scholarly journals Research on Short-Term Urban Traffic Congestion Based on Fuzzy Comprehensive Evaluation and Machine Learning

2020 ◽  
pp. 95-107
Author(s):  
Yuan Mei ◽  
Ting Hu ◽  
Li Chun Yang
Keyword(s):  
Machine Learning ◽  
Traffic Congestion ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Urban Traffic ◽  
Short Term

A Study of Transport-Oriented Development (TOD) Based on the Frequency Scale-Fuzzy Comprehensive Evaluation Method

2013 ◽  
Vol 291-294 ◽  
pp. 2899-2904
Author(s):  
Fei Han ◽  
Gui Ping Xiao
Keyword(s):  
Urban Development ◽  
Traffic Congestion ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Noise Pollution ◽  
Fuzzy Comprehensive Evaluation ◽  
Urban Traffic ◽  
Fuzzy Comprehensive Evaluation Method ◽  
Comprehensive Evaluation Method ◽  
Frequency Scale

Transport-Oriented Development (TOD) is an effective solution to urban traffic congestion, environmental pollution, noise pollution, and reduce energy consumption. And it is the best mode to solve urban development problems currently. This paper uses frequency scale-fuzzy comprehensive evaluation method to explore the inadequacies of modern urban development. And use Matlab to visualize this approach. Lastly put forward feasible suggestions for the city's TOD development in order to guide the cities’ sustainable development.

scholarly journals A Review of Traffic Congestion Prediction Using Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mahmuda Akhtar ◽  
Sara Moridpour
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Traffic Congestion ◽  
Historical Data ◽  
Research Area ◽  
Short Term ◽  
Real Time Traffic ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Congestion Prediction

In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI). With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research area has expanded extensively. Traffic congestion prediction, especially short-term traffic congestion prediction is made by evaluating different traffic parameters. Most of the researches focus on historical data in forecasting traffic congestion. However, a few articles made real-time traffic congestion prediction. This paper systematically summarises the existing research conducted by applying the various methodologies of AI, notably different machine learning models. The paper accumulates the models under respective branches of AI, and the strength and weaknesses of the models are summarised.

Vehicle Air Quality Analysis and Comprehensive Evaluation

2014 ◽  
Vol 507 ◽  
pp. 786-789
Author(s):  
Ruo Jun Wang ◽  
Yan Ying Xu
Keyword(s):  
Air Quality ◽  
Pollution Control ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Quality Analysis ◽  
Air Pollution Control ◽  
Short Term ◽  
Comprehensive Evaluation Method

Vehicle air quality is attracted attention more and more with the increase of private vehicles popularization rate but the air quality evaluation is difficult to achieve standardization in the short term. The main pollutants affecting vehicle air quality were analyzed. Index factors were identified and the classification method of vehicle air quality evaluation were determined combining with China and international air quality standards. Fuzzy comprehensive evaluation method was established for vehicle air quality evaluation. According to the degree of different pollutants harm to human body, weight of each index factor was determined. The evaluation results would provide theoretical basis for the comparison of different vehicle air quality conditions and vehicle air pollution control.

scholarly journals Fuzzy Comprehensive Evaluation Model for Road Capacity of Open Neighborhood -- Based on Improved NS Cellular Automata

2018 ◽  
Vol 12 (7) ◽  
pp. 144
Author(s):  
XiaoYan Cao ◽  
Bing-Qian Liu ◽  
Jian Cao ◽  
Yuan-Biao Zhang
Keyword(s):  
Cellular Automata ◽  
Evaluation System ◽  
Comprehensive Evaluation ◽  
Rapid Development ◽  
Fuzzy Comprehensive Evaluation ◽  
Gated Communities ◽  
Urban Traffic ◽  
Urban District ◽  
Road Capacity ◽  
The Impact

With the rapid development of motorization in our country, gated communities have become the obstacle of the development of urban traffic network. In this paper, focusing on the impact of gated communities on the traffic situation in surrounding area before and after it is opened, we firstly establish a comprehensive evaluation system by fuzzy comprehensive evaluation method and then build up the improved NS model based on Cellular Automata, which put forward the concept of internal road sharing rate in communities. Finally, we select the urban district of different types and surrounding roads in Shanghai as a case, and through simulation and evaluation, we found that the opening of communities is feasible. What’s more, compared with gated communities, the two-way 2-lane urban opening communities have the most optimal effect on improving road capacity.

Machine Learning Approach to Short-Term Traffic Congestion Prediction in a Connected Environment

2018 ◽  
Vol 2672 (45) ◽  
pp. 185-195 ◽  
Author(s):  
Amr Elfar ◽  
Alireza Talebpour ◽  
Hani S. Mahmassani
Keyword(s):  
Machine Learning ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Congested Traffic ◽  
Vehicle Trajectories ◽  
Congestion Prediction ◽  
The Individual

Traffic congestion is a complex phenomenon triggered by a combination of multiple interacting factors. One of the main factors is the disturbances caused by individual vehicles, which cannot be identified in aggregate traffic data. Advances in vehicle wireless communications present new opportunities to measure traffic perturbations at the individual vehicle level. The key question is whether it is possible to find the relationship between these perturbations and shockwave formation and utilize this knowledge to improve the identification and prediction of congestion formation. Accordingly, this paper explores the use of three machine learning techniques, logistic regression, random forests, and neural networks, for short-term traffic congestion prediction using vehicle trajectories available through connected vehicles technology. Vehicle trajectories provided by the Next Generation SIMulation (NGSIM) program were utilized in this study. Two types of predictive models were developed in this study: (1) offline models which are calibrated based on historical data and are updated (re-trained) whenever significant changes occur in the system, such as changes/updates to the infrastructure, and (2) online models which are calibrated using historical data and updated regularly using real-time information on prevailing traffic conditions obtained through V2V/V2I communications. Results show that the accuracy of the models built in this study to predict the congested traffic state can reach 97%. The models presented can be used in various potential applications including improving road safety by warning drivers of upcoming traffic slowdowns and improving mobility through integration with traffic control systems.

The Urban Road Short-Term Traffic Flow Prediction Research

2013 ◽  
Vol 423-426 ◽  
pp. 2954-2956 ◽  
Author(s):  
Zhen Hai Qin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Evaluation Model ◽  
Social Resources ◽  
Urban Traffic ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Short Period

To predict the future traffic flow status more accurately is of great significance to alleviate urban traffic congestion for a short period of time and avoid the waste of social resources. At first, this paper summarizes the characteristics of urban expressway traffic flow. Then establishes BP neural network short-term traffic flow evaluation model based on MATLAB, and finally through the instance, verify the validity of the model.

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