scholarly journals MF-TCPV: A Machine Learning and Fuzzy Comprehensive Evaluation-Based Framework for Traffic Congestion Prediction and Visualization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 227113-227125
Author(s):  
Leixiao Li ◽  
Hao Lin ◽  
Jianxiong Wan ◽  
Zhiqiang Ma ◽  
Hui Wang
Keyword(s):  
Machine Learning ◽  
Traffic Congestion ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Congestion Prediction

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.

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 Traffic congestion prediction based on GPS trajectory data

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771984744 ◽  
Author(s):  
Shuming Sun ◽  
Juan Chen ◽  
Jian Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Traffic Congestion ◽  
Learning Models ◽  
Trajectory Data ◽  
Congestion Prediction ◽  
Gps Trajectory Data ◽  
Conventional Machine ◽  
Gps Trajectory ◽  
Machine Learning Models

Since speed sensors are not as widely used as GPS devices, the traffic congestion level is predicted based on processed GPS trajectory data in this article. Hidden Markov model is used to match GPS trajectory data to road network and the average speed of road sections can be estimated by adjacent GPS trajectory data. Four deep learning models including convolutional neural network, recurrent neural network, long short-term memory, and gated recurrent unit and three conventional machine learning models including autoregressive integrated moving average model, support vector regression, and ridge regression are used to perform congestion level prediction. According to the experimental results, deep learning models obtain higher accuracy in traffic congestion prediction compared with conventional machine learning models.

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.

Fuzzy Comprehensive Evaluation for Development Status of Urban Bus Rapid Transit (BRT) in China

2012 ◽  
Vol 253-255 ◽  
pp. 1930-1935
Author(s):  
Li Chi ◽  
Li Lei
Keyword(s):  
Traffic Congestion ◽  
Comprehensive Evaluation ◽  
Public Awareness ◽  
Fuzzy Comprehensive Evaluation ◽  
Bus Rapid Transit ◽  
Developmental Trend ◽  
Rapid Transit ◽  
Bus Priority ◽  
Average Travel Time ◽  
Priority Needs

Under the situation of economic development and traffic congestion in Chinese city, public transport has become an inevitable developmental trend. Bus Rapid Transit (BRT) has gained more and more attention for its fast speed, low investment, high safety. In current there are a dozen cities opening BRT in China. Although they achieve certain effect, some problems emerge. BRT development level in three cities was analyzed by Fuzzy Comprehensive Evaluation. In these three cities, the proportion of BRT intersection signal priority is low and passengers’ average travel time is long. BRT in China is still in its infancy. Technology and management needs to be strengthened and public awareness of bus priority needs to be improved.

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