Component Of Intelligent Transportation System Research Articles

Internet of Things-Based Intelligent Ontology Model for Safety Purpose Using Wireless Networks

Intelligent transportation systems (ITS) are a newer trend in technology that improve the safety and performance in transportation systems. The exchange of information is considered as one of the key elements for ITS since it involves communication between vehicles and other components of ITS on road. On other hand, the data collections via Internet of Things (IoT) sensors play a major role for data collection and transmission between the vehicles and road segments in ITS. The data collection provides the current traffic and weather conditions that is considered necessary for driving in traffic. However, energy savings is one of the predominant objectives of electric vehicle (EV) while it is connected with ITS. In this research, we propose an ontology-based architecture for EVs using the data collected from IoT sensor network, which is intended to improve the overall driving experience. The system uses IoT sensor data to execute a range of activities in order to ensure the driver safety and comfort while on the road. The simulation is conducted on an Eclipse SUMO simulator, and the performance is reported. The results of simulation shows that the proposed intelligent model on making decisions along with weather and traffic conditions is reported efficient than existing ITS models.

Blockchain and deep learning based trust management for Internet of Vehicles

Internet of Vehicles (IoVs) works as the most advanced component of Intelligent Transportation Systems (ITSs). In IoVs, vehicles are able to communicate with nearby vehicles or RoadSide Units (RSUs). Traffic safety and efficiency can be provided by collecting and uploading real-time traffic information through vehicles, as well as broadcasting information by RSUs. However, there may be malicious vehicles in the network uploading false information, which will lead to serious traffic problems. To alleviate this problem, a trust management system based on blockchain technology is proposed in this paper. In this system, vehicles in the network firstly collect information about their surroundings and then upload valid information to nearby RSUs. To prevent malicious vehicles from uploading false messages, this paper designs a deep learning based verification model to calculate the trustworthiness of uploaded messages, and to further obtain the credibility scores of vehicles using the calculated results, and detect malicious vehicles accordingly. Moreover, a public blockchain framework is proposed and a Proof-Of-Trust (POT) consensus algorithm is designed. Vehicles are motivated to report true and valid information, and are penalized for uploading false data under this framework. Simulation results show that this mechanism can effectively detect malicious vehicles and motivate unfamiliar vehicles to upload true and reliable information to achieve trust management in the open and dynamic vehicular network environments.

A Multifeature Fusion Short-Term Traffic Flow Prediction Model Based on Deep Learnings

Short-term traffic flow prediction is an important component of intelligent transportation systems, which can support traffic trip planning and traffic management. Although existing predicting methods have been applied in the field of traffic flow prediction, they cannot capture the complex multifeatures of traffic flows resulting in unsatisfactory short-term traffic flow prediction results. In this paper, a multifeature fusion model based on deep learning methods is proposed, which consists of three modules, namely, a CNN-Bidirectional GRU module with an attention mechanism (CNN-BiGRU-attention) and two Bidirectional GRU modules with an attention mechanism (BiGRU-attention). The CNN-BiGRU-attention module is used to extract local trend features and long-term dependent features of the traffic flow, and the two BiGRU-attention modules are used to extract daily and weekly periodic features of the traffic flow. Moreover, a feature fusion layer in the model is used to fuse the features extracted by each module. And then, the number of neurons in the model, the loss function, and other parameters such as the optimization algorithm are discussed and set up through simulation experiments. Finally, the multifeature fusion model is trained and tested based on the training and test sets from the data collected from the field. And the results indicate that the proposed model can better achieve traffic flow prediction and has good robustness. Furthermore, the multifeature fusion model is compared and analyzed against the baseline models with the same dataset, and the experimental results show that the multifeature fusion model has superior predictive performance compared to the baseline models.

Vehicle Collision Detection & Prevention using VANET Based IoT With V2V

EMERGENCY alert in case of any accident is vitally necessitated to rescue the victims. And so, this paper is made to present the results of a major analysis relating to emergency alert conditions at the time of collision (automobile). In this study, the authors have investigated modern Internet of Things (IoT) and VANET (Vehicular Ad hoc Networks) technologies and developed a collection of modern and specialized techniques as well as their characteristics. It has sensors that detect unbalanced circumstances and provide with a warning to the microcontroller if a collision occurs. Additionally, the technique can be implemented in such a way that vehicles are alerted of possible closing barriers. Vehicle-to-Vehicle communication (V2V) has a huge impact since it allows vehicles to communicate with each other while in proximity and the buzzer together with the LEDs serves as a safety feature. The primary goal of the system is to carry out the microcontroller functions in every environment and moreover, the concept refers to detect and prevent the collision specially in a foggy weather as well as at night and in other odd circumstances. The Internet of Things (IoT) and the Vehicular Ad-Hoc Network (VANET) have now been merged as the fundamental and central components of Intelligent Transportation System (ITS). Furthermore, while the procedure of obtaining the insurance may be longer for certain people. On the other hand, others may avoid the law after being involved in severe collisions which makes it difficult for the authorities to discriminate between criminal and non-criminal evidence.

A multi-view bidirectional spatiotemporal graph network for urban traffic flow imputation

Accurate estimation of missing traffic data is one of the essential components in intelligent transportation systems (ITS). The non-Euclidean data structure and complex missing traffic flow patterns make it challenging to capture nonlinear spatiotemporal correlations of missing traffic flow, which are critical for the imputation of missing traffic data. In this study, we propose a novel multi-view bidirectional spatiotemporal graph network called Multi-BiSTGN to impute urban traffic data with complex missing patterns. First, three spatiotemporal graph sequences are constructed to comprehensively describe traffic conditions from different temporal correlation views, i.e. temporal closeness view, daily periodicity view, and weekly periodicity view. Then, three bidirectional spatiotemporal graph networks are fused by a parametric-matrix-based method to obtain the final imputation results. To train the Multi-BiSTGN model, a novel loss function that considers the interactions between three temporal correlation views is designed to optimize the parameters of the Multi-BiSTGN model. The proposed model was validated on real-world traffic datasets collected in Wuhan, China. Experimental results showed that Multi-BiSTGN outperformed ten existing baselines under different missing types (random missing, block missing, and mixed missing) and missing rates.

An Area Partitioning and Subgraph Growing (APSG) Approach to the Conflation of Road Networks.

A road network represents a set of road objects in a geographic area and their interconnections, and it is an essential component of intelligent transportation systems (ITS) enabling emerging new applications such as dynamic route guidance, driving assistance systems, and autonomous driving. As the digitization of geospatial information becomes prevalent, a number of road networks with a wide variety of characteristics may coexist. In this paper, we present an area partitioning and subgraph growing (APSG) approach to the conflation of two road networks with a large difference in the level of details and representation rules. Our area partitioning (AP) scheme partitions the geographic area using the Network Voronoi Area Diagram (NVAD) of the low-detailed road network. Next, a subgraph of the high-detailed road network corresponding to a complex intersection is extracted and aggregated into a supernode so that high precision can be achieved via 1:1 road object matching. For the unmatched road objects due to missing road objects and different representation rules, we also propose a subgraph growing (SG) scheme that sequentially inserts a new road object while keeping the consistency of its connectivity to the matched road objects by the AP scheme. From the numerical results at Yeouido, Seoul, Korea, we show that our APSG scheme can achieve an outstanding matching performance in terms of the precision, recall, and F1-score.

A GATs-GAN framework for road traffic states forecasting

Short-term traffic states forecasting of road networks based on real-time data is an important component of intelligent transportation systems, especially advanced traffic management systems and traveller information systems. By considering the influence of both space and time dimensions, we proposed a novel GATs-GAN framework for the forecasting of traffic states. First, to capture spatial traffic relationships, the traffic topological graph network is set up based on the connection of traffic sections. Then, the first-order neighbours and high-order neighbours of traffic networks can be structured. Graph attention networks (GATs) are used to obtain the hidden features of input traffic data by training the attention between nodes in high-order neighbours. Based on two traffic networks in California and Seattle in the United States, we find that the GATs-GAN with high-order neighbours can satisfactorily estimate the traffic data and performs better than the baseline methods and comparative experiments.

Metaheuristic enabled modified hidden Markov model for traffic flow prediction

Traffic flow prediction is essential for traffic management, pollution reduction, and public safety, and is an important component of Intelligent Transportation Systems (ITS). Since most of the links are not outfitted with traffic sensors, extracting traffic flow data in real-time scenarios has proven to be challenging. Moreover, the parameters that affect traffic flows (such as accidents, road closures, and public events) are mostly unexpected, which makes traffic flow prediction a complex task. Hence, this paper plans to design an enhanced prediction model for traffic flow using a Modified Hidden Markov Model (MHMM). The input features subjected to prediction via MHMM are “Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Indicator (RSI), and Rate of Change (ROC)” respectively. In fact, the modification in HMM relies on the optimal tuning of state numbers using the Mean Fitness-oriented Dragonfly Algorithm (MF-DA). Finally, the betterment of implemented work is compared and proved over the conventional models.

GCN-GAN: Integrating Graph Convolutional Network and Generative Adversarial Network for Traffic Flow Prediction

As a necessary component in intelligent transportation systems (ITS), traffic flow-based prediction can accurately estimate the traffic flow in a certain period and area in the future. However, despite the success of traditional research and current machine learning methods, traffic flow prediction models have limitations in terms of prediction accuracy and efficiency. In this work, we propose a novel traffic flow prediction model named Graph Convolution and Generative Adversative Neural Network (GCN-GAN), which leverages Graph Convolution Neural Network (GCN) module and Generative Adversative Neural Network (GAN) module to predict urban traffic flow. Firstly, the GCN module extracts historical traffic flow information in the graph structure. Secondly, the GAN module generates reliable traffic flow prediction results by adversative training. Additionally, GCN-GAN can parallelly generate prediction results rather than traditional one by one. Through experiments on the traffic flow dataset at multiple intersections, our GCN-GAN model outperforms the baseline methods by over 30.54% and has apparent advantages in multi-step prediction.

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions.

Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.

Reliability of variable message signs information: A field experiment study in Chongqing

A variable message sign (VMS), which is a key component of intelligent transportation systems, has been frequently used in the management of urban roads and motorways to provide drivers with real-time traffic condition information about a road section or area. Nevertheless, there is a lack of unified regulations for VMS design, which makes it difficult to accurately determine the understandability and legibility of VMS information. In practice, inappropriate designs of VMSs are common, such as overload of VMS information and excessive number of phases, particularly on the urban roads of China. Building on our earlier findings obtained by surveys, in this study, field tests were conducted to assess the reliability of VMS information in terms of the visual perception characteristics of drivers. For a full visual perception, the information displayed on a VMS must be reliable, i.e., it should be easy for drivers to detect, obtain, and understand the messages they require. Two basic theories—information theory and visual perception theory—were introduced to build a quantification model of VMS information, select evaluation indices, and design the experimental process. A total of 24 drivers participated in the field experiments and questionnaires. Five indices—information obtainment rate, periodic validity, driver subjective scoring and the corresponding UI (represents information intelligibility), and CE (represents information legibility)—were analyzed to evaluate their relationships to VMS information reliability. The results confirmed that the amount of information, and the scrolling period considering redundant distance, significantly affect the reliability of VMSs information. For static VMSs, the information obtainment rate and the subjective scoring decrease with increasing amount of information. The recollection accuracy of the drivers significantly declines when the amount of information shown increases to 90 bits, corresponding to an information obtainment rate of less than 0.8 and UI equal to 0. For dynamic VMSs, the information reliability deteriorates as the scrolling period shortens and the number of phases increases. Unreasonable reliability is found when the periodic validity is less than 0.9, i.e., the actual scrolling period is more than 10% less than the calculated one, corresponding to CE equal to 0. The reliability of information was evaluated by combining the subjective scoring of the drivers and a data-based statistical analysis and considering driving safety. Accordingly, it was recommended that 90 bit is the maximum amount of information to be shown on a VMS and that the preferred scrolling period of each phase of a dynamic VMS is 5 s. The results of this study support the objective of providing reliable information to drivers by addressing the problems related to the amount of information and presentation time for VMSs. These findings provide a basis for determining the thresholds for VMS information to promote practical and user-friendly designs of VMSs on urban roads.

Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction

As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing availability of massive traffic mobility data. Its key challenge lies in how to integrate diverse factors (such as temporal rules and spatial dependencies) to infer the evolution trend of traffic flow. To address this problem, we propose a unified neural network called Attentive Traffic Flow Machine (ATFM), which can effectively learn the spatial-temporal feature representations of traffic flow with an attention mechanism. In particular, our ATFM is composed of two progressive Convolutional Long Short-Term Memory (ConvLSTM <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> ) units connected with a convolutional layer. Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference. The second ConvLSTM unit aims at learning the dynamic spatial-temporal representations from the attentionally weighted traffic flow features. Further, we develop two deep learning frameworks based on ATFM to predict citywide short-term/long-term traffic flow by adaptively incorporating the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks well demonstrate the superiority of the proposed method for traffic flow prediction. Moreover, to verify the generalization of our method, we also apply the customized framework to forecast the passenger pickup/dropoff demands in traffic prediction and show its superior performance. Our code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liulingbo918/ATFM</uri> .

Decentralized Platoon Join-in-Middle Protocol Considering Communication Delay for Connected and Automated Vehicle.

Cooperative driving is an essential component of intelligent transport systems (ITSs). It promises greater safety, reduced accidents, efficient traffic flow, and fuel consumption reduction. Vehicle platooning is a representative service model for ITS. The principal sub-systems of platooning systems for connected and automated vehicles (CAVs) are cooperative adaptive cruise control (CACC) systems and platoon management systems. Based on vehicle state information received through vehicle-to-vehicle (V2V) communication, the CACC system allows platoon vehicles to maintain a narrower safety distance. In addition, the platoon management system using V2V communications allows vehicles to perform platoon maneuvers reliably and accurately. In this paper, we propose a CACC system with a variable time headway and a decentralized platoon join-in-middle maneuver protocol with a trajectory planning system considering the V2V communication delay for CAVs. The platoon join-in-middle maneuver is a challenging research subject as the research must consider the requirement of a more precise management protocol and lateral control for platoon safety and string stability. These CACC systems and protocols are implemented on a simulator for a connected and automated vehicle system, PreScan, and we validated our approach using a realistic control system and V2V communication system provided by PreScan.

Attention meets long short-term memory: A deep learning network for traffic flow forecasting

Accurate forecasting of future traffic flow has a wide range of applications, which is a fundamental component of intelligent transportation systems. However, timely and accurate traffic forecasting remains an open challenge due to the high nonlinearity and volatility of traffic flow data. Canonical long short-term memory (LSTM) networks are easily drawn to focus on min-to-min fluctuations rather than the long term dependencies of the traffic flow evolution. To address this issue, we propose to introduce an attention mechanism to the long short-term memory network for short-term traffic flow forecasting. The attention mechanism helps the network model to assign different weights to different inputs, focus on critical and important information, and make accurate predictions. Extensive experiments on four benchmark data sets show that the LSTM network equipped with an attention mechanism has superior performance compared with commonly used and state-of-the-art models.

VANET-Based Traffic Monitoring and Incident Detection System: A Review

&lt;span&gt;As a component of intelligent transport systems (ITS), vehicular ad hoc network (VANET), which is a subform of manet, has been identified. It is established on the roads based on available vehicles and supporting road infrastructure, such as base stations. An accident can be defined as any activity in the environment that may be harmful to human life or dangerous to human life. In terms of early detection, and broadcast delay. VANET has shown various problems. The available technologies for incident detection and the corresponding algorithms for processing. The present problem and challenges of incident detection in VANET technology are discussed in this paper. The paper also reviews the recently proposed methods for early incident techniques and studies them.&lt;/span&gt;

Modeling and verifying NDN‐based IoV using CSP

AbstractAs a crucial component of intelligent transportation system, Internet of Vehicles (IoV) plays an important role in the smart and intelligent cities. However, current Internet architectures cannot guarantee efficient data delivery and adequate data security for IoV. Therefore, Named Data Networking (NDN), a leading architecture of Information‐Centric Networking (ICN), is introduced into IoV. Although problems about data distribution can be resolved effectively, the combination of NDN and IoV causes some new security issues. In this paper, we apply Communicating Sequential Processes (CSP) to formalize NDN‐based IoV. We mainly focus on its data access mechanism and model this mechanism in detail. By feeding the formalized model into the model checker Process Analysis Toolkit (PAT), we verify four vital properties, namely, deadlock freedom, data reliability, PIT deletion faking, and CS caching pollution. According to verification results, the model cannot ensure the security of data with the appearance of intruders. To solve these problems, we construct a blockchain‐based mechanism by creating a blockchain‐based distribution trusted platform on top of NDN‐based IoV. Through the analysis of the improved model, the blockchain‐based mechanism can truly guarantee the security of NDN‐based IoV.

Context-Aware Naming and Forwarding in NDN-Based VANETs.

Vehicular ad-hoc network (VANET) is a technology that allows ubiquitous mobility to mobile users. Inter-vehicle communication is an integral component of intelligent transportation systems that enables a wide variety of applications where vehicles interact and cooperate with each other, from safety applications to non-safety applications. VANETs applications have different needs (e.g., latency, reliability, delivery priorities, etc.) in terms of delivery effectiveness. In the last decade, named data networking (NDN) gained the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. In NDN, the content’s name has a vital role in storing and retrieving the content effectively and efficiently. In NDN-based VANETs, adaptive content dissemination solutions must be introduced that can make decisions related to forwarding, cache management, etc., based on context information represented by a content name. In this context, our main contributions are two-fold: (i) we present the hierarchical context-aware content-naming (CACN) scheme for NDN-based VANETs that enables naming the safety and non-safety applications, and (ii) we present a decentralized context-aware notification (DCN) protocol that broadcasts event notification information for awareness within the application-based geographical area. Simulation results show that the proposed DCN protocol succeeds in achieving reduced transmissions, bandwidth, and energy compared to existing critical contents dissemination protocols.

Factor Analysis of the Sydney Metro's ITS

This paper aspires to examine advanced rail transportation systems via the utilization of intelligent transportation systems (ITS) as the basis of the key integration. In doing so, the Sydney Metro ITS will be used as the basis for the case study. This paper also investigates the different perceptions for diverse rail transportation projects. Ordinarily, cities are composed of complex configurations which are supported by various disparate transport systems. These transport infrastructure in turn require comprehensive system thinking as the base research methodologies. ITS provides an excellent mechanism to simplify a city's complex transport configurations. Nevertheless, the core ITS components require careful integration and detailed attention to detail in their design. The ITS integration strategy would also amalgamate crucial transportation issues, including optimized system performance through increased productivity, improved road and traffic safety, and so on. Accordingly, this integration and consolidation of ITS would result in superior overall transportation planning schemes. The resulting improved planning methods will ultimately facilitate the various transportation operators to become better acquainted with the ITS, and to support the making of safer and more synchronized ITS. A synchronized ITS requires a comprehensive process to successfully integrate its components. To assess the Sydney Metro's ITS integration, this research utilized confirmatory factor analysis as the statistical method to produce a model indicating the existence of four sub-systems and emphasized safety standard as the key attribute for a successful deployment. The very high safety benchmark is the pinnacle of the Sydney's advanced rail transportation system.

Fuzzy Logic Based Secured Routing In Vanets

Vehicular Adhoc Network (VANET) is becoming a fundamental important component of Intelligent Transport System (ITS). VANET is one of the evolutionary network for providing safety related services, management of traffic and other user related services. VANET communication involves either Vehicle to Infrastructure (V2I) or Vehicle to Vehicle (V2V). In the current scenario, Multi-hop Authenticated Proxy Mobile IP (MA-PMIP) scheme is used in VANET where only 3G networks is considered and the scheme is unable to support transmission of substantial massive data while maintaining the security. The performance of MAPMIP drastically degrades during transmission of huge volume of data which leads to huge power dissipation. To overcome these problems a novel Fuzzy logic Based Secured Routing (FBSR) mechanism is proposed. The FBSR prevents Sink hole attack and Sybil attack. In order to avoid collision during large file transfer, TDMA and multi-threading concepts are introduced in FBSR. Furthermore, the performance of FBSR is compared with contemporary routing protocols OLSR and AODV.

Explanatory prediction of traffic congestion propagation mode: A self-attention based approach

Short-term traffic flow forecasting, an important component of intelligent transportation systems (ITS), is a challenging research direction as forecasting itself is affected by a series of complex factors. As more and more attention is paid to the data itself, deep learning methods have attained mainstream popularity for accomplishing traffic flow prediction tasks. In recent years, the attention mechanism has been widely used in various fields thanks to its excellent result interpretation ability and its capability to improve the performance of neural network models. In terms of time series data prediction, LSTM has demonstrated its powerful time feature extraction capability. Because of its ability to efficiently and quickly extract spatial–temporal features, CNN is often used in combination with LSTM and attention mechanisms to obtain accurate traffic flow prediction forecast results. In this paper, we propose a short-term traffic flow prediction model based on self-attention, and test the performance of the model experimentally with real data. The model can achieve the best prediction results compared with other classical models. In addition, the temporal and spatial features extracted by the model have certain physical characteristics making results easier to interpret.