Intelligent Transportation System Environment Research Articles

Edge Intelligence in Intelligent Transportation Systems: A Survey

Edge intelligence (EI) is becoming one of the research hotspots among researchers, which is believed to help empower intelligent transportation systems (ITS). ITS generates a large amount of data at the network edge by millions of devices and sensors. Data-driven artificial intelligence (AI) is at the core of ITS development. By pushing the AI frontier to the network edge, EI enables ITS AI applications to have lower latency, higher security, less pressure on the backbone network and better use edge big data. This paper surveys Edge Intelligence in Intelligent Transportation Systems. We first introduce the challenges ITS faces and explain the motivation of using EI in ITS. We then explore the framework of using EI in ITS, including the EI-based ITS architecture, the data gathering and communication methods, the data processing and service delivery, and the performance indexes. The enabling technologies, such as AI models, the Internet of Things, and Edge Computing technologies used in EI-based ITS, are reviewed intensively. We discuss the edge intelligence applications and research fields in ITS in depth. Typical application scenarios, such as autonomous driving, vehicular edge computing, intelligent vehicular transportation system, unmanned aerial vehicle (UAV) in ITS environment, and rail transportation control and management, are explored. The general platforms of EI, the EI training and inference in ITS, as well as the benchmark datasets, are introduced. Finally, we discuss some of the challenges and future directions of using EI in ITS.

Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images

Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with the power of computer vision (CV) and machine learning (ML) for mitigating potential safety hazards and identifying anomalies (i.e., vehicles) in pedestrian walkways. The real-world challenges of scenes and dynamics of environmental complexity cannot be handled by the conventional offline learning-based vehicle detection method and shallow approach. With recent advances in deep learning (DL) and ML areas, authors have found that the image detection issue ought to be devised as a two-class classification problem. Therefore, this study presents an Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems using Federated Learning and Harris Hawks Optimizer (ADPW-FLHHO) algorithm on remote sensing images. The presented ADPW-FLHHO technique focuses on the identification and classification of anomalies, i.e., vehicles in the pedestrian walkways. To accomplish this, the ADPW-FLHHO technique uses the HybridNet model for feature vector generation. In addition, the HHO approach is implemented for the optimal hyperparameter tuning process. For anomaly detection, the ADPW-FLHHO technique uses a multi deep belief network (MDBN) model. The experimental results illustrated the promising performance of the ADPW-FLHHO technique over existing models with a maximum AUC score of 99.36%, 99.19%, and 98.90% on the University of California San Diego (UCSD) Ped1, UCSD Ped2, and avenue datasets, respectively. Therefore, the proposed model can be employed for accurate and automated anomaly detection in the ITS environment.

An Optimal Security Framework Based on Driver Authentication for Intelligent Transportation Systems

Abstract Security and safety have been a critical issue in the field of Intelligent Transportation Systems. Security of both people and vehicles is a matter of concern. In this paper, an authentication-based security framework is designed to serve the purpose. Only authorized users are provided access to the vehicle. The drivers are first registered for driving the vehicle from an authorized and valid source. The next step is the authentication check before one can be given access to the vehicle. There are two alternative methods provided for the authentication check in the solution depending upon the situation the vehicle is in. The solution is an optimally viable solution from the security perspective of the real-time Intelligent Transportation System environment. The results have shown that the security solution fits into the domain very well and performs as per expectations. The solution has been tested on a Vehicle Tracking Device connected to a vehicle. The same solution can be extended to a fleet of vehicles.

Density waves in an improved car-following model under intelligent transportation system environment

The intelligent transportation system (ITS) can effectively utilize the existing transportation facilities and improve traffic safety and efficiency. To study and evaluate the dynamic characteristics of traffic flow, a novel car-following model is proposed based on the two-velocity difference model (TVDM) with connected consideration of headway memory and backward looking effect under ITS environment. The linear stability theory is used to acquire the stability condition of the improved model, which shows that the proposed model can effectively strengthen the traffic flow stability. According to the nonlinear theory, the time-dependent Ginzburg–Landau (TDGL) equation and modified Korteweg–de Vries (mKdV) equation are obtained to exhibit the evolution characteristics of traffic flow density wave. At last, numerical simulation is performed to further validate that the improved model can enhance the traffic flow stability and eliminate traffic congestion.

Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

Intelligent Transportation Systems (ITS) have become a vital part in improving human lives and modern economy. It aims at enhancing road safety and environmental quality. There is a tremendous increase observed in the number of vehicles in recent years, owing to increasing population. Each vehicle has its own individual emission rate; however, the issue arises when the emission rate crosses a standard value. Owing to the technological advances made in Artificial Intelligence (AI) techniques, it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution. The current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring (OSSO-HDLAPM) in ITS environment. The proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of pollutants. In addition, hybridized Convolution Neural Network with Long Short-Term Memory (HCNN-LSTM) model is used to predict pollutant level based on the data attained earlier by the sensors. In HCNN-LSTM model, the hyperparameters are selected and optimized using OSSO algorithm. In order to validate the performance of the proposed OSSO-HDLAPM technique, a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

Study on the integration effect of multiple vehicles’ delayed velocities on traffic stability in intelligent transportation system environment

PurposeIn an intelligent transportation system (for short, ITS) environment, a vehicle’s motion is affected by the information in a large scale. The purpose of this paper is to study the integration effect of multiple vehicles’ delayed velocities on traffic flow.Design/methodology/approachThis paper constructed a new car-following model to study the integration effect of multiple vehicles’ delayed velocities on traffic flow. The new model is analyzed by linear and nonlinear perturbation method theoretically and also verified by simulation.FindingsIt is found out that the integration of preceding vehicles’ delayed velocities affect the stability of traffic flow importantly, and three preceding vehicles’ delayed velocities information should be considered in real traffic.Originality/valueThe new car-following model by considering the integration effect of multiple vehicles’ delayed velocities is firstly proposed in this paper. The research result shows that three preceding vehicles’ delayed velocities information is the best choice to stabilizing traffic flow.

A Review on IEEE 802.11p for Intelligent Transportation Systems

Road safety is an active area of research for the automotive industry, and certainly one of ongoing interest to governments around the world. The intelligent transportation system (ITS) is one of several viable solutions with which to improve road safety, where the communication medium (e.g., among vehicles and between vehicles and the other components in an ITS environment, such as roadside infrastructure) is typically wireless. A typical communication standard adopted by car manufacturers is IEEE 802.11p for communications. Thus, this paper presents an overview of IEEE 802.11p, with a particular focus on its adoption in an ITS setting. Specifically, we analyze both MAC and PHY layers in a dedicated short-range communication (DSRC) environment.

Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview

Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultralow latency and reliable data analytics solutions that combine, in real time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this article, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level to overcome the ITS's latency and reliability challenges. With a higher capability of passengers' mobile devices and intravehicle processors, such a distributed edge computing architecture leverages deep-learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyberphysical security are investigated. Then, different deep-learning solutions for such challenges are revealed. The discussed deep-learning solutions enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the introduced edge analytics architecture, coupled with the power of deep-learning algorithms, provides a reliable, secure, and truly smart transportation environment.

Trust-based secure clustering in WSN-based intelligent transportation systems

Increasing the number of vehicles on roads leads to congestion and safety problems. Wireless Sensor Network (WSN) is a promising technology providing Intelligent Transportation Systems (ITS) to address these problems. Usually, WSN-based applications, including ITS ones, incur high communication overhead to support efficient connectivity and communication activities. In the ITS environment, clustering would help in addressing the high communication overhead problem. In this paper, we introduce a bio-inspired and trust-based cluster head selection approach for WSN adopted in ITS applications. A trust model is designed and used to compute a trust level for each node and the Bat Optimization Algorithm (BOA) is used to select the cluster heads based on three parameters: residual energy, trust value and the number of neighbors. The simulation results showed that our proposed model is energy efficient (i.e., its power consumption is more efficient than many well-known clustering algorithm such as LEACH, SEP, and DEEC under homogeneous and heterogeneous networks). In addition, the results demonstrated that our proposed model achieved longer network lifetime, i.e., nodes are kept alive longer than what LEACH, SEP and DEEC can achieve. Moreover, the the proposed model showed that the average trust value of selected Cluster Head (CH) is high under different percentage (30% and 50%) of malicious nodes.

Sensor Technologies for Intelligent Transportation Systems.

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment.

An extended car-following model accounting for the average headway effect in intelligent transportation system

In this paper, an extended car-following model is proposed to simulate traffic flow by considering average headway of preceding vehicles group in intelligent transportation systems environment. The stability condition of this model is obtained by using the linear stability analysis. The phase diagram can be divided into three regions classified as the stable, the metastable and the unstable ones. The theoretical result shows that the average headway plays an important role in improving the stabilization of traffic system. The mKdV equation near the critical point is derived to describe the evolution properties of traffic density waves by applying the reductive perturbation method. Furthermore, through the simulation of space–time evolution of the vehicle headway, it is shown that the traffic jam can be suppressed efficiently with taking into account the average headway effect, and the analytical result is consistent with the simulation one.

A Heuristic for the Doubly Constrained Entropy Distribution/Assignment Problem

The doubly constrained entropy distribution/assignment (DEDA) problem that combines a gravity-based trip distribution (TD) problem and a traffic assignment (TA) problem has long been formulated as an optimization model and solved by two solution algorithms, i.e., partial- and full-linearization solution algorithms. As an alternative, this research first treats the DEDA problem by the augmented Lagrangian dual (ALD) method as the singly constrained entropy distribution/assignment (SEDA) problem, which in turn is addressed, via a tactical supernetwork representation, as an “extended” 1-origin-to-1-destination TA problem. A quick-precision TA solution algorithm, − called TAPAS (Traffic Assignment by Paired Alternative Segments), − is then adopted for solutions. The proposed approach is demonstrated with a numerical example for the correctness of the result, using Lingo 11 solver and the partial linearization solution (PLS) algorithm. Moreover, through the use of TAPAS in the innermost loop, the proposed approach also has the merit of generating unique path flow solution, which is very useful in route guidance under the intelligent transportation systems environment, among other academic applications. In addition, the proposed approach can be easily applied, or with minor modification at most, to various combined models in travel demand forecasting.

Impact of the Next-Nearest-Neighbor Interaction on Traffic Flow of Highway with Slopes

In this paper, we study the motion course of traffic flow on the slopes of a highway by applying a microscopic traffic model, which takes into account the next-nearest-neighbor interaction in an intelligent transportation system environment. Three common gradients of the highway, which are sag terrain, uphill terrain, and downhill terrain on a single-lane roadway, are selected to clarify the impact on the traffic flow by the next-nearest-neighbor interaction in relative velocity. We obtain the current-density relation for traffic flow on the sag, the uphill and the downhill under the next-nearest-neighbor interaction strategy. It is observed that the current saturates when the density is greater than a critical value and the current decreases when the density is greater than another critical value. When the density falls into the intermediate range between the two critical densities it is also found that the oscillatory jam, easily leads to traffic accidents, often appears in the downhill stage, and the next-nearest-neighbor interaction in relative velocity has a strong suppressing effect on this kind of dangerous congestion. A theoretical analysis is also presented to explain this important conclusion.

Wireless Communication Alternatives for Intelligent Transportation Systems: A Case Study

Communication systems are the basis of every effective and reliable traffic control and management application. While cellular-based communication through commercial carriers is widely used for online traffic management applications, public agencies have also begun to consider other technologies, such as WiFi and WiMAX. Most such agencies still seek additional guidelines for the selection of suitable wireless options for different traffic control and management applications under different physical and environmental conditions. Performance and reliability are among the most important parameters to be considered when examining wireless communication options for traffic control and management applications. The authors first conducted interviews with selected traffic agencies regarding their experiences with performance of wireless communication infrastructure, as well as their interests and plans on future expansion. Next, they conducted a thorough literature review focusing on various wireless technologies that could be used in an intelligent transportation system environment. Last, the authors conducted a case study in which a section of the South Carolina Department of Transportation traffic camera system was wirelessly connected via either WiFi or WiMAX network architecture. This case study followed the proposed network design process presented in the article using WiFi and WiMAX technologies to support a traffic surveillance system that considered coverage range and two different network topologies: mesh/ad hoc (devices forward data to neighboring devices to reach the destination) and infrastructure (devices send data to an access point directly). The network simulator ns-2 was used to assess the average throughput that each camera can receive in different network topologies. Throughput/cost analysis of WiFi and WiMAX mesh and infrastructure topologies indicated that for given number of devices, a mesh network has better throughput for every dollar spent than infrastructure based topology. This article provides a foundation for further investigation of WiFi and WiMAX performance and reliability under different network topologies. Findings from this research will benefit transportation agencies and other stakeholders in evaluating and selecting wireless communication options and network topologies for various traffic control and management applications.

Push-to-Talk Service for Intelligent Transportation Systems

Push to talk (PTT) is a walkie-talkie like service. In this service, several predefined group members participate in one PTT session. At any time, only one group member is allowed to speak. Therefore, a mechanism is required to determine the member that is permitted to speak. In Open Mobile Alliance (OMA), a central arbitrator coordinates the permission to speak among the group members. In the Intelligent Transportation System (ITS) environment, the centralized OMA approach is not appropriate. This paper proposes a distributed PTT mechanism for the ITS environment, which does not require any central arbitrator. The group member permitted to speak is automatically determined through distributed learning interaction. We also explore the properties and model the performance of the proposed PTT mechanism.

A Stochastic Form of a Human Driver Steering Dynamics Model

This work develops a stochastic form of a human driver model which can be used for simulating vehicle guidance and control. The human motor-control function is complex and can be affected by factors such as driver’s training and experience, fatigue, road conditions, and attention. The variations in these effects become more pronounced in hazardous driving conditions such as in snow and ice. One example of such driving conditions is snow removal operation in highway maintenance, where the use of a stochastic driver model seems to be more desirable. This work evaluates and extends existing models of a human driver including stochastic or statistical considerations related to differences in drivers’ experiences and their conditions as well as variations in the effect of disturbances such as plowing forces. The aim is to develop a simulation environment that can be used in design and evaluation of driver assistance systems for snow removal operation in an Intelligent Transportation System environment.

Short‐term traffic forecasting: Overview of objectives and methods

In the last two decades, the growing need for short‐term prediction of traffic parameters embedded in a real‐time intelligent transportation systems environment has led to the development of a vast number of forecasting algorithms. Despite this, there is still not a clear view about the various requirements involved in modelling. This field of research was examined by disaggregating the process of developing short‐term traffic forecasting algorithms into three essential clusters: the determination of the scope, the conceptual process of specifying the output and the process of modelling, which includes several decisions concerning the selection of the proper methodological approach, the type of input and output data used, and the quality of the data. A critical discussion clarifies several interactions between the above and results in a logical flow that can be used as a framework for developing short‐term traffic forecasting models.

Investigation for a global AVL system

Real-time reporting systems are now becoming widely available, in order to aid emergency dispatch assistance and traffic control management as being an important part of intelligent transportation systems (ITS). Based on the fact that the global system mobile (GSM) standard is widely accepted as being the most comprehensively deployed of all the various second generation digital cellular telephony standards, studies have been focused on using it as a communication link for real-time application reporting. This paper introduces a study made on an integrated system incorporating a global positioning system (GPS) receiver, a control area network (CAN) and the short message service (SMS) of GSM as the transmission media. The goal of the research study conducted, is to measure the end to end delays experienced, and consequently its practicality, when using a CAN bus and the GSM-SMS bearer service for position reporting for use in an ITS environment. The paper initially presents the previous research that has been carried out relevant to this study. Following it outlines the system architecture of the integrated system studied together with its functionality in an ITS environment. The results obtained from the various simulation scenarios are presented as well as an estimation algorithm that aims to diminish the positioning error incorporated from the transmission delays occurred. Concluding this paper discusses the advantages of using this integrated system as a solution for position reporting in ITS as well as proposing further research to be conducted.