scholarly journals Road Traffic Analysis Using Computer Vision

2021 ◽  
Vol 9 (8) ◽  
pp. 2002-2006
Author(s):  
Needhi U. Gaonkar
Keyword(s):  
Computer Vision ◽  
Traffic Flow ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Traffic Analysis ◽  
Transportation Systems ◽  
Visual Object ◽  
Highway Traffic ◽  
Analysis System

Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply with information about traffic flow, traffic summit times on road. The motivation of visual object detection is to track the vehicle position and then tracking in successive frames is to detect and connect target vehicles for frames. Recognising vehicles in an ongoing video is useful for traffic analysis. Recognizing what kind of vehicle in an ongoing video is helpful for traffic analysing. this system can classify the vehicle into bicycle, bus, truck, car and motorcycle. In this system I have used a video-based vehicle counting method in a highway traffic video capture using cctv camera. Project presents the analysis of tracking-by-detection approach which includes detection by YOLO(You Only Look Once) and tracking by SORT(simple online and realtime tracking) algorithm. Keywords: Vehicle detection, Vehicle tracking, Vehicle counting, YOLO, SORT, Analysis, Kalman filter, Hungarian algorithm.

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

2021 ◽  
Vol 17 (2) ◽  
pp. 46-71
Author(s):  
Manipriya Sankaranarayanan ◽  
Mala C. ◽  
Samson Mathew
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Characteristics ◽  
Traffic Video ◽  
Virtual Loop

Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.

scholarly journals EVALUATING THE EFFECT OF HEAVY VEHICLES ON TRAFFIC PARAMETERS BY USING HEAVY VEHICLE FACTOR

2018 ◽  
Vol 6 (4) ◽  
pp. 95-104
Author(s):  
Parthkumar Patel ◽  
H.R. Varia
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Road Traffic ◽  
Freight Transport ◽  
Heavy Vehicle ◽  
Mixed Traffic ◽  
Heavy Vehicles ◽  
Highway Traffic ◽  
Ideal Situation ◽  
The Impact

Safe, convenient and timely transportation of goods and passengers is necessary for development of nation. After independence road traffic is increased manifold in India. Modal share of freight transport is shifted from Railway to roadways in India. Road infrastructures continuously increased from past few decades but there is still need for new roads to be build and more than three forth of the roads having mixed traffic plying on it. The impact of freight vehicles on highway traffic is enormous as they are moving with slow speeds. Nature of traffic flow is dependent on various traffic parameters such as speed, density, volume and travel time etc. As per ideal situation these traffic parameters should remain intact, but it is greatly affected by presence of heavy vehicle in mixed traffic due to Svehicles plying on two lane roads. Heavy vehicles affect the traffic flow because of their length and size and acceleration/deceleration characteristics.  This study is aimed to analyse the impact of heavy vehicles on traffic parameters.

scholarly journals Utilization of the methods of artificial intelligence for organization of unmanned traffic

2020 ◽  
pp. 51-58
Author(s):  
Roman Dushkin ◽  
Mikhail Grigor'evich Andronov
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Traffic Management ◽  
Road Traffic ◽  
Multi Agent Systems ◽  
Special Contribution ◽  
Agent Systems ◽  
Multi Agent ◽  
Computer Vision Systems ◽  
Central Control System

This article meticulously examines the questions of application of certain technologies of multi-agent systems theory in the area of unmanned traffic management for combatting the so-called “generative adversarial attacks” on the computer vision systems that are used in such vehicles. The article provides examples of generative-adversarial attacks on various types of neural networks, as well as describes the problems that arise when using computer vision. Possible solutions to these problems are proposed. Research methodology includes the theory of multi-agent systems applicable to automobile transport, which suggests using the so-called V2X-interaction, i.e. constant exchange of information between the vehicle and various actors involved in road traffic – a central control system, other vehicles, roadside infrastructure and pedestrians. The authors’ special contribution to this research lies in application of the theory of multi-agent systems for traffic arrangement with consideration of its actors as the agents with diverse roles. The novelty consists in employment of one of the methods of artificial intelligence in solution of the problems, obtained due to the use of other methods of artificial intelligence (recognition of images in computer vision). The relevance of the study is based on the detailed coverage of the questions of organization of unmanned traffic on training grounds and public roads.

Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling

2018 ◽  
Vol 22 (5) ◽  
pp. 704-710 ◽  
Author(s):  
Robert Kerwin C. Billones ◽  
◽  
Argel A. Bandala ◽  
Laurence A. Gan Lim ◽  
Edwin Sybingco ◽  
...  
Keyword(s):  
Computer Vision ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Volume Estimation ◽  
Public Utility ◽  
Scene Analysis ◽  
Volume Distribution ◽  
Distribution Analysis ◽  
Traffic Scene Analysis

This paper presents the development of a vision-based system for microscopic road traffic scene analysis and understanding using computer vision and computational intelligence techniques. The traffic flow model is calibrated using the information obtained from the road-side cameras. It aims to demonstrate an understanding of different levels of traffic scene analysis from simple detection, tracking, and classification of traffic agents to a higher level of vehicular and pedestrian dynamics, traffic congestion build-up, and multi-agent interactions. The study used a video dataset suitable for analysis of a T-intersection. Vehicle detection and tracking have 88.84% accuracy and 88.20% precision. The system can classify private cars, public utility vehicles, buses, and motorcycles. Vehicular flow of every detected vehicles from origin to destination are also monitored for traffic volume estimation, and volume distribution analysis. Lastly, a microscopic traffic model for a T-intersection was developed to simulate a traffic response based on actual road scenarios.

Navigation and Intelligent Transportation Systems

1998 ◽  
Keyword(s):  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Cellular Phone ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Navigation Systems ◽  
Route Guidance ◽  
Highway Traffic ◽  
National Highway Traffic Safety

Navigation and Intelligent Transportation Systems contains 40 papers covering the technical and functional aspects of these systems including: 3D mapping, route guidance, cellular phone access, electronic compasses, and the history and future of navigation systems. The book also covers the important role of navigation in Intelligent Transportation Systems concerned with traffic management, traveler information, vehicle control systems, commercial vehicle operations, and public and rural transportation systems. The book concludes with a chapter on the Intelligent Vehicle Initiative, a joint program between the National Highway Traffic Safety Administration, the Federal Highway Administration, and the Federal Transit Administration.

scholarly journals Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3431
Author(s):  
Lin Li ◽  
Serdar Coskun ◽  
Jiaze Wang ◽  
Youming Fan ◽  
Fengqi Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Transportation Systems ◽  
Vehicle Speed ◽  
Extensive Discussion ◽  
Lateral Dynamics ◽  
Speed Prediction ◽  
Velocity Prediction ◽  
Vehicle Lateral Dynamics

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.

scholarly journals A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data

2021 ◽  
Vol 10 (9) ◽  
pp. 624
Author(s):  
Kaiqi Chen ◽  
Min Deng ◽  
Yan Shi
Keyword(s):  
Traffic Flow ◽  
Directed Graph ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
A Priori ◽  
Transportation Systems ◽  
Superior Performance ◽  
Trajectory Data ◽  
Temporal Dependence ◽  
Traffic Forecasting

Traffic forecasting plays a vital role in intelligent transportation systems and is of great significance for traffic management. The main issue of traffic forecasting is how to model spatial and temporal dependence. Current state-of-the-art methods tend to apply deep learning models; these methods are unexplainable and ignore the a priori characteristics of traffic flow. To address these issues, a temporal directed graph convolution network (T-DGCN) is proposed. A directed graph is first constructed to model the movement characteristics of vehicles, and based on this, a directed graph convolution operator is used to capture spatial dependence. For temporal dependence, we couple a keyframe sequence and transformer to learn the tendencies and periodicities of traffic flow. Using a real-world dataset, we confirm the superior performance of the T-DGCN through comparative experiments. Moreover, a detailed discussion is presented to provide the path of reasoning from the data to the model design to the conclusions.

scholarly journals Operational and Safety Assessment of Motorised Three-Wheel Vehicles for Public Transport in the Tamale Metropolis

2020 ◽  
Vol 2 (1) ◽  
pp. 26-35
Author(s):  
Lukuman Wahab ◽  
Mohammed Salifu
Keyword(s):  
Public Transport ◽  
Traffic Management ◽  
Road Traffic ◽  
Transportation Systems ◽  
Traffic Classification ◽  
Contributing Factors ◽  
Middle Income ◽  
Road Traffic Crashes ◽  
Tamale Metropolis ◽  
Mode Of Transport

Motorised three-wheel vehicles are important modes of transportation in the Tamale metroplis because they provide alternative mobility solutions for low and middle income earners and fill the gaps in transportation systems in Ghana. The fact that motorised three-wheel vehicles are inexpensive to manufacture, sell, operate and repair compared to cars have also catalysed the surge in their ownership. This study evaluates the operations and safety of motorised three-wheel vehicles as a means of public transport for goods and humans in the Tamale Metropolis. To achieve this objective, a manual traffic classification count was carried out on the following main roads: Hospital Road, Bolgatanga Road, Choogu Road and Nyohini Road within the study area; a survey questionnaire was designed to elicit information from operators as well as users of motorised three-wheel vehicles. Road traffic crashes data were obtained and analysed. Direct field observations were also carried out along selected roads. The manual traffic classification count revealed that motorised three-wheel vehicles constitute the third most significant mode of transport in the study area whereas light vehicles and motorcycles are first and second respectively. Additionally, operation of motorised three-wheel vehicles provides employment or livelihood to people who are otherwise unemployable and have families to take care of. In terms of safety, 94.6% are completely unlicensed, 3.1% have motorcycle license, 1.5% have tractor operator license and 0.8% have driving license. Lack of required driving skills could be one of contributing factors of crashes in the study area. The vehicles also stop or park at unauthorised places, leading to congestion and traffic management problems on roads. It is therefore recommended that the operation of motorised three-wheel vehicles within Tamale Metropolis Area be regulated and designated spaces provided for parking. Keywords: Motorised three-wheel Vehicles, Public Transport, Operational, Safety; Tamale

Modeling traffic flow in work zone sections considering the effect of traffic lights

2021 ◽  
pp. 2150113
Author(s):  
Xingyu Lu ◽  
Li Fei ◽  
Huibing Zhu ◽  
Wangjun Cheng ◽  
Zijie Wang
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Traffic Accidents ◽  
Red Light ◽  
Work Zone ◽  
Traffic Model ◽  
Traffic Density ◽  
Highway Traffic ◽  
Traffic Lights

Based on the two-lane highway traffic model with a work zone presented previously, a new traffic model with a work zone under the control of traffic lights is proposed. The length of the waiting area for vehicles before traffic lights is recommended cautiously after numerical simulation. The relationship between the vehicles’ queuing time and the cycle of traffic lights is studied, and the cycle time of traffic lights is obtained also considering people’s endurance to the red light. It is found that the traffic lights are effective to ease the traffic congestion in the work zone when the density is medium, and help to eliminate the inducement of traffic accidents. On the other hand, the simulation results show that traffic lights are not needed in the work zone when the traffic density is small. It indicates that the traffic flow in the work zone area can be optimized by using appropriate traffic management when the traffic density varies.

Sign in / Sign up

Export Citation Format

Share Document