Estimating Dynamic Origin-Destination Data and Travel Demand Using Cell Phone Network Data

In recent years, tensor has been used in traffic flow data processing. However, the influx of the large number of real-time traffic flow data will cause dimension disaster and computational explosion. In order to deal the problem, this paper presents a Cyber-Physical-Social tensor framework (CPST) and an incremental SVD-based tensor train decomposition algorithm (ITTD), which effectively solves the problem of integrating incremental traffic flow data with original traffic flow data. Firstly, the singular value decomposition (SVD) is used to extract the core tensor of the new traffic flow data, and then the zero-padding operation is performed on the decomposed tensor-train (TT) to make it in the same dimension with the original tensor. After that the new data tensor is fused with the original data tensor. Finally, the fused data tensor train is obtained by orthogonal compression, and the superiority of the algorithm is verified on the UK Highway data set. Our work provides a new data model for traffic flow processing.

Traffic flow prediction is an important part of intelligent transportation systems (ITS). However, sensor failures or the transmission distortion often occur in the process of data acquisition, which will inevitably cause the loss or abnormality of traffic flow data transmitted to the edge server. In this situation, it is necessary to share traffic flow data among different platforms. However, existing traffic flow prediction methods are facing two challenges in the process of traffic flow data sharing. First, user privacy is often leaked in the process of sharing traffic data on various platforms. Moreover, with the continuous updating of data, the efficiency and scalability of data sharing between different platforms will become lower and lower. In view of the above challenges, in this paper, we propose a novel prediction method for the missing traffic flow data caused by abnormal sensors, named ASMVP_{distr-LSH} based on distributed locality-sensitive hashing (LSH) technique. At last, a case study is presented to illustrate the feasibility and effectiveness of our approach ASMVP_{distr-LSH}.

Traffic data is a challenging spatio-temporal data, and a multivariate time series data with spatial similarities. Clustering of traffic data is a fundamental tool for various machine learning tasks including anomaly detection, missing data imputation and short term forecasting problems. In this paper, first, we formulate a spatio-temporal clustering problem and define temporal and spatial clusters. Then, we propose an approach for finding temporal and spatial clusters with a deep embedded clustering model. The proposed approach is examined on traffic flow data. In the analysis, we present the properties of clusters and patterns in the dataset. The analysis shows that the temporal and spatial clusters have meaningful relationships with temporal and spatial patterns in traffic data, and the clustering method effectively finds similarities in traffic data.

With the continuous development of data mining technology, to apply the data mining techniques to transportation sector will provide service to transportation scientifically and reasonably. In intelligent transportation, the analysis of traffic flow data is very important, how to analyze the traffic data intelligently is more difficult problem, so using a new data mining techniques to replace the traditional data analysis and interpretation methods is very necessary and meaningful, clustering algorithm is the collection of physical or abstracting objects into groups of similar objects from the multiple classes of processes. This paper describes all kinds of the data mining clustering algorithms, clustering algorithm is proposed in the method of dealing with traffic flow data, and applied to the actual traffic data processing, and finally the clustering algorithm is applied to each of highway toll station Various types of car traffic volume data analysis.

Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of traffic flow prediction, we developed a traffic flow prediction framework—namely, traffic flow multicomponent network—that appropriately processes the noise, volatility, and nonlinearity in traffic flow data. This framework comprises three components: a factor selection component, traffic flow decomposition component, and traffic flow prediction component. The factor selection component considers the dynamic effects of weather‐related, environmental, and spatiotemporal factors on traffic flow; it then extracts and analyzes factors exhibiting strong correlations with traffic flow. The traffic flow decomposition component optimizes the parameters of variational mode decomposition on the basis of the envelope entropy by using the sparrow search algorithm; it then transforms traffic flow into multiple intrinsic mode functions to enable accurate traffic flow prediction. Finally, the traffic flow prediction component constructs dynamic feature matrices by using a bidirectional gated recurrent unit model to identify relationships within the data. Moreover, it uses an attention mechanism to assign different weights to different features on the basis of the importance of these features to traffic flow prediction, thereby enabling the efficient processing of a large volume of data. The performance of the proposed framework was examined in experiments conducted on large volumes of traffic flow data with different time granularities. The results indicated that the proposed framework achieved high prediction accuracy and stability for various time granularities, data samples, dataset sizes, and noise conditions. Moreover, it generally outperformed existing traffic flow prediction models under all experimental conditions.

Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: 1) without traffic flow information; 2) with static traffic flow information; and 3) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.

Accurate short‐term traffic flow modeling is an essential prerequisite to analyze and control traffic flow. Canonical data‐driven methods are a large account of parameters that may be underfitted with limited training samples, yet they cannot adaptively boost their understanding of the spatiotemporal dependencies of the traffic flow. The noisy and unstable traffic flow data also prevent the models from effectively learning the underlying patterns for forecasting future traffic flow. To address these issues, we propose an easy‐to‐implement yet effective boosting model based on extreme gradient boosting and enhance it by wavelet denoising for short‐term traffic flow forecasting. The discrete wavelet denoising is employed to preprocess the noisy traffic flow data. Then, the denoised training datasets are reconstructed to train the extreme gradient boosting model. These two components are integrated seamlessly in a unified framework, and the whole framework can retain the features in the data as much as possible. Our model can precisely capture the hidden spatial dependency in the data. Extensive experiments are conducted on four benchmark datasets compared with frequently used models. The results demonstrate that the proposed model can precisely capture the hidden spatial dependency of the traffic flow data and achieve superior performance.

A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model

Estimating multi-year [formula omitted] origin-destination demand using high-granular multi-source traffic data

With the acceleration of urbanization and the continuous growth of transportation demand, traffic flow prediction has become a key research issue in ITS (Intelligent Transportation System). In this paper, a traffic flow prediction model based on VMD-ARO-BiLSTM is proposed, which can make full use of the spatio-temporal characteristics of traffic data to achieve high-precision traffic flow prediction. First, the traffic flow data are input into VMD (Variational Mode Decomposition) for data reconstruction. Then, the optimal parameter values of BiLSTM (Bi-directional Long Short-Term Memory) are solved using ARO (Artificial Rabbits Optimization) to complete the optimization of BiLSTM. Finally, the reconstructed data are input into BiLSTM to realize the accurate prediction of traffic flow. To verify the model performance, we conducted experiments using traffic flow data from different time periods. The experimental results show that on weekdays, the combined VMD-ARO-BiLSTM prediction model decreases the RMSE (Root Mean Square Error) by 47.2% and the MAE (Mean Absolute Error) by 44.6% compared to BiLSTM; on weekends, compared to BiLSTM, the RMSE decreases by 26.1% and MAE decreases by 23.4%, reflecting the strong robustness and applicability of this model, which has certain application value.

The development of applications for advanced Intelligent Transportation Systems (ITS), is a growing research area, pushed especially by the need to mitigate problems related to transportation inefficiency, overuse and pollution, mainly caused by the increasing of traffic congestions in big cities. Nowadays, traffic related data coming from the road infrastructure, user's cars or mobile phones, is generated in unprecedent volume and speed. Therefore, ITS face new challenges, with respect to growing demand to process large volumes of traffic data in real-time, and extract value that can be immediately used for decision making. The work presented here, proposes a scalable architecture supported by Big Data technologies, capable of processing real-time traffic data captured from 349 inductive loop counters, placed in Slovenian road network. The main goal is to propose an approach which can be adopted by national road operators, capable of monitoring, in real-time, the current status of the road network. Traffic events need to be detected in real-time and require the collection of data from several fixed sensors. To deal with data volume and velocity challenges, a data stream management system was used, able to collect traffic data that arrives and process it on a continuous (24×7) basis. Results achieved, suggest that there's a great advantage in the adoption of a data stream management system, with respect to traditional database management systems, illustrated by practical examples, providing quantitative metrics about data processing performance.

Reliable traffic flow data is not only crucial for traffic management and planning, but also the foundation for many intelligent applications. However, the phenomenon of missing traffic flow data often occurs, so we propose an imputation model for missing traffic flow data to overcome the randomness and instability bands of traffic flow. First, k-means clustering is used to classify road segments with traffic flow belonging to the same pattern into a group to utilize the spatial characteristics of roads fully. Then, the LSTM networks optimized with an attention mechanism are used as the base learner to extract the temporal dependence of the traffic flow. Finally, the AdaBoost algorithm is used to integrate all the LSTM-attention networks into a reinforced learner to impute the missing data. To validate the effectiveness of the proposed model, we use the PeMS dataset for validation, we impute the data with missing data rate from 10 to 60% under three missing modes, and we use multiple baseline models for comparison, which confirms that our proposed model improves the stability and accuracy of imputing the missing data of the traffic flow with different scenarios.

Traffic flow prediction is a core component of intelligent transportation systems, providing accurate decision support for traffic management and urban planning. Traffic flow data exhibits highly complex spatiotemporal characteristics due to the intricate spatial correlations between nodes and the significant temporal dependencies across different time intervals. Despite substantial progress in this field, several challenges still remain. Firstly, most current methods rely on Graph Convolutional Networks (GCNs) to extract spatial correlations, typically using predefined adjacency matrices. However, these matrices are inadequate for dynamically capturing the complex and evolving spatial correlations within traffic networks. Secondly, traditional prediction methods predominantly focus on short-term forecasting, which is insufficient for long-term prediction needs. Additionally, many approaches fail to fully consider the local trend information in traffic flow data which reflects short-term temporal variations. To address these issues, a novel deep learning-based traffic flow prediction model, TDMGCN, is proposed. It integrates the Transformer and a multi-graph GCN to tackle the limitations of long-term prediction and the challenges of using the predefined adjacency matrices for spatial correlation extraction. Specifically, in the temporal dimension, a convolution-based multi-head self-attention module is designed. It can not only capture long-term temporal dependencies but also extract local trend information. In the spatial dimension, the model incorporates a spatial embedding module and a multi-graph convolutional module. The former is designed to learn traffic characteristics of different nodes, and the latter is used to extract spatial correlations effectively from multiple graphs. Additionally, the model integrates the periodic features of traffic flow data to further enhance prediction accuracy. Experimental results on five real-world traffic datasets demonstrate that TDMGCN outperforms the current most advanced baseline models.

This brief presents an innovative algorithm integrated with particle swarm optimization and artificial neural networks to develop short-term traffic flow predictors, which are intended to provide traffic flow forecasting information for traffic management in order to reduce traffic congestion and improve mobility of transportation. The proposed algorithm aims to address the issues of development of short-term traffic flow predictors which have not been addressed fully in the current literature namely that: 1) strongly non-linear characteristics are unavoidable in traffic flow data; 2) memory space for implementation of short-term traffic flow predictors is limited; 3) specification of model structures for short-term traffic flow predictors which do not involve trial and error methods based on human expertise; and 4) adaptation to newly-captured, traffic flow data is required. The proposed algorithm was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information is newly-captured. These results clearly demonstrate the effectiveness of using the proposed algorithm for real-time traffic flow forecasting.

Traffic incidents continue to cause a significant loss in deaths, injuries, and property damages. Reported traffic accident data contains a considerable amount of human errors, hindering the studies on traffic accidents. Several approaches have been developed to detect accidents using traffic data in real time. However, those approaches do not consider the spatiotemporal patterns inherent in traffic data, resulting in high false alarm rates. In this paper, we study the problem of traffic accident detection by considering multiple traffic speed time series collected from road network sensors. To capture the spatiotemporal impact of traffic accidents to upstream locations, we adopt Impact Interval Grouping (IIG), which compares real-time traffic speed with historical data, and generates impact intervals to determine the presence of accidents. Furthermore, we take a multivariate time series classification approach and extract three novel features to measure the severity of traffic accidents. We use real-world traffic speed and accident datasets in our empirical evaluation, and our solutions outperform state-of-the-art approaches in multivariate time series classification.