scholarly journals Coordinated ramp signal optimization framework based on time series flux-correlation analysis

2021 ◽  
Vol 7 ◽  
pp. e446
Author(s):  
Zhi Liu ◽  
Wendi Shu ◽  
Guojiang Shen ◽  
Xiangjie Kong
Keyword(s):  
Correlation Analysis ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Time Lag ◽  
Spatial Distance ◽  
Signal Optimization ◽  
Traffic Demand ◽  
Traffic Flow Prediction ◽  
Optimization Framework ◽  
Ramp Signal

Urban expressways provide an effective solution to traffic congestion, and ramp signal optimization can ensure the efficiency of expressway traffic. The existing methods are mainly based on the static spatial distance between mainline and ramp to achieve multi-ramp coordinated signal optimization, which lacks the consideration of the dynamic traffic flow and lead to the long time-lag, thus affecting the efficiency. This article develops a coordinated ramp signal optimization framework based on mainline traffic states. The main contribution was traffic flow-series flux-correlation analysis based on cross-correlation, and development of a novel multifactorial matric that combines flow-correlation to assign the excess demand for mainline traffic. Besides, we used the GRU neural network for traffic flow prediction to ensure real-time optimization. To obtain a more accurate correlation between ramps and congested sections, we used gray correlation analysis to determine the percentage of each factor. We used the Simulation of Urban Mobility simulation platform to evaluate the performance of the proposed method under different traffic demand conditions, and the experimental results show that the proposed method can reduce the density of mainline bottlenecks and improve the efficiency of mainline traffic.

scholarly journals Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

2019 ◽  
Vol 9 (4) ◽  
pp. 615 ◽  
Author(s):  
Panbiao Liu ◽  
Yong Zhang ◽  
Dehui Kong ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Network ◽  
Temporal Correlation ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Building Models ◽  
Spatio Temporal ◽  
Specific Scenario

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.

Path Reconstruction of Intelligent Traffic Based on Positive Feedback System

2014 ◽  
Vol 513-517 ◽  
pp. 3160-3164
Author(s):  
Xue Li Zhang
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Intelligent Systems ◽  
Supply And Demand ◽  
Demand Management ◽  
Reference Value ◽  
Feedback System ◽  
Urban Traffic ◽  
Urban Transport ◽  
Traffic Demand

Traffic congestion are prevalent in worldwide cities. The imbalance between demand and supply of urban traffic is the root cause of this problem. So taking effective measures to regulate traffic demand, and guiding the traffic problems of the supply and demand balance is the best way to solve traffic congestion. This paper improves the TDM measure, and combines with intelligent information platform for the design of a new urban transport demand management adaptability of dynamic traffic data analysis platform. The platform supported by the technology of wireless sensor communications, intelligent terminals, the Internet and cloud computing is facing with the dynamic needs of traffic flow and traffic congestion state to carry out the operations of spatiotemporal data mining, clustering, and track detection, and to apply it into the traffic hot spots, abnormal driving track, traffic congestion trends and traffic flow detection and analysis, which has a good reference value for the improvement of management and service level of traffic intelligent systems.

Characteristic Parameters Identification of Macro Traffic Flow on Urban Expressway

2013 ◽  
Vol 409-410 ◽  
pp. 1209-1212
Author(s):  
Da Shan Chen
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Network ◽  
Large Scale ◽  
Characteristic Parameters ◽  
Traffic Demand ◽  
Urban Expressway ◽  
Back Ground ◽  
Control And Management

The macroscopic traffic flow parameters characteristic is an important research content in traffic flow theory. Urban expressway plays an important role in the urban road network. It is gradually shifting from large-scale infrastructure-oriented to refinement of traffic management. With the growing of traffic demand and much more traffic congestion and accidents, integrated active traffic management should be involved in urban expressway management on the back ground of car-road coordination. As the backbone road network, traffic flow characteristic parameters have great value for the control and management of urban expressway. Then the characteristic variables of the expressway traffic flow were identified which support meticulous management for urban expressway.

scholarly journals Traffic signal coordination control along oversaturated two-way arterials

2020 ◽  
Vol 6 ◽  
pp. e319
Author(s):  
Haitao Xu ◽  
Zuozhang Zhuo ◽  
Jing Chen ◽  
Xujian Fang
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Control Method ◽  
Traffic Signal ◽  
Coordination Control ◽  
Minimization Method ◽  
Traffic Condition ◽  
Traffic Demand ◽  
Signal Coordination ◽  
Arterial Coordination

As an effective method to alleviate traffic congestion, traffic signal coordination control has been applied in many cities to manage queues and to regulate traffic flow under oversaturated traffic condition. However, the previous methods are usually based on two hypotheses. One is that traffic demand is constant. The other assumes that the velocity of vehicle is immutable when entering the downstream section. In the paper, we develop a novel traffic coordination control method to control the traffic flow along oversaturated two-way arterials without both these hypotheses. The method includes two modules: intersection coordination control and arterial coordination control. The green time plan for all intersections can be obtained by the module of intersection coordination control. The module of arterial coordination control can optimize offset plan for all intersections along oversaturated two-way arterials. The experiment results verify that the proposed method can effectively control the queue length under the oversaturated traffic state. In addition, the delay in this method can be decreased by 5.4% compared with the existing delay minimization method and 13.6% compared with the traffic coordination control method without offset optimization. Finally, the proposed method can balance the delay level of different links along oversaturated arterial, which can directly reflect the efficiency of the proposed method on the traffic coordination control under oversaturated traffic condition.

scholarly journals Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Noor Afiza Mat Razali ◽  
Nuraini Shamsaimon ◽  
Khairul Khalil Ishak ◽  
Suzaimah Ramli ◽  
Mohd Fahmi Mohamad Amran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Short Term Memory ◽  
Smart Cities ◽  
Experimental Studies ◽  
Machine Learning Techniques ◽  
Traffic Flow Prediction ◽  
Flow Prediction

AbstractThe development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques.

scholarly journals Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

2014 ◽  
Vol 26 (5) ◽  
pp. 393-403 ◽  
Author(s):  
Seyed Hadi Hosseini ◽  
Behzad Moshiri ◽  
Ashkan Rahimi-Kian ◽  
Babak Nadjar Araabi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Weather Conditions ◽  
Traffic Data ◽  
Data Loss ◽  
Traffic Lights ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Flow Prediction ◽  
Forecasting Method

Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.

scholarly journals A Short-Term Traffic Flow Prediction to Control Traffics in Large Scale Transportation using Internet of Things

2019 ◽  
Vol 8 (4) ◽  
pp. 8323-8330
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Large Scale ◽  
Green Light ◽  
Traffic Density ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Road Section ◽  
Time Calculation ◽  
Short Time

Traffic congestion is the key problem that occurs across urban metropolises around the world. Due to the increase in transportation vehicles the fixed light time on traffic signals not able to solve the traffic congestion problem. In this paper, First, we develop an IoT based system which is capable of streaming the traffic surveillance footages to cloud storage, then the vehicle count is recorded every 30 sec interval and updated in the traffic flow dataset. Second the traffic flow is predicted using our CNN-LSTM residual learning model. Finally, the predicted value is classified and traffic density at each road section is identified, thereby passing this density value to green light time calculation to set an optimal green time to reduce the traffic congestion. The traffic flow dataset, China is used for training and testing to forecast the short time traffic flow across the road section. Experiment results shows that our model has best accuracy by lowering the RMSE value.

Land Use-Slow Traffic and Demand Forecasting

2017 ◽  
Vol 42 (3) ◽  
pp. 130-134
Author(s):  
Ren Hong ◽  
Zhang Zhengtong ◽  
Ma Xianrui ◽  
Tang Xilai
Keyword(s):  
Land Use ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Population Distribution ◽  
Demand Forecasting ◽  
Urban Traffic ◽  
High Tech ◽  
Traffic Demand ◽  
The Cross ◽  
Traffic Systems

In the face of solving the urban traffic congestion problem radically, emphasis has been laid on the research on slow traffic planning of urban built environment. Hence, research on slow traffic demand forecasting can provide a basis for the planning of urban slow traffic systems. Based on land use, the overall planning of the new Guangming (GM) district, and the population prediction results, the slow traffic demand within the scope of the new district was forecasted by combining the per capita trip frequency, and the spatial distribution of the slow traffic flow of the new GM district was forecasted per the forecasted demand quantity for slow traffic. The following research conclusions were obtained. Within the new GM district, the correlation of the total demand for slow traffic with the land use functions and population distribution was high, and the cross-zone traffic was mainly decided by the land usage of this district. The cross-unit slow traffic flow was concentrated in the Gongming central, Guangming central, high-tech zone, and Yutian zones. This research provides a guideline for the layout of slow traffic facilities in the future.

scholarly journals Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzyc-Mean Clustering and Advanced Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
He Huang ◽  
Qifeng Tang ◽  
Zhen Liu
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Prediction Accuracy ◽  
Intelligent Transportation System ◽  
Prediction Models ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Flow Prediction ◽  
Adaptive Correction

Forecasting of urban traffic flow is important to intelligent transportation system (ITS) developments and implementations. The precise forecasting of traffic flow will be pretty helpful to relax road traffic congestion. The accuracy of traditional single model without correction mechanism is poor. Summarizing the existing prediction models and considering the characteristics of the traffic itself, a traffic flow prediction model based on fuzzyc-mean clustering method (FCM) and advanced neural network (NN) was proposed. FCM can improve the prediction accuracy and robustness of the model, while advanced NN can optimize the generalization ability of the model. Besides these, the output value of the model is calibrated by the correction mechanism. The experimental results show that the proposed method has better prediction accuracy and robustness than the other models.

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