scholarly journals Real-time predication and navigation on traffic congestion model with equilibrium Markov chain

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771876978 ◽  
Author(s):  
Yan Zheng ◽  
Yanran Li ◽  
Chung-Ming Own ◽  
Zhaopeng Meng ◽  
Mengya Gao
Keyword(s):  
Markov Chain ◽  
Real Time ◽  
Traffic Congestion ◽  
Field Experiments ◽  
Road Traffic ◽  
Markov Chain Model ◽  
Chain Model ◽  
The Road ◽  
Shortest Path Algorithms ◽  
On The Road

With the explosive growth of vehicles on the road, traffic congestion has become an inevitable problem when applying guidance algorithms to transportation networks in a busy and crowded city. In our study, the authors proposed an advanced prediction and navigation models on a dynamic traffic network. In contrast to the traditional shortest path algorithms, focused on the static network, the first part of our guiding method considered the potential traffic jams and was developed to provide the optimal driving advice for the distinct periods of a day. Accordingly, by dividing the real-time Global Positioning System data of taxis in Shenzhen city into 50 regions, the equilibrium Markov chain model was designed for dispatching vehicles and applied to ease the city congestion. With the reveals of our field experiments, the traffic congestion of city traffic networks can be alleviated effectively and efficiently, the system performance also can be retained.

scholarly journals INTELLIGENT CAR PARKING SYSTEM

2014 ◽  
pp. 261-267
Author(s):  
S. AVINASH ◽  
SNEHA MITTRA ◽  
SUDIPTA NAYAN GOGOI ◽  
C. SURESH
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Empty Space ◽  
The West ◽  
The Road ◽  
Design And Implementation ◽  
Dc Motors ◽  
Parking System ◽  
Machine Interface ◽  
On The Road

Due to the proliferation in the number of vehicles on the road, traffic problems are bound to exist. This is due to the fact that the current transportation infrastructure and car parking facility developed are unable to cope with the influx of vehicles on the road. In India, the situation are made worse by the fact that the roads are significantly narrower compared to the west. Therefore problems such as traffic congestion and insufficient parking space inevitably crops up. In his paper we describe an Intelligent Car Parking System, which identifies the available spaces for parking using sensors, parks the cars in an identified empty space and gets the car back from its parked space without the help of any human personnel. A Human Machine Interface (HMI) helps in entering a unique identification number while entry of any car which helps in searching for the space where the car is parked while exit. An Indraconrol L10 PLC controls the actions of the parking system. The PLC is used to sequence the placing and fetching of the car via DC motors. We have implemented a prototype of the system. The system evaluation demonstrates the effectiveness of our design and implementation of car parking system.

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 A Framework to Reduce Road Congestion by means of Data Analytics

2021 ◽  
Vol 4 (1) ◽  
pp. 287-297
Author(s):  
Anosha Arooj Yousaf ◽  
Najia Saher ◽  
Faisal Shahzad ◽  
Sara Fareed
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Data Analytics ◽  
Road Traffic ◽  
Image Data ◽  
The Road ◽  
Road Planning ◽  
On The Road ◽  
Critical Traffic ◽  
Processing Techniques

The density of vehicles on the road especially in urban areas keeps on increasing to large amount day by day. Especially during the peak hours of the day, large amount of people wastes much of their time in traffic signals. Not only they waste energy by burning excess fuel and releasing CO2 emissions in the environment as well as their time and money. An idea has been proposed to monitor the traffic congestion by means of data analytics on image data and solve the critical traffic congestion issue. The CCTV or surveillance cameras installed at the top points on the roads acts as a medium to provide image data as an input to analyze road traffic congestion by counting the number of vehicles under specified interval of time. Monitoring of traffic congestion using image processing techniques is very useful for the future urban road planning such as: 1) if there is a need to make the road wider, 2) if there is a need to add more lanes on the road, 3) if there is need to make flyover or a bridge to control the traffic on the roads. It will help municipalities to structure and expansion of the roads.

Prediction of Road Handling Cost Using Markov Chain Method in Regency Road Network

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Ahmad Sazali ◽  
◽  
Bagus Hario Setiadji ◽  
Bambang Haryadi ◽  
◽  
...  
Keyword(s):  
Markov Chain ◽  
Road Network ◽  
Transition Probability ◽  
Management Program ◽  
Chain Model ◽  
The Road ◽  
Road Pavement ◽  
Road Management ◽  
Markov Chain Method ◽  
On The Road

The use of budget in road management must be effective and efficient by providing the optimal pavement performance values. The problem in cost optimization of the road handling programs in West Bangka Regency is the lack of information regarding with the changes in conditions that will occur in the future due to the pattern of road handling that carried out, so that required a method that capable to predict road pavement conditions. The purpose of this study is to determine the cost of future road handling programs based on the value of road pavement conditions from the prediction results using Markov chain method. Modeling requires an initial condition vector and a Transition Probability Matrix (TPM). The main data used in the development of this model is pavement condition data and road handling history data for 2012 - 2017. The application of the Markov chain model on the road network in West Bangka Regency in the period of 2018 - 2022 shows a drastic decrease in pavement conditions, if not conducted the handling action on the road damage, with a change in the value of Good (B) condition from 63.7% in 2017 become 12.3% in 2022. Based on a simulation of a road management program during this period, produce an estimated cost of Rp. 45.338.471.000 by providing a change in the value of Good (B) conditions at the end of 2022 by 58.6%. The results of the study are expected could assist road managers in the context of the preparation of an optimal road management program.

Video Stream Mining for On-Road Traffic Density Analytics

2012 ◽  
pp. 182-194
Author(s):  
Rudra Narayan Hota ◽  
Kishore Jonna ◽  
P. Radha Krishna
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Video Stream ◽  
Traffic Density ◽  
Svm Classifier ◽  
Stream Mining ◽  
The Road ◽  
Video Frames ◽  
Traffic Congestion Control ◽  
On The Road

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

scholarly journals Applications of Surveying and Geoinformatics for Planning New Routes to Solve Traffic Congestion in part of Minna Metropolis (Kpakungu, a case study)

2019 ◽  
Vol 3 (1) ◽  
pp. 149-160
Author(s):  
I. C. Onuigbo ◽  
T. Adewuyi ◽  
J. O. Odumosu ◽  
G. A. Oluibukun
Keyword(s):  
Land Use ◽  
Traffic Congestion ◽  
Road Network ◽  
Road Traffic ◽  
Urban Land Use ◽  
Land Use Pattern ◽  
The Road ◽  
National Examination ◽  
Traffic Volumes ◽  
On The Road

The volume of traffic generated by land-use pattern varies during different periods of the day but there is usually a predictable pattern of such traffic volumes. Most often, the structure of urban land-use fails to provide easy and convenient traffic movement, which in the case of the study area is usually that of vehicles and pedestrian traffic. The fact is that Minna is presently experiencing rapid urban growth. Both the authorities and citizens seem to simply ignore this and its impact on human existence. The research is based on Road Traffic Network Analysis in Minna, to develop a road network map and determine the causes of Traffic Congestion in Kpakungu specifically. Quickbird satellite imagery was used in analyzing and mapping out the existing road network within the study area. Field survey aspects involving measuring of roads, traffic count, coordinates captured were also undertaken. It was discovered that the causes of the traffic pressure in the study area was as a result of the relocation of Federal University of Technology, Minna to its permanent site in Gidan Kwanu and the relocation of National Examination Council(NECO) Headquarter. Majority of the traffic pressure in the area were as a result of vehicles coming from Maikunkele, Bosso, Maitumbi, Minna central, Dutsen Kura, Chanchaga, Tunga, Sahuka-kahuta and BarikinSale going to Bida, Gidan-Kwanu or NECO office. It was concluded that alternative roads should be provided for vehicle diversion to limit the congestion of traffic on the road.

Video Stream Mining for On-Road Traffic Density Analytics

2013 ◽  
pp. 1019-1030
Author(s):  
Rudra Narayan Hota ◽  
Kishore Jonna ◽  
P. Radha Krishna
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Video Stream ◽  
Traffic Density ◽  
Svm Classifier ◽  
Stream Mining ◽  
The Road ◽  
Video Frames ◽  
Traffic Congestion Control ◽  
On The Road

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

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