scholarly journals Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7705
Author(s):  
Selim Reza ◽  
Hugo S. Oliveira ◽  
José J. M. Machado ◽  
João Manuel R. S. Tavares
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Traffic Management ◽  
Resource Constraints ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic State ◽  
Wide Range ◽  
Intersection Signal Control ◽  
And Control

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

scholarly journals Deep Learning Based Ethiopian Car’s License Plate Detection and Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 5730-5737
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Learning Model ◽  
Motor Vehicles ◽  
License Plate ◽  
License Plate Recognition ◽  
Computer Algorithms ◽  
Wide Range ◽  
Processing Techniques ◽  
Deep Learning Model

Digital Image Processing is application of computer algorithms to process, manipulate and interpret images. As a field it is playing an increasingly important role in many aspects of people’s daily life. Even though Image Processing has accomplished a great deal on its own, nowadays researches are being conducted in using it with Deep Learning (which is part of a broader family, Machine Learning) to achieve better performance in detecting and classifying objects in an image. Car’s License Plate Recognition is one of the hottest research topics in the domain of Image Processing (Computer Vision). It is having wide range of applications since license number is the primary and mandatory identifier of motor vehicles. When it comes to license plates in Ethiopia, they have unique features like Amharic characters, differing dimensions and plate formats. Although there is a research conducted on ELPR, it was attempted using the conventional image processing techniques but never with deep learning. In this proposed research an attempt is going to be made in tackling the problem of ELPR with deep learning and image processing. Tensorflow is going to be used in building the deep learning model and all the image processing is going to be done with OpenCV-Python. So, at the end of this research a deep learning model that recognizes Ethiopian license plates with better accuracy is going to be built.

An Advanced Fuzzy Logic Based Traffic Controller

2014 ◽  
Vol 5 (1) ◽  
pp. 31-40
Author(s):  
Bilal Ahmed Khan ◽  
Nai Shyan Lai
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Traffic Management ◽  
Fuzzy Logic Controller ◽  
Current Model ◽  
Traffic Signals ◽  
Processing Technique ◽  
Urban Traffic ◽  
Image Processing Technique ◽  
Traffic Light

Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisition, the process is further linked to the fuzzy logic controller which generates a unique output for each input pattern. Here image processing and fuzzy logic tool boxes of MATLAB are used where the final output is sent to Peripheral Interface Controller (PIC) microcontroller to drive the traffic signals in the desired manner. The results obtained show an improvement of 44% in the overall outcome of traffic management as compared to the conventional traffic controller, marking great feasibility and practicality of the current model.

scholarly journals A Novel Left-Turn Signal Control Method for Improving Intersection Capacity in a Connected Vehicle Environment

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1058 ◽  
Author(s):  
Chuanxiang Ren ◽  
Jinbo Wang ◽  
Lingqiao Qin ◽  
Shen Li ◽  
Yang Cheng
Keyword(s):  
Traffic Safety ◽  
Control Strategy ◽  
Control Method ◽  
Control Model ◽  
Signal Control ◽  
Signal Phase ◽  
Phase Duration ◽  
Left Turn ◽  
Intersection Signal Control ◽  
And Control

Setting up an exclusive left-turn lane and corresponding signal phase for intersection traffic safety and efficiency will decrease the capacity of the intersection when there are less or no left-turn movements. This is especially true during rush hours because of the ineffective use of left-turn lane space and signal phase duration. With the advantages of vehicle-to-infrastructure (V2I) communication, a novel intersection signal control model is proposed which sets up variable lane direction arrow marking and turns the left-turn lane into a controllable shared lane for left-turn and through movements. The new intersection signal control model and its control strategy are presented and simulated using field data. After comparison with two other intersection control models and control strategies, the new model is validated to improve the intersection capacity in rush hours. Besides, variable lane lines and the corresponding control method are designed and combined with the left-turn waiting area to overcome the shortcomings of the proposed intersection signal control model and control strategy.

Measuring Vehicle Queue Length with Video

2011 ◽  
Vol 179-180 ◽  
pp. 109-114
Author(s):  
Zhong Qin ◽  
Guang Ting Su ◽  
Yi Chen ◽  
Qi Zhou Liu ◽  
Min Huang
Keyword(s):  
Traffic Control ◽  
Queue Length ◽  
Image Sequences ◽  
Urban Traffic ◽  
Experimental Results ◽  
Signal Control ◽  
Detection Algorithms ◽  
Intersection Signal Control ◽  
Urban Traffic Control ◽  
Stop Line

Queue length behind the stop line is an important parameter in the model of intersection signal control which is the base of urban traffic control. In this paper, the detection algorithms of queue length by the image information are proposed. At first, the background differential is used to extract the vehicle after the stop line, and then the three regional of the left, straight and right are identified, and finally at the different regions, tail of the vehicles queue is detected based on the change of image sequences gray, so the queue length is measured. The experimental results confirmed the effectiveness of the algorithm.

Traffic management in cities

2019 ◽  
Author(s):  
Елена Андреева ◽  
Elena Andreeva ◽  
Кристиан Бёттгер ◽  
Kristian Bettger ◽  
Екатерина Белкова ◽  
...  
Keyword(s):  
Traffic Management ◽  
Engineering Students ◽  
Urban Traffic ◽  
Urban Transport ◽  
Population Increase ◽  
Transport Systems ◽  
Traffic Flows ◽  
Organization And Management ◽  
Wide Range ◽  
Traffic Management System

The monograph is devoted to the consideration of issues relevant to the vast majority of cities-the organization and management of traffic flows to improve the mobility of the population, increase the speed and reduce the cost of transportation of passengers and goods, reduce the burden on the environment, etc. The book provides an overview of existing models, methods and tools for modeling and managing traffic flows in cities. The author identifies the main modern challenges to sustainable development of urban transport systems, which should be taken into account in the development of urban traffic management system. The authors substantiate the need for a systematic approach in the development of traffic management systems in cities and propose a practical tool for its implementation — an integrated digital platform for urban traffic management. Describes the experience of creation and application of an integrated automated control system of traffic management TransInfo and its improved version RITM, for the city of Moscow. In conclusion, the forecast of further development of research and development in the field of modeling and management of transport mobility is given. The book is of interest to a wide range of readers involved in the modeling and management of traffic flows, experts in the field of transport planning, scientists, engineers, economists and mathematicians, as well as graduate students and engineering students.

scholarly journals What are the challenges of pedestrian safety from the viewpoints of traffic and transport stakeholders? A qualitative Study

2020 ◽  
Author(s):  
Morteza Haghighi ◽  
Fatemeh Bakhtari Aghdam ◽  
Homayoun Sadeghi-Bazargani ◽  
Haidar Nadrian
Keyword(s):  
Traffic Management ◽  
Management System ◽  
Traffic Accidents ◽  
Holistic Approach ◽  
Pedestrian Safety ◽  
Urban Traffic ◽  
Vulnerable Groups ◽  
Individual Interviews ◽  
Wide Range ◽  
Traffic Management System

Abstract Background Pedestrians are among the most vulnerable groups in traffic accidents. Our aim in this study was to explore the challenges associated to pedestrian safety from the perspective of traffic and transport stakeholders. Methods In 2018, applying a qualitative approach, twenty-four traffic and transport stakeholders were invited to participate in semi-structured individual interviews in Tabriz, Iran. To analyze data, conventional content analysis approach was used. MAXQDA software version 11 was applied to manage data analysis process. Findings: Participants reported a wide range of challenges which were grouped into six categories: "Challenges related to pedestrians", "Challenges related to drivers", "Management system challenges", "Environmental infrastructure challenges", "Educational and media challenges", and "Challenges of legislation and enforcement". Conclusion We identified pedestrian safety as a challenging urban traffic and transport issue with specific complexities, particularly in the management system. With a holistic approach to the challenges, as discussed inside, all reported obstacles seem to be overshadowed by one core challenge, namely the lack of a traffic management system with health-oriented approach and enough authority. Using evidence while policy-making and intervention planning, as well as media support is recommended.

Cycling and Urban Traffic Management and Control Systems

2000 ◽  
Vol 1705 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Stephen D. Clark ◽  
Matthew W. Page
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Urban Traffic ◽  
The United Kingdom ◽  
Explicit Consideration ◽  
Additional Costs ◽  
And Performance ◽  
Urban Traffic Control ◽  
Motorized Vehicles ◽  
And Control

Since the 1950s, cycling has been a declining mode of travel in the United Kingdom. During this same period, sophisticated techniques for managing traffic in the urban environment have been developed. Given these circumstances, the presence of cyclists is often ignored by urban traffic control (UTC) systems, which are dominated by consideration of the flows and journey times of private motorized vehicles. Authorities are enthusiastic about the promotion of cycling as a mode of travel and are looking to see if this can be assisted by use of traffic management systems. The fact that cyclists and potential cyclists vary considerably in their abilities and performance, as well as in their attitudes to timesaving and safety, is highlighted. The context of the problem is set, the specific issue of detection of cycles is examined, the potential for implementation of priority measures in different types of UTC systems is discussed, and the issue is illustrated with some actual installations. Limited European evidence would suggest that only minimum effort is needed to take explicit account of cycling when a UTC system is being implemented. This supports the idea that cyclists can be given a higher degree of consideration within a UTC system without incurring significant additional costs. Only when cycling achieves a near-dominant proportion of the trips within a city and is growing in volume, as is the case in China, is explicit consideration to cyclists given.

scholarly journals Dynamic Analysis of Traffic State and Congestion Propagation on Bidirectional Grid Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Shu-bin Li ◽  
Bai-bai Fu ◽  
Jian-feng Zheng
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Network Performance ◽  
Simulation Software ◽  
Urban Traffic ◽  
Grid Network ◽  
Traffic State ◽  
Proposed Model ◽  
Air Pollutions ◽  
Density Relationship

Many traffic problems in China such as traffic jams and air pollutions are mainly caused by the increasing traffic volume. In order to alleviate the traffic congestion and improve the network performance, the analysis of traffic state and congestion propagation has attracted a great interest. In this paper, an improved mesoscopic traffic flow model is proposed to capture the speed-density relationship on segments, the length of queue, the flow on links, and so forth, The self-developed dynamic traffic simulation software (DynaCHINA) is used to reproduce the traffic congestion and propagation in a bidirectional grid network for different demand levels. The simulation results show that the proposed model and method are capable of capturing the real traffic states. Hence, our results can provide decision supports for the urban traffic management and planning.

scholarly journals Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus

2020 ◽  
Vol 21 (4) ◽  
pp. 295-302
Author(s):  
Haris Ballis ◽  
Loukas Dimitriou
Keyword(s):  
Reinforcement Learning ◽  
Environmental Sustainability ◽  
Urban Areas ◽  
Traffic Management ◽  
Large Scale ◽  
Smart Cities ◽  
Control Strategies ◽  
Urban Traffic ◽  
Signal Control ◽  
Urban Network

AbstractSmart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.

scholarly journals Image Processing in Intelligent Traffic Management

2019 ◽  
Vol 8 (2S4) ◽  
pp. 213-218
Keyword(s):  
Image Processing ◽  
Traffic Management ◽  
Heavy Traffic ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Traffic Density ◽  
Control Mechanisms ◽  
Image Processing Techniques ◽  
Wide Range ◽  
Processing Techniques

Traffic monitoring and traffic control have always been challenging tasks. Intelligent Transportation Systems (ITS) based on wide range of technologies have certain practical challenges in their application and implementation. Video surveillance has proven advantageous over traditional systems based on inductive loops sensors and detectors for traffic monitoring. Accurate traffic density estimation which is basic to tackling traffic congestions requires detection of vehicles, assessing their speed, and tracking vehicles passing through surveillance zones. Image processing techniques require processing of large number of image frames for real-time applications in traffic management. More efficient and less costly image processing techniques for accurate vehicle detection and density determination are required for developing more effective traffic management systems. There is a need for developing algorithms with robust performance under heavy traffic loads and varied environmental conditions. Developments in artificial intelligence offer new vistas in image processing for regulation and management of traffic by signal control mechanisms and creation of neural networks for unhindered traffic flow.

Sign in / Sign up

Export Citation Format

Share Document