Traffic pattern analysis and traffic state prediction of urban traffic road network based on correlated routes

2020 ◽  
Author(s):  
Zhuowei Zhang ◽  
Weibin Zhang
Keyword(s):  
Road Network ◽  
Pattern Analysis ◽  
Urban Traffic ◽  
Traffic Pattern ◽  
Traffic State ◽  
State Prediction

scholarly journals Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information

2018 ◽  
Author(s):  
Yang Wang ◽  
Yiwei Xiao ◽  
Xike Xie ◽  
Ruoyu Chen ◽  
Hengchang Liu
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Pattern Analysis ◽  
Transition Probabilities ◽  
Urban Traffic ◽  
Third Party ◽  
Distribution Model ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Real Time Traffic

Recent advances in  video surveillance systems enable a new paradigm for intelligent urban traffic management systems. Since surveillance cameras are usually sparsely located to cover key regions of the road under surveillance, it is a big challenge to perform a complete real-time traffic pattern analysis based on incomplete sparse surveillance information. As a result, existing works mostly focus on predicting traffic volumes with historical records available at a particular location  and may not provide a complete picture of real-time traffic patterns. To this end, in this paper, we go beyond existing works and tackle the challenges of traffic flow analysis from three perspectives. First, we train the transition probabilities to capture vehicles' movement patterns. The transition probabilities are trained from third-party vehicle GPS data, and thus can work in the area even if there is no camera. Second, we exploit the Multivariate Normal Distribution model together with the transferred probabilities to estimate the unobserved traffic patterns. Third, we propose an algorithm for real-time traffic inference with  surveillance as a complement source of information. Finally, experiments on real-world data show the effectiveness of our approach.

scholarly journals Urban Traffic State Prediction Based on SA-LSTM

2021 ◽  
Vol 783 (1) ◽  
pp. 012153
Author(s):  
Jingyu Yu ◽  
Haiping Wei ◽  
Hongwei Guo ◽  
Yafeng Cai
Keyword(s):  
Urban Traffic ◽  
Traffic State ◽  
State Prediction

scholarly journals Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network

Technologies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 71 ◽  
Author(s):  
Lin Dong ◽  
Akira Rinoshika ◽  
Zhixian Tang
Keyword(s):  
Road Network ◽  
Entropy Method ◽  
Urban Traffic ◽  
Traffic Information ◽  
Gated Community ◽  
Entropy Model ◽  
Evaluation Standard ◽  
Traffic System ◽  
Traffic State ◽  
Selection Framework

The opening of a gated community to expand the micro-road network in an urban traffic system is an importance research topic related to urban congestion. To satisfy the demands of opening an early choosing case, this paper proposes a comprehensive selection framework on qualified communities and their appropriate opening times by describing the traffic state at the boundary road network accurately. The traffic entropy model and fuzzy c-means (FCM) method are used in this paper. In the framework, a new opening evaluation entropy model is built using basic theory of the thermodynamic traffic entropy method. The traffic state entropy values of the boundary road network and entropy production are calculated to determinate the opening time. In addition, a specific fuzzy range evaluation standard at a preset gated community is drawn with an FCM algorithm to verify the opening determination. A case study based on the traffic information in a simulated gated community in Shanghai is evaluated and proves that the findings of opening evaluation are in accordance with the actual situation. It is found that the micro-inter-road network of a gated community should be opened as the entropy value reaches 2.5. As the travel time is less than 20 s, the correlation between the opening entropy value and the journey delay time exhibits a good linear correlation, which indicates smooth traffic flow.

scholarly journals Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tian Ma ◽  
Guanghong Gong ◽  
Yilong Ren
Keyword(s):  
Road Network ◽  
Large Scale ◽  
Short Term Memory ◽  
Transportation Networks ◽  
Prediction Method ◽  
Short Term ◽  
Traffic State ◽  
State Prediction ◽  
Network Experiment ◽  
Maximum Proportion

Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

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