Intelligent Transportation Systems Deployment Research Articles

VESecure: Verifiable authentication and efficient key exchange for secure intelligent transport systems deployment

The Intelligent Transportation Systems (ITS) is a leading-edge, developing idea that seeks to revolutionize how people and things move inside and outside cities. Internet of Vehicles (IoV) forms a networked environment that joins infrastructure, pedestrians, fog, cloud, and vehicles to develop ITS. The IoV has the potential to improve transportation systems significantly, but as it is networked and data-driven, it poses several security issues. Numerous solutions to these IoV issues have recently been put forth. However, significant computing overhead and security concerns afflict the majority of them. Moreover, malicious vehicles may be injected into the network to access or use unauthorized services. To improve the security of the IoV network, the Mayfly algorithm is used to optimize the private keys continuously. To address these difficulties, we propose a novel VESecure system that provides secure communication, mutual authentication, and key management between vehicles, roadside units (RSU), and cloud servers. The scheme undergoes extensive scrutiny for security and privacy using the Real-or-Random (ROR) oracle model, Tamarin, and Scyther tools, along with the informal security analysis. An Objective Modular Network Testbed in OMNet++ is used to simulate the scheme. We prove our scheme's efficiency by comparing it with other existing methods regarding communication and computation costs.

Cyber Resilience and Intelligent Transport Systems, a Scoping Review

The scoping review reported by this article aimed to analyze and synthesize state-of-the-art studies focused on the integration of cyber resilience in the implementation and deployment of intelligent transport systems, namely in terms of (i) cyber resilience functions being considered, (ii) relative importance of the different transport types, and (iii) how the proposed solutions are being validated. An electronic search was conducted, and 23 studies were included in this review after the selection process. According to the findings, cyber resilience represents a gap of the current research related to intelligent transport systems and, therefore, additional efforts are required to guarantee that they are resilient to cyber-attacks and other adverse events.

Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?

Intelligent transportation system (ITS) refers to advanced applications to make transportation safer and more intelligent. The dynamic and diverse natures of the system have been creating many challenges in ITS deployment and security. The progression in recent years of machine learning provides potentially strong methods to exploit data sources from transportation networks. Machine learning-based approaches promise to deal with various challenges in networks thanks to their ability to adapt to the changing network topology and network scale. This paper investigates the application of machine-learning models in jamming detection by analyzing how the approach works on individual observations of vehicles in different scenarios. We propose a machine learning-based approach that explores hidden rules of how the observation changes under a reactive jamming attack. Our proposed machine learning-based approach improves detection accuracy compared to existing methods. The performance of the proposed method depends closely on dataset selection. Though we evaluate our approach with synthetic data, the data are generated with justifiable simulations calibrated to match a real-referenced dataset. Our analysis provides a connection between the machine-learning domain and vehicular networks by utilizing specific domain techniques for jamming detection.

A three-stage framework for efficient deployment of intelligent transportation systems in urban areas

Rapid urbanization in big cities worldwide leads to severe traffic congestion and damages the urban living environment. Intelligent Transportation Systems (ITS) play a crucial role in addressing the growing challenges of urban transportation, such as congestion, pollution, and safety concerns. However, there is a lack of studies to present an overall framework to deploy ITS efficiently in urban areas considering each city's unique conditions. Therefore, this paper develops a three-stage framework to simplify the process of efficient deployment of ITS application services in a specific city. Three stages include identifying potential ITS services, evaluating and ranking the priority of ITS application services, and analyzing legal requirements as well identifying any gaps or areas where the current legal framework may fall short in accommodating ITS technologies. The three-stage framework considers the specific transportation challenges and requirements of each urban area/city, taking into account factors such as traffic patterns, infrastructure limitations, environmental considerations, and future development plans. A case study in Hai Phong city was presented where expert surveys were conducted to assess the current transport situation, evaluate satisfaction and expectation regarding applying ITS services. By providing a comprehensive framework for the efficient deployment of ITS in urban areas, this paper offers valuable insights and guidance to policymakers, transportation planners, and researchers. The framework can be as reference for other similar cities grappling with transportation challenges, facilitating the implementation of effective ITS solutions to transform advanced and sustainable urban transportation systems.

A Dual Attention-Based Recurrent Neural Network for Short-Term Bike Sharing Usage Demand Prediction

Improved network technology and data openness have generated a large number of trajectory-based data, and trajectory-based knowledge graphs help to provide consecutive and effective development and optimization for intelligent transportation systems (ITS). Bike-sharing systems (BSS) are increasingly important in the traffic planning, management, and deployment of ITS. Therefore, it is crucial to determine how trajectory data are best used to accurately predict the demand for short-term bike sharing usage. This study proposed a recurrent neural network (RNN) model based on the dual attention mechanism to extract spatial and temporal features. The attention mechanism is able to determine and weight all location features of the data in time series to learn mutual correlations. The method applied in this paper can effectively conduct an adaptive combination of local- and global-feature dependencies for trajectory data, in order to effectively predict the trend of short-term bike sharing usage demand. In addition, this study adopted the random walk mechanism to maintain local relations between bike stations in the preprocessing of time series data, which makes it more adaptive to the local location changes of different stations. Finally, the experimental results show that the model architecture in this study combined the attention and random walk mechanisms to achieve better prediction performance.

Intelligent Transportation Systems in Developing Countries: Challenges and Prospects

Developed countries have paved the way for the implementation of intelligent transport systems to improve the safety, efficiency, and environmental impact of transport. With developing countries entering the fray, the question is: Is ITS as implemented in developed countries relevant to developing countries? This research question is discussed in this paper for the case of sub-Saharan countries that are among the poorest countries worldwide. To this end, the paper outlines the main differences in transportation scenarios between developed and sub-Saharan countries, and then considering the main constraints of sub-Saharan countries, a guide towards an affordable intelligent transportation system in these countries is discussed. Finally, the paper proposes two main novel ideas for the deployment of intelligent transport systems on dirt roads.

Analysis for the sustainability of intelligent transport systems in the United States

The economic, social, and ecological wellbeing of a society are all negatively impacted by the traffic congestion issue. Intelligent Transportation Systems (ITS) have been used to better sustainable transportation systems across the globe, helping to lessen the unsustainable effects of the clogged highway issue. The goal may be reached by examining the positive effects ITS deployments have had on urban areas in the United States. In addition to the statewide findings, we also do in-depth research on the economies of seven major Florida communities. A fuzzy-data envelopment analysis (DEA) technique is used instead of the conventional DEA approach for sustainability performance analysis in order to take into account the uncertainty in benefit and cost evaluations. While all of the ITS investments are very successful when looking at the cost-benefit ratio, the findings from the fuzzy-DEA method show that some of them are less efficient than others. This research's TBL findings give a fuller picture of the societal and economic benefits of ITS-related congestion reduction, including its drawbacks, indirect indicators, and environmental advantages. In addition, decision makers had positive findings from TBL analysis of ITS investments and comparisons of sustainability performance of ITS initiatives.

Environmental benefits of intelligent transport systems implementing

Intelligent Transportation Systems (ITS) have been shown to have positive effects on the economy and on efficiency, but this study aims to show that they also have a positive effect on carbon emissions. This article is based on the premise that implementing these technological steps is a winwin for long-term economic and ecological viability. The first research question asks if there is evidence that ITS systems can be implemented and operated in a way to generate environmental benefits, and the second asks if the policy priorities of national and international stakeholders reflect a propensity for ITS deployment in order to yield those benefits. Both fundamental drivers and a synthesis of the empirical facts are used to provide an answer to the first question. New propensity models to accomplish climatic and environmental objectives are one solution to the second problem. According to the findings, the likelihood that ITS would be included as a high priority policy instrument in future transport policies is significantly influenced by the Vehicle Density and High Technology exports of a country. The study's significance lies in its promotion of ITS as a policy instrument with the potential to improve economic performance and environmental performance. It's useful for transport planners and policy experts on a global, national, and even regional scale.

Traffic Congestion Classification Using GAN-Based Synthetic Data Augmentation and a Novel 5-Layer Convolutional Neural Network Model

Private automobiles are still a widely prevalent mode of transportation. Subsequently, traffic congestion on the roads has been more frequent and severe with the continuous rise in the numbers of cars on the road. The estimation of traffic flow, or conversely, traffic congestion identification, is of critical importance in a wide variety of applications, including intelligent transportation systems (ITS). Recently, artificial intelligence (AI) has been in the limelight for sophisticated ITS solutions. However, AI-based schemes are typically heavily dependent on the quantity and quality of data. Typical traffic data have been found to be insufficient and less efficient in AI-based ITS solutions. Advanced data cleaning and preprocessing methods offer a solution for this problem. Such techniques enable quality improvement and augmenting additional information in the traffic congestion dataset. One such efficient technique is the generative adversarial network (GAN), which has attracted much interest from the research community. This research work reports on the generation of a traffic congestion dataset with enhancement through GAN-based augmentation. The GAN-enhanced traffic congestion dataset is then used for training artificial intelligence (AI)-based models. In this research work, a five-layered convolutional neural network (CNN) deep learning model is proposed for traffic congestion classification. The performance of the proposed model is compared with that of a number of other well-known pretrained models, including ResNet-50 and DenseNet-121. Promising results present the efficacy of the proposed scheme using GAN-based data augmentation in a five-layered convolutional neural network (CNN) model for traffic congestion classification. The proposed technique attains accuracy of 98.63% compared with the accuracies of ResNet-50 and DenseNet-121, 90.59% and 93.15%, respectively. The proposed technique can be used for urban traffic planning and maintenance managers and stakeholders for the efficient deployment of intelligent transportation system (ITS).

Robust ensemble method for short-term traffic flow prediction

Accurate and real-time prediction of short-term traffic flow plays an increasingly vital role in the successful deployment of Intelligent Transportation Systems. Although existing studies have been done for traffic flow prediction problem, their efficacy relies heavily on traffic data. However, collected traffic data are usually affected by various external factors (e.g, weather, traffic jams and accidents), leading to errors and missing data. This makes it difficult to pick a single method that works well all the time. This paper concentrates on investigating ensemble learning that benefits from multiple base methods and presents an effective and robust ensemble method by using the bagging ensemble technique averaging to improve the traffic flow prediction performance. To enhance the robustness of constructed ensemble method, three improved least squares twin support vector regression methods are proposed based on robust L1-norm, L2,p-norm and Lp-norm distance to alleviate the negative effect of traffic data with outliers. In addition, a pruning scheme is utilized to remove anomalous individual components. This makes the proposed method more effective for traffic flow prediction. Further, a comprehensive traffic flow indicator system based on speed, traffic volume, occupancy and ample degree is utilized to forecast the traffic flow. To promote the prediction performance, we optimize the parameters of each component in ensemble method with the adaptive particle swarm optimization. The results on real traffic data demonstrate that the proposed ensemble method yields better prediction performance and robustness even when the standalone components and other competitors make unsatisfactory predictions.

Transport Inequalities and the Adoption of Intelligent Transportation Systems in Africa: A Research Landscape

Intelligent Transportation Systems (ITS), also known as Smart Transportation, is an infusion of information and communication technologies into transportation. ITS are a key component of smart cities, which have seen rapid global development in the last few decades. This has in turn translated to an increase in the deployment and adoption of ITS, particularly in countries in the Western world. Unfortunately, this is not the case with the developing countries of Africa and Asia, where dilapidated road infrastructure, poorly maintained public/mass transit vehicles and poverty are major concerns. However, the impact of Westernization and “imported technologies” cannot be overlooked; thus, despite the aforementioned challenges, ITS have found their way into African cities. In this paper, a systematic review was performed to determine the state of the art of ITS in Africa. The output of this systematic review was then fed into a hybrid multi-criteria model to analyse the research landscape, identify connections between published works and reveal research gaps and inequalities in African ITS. African peculiarities inhibiting the widespread implementation of ITS were then discussed, followed by the development of a conceptual architecture for an integrated ITS for African cities.

VEHICLE TO EVERYTHING (V2X) COMMUNICATIONS TECHNOLOGY FOR SMART MOBILITY IN MALAYSIA: A COMPREHENSIVE REVIEW

Vehicle to Everything (V2X) communication technology assesses the potential as the new phenomenon for Intelligent Transportation Systems (ITS) in the context of vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-network (V2N). Dedicated Short-Range Communications (DSRC) is the conventional vehicular communication standard for ITS. The cellular network based on 4G/5G for ITS deployment has become a competitor to DSRC. Recent advancements in technologies have motivated the research community to develop a hybrid DSRC, and cellular networks approach to support reliable ITS applications. Nevertheless, as new techniques come forward, the technical and regulatory challenges may also vary across countries. Given that the existing comparative studies have not been covered as a whole, we evaluated the V2X communication technology to classify the adaptability of DSRC, cellular networks, and hybrid methods. The study also includes available V2X technology platforms and products. In addition, the challenges of deployments are also depicted in this study. The outcome indicates that many automotive industries and telecommunication providers recognize V2X substantial effect on ITS. The work is underway to decide which capabilities will be added since this is a long-term benefit for our future transportation.

Selection and Implementation of Intelligent Transportation Systems for Work Zone Construction Projects

The extent of the deployment of Intelligent Transportation Systems (ITSs) for work zone construction projects has increased in recent years. However, highway agencies are unable to meet the full demand of the deployment of ITSs in work zones in a fiscally constrained environment. Therefore, it is desirable to establish guidelines to help highway agencies to consider installing ITS in work zones as funding becomes available. The goal of this research is to develop a methodology and guideline to assist project designers in assessing whether a particular work zone construction or maintenance project should be considered for the deployment of one or more ITSs. If so, the guideline would assist in determining the ITSs that would be most appropriate for the project. To achieve this goal, the researchers: (1) investigated technologies and evaluated different ITSs that could be used in work zone projects, (2) selected the criteria that would have to be evaluated to identify the eligible work zone projects for the deployment of ITSs, and (3) developed a selection methodology to assist project designers in selecting one or more work zone ITSs in order to be deployed in the project. The outcomes of this study provide a guideline for use in selecting and implementing ITSs for a work zone construction or maintenance project.

Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, deployment of intelligent transport systems, traffic operations, to name a few. In this work, we cast the problem of human trajectory forecasting as learning a representation of human social interactions. Early works handcrafted this representation based on domain knowledge. However, social interactions in crowded environments are not only diverse but often subtle. Recently, deep learning methods have outperformed their handcrafted counterparts, as they learn about human-human interactions in a more generic data-driven fashion. In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two domain-knowledge inspired data-driven methods to effectively capture these social interactions. To objectively compare the performance of these interaction-based forecasting models, we develop a large scale interaction-centric benchmark <i>TrajNet</i>&#x002B;&#x002B;, a significant yet missing component in the field of human trajectory forecasting. We propose novel performance metrics that evaluate the ability of a model to output socially acceptable trajectories. Experiments on TrajNet&#x002B;&#x002B; validate the need for our proposed metrics, and our method outperforms competitive baselines on both real-world and synthetic datasets.

Mobility Prediction in Vehicular Ad-Hoc Networks: Prediction Aims, Techniques, Use Cases, and Research Challenges

The accurate prediction of the vehicle mobility in Vehicular Ad-hoc Networks (VANETs) has a significant effect in the rapid deployment of Intelligent Transport Systems (ITSs) as well as in building efficient vehicular communication protocols. Thus, this article aims to briefly review the contemporary research progress in the field of mobility prediction in VANETs. The fundamental objective here is to explore the major mobility prediction aims, techniques, and use cases in VANETs, with focusing on the prerequisites, advantages, and limitations. Furthermore, performance and usage analysis are presented with the aim to deduce the appropriateness of utilizing each prediction aim/technique for the varied use cases. Finally, the relevant research challenges are identified to shape the future of the mobility prediction in VANETs. A thorough knowledge and understanding of the mobility prediction are provided, with the aim of enhancing the robustness and reliability of the mobility prediction in VANETs.

Support Vector Machine for Short-Term Traffic Flow Prediction and Improvement of Its Model Training using Nearest Neighbor Approach

Short-term prediction of traffic flow is essential for the deployment of intelligent transportation systems. In this paper we present an efficient method for short-term traffic flow prediction using a Support Vector Machine (SVM) in comparison with baseline methods, including the historical average, the Current Time Based, and the Double Exponential Smoothing predictors. To demonstrate the efficiency and accuracy of the SVM method, we used one-month time-series traffic flow data on a segment of the Pan Island Expressway in Singapore for training and testing the model. The results show that the SVM method significantly outperforms the baseline methods for most prediction intervals, and under various traffic conditions, for the rolling horizon of 30 min. In investigating the effect of the input-data dimension on prediction accuracy, we found that the rolling horizon has a clear effect on the SVM’s prediction accuracy: for the rolling horizon of 30–60 min, the longer the rolling horizon, the more accurate the SVM prediction is. To look for a solution for improvement of the SVM’s training performance, we investigate the application of k-Nearest Neighbor method for SVM training using both actual data and simulated incident data. The results show that the k- Nearest Neighbor method facilitates a substantial reduction of SVM training size to accelerate the training without compromising predictive performance.

Mapping of bus travel time to traffic stream travel time using econometric modeling

Travel time is one of the most important traffic parameters for travelers, traffic managers, planners, and operators alike. Travel time estimation is a significant component of any intelligent transportation systems (ITS) operations. In countries like India, which are at a nascent stage of ITS deployment, one of the main hurdles is the lack of accurate and automated systems for travel time data collection. However, in most of the cities in India, public transit buses are equipped with global positioning system (GPS) devices providing rich bus travel time data. Privacy issues deter the use of such GPS devices in private vehicles present in the stream. Hence, estimating the stream travel time using buses as probes is a relevant problem. In countries like India, where a heterogeneous and less lane disciplined traffic condition exists and the buses have to share the road space with other vehicles (no separate bus lanes); this mapping of bus travel time to stream travel time becomes challenging. The present study attempts to estimate stream travel time using buses as probes in a heterogeneous and less lane disciplined urban traffic scenario. Preliminary analysis of the collected travel time data indicated significant variability across time and links. Panel modeling can capture both this cross-sectional and time-dependent behavior and hence is used in this study. The estimation model is corroborated with field data and the performance is found to be satisfactory.

A Hybrid Machine Learning Approach for Freeway Traffic Speed Estimation

Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.

A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction

Accurate short-time traffic flow prediction has gained gradually increasing importance for traffic plan and management with the deployment of intelligent transportation systems (ITSs). However, the existing approaches for short-term traffic flow prediction are unable to efficiently capture the complex nonlinearity of traffic flow, which provide unsatisfactory prediction accuracy. In this paper, we propose a deep learning based model which uses hybrid and multiple-layer architectures to automatically extract inherent features of traffic flow data. Firstly, built on the convolutional neural network (CNN) and the long short-term memory (LSTM) network, we develop an attention-based Conv-LSTM module to extract the spatial and short-term temporal features. The attention mechanism is properly designed to distinguish the importance of flow sequences at different times by automatically assigning different weights. Secondly, to further explore long-term temporal features, we propose a bidirectional LSTM (Bi-LSTM) module to extract daily and weekly periodic features so as to capture variance tendency of the traffic flow from both previous and posterior directions. Finally, extensive experimental results are presented to show that the proposed model combining the attention Conv-LSTM and Bi-LSTM achieves better prediction performance compared with other existing approaches.

Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method

As an industrial application of Internet of Things (IoT), Internet of Vehicles (IoV) is one of the most crucial techniques for Intelligent Transportation System (ITS), which is a basic element of smart cities. The primary issue for the deployment of ITS based on IoV is the security for both users and infrastructures. The Intrusion Detection System (IDS) is important for IoV users to keep them away from various attacks via the malware and ensure the security of users and infrastructures. In this paper, we design a data-driven IDS by analyzing the link load behaviors of the Road Side Unit (RSU) in the IoV against various attacks leading to the irregular fluctuations of traffic flows. A deep learning architecture based on the Convolutional Neural Network (CNN) is designed to extract the features of link loads, and detect the intrusion aiming at RSUs. The proposed architecture is composed of a traditional CNN and a fundamental error term in view of the convergence of the backpropagation algorithm. Meanwhile, a theoretical analysis of the convergence is provided by the probabilistic representation for the proposed CNN-based deep architecture. We finally evaluate the accuracy of our method by way of implementing it over the testbed.