cycles and phases of traffic lights by exploiting the road traffic data collected by a wireless sensor network installed on the road. The traffic light controller determines the next phase of traffic lights by applying the Ant Colony Optimazation metaheuristics to the information collected by WSN. The objective of this system is to find an optimal solution that gives the best possible results in terms of reducing the waiting time of vehicles and maximizing the flow crossing the intersection during the green light. The results of simulations by the SUMO traffic simulator confirm the preference of the developed algorithm over the predefined time controller and other dynamic controllers.

scholarly journals IoT based smart traffic signal monitoring system using vehicles counts

2018 ◽  
Vol 7 (2.21) ◽  
pp. 309 ◽  
Author(s):  
Senthil Kumar Janahan ◽  
M R.M. Veeramanickam ◽  
S Arun ◽  
Kumar Narayanan ◽  
R Anandan ◽  
...  
Keyword(s):  
Waiting Time ◽  
Clustering Algorithms ◽  
Traffic Signal ◽  
Traffic Network ◽  
The Road ◽  
Real Time Traffic ◽  
Proposed Model ◽  
Timing Interval ◽  
On The Road ◽  
Smart Traffic

Traffic signal management is one of the major problematic issues in the current situation. Such scenarios, every signal are getting 60 seconds of timing on the road at a regular interval, even when traffic on that particular road is dense. As per this proposed model in this article, which will be optimized the timing interval of the traffic signal purely depends on the number of vehicles on that particular roadside. The major advantage of this system is that it can able to decrease the more waiting time for the drivers to cross road signal.  In this model, we are using the clustering algorithms model which is based on KNN algorithm. Using this algorithm new model will be liable to determine expected required timing as per provided inputs to the signal which is vehicles count. The input of these systems is vehicles counts on each side of the road from crossing signal.  And this input will be determined on much time is to be provided. “Case studies on this system are traffic network and real-time traffic sub-networks are organized to get the effectiveness of the proposed model.”  

scholarly journals Towards a Real-Time Driver Workload Estimator: An On-the-Road Study

2016 ◽  
pp. 1151-1164
Author(s):  
Peter van Leeuwen ◽  
Renske Landman ◽  
Lejo Buning ◽  
Tobias Heffelaar ◽  
Jeroen Hogema ◽  
...  
Keyword(s):  
Real Time ◽  
The Road ◽  
Driver Workload ◽  
On The Road ◽  
Road Study

scholarly journals Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yongchao Song ◽  
Jieru Yao ◽  
Yongfeng Ju ◽  
Yahong Jiang ◽  
Kai Du
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Minimum Energy ◽  
Automatic Detection ◽  
Initial Contour ◽  
Target Area ◽  
Threshold Method ◽  
The Real ◽  
The Road ◽  
On The Road

In order to solve the problems of traffic object detection, fuzzification, and simplification in real traffic environment, an automatic detection and classification algorithm for roads, vehicles, and pedestrians with multiple traffic objects under the same framework is proposed. We construct the final V view through a considerate U-V view method, which determines the location of the horizon and the initial contour of the road. Road detection results are obtained through error label reclassification, omitting point reassignment, and so an. We propose a peripheral envelope algorithm to determine sources of vehicles and pedestrians on the road. The initial segmentation results are determined by the regional growth of the source point through the minimum neighbor similarity algorithm. Vehicle detection results on the road are confirmed by combining disparity and color energy minimum algorithms with the object window aspect ratio threshold method. A method of multifeature fusion is presented to obtain the pedestrian target area, and the pedestrian detection results on the road are accurately segmented by combining the disparity neighbor similarity and the minimum energy algorithm. The algorithm is tested in three datasets of Enpeda, KITTI, and Daimler; then, the corresponding results prove the efficiency and accuracy of the proposed approach. Meanwhile, the real-time analysis of the algorithm is performed, and the average time efficiency is 13 pfs, which can realize the real-time performance of the detection process.

scholarly journals Raspberry Pi-Based Low-Cost Connected Device for Assessing Road Surface Friction

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 341 ◽  
Author(s):  
Miha Ambrož ◽  
Uroš Hudomalj ◽  
Alexander Marinšek ◽  
Roman Kamnik
Keyword(s):  
Traffic Safety ◽  
Road Network ◽  
Low Cost ◽  
Measurement Data ◽  
Automatic Measurement ◽  
Raspberry Pi ◽  
Measuring Device ◽  
The Road ◽  
Measuring Devices ◽  
On The Road

Measuring friction between the tyres of a vehicle and the road, often and on as many locations on the road network as possible, can be a valuable tool for ensuring traffic safety. Rather than by using specialised equipment for sequential measurements, this can be achieved by using several low-cost measuring devices on vehicles that travel on the road network as part of their daily assignments. The presented work proves the hypothesis that a low cost measuring device can be built and can provide measurement results comparable to those obtained from expensive specialised measuring devices. As a proof of concept, two copies of a prototype device, based on the Raspberry Pi single-board computer, have been developed, built and tested. They use accelerometers to measure vehicle braking deceleration and include a global positioning receiver for obtaining the geolocation of each test. They run custom-developed data acquisition software on the Linux operating system and provide automatic measurement data transfer to a server. The operation is controlled by an intuitive user interface consisting of two illuminated physical pushbuttons. The results show that for braking tests and friction coefficient measurements the developed prototypes compare favourably to a widely used professional vehicle performance computer.

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