Performance Of Intelligent Transportation Systems Research Articles

Dynamic Decision Making for Connected Vehicles in IoT System

Intelligent Transportation System (ITS) which focuses on dynamic decision making for IoT data processes using distributed and decentralized systems has attracted much attention in recent years. In this paper, we propose a novel approach for enabling computational intelligence into dynamic decision-making for connected vehicles to provide various applications of smart cities. The proposed approach is based on the ant colony optimization (ACO) approach, which is able to improve the performance of intelligent transportation systems. In addition, the simulated results are compared with previous works in the literature.

Video Super-Resolution Based on Inter-Frame Information Utilization for Intelligent Transportation

Intelligent transportation infrastructure is essential to intelligent transportation system (ITS). With the continuous development of Internet of Things (IoT) technology, remote monitoring has become a critical part of ITS. However, due to the limitations of network transmission, production cost, and other factors, some video monitoring can obtain only low-resolution (LR) video. LR video features are seriously lost, thus affecting the performance of ITS. In this paper, based on the research of existing super-resolution algorithms, we focus on improving the reconstruction quality of video frame sequences by aiming at the insufficient utilization of inter-frame information and low reconstruction quality of existing video super-resolution algorithms. This paper proposes a video super-resolution algorithm based on inter-frame information utilization, which can effectively improve the performance of ITS. First, a novel U-shaped feature extractor is designed to fully extract the feature expression of video frame sequences. Second, a deformable inter-frame alignment module based on residual learning is constructed to make the inter-frame alignment more accurate and thus promote the mutual utilization of inter-frame information. Finally, an up and down sampling residual block is proposed to extract features that better match the upsampling reconstruction requirements. The experimental results show that the method has better reconstruction quality for monitoring video and is advanced and applicable compared to mainstream video oversampling methods.

A novel grey prediction model based on tensor higher-order singular value decomposition and its application in short-term traffic flow

Short-term traffic flow prediction is the key to traffic guidance and control and can directly affect the performance of intelligent transportation systems. Traffic flow data has the characteristics of volatility, chaos, and randomness, which affect the accuracy of the prediction model. Based on the multi-dimensional spatio-temporal data characteristics of traffic flow data, this paper combines the tensor higher-order singular value decomposition theory and the modeling mechanism of the classical grey model GM(1,1) model and establishes the grey GM(1,1) model with tensor higher-order singular values. The tensor higher-order singular value decomposition reflects the periodic, multi-modal, and holistic nature of traffic flow data, which can mitigate the volatility and randomness of traffic flow data and improve the accuracy of the model. Then, the new model is applied to highway short-time traffic flow prediction, analyzing the spatio-temporal nature of traffic flow data, giving the detailed steps of model modeling, and analyzing the correlation between the original traffic flow data and tensor approximation data using grey correlation degrees. There are three cases to illustrate the effectiveness of the model. Case 1 shows that the results of MAPE from nine modeling objects are stable at about 5%, which indicates that the new model has some stability; Case 2 shows that the new model is more adaptable to short-time traffic flow prediction based on the results of three different modeling and prediction objects; Case 3 uses the new model to compare it with two traditional grey forecasting models and two optimization models, and the results indicate that the new model has a total MAPE value of 5.172%, which is better than the other four grey forecasting models. Finally, the new model is applied to short-time traffic flow prediction, and its prediction results are consistent with the trend of the original traffic flow data, indicating that it can reveal the real-time characteristics of the traffic system to a certain extent and provide a reliable theoretical basis for traffic planning, control, and optimization.

STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation

The traffic data corrupted by noise and missing entries often lead to the poor performance of Intelligent Transportation Systems (ITS), such as the bad congestion prediction and route guidance. How to efficiently impute the traffic data is an urgent problem. As a classic deep learning method, Generative Adversarial Network (GAN) achieves remarkable success in image recovery fields, which opens up a new way for the traffic data imputation. In this paper, we propose a novel spatio-temporal GAN model for the traffic data imputation (STGAN). Firstly, we design the generative loss and center loss, which not only minimizes the reconstructed errors of the imputed entries, but also ensures each imputed entry and its neighbors conform to the local spatio-temporal distribution. Then, the discriminator uses the convolution neural network classifier to judge whether the imputed matrix conforms to the global spatio-temporal distribution. As for the network architecture of the generator, we introduce the skip-connection to keep all well preserved data unchanged, and employ the dilated convolution to capture the spatio-temporal correlation in the traffic data. The experimental results show that our proposed method obviously outperforms other competitive traffic data imputation methods.

WASPAS Multi-Criteria Decision-Making Method for Assessment Effectiveness and Performance Intelligent Transportation Systems Alternatives

The assessment of Intelligent Transportation Systems (ITS) plays a vital role in understanding their effectiveness, efficiency, and impact on transportation networks. This abstract provides an overview of the criteria for assessing ITS and highlights the importance of a comprehensive and multidimensional approach. The requirements discussed include safety, efficiency, mobility, environmental impact, user satisfaction, cost-effectiveness, scalability and interoperability, data security and privacy, technological reliability and resilience, regulatory and policy compliance, equity and accessibility, system integration, innovation and future-readiness, stakeholder engagement, performance monitoring and evaluation, resilience and disaster preparedness, social and economic impact, and continuous improvement and adaptation. By considering these criteria, stakeholders can gain valuable insights into the performance and benefits of ITS, aiding in decision-making, policy development, and future planning for transportation systems. This study uses multi-criteria decision-making (MCDM) methodologies, such as the assessing attractiveness method and the weighted aggregated sum product assessment (WASPAS) method. The WASPAS method is used to rank the alternatives. We used 18 criteria and 8 alternatives to be organised. The sensitivity analysis is conducted to check the stability of the results.

Construction of Regional Intelligent Transportation System in Smart City Road Network via 5G Network

This purpose of the research is to explore the construction status and prediction performance of intelligent transportation systems in the road network of smart cities based on 5G (5th Generation Mobile Communication Technology) network, and further intellectualize the smart city. Aiming at the diversity and complexity of regional traffic influencing factors of road network in the construction of smart city, this research carries out resource real-time load balancing scheduling from the perspective of 5G heterogeneous network. Meanwhile, CNN (convolutional neural network) in the introduced deep learning algorithm is improved, and finally an intelligent traffic prediction model is constructed based on 5G load balancing and AlexNet network. The model is simulated and its performance is analyzed. The results show that the algorithm proposed is compared with LSTM (Long Short-Term Memory), CNN, RNN (Recurrent Neural Network), VGGNet (Visual Geometry Group Network), and BN (Bayesian network) models regarding Accuracy, Precision, Recall, and F1. It is found that the road network prediction accuracy of the algorithm proposed is 94.05%, which is at least 4.29% higher than that of the model algorithm proposed by other scholars. The analysis of network data transmission synchronization performance suggests that there are obvious performance improvements in access delay, access collision rate, reliability, and network throughput. Among them, the packet loss rate is lower than 0.1, the access collision rate is basically stable at about 0, the access time is stable at about 75ms, and the sending throughput is basically maintained at about 1, which is significantly better than the performance of other algorithms. Therefore, the intelligent transportation system can achieve better data transmission performance under the premise of ensuring high prediction performance, with prominent instantaneity, which can provide experimental basis for the intelligent development of transportation in smart cities.

Meta Graph Transformer: A Novel Framework for Spatial–Temporal Traffic Prediction

Accurate traffic prediction is critical for enhancing the performance of intelligent transportation systems. The key challenge to this task is how to properly model the complex dynamics of traffic while respecting and exploiting both spatial and temporal heterogeneity in data. This paper proposes a novel framework called Meta Graph Transformer (MGT) to address this problem. The MGT framework is a generalization of the original transformer, which is used to model vector sequences in natural language processing. Specifically, MGT has an encoder-decoder architecture. The encoder is responsible for encoding historical traffic data into intermediate representations, while the decoder predicts future traffic states autoregressively. The main building blocks of MGT are three types of attention layers named Temporal Self-Attention (TSA), Spatial Self-Attention (SSA), and Temporal Encoder-Decoder Attention (TEDA), respectively. They all have a multi-head structure. TSAs and SSAs are employed by both the encoder and decoder to capture temporal and spatial correlations. TEDAs are employed by the decoder, allowing every position in the decoder to attend all positions in the input sequence temporally. By leveraging multiple graphs, SSA can conduct sparse spatial attention with various inductive biases. To facilitate the model’s awareness of temporal and spatial conditions, Spatial–Temporal Embeddings (STEs) are learned from external attributes, which are composed of temporal attributes (e.g. sequential order, time of day) and spatial attributes (e.g. Laplacian eigenmaps). These embeddings are then utilized by all the attention layers via meta-learning, hence endowing these layers with Spatial–Temporal Heterogeneity-Aware (STHA) properties. Experiments on three real-world traffic datasets demonstrate the superiority of our model over several state-of-the-art methods. Our code and data are available at ( http://github.com/lonicera-yx/MGT).

A throughput-aware joint vehicle route and access network selection approach based on SMDP

In intelligent transportation system (ITS), the interworking of vehicular networks (VN) and cellular networks (CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading (MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process (SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio. Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.

Providing Performance Evaluation Indicators for Intelligent Transportation Systems (The Case Study of Tehran-Karaj Freeway Located in Iran)

Intelligent Transportation Systems (ITS) play a fundamental role in reducing traffic congestion and increasing safety during daily transportation. These systems can also be useful in improving social welfare leading to general satisfaction. Proper performance evaluation can be efficient in improving the performance of these systems, and providing a scientific assessment index system can assist decision-makers in smart communities to plan for the development of ITS. However, the evaluation of these systems requires identifying appropriate indicators of performance evaluation that are consistent with the views of the beneficiaries of these systems. In this paper, performance evaluation indicators of ITS have been identified, and three indicators entitled “environmental and safety”, “assistance in reducing traffic congestion” and “attractive public transport” are presented to evaluate the performance of these systems. Moreover, the intelligent transport systems of the Tehran-Karaj Freeway in Iran are studied, and inferential statistical methods are employed to test the research hypotheses. It is worth noticing that in this study, a one-sample T-test method is used for hypotheses assessment and the SPSS software was used to analyze the findings. Also, the results demonstrated that the performance of ITS in the Tehran-Karaj Freeway regarding the indicators, such as “Declaration of route blocking information due to maintenance or reconstruction” and “Declaration of path geometry conditions” has not been acceptable.

An Improved Stacked Denoise Autoencoder with Elu Activation Function for Traffic Data Imputation

Traffic data plays a major role in transport related applications. The problem of missing data has greatly impact the performance of Intelligent transportation systems(ITS). In this work impute the missing traffic data with spatio-temporal exploitation for high precision result under various missing rates. Deep learning based stacked denoise autoencoder is proposed with efficient Elu activation function to remove noise and impute the missing value.This imputed value will be used in analyses and prediction of vehicle traffic. Results are discussed that the proposed method outperforms well in state of the art approaches.

Multi-view Low Rank Representation for Multi-Source Traffic Data Completion

Intelligent Transportation System (ITS) has been widely applied in major cities to relieve congestion and decrease accidents. However, the hardware failure of detectors or transformation failure of data cause data loss, which seriously decreases the performance of ITS. How to ensure the completeness of observed traffic data becomes is a current key problem. Recently, the low rank constraint which can exploit the global relation hidden in data has been successfully used in matrix completion, such as the classic robust principal component analysis (RPCA) and its variants. The spatio-temporal correlation among traffic data make traffic data contain low rank property; therefore, we naturally apply the low rank constraint on traffic data completion. In addition, most traffic detectors installed on the road can collect various types of traffic data, so-called multi-source traffic data. Due to describing the same traffic condition, these various type of traffic data usually have similar intrinsic structure. Therefore, we consider fuse these various type of traffic data to complete the missing data. In this paper, we propose multi-view low-rank representation model for multi-source data completion and provide an efficient optimization algorithm. To variety the performance of the proposed method, some traditional traffic data completion methods are compared with our method on a highway microwave dataset. The experimental results show that our proposed method is obviously superior to other state-of-the-art traffic data completion methods.

Simulated Studies on the Performance of Intelligent Transportation System Using Vehicular Networks

Simulated Studies on the Performance of Intelligent Transportation System Using Vehicular Networks

A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series

Short-term traffic flow forecasting is a key step to achieve the performance of intelligent transportation system (ITS). Timely and accurate traffic information prediction is also the prerequisite of realizing proactive traffic control and dynamic traffic assignment effectively. Based on the fact that univariate forecasting methods have limited forecasting abilities when the data is missing or erroneous and that single models make no full use of information underline data, a new hybrid method with multivariate for short-term traffic flow forecasting is proposed. This method combines statistical analysis method with computational intelligence techniques to mine the characteristic of traffic flow as well as forecast short-term traffic state. First, the wavelet de-noising is employed to remove the noise information. Then, time series analysis is used to analyze time-varying and periodic characteristic of traffic flow. Furthermore, the seasonal auto-regressive moving average with external input (SARIMAX) is established to fit traffic flow with occupancy as exogenous variables. Finally, wavelet forecast is adopted to forecast the values of occupancy which are used as exogenous input, and a WSARIMAX is constructed to forecast traffic flow. Using the relationship of flow and occupancy at the same road section and taking traffic flow and occupancy data from freeway I-694 EB in the Twin Cities as endogenous variables and exogenous variables respectively, this paper studies the forecasting performance of the proposed method. The study results are encouraging. Compared with SARIMA newly proposed in literature, WSARIMA and SARIMAX improved method with wavelet analysis and multivariate modeling method, the proposed method gets improvements of 12.95%, 12.62% and 10.41% in forecasting accuracy of one-step ahead respectively. For ten-steps ahead forecasting, it gets improvements of 18.87%, 17.05% and 2.57% in forecasting accuracy respectively.

A Trust-Based Framework for Increasing MAC Layer Reliability in Cognitive Radio VANETs

Vehicular Ad hoc Networks (VANETs) are introduced to improve the performance of Intelligent Transportation Systems (ITS). As safety is the most important purpose of VANETs, reliability is one of their main necessities. In the other hand, regarding the popularity of VANETs in the future of auto-technologies, the lack of bandwidth in peak traffic hours seems to be one of the most important challenges. For this purpose, utilizing Cognitive Radio (CR) technology in VANETs appears to be a proper candidate. Making the most of CR-VANETS capabilities involves cooperation between the vehicles which in turns necessitates a comprehensive trust management system among them. Considering the concept of reliability in CR-VANETs in this article, employment of trust as a mean of improving reliability is investigated. Classifying the node’s misbehaviors into three categories of selfish, malicious and incapable, a model for evaluating reliability is derived, based on which a trust based framework is presented to improve the reliability of VANET’s transmission media access. Simulation results show that considering cognitive radio and trust together can improve the reliability of VANET and can decrease the number of accidents in peak traffic hours.

Shadow Detection Based on Combinations of Hessenberg Decomposition and Principal Component Analysis in Surveillance Applications

ABSTRACTShadow removing is an important issue in performance of intelligent transportation systems. Moving shadows cause confusion and errors in moving object detection, since they cannot be removed from foreground using commercial background subtraction methods. In this paper, a synthetic shadow detection algorithm using combination of Hessenberg decomposition (HD) and principal component analysis (PCA) is proposed. The HD and PCA are applied to improve the performance of shadow detection. At first, the background image is obtained using moving average method and adaptive background estimation. The moving objects in the foreground image can be accurately obtained via the background subtraction method. In this algorithm, the candidate shadow region is estimated by using HD. With regard to ability of HD in the decomposition of foreground image into two parts as shadow and object, we should calculate the HD of foreground image and determine the shadow and object regions. In this procedure, some parts of vehicles like dark region and windshield that is similar to shadow are detected as moving shadow. Therefore, PCA is applied to compensate misclassified regions in the previous stage. The proposed algorithm is evaluated on real and operational conditions such as different perspectives and illuminations. Experimental results demonstrate the efficiency and effectiveness of our proposed algorithm in the intelligent transportation systems.

Traffic speed data imputation method based on tensor completion.

Traffic speed data plays a key role in Intelligent Transportation Systems (ITS); however, missing traffic data would affect the performance of ITS as well as Advanced Traveler Information Systems (ATIS). In this paper, we handle this issue by a novel tensor-based imputation approach. Specifically, tensor pattern is adopted for modeling traffic speed data and then High accurate Low Rank Tensor Completion (HaLRTC), an efficient tensor completion method, is employed to estimate the missing traffic speed data. This proposed method is able to recover missing entries from given entries, which may be noisy, considering severe fluctuation of traffic speed data compared with traffic volume. The proposed method is evaluated on Performance Measurement System (PeMS) database, and the experimental results show the superiority of the proposed approach over state-of-the-art baseline approaches.

Energy efficient continuous phase modulation signals for satellite intelligent transportation systems

In this study, performance of intelligent transportation systems (ITS), which consists of satellite mesh networks, is investigated. These networks are characterised by the absence of the on ground satellite gateway and many power limited user terminals with small antennas which are able to direct connection with a satellite. In such scenario, energy efficiency signals, for example, continuous phase modulation (CPM) should be used for transmission. The authors analyse performance of a mesh ITS which apply multiuser frequency division multiplexed (FDM) transmission with coded CPM via computer simulations. The spectral efficiency of investigated system is improved by reducing the inter-carrier frequency (ICI) spacing and using simple iterative ICI cancellation algorithm at the receiver. The results suggest that FDM CPM transmission might constitute a desirable option in ITS satellite mesh, ad-hoc networks.

Editoral: Special Issue on Photonic and RF Communications Systems

Editoral: Special Issue on Photonic and RF Communications Systems

Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can significantly vary across the network and during different time periods. Insight into those spatiotemporal trends can improve the performance of intelligent transportation systems. Traditional prediction error measures, such as the mean absolute percentage error, provide information about the individual links in the network but do not capture global trends. We propose unsupervised learning methods, such as k-means clustering, principal component analysis, and self-organizing maps, to mine spatiotemporal performance trends at the network level and for individual links. We perform prediction for a large interconnected road network and for multiple prediction horizons with an SVR-based algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of the SVR.

Correlation Analysis for Tensor-based Traffic Data Imputation Method

The phenomenon of missing data in traffic has a great impact on the performance of Intelligent Transportation System (ITS). Many imputation methods have been proposed to estimate the missing traffic data. Recently,a tensor-based traffic volume imputation method has been proposed. In this paper, we focus on the underlying mechanism of tensor-based method from the viewpoint of intrinsic multi-correlations/principle components of the traffic data, and try to recommend suitable tensor pattern for traffic volume imputation. Experiments on PeMS database show that the tensor-based method outperforms matrix-based methods, and using the recommended tensor pattern achieves better performances.