Prediction of Road Congestion Level Based on Bayes Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1593-1596
Author(s):  
Shou Bai Xiao
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Evaluation Model ◽  
The State ◽  
Assessment Model ◽  
Bayesian Algorithm ◽  
Processing Efficiency ◽  
Model Based ◽  
Bayes Algorithm ◽  
Road Traffic Congestion

Traffic jams increasingly threaten the normal city traffic, so our paper analyzes the state of the existing road traffic congestion, road traffic congestion found in the state is a relatively vague and random dynamic data model. Based on these two characteristics, we propose a road traffic congestion degree assessment model based on Bayesian algorithm. Based on the theoretical analysis of Bayesian algorithms to improve the processing efficiency of the algorithm to construct the road traffic congestion degree evaluation model based on Bayesian algorithm set, and the simulation experiments.

scholarly journals Integrating Model-Based Observer and Kullback–Leibler Metric for Estimating and Detecting Road Traffic Congestion

2018 ◽  
Vol 18 (20) ◽  
pp. 8605-8616 ◽  
Author(s):  
Abdelhafid Zeroual ◽  
Fouzi Harrou ◽  
Ying Sun ◽  
Nadhir Messai
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Model Based ◽  
Road Traffic Congestion

scholarly journals Road Traffic Congestion Management Based on a Search-Allocation Approach

2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Smart Cities ◽  
Self Regulation ◽  
Congestion Management ◽  
Shortest Route ◽  
Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

scholarly journals Research on Decision Evaluation Model of HOV Lane Setting

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ding Lv ◽  
Qunqi Wu ◽  
Bo Chen ◽  
Yahong Jiang
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Evaluation Model ◽  
Path Selection ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Travel Efficiency ◽  
Hov Lanes ◽  
The City

In order to achieve the purpose of improving the travel efficiency of commuters in the periphery of the city, expanding the beneficiary groups of urban rail transit, and alleviating urban road traffic congestion, when planning and setting up HOV in the periphery of the city, it is necessary to analyze the feasibility of HOV lane setting from both the demand conditions and the setting conditions. This paper combines machine learning to construct a decision-making evaluation model for HOV lane setting and studies the optimal layout model and algorithm of HOV lanes in service rail transit commuter chain. The setting, planning, and layout of HOV lanes are a two-way interactive process of traveler's path selection and designer's road planning. Finally, after the model is constructed, the performance of the system model is verified. The results show that the system studied in this paper can be used for traffic data and lane planning analysis. Therefore, in the process of urban operation, the HOV model constructed in this paper is mainly used to alleviate urban traffic and improve urban operation efficiency.

scholarly journals V2V-based method for the detection of road traffic congestion

2019 ◽  
Vol 13 (5) ◽  
pp. 880-885 ◽  
Author(s):  
Runmin Wang ◽  
Zhigang Xu ◽  
Xiangmo Zhao ◽  
Jinchao Hu
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Road Traffic Congestion

Reliability-based preventive maintenance strategies of road junction systems

2019 ◽  
Vol 36 (5) ◽  
pp. 752-781 ◽  
Author(s):  
Kong Fah Tee ◽  
Ejiroghene Ekpiwhre
Keyword(s):  
Preventive Maintenance ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Case Based Reasoning ◽  
Time Data ◽  
Accident Rate ◽  
Content Type ◽  
Network Maintenance ◽  
Road Traffic Congestion ◽  
Road Junction

PurposeThe purpose of this paper is to present a study of reliability-centred maintenance (RCM), which is conducted on the key sub-assets of a newly constructed road junction infrastructure in Nigeria.Design/methodology/approachThe classical RCM methodology, a type of RCM, which has a top down, zero-based approach for maintenance analysis, is implemented in this study.FindingsThe implementation of the classical RCM is successful in its application of various PM policies assigned to the assets and it shows that its application in the highway industry could reduce excessive maintenance backlog and frequent reactive maintenance by effective optimisation of its preventive maintenance (PM) intervals.Practical implicationsRoad junctions are originators of more than 70 per cent of road traffic congestion and account for high accident rate. The traditional methods of reliability assurance used in the highway industry such as reactive maintenance and routine maintenance are often inadequate to meet the round the clock usage demands of these assets, thus the consideration for the application of a systematic RCM process for maintaining the system function by selecting and applying effective PM tasks.Originality/valueIt uses an approach that critically develops and analyses thoroughly preventive and continuous maintenance strategy in a new circumstance with environment of uncertainty and limited operating data. The case-based reasoning cycle has been applied in the RCM approach with real-time data obtained from a UK-based network maintenance management system for highway infrastructures.

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