Multiple-Interval Freeway Traffic Flow Forecasting

1996 ◽  
Vol 1554 (1) ◽  
pp. 136-141 ◽  
Author(s):  
Brian L. Smith ◽  
Michael J. Demetsky
Keyword(s):  
Traffic Flow ◽  
Nonparametric Regression ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Past Research ◽  
Transportation Systems ◽  
Forecasting Model ◽  
Freeway Traffic ◽  
Traffic Forecasting ◽  
Traffic Flow Forecasting

Freeway traffic flow forecasting will play an important role in intelligent transportation systems. The TRB Committee on Freeway Operations has included freeway flow forecasting in its 1995 research program. Much of the past research in traffic flow forecasting has addressed short-term, single-interval predictions. Such limited forecasting models will not support the development of the longer-term operational strategies needed for such events as hazardous material incidents. A multiple-interval freeway traffic flow forecasting model has been developed that predicts traffic volumes in 15-min intervals for several hours into the future. The nonparametric regression modeling technique was chosen for the multiple-interval freeway traffic flow forecasting problem. The technique possesses a number of attractive qualities for traffic forecasting. It is intuitive and uses a data base of past conditions to generate forecasts. It can also be implemented as a generic algorithm and is easily calibrated at field locations, suiting it for wide-scale deployment. The model was applied at two sites on the Capital Beltway monitored by the Northern Virginia Traffic Management System. The nonparametric regression forecasting model produced accurate short- and long-term volume estimates at both sites.

scholarly journals A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data

2021 ◽  
Vol 10 (9) ◽  
pp. 624
Author(s):  
Kaiqi Chen ◽  
Min Deng ◽  
Yan Shi
Keyword(s):  
Traffic Flow ◽  
Directed Graph ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
A Priori ◽  
Transportation Systems ◽  
Superior Performance ◽  
Trajectory Data ◽  
Temporal Dependence ◽  
Traffic Forecasting

Traffic forecasting plays a vital role in intelligent transportation systems and is of great significance for traffic management. The main issue of traffic forecasting is how to model spatial and temporal dependence. Current state-of-the-art methods tend to apply deep learning models; these methods are unexplainable and ignore the a priori characteristics of traffic flow. To address these issues, a temporal directed graph convolution network (T-DGCN) is proposed. A directed graph is first constructed to model the movement characteristics of vehicles, and based on this, a directed graph convolution operator is used to capture spatial dependence. For temporal dependence, we couple a keyframe sequence and transformer to learn the tendencies and periodicities of traffic flow. Using a real-world dataset, we confirm the superior performance of the T-DGCN through comparative experiments. Moreover, a detailed discussion is presented to provide the path of reasoning from the data to the model design to the conclusions.

Parallel SMO for Traffic Flow Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 843-848 ◽  
Author(s):  
Fan Wang ◽  
Guo Zhen Tan ◽  
Chao Deng
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Good Method ◽  
Transportation Systems ◽  
Training Data ◽  
Support Vector ◽  
Training Time ◽  
The Real ◽  
Traffic Flow Forecasting

Accurate traffic flow forecasting is crucial to the development of intelligent transportation systems and advanced traveler information systems. Since Support Vector Machine (SVM)have better generalization performance and can guarantee global minima for given training data, it is believed that SVR is an effective method in traffic flow forecasting. But with the sharp increment of traffic data, traditional serial SVM can not meet the real-time requirements of traffic flow forecasting. Parallel processing has been proved to be a good method to reduce training time. In this paper, we adopt a parallel sequential minimal optimization (Parallel SMO) method to train SVM in multiple processors. Our experimental and analytical results demonstrate this model can reduce training time, enhance speed-up ratio and efficiency and better satisfy the real-time demands of traffic flow forecasting.

Application of Intelligent Data Mining Method for Traffic Forecasting

2011 ◽  
Vol 84-85 ◽  
pp. 405-409
Author(s):  
Wei He ◽  
Jie Xiong
Keyword(s):  
Data Mining ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Pso Algorithm ◽  
Transportation Systems ◽  
Support Vector ◽  
Self Organizing Map ◽  
Traffic Forecasting ◽  
Mining Model ◽  
The Impact

Potential knowledge useful for traffic management optimization is hidden in a huge amount of data. Previous works use the prior data pattern labels to train the artificial neural network to attain the intelligent data mining models. The performance of the models suffers from the experts’ experience. To relieve the impact of the human factor, a new hybrid intelligent data mining model is proposed in this work based on self-organizing map (SOM) and support vector machine (SVM). The SOM was firstly used to capture the clustering information of the database through an unsupervised manner. Then the identified samples were treated as input to train the SVM. To optimize the SVM model, the particle swarm optimization (PSO) algorithm was employed to tune the SVM parameters and hence the satisfactory SVM data mining model was obtained. 2000 practical data sets from the Intelligent Transportation Systems (ITS) were applied to the validation of the proposed mining model. The analysis results show that the proposed method can extract the underlying rules of the testing data and can predict the future traffic state with the accuracy beyond 97%. Hence, the new SOM-PSO-SVM data mining model can provide practical application for the ITS.

scholarly journals Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 356 ◽  
Author(s):  
Zhenbo Lu ◽  
Jingxin Xia ◽  
Man Wang ◽  
Qinghui Nie ◽  
Jishun Ou
Keyword(s):  
Traffic Flow ◽  
Ensemble Learning ◽  
Traffic Management ◽  
Feasible Solution ◽  
Learning Strategy ◽  
Percentage Error ◽  
Forecasting Model ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
And Control

Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures.

scholarly journals Urban Traffic Flow Forecast Based on FastGCRNN

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ya Zhang ◽  
Mingming Lu ◽  
Haifeng Li
Keyword(s):  
Computational Complexity ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Geometric Graph ◽  
Transportation Systems ◽  
Urban Traffic ◽  
The Road ◽  
Traffic Forecasting ◽  
Traffic Flow Forecast

Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state-of-the-art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large-scale road networks due to high computational complexity. To address this problem, we propose abstracting the road network into a geometric graph and building a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, we use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.

scholarly journals Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yiming Xing ◽  
Xiaojuan Ban ◽  
Chong Guo
Keyword(s):  
Traffic Flow ◽  
Extreme Learning Machine ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Transportation Systems ◽  
Probabilistic Prediction ◽  
Probabilistic Forecasting ◽  
Practical Applications ◽  
Optimal Output ◽  
Learning Machine

Real-time and accurate prediction of traffic flow is the key to intelligent transportation systems (ITS). However, due to the nonstationarity of traffic flow data, traditional point forecasting can hardly be accurate, so probabilistic forecasting methods are essential for quantification of the potential risks and uncertainties for traffic management. A probabilistic forecasting model of traffic flow based on a multikernel extreme learning machine (MKELM) is proposed. Moreover, the optimal output weights of MKELM are obtained by utilizing Quantum-behaved particle swarm optimization (QPSO) algorithm. To verify its effectiveness, traffic flow probabilistic prediction using QPSO-MKELM was compared with other learning methods. Experimental results show that QPSO-MKELM is more effective for practical applications. And it will help traffic managers to make right decisions.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

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