Intelligent Transportation Systems Applications Research Articles

An Application-Driven Framework for Intelligent Transportation Systems Using 5G Network Slicing

Vehicular networks are critical pieces in support of advanced intelligent transportation systems (ITS). These networks are formed by vehicles that can be connected to one another as well as to the infrastructure, and are subject to constant topology changes, disconnections, and data congestion. Each ITS application could have a different set of communication requirements, such as delay, bandwidth, and packet delivery ratio. Meeting these heterogeneous requirements in the complex dynamic environment of vehicular networks is a challenge. This paper develops a new framework for application-driven vehicular networks using 5G network slicing. We present the architecture of the proposed solution and design algorithms for heterogeneous traffic in a dynamic vehicular environment. Our simulations on realistic vehicular scenarios show significant improvements in network performance compared to the state-of-the-art approaches.

Interference-Aware Antenna Synthesis for Enhanced Coverage in Intelligent Transportation System

In this paper, an antenna synthesis process is investigated for Intelligent Transportation System (ITS) application. The wireless communication between Road Side Units (RSUs) and On Board Units (OBUs) located in the vehicles enables smart mobility. However, to support high speed vehicles and high data rates, a wider coverage area by the RSU antennas projected on the highway is required. This is defined by the radiation patterns of the RSU antennas. On a multi-lane highway, interference from the adjacent lanes is a critical issue which determines the communication reliability in terms of Single-to-Interference plus Noise Ratio (SINR). Hence, in this paper, an interference-aware antenna synthesis process is proposed and a planar array is optimized based on the objective function of obtaining a wide coverage area in terms of SINR distribution for a given modulation scheme. The proposed antenna array enhances the reliable communication area and provides improved SINR distribution across the lane for misaligned vehicles from the center of the lane. The results indicate that the proposed antenna array performs better than the existing designs in terms of communication reliability.

Automating Road Traffic Monitoring Using Computer Vision

The objective of the article is to describe application of computer vision and artificial intelligence technologies for solving the problems of road infrastructure design.The article evaluates the traditional methods of quantitative and qualitative analysis of traffic flows in terms of labour intensity and accuracy using the method of comparative analysis, the advantages and disadvantages of the considered methods are indicated. A new method of traffic flow analysis using unmanned aerial vehicles and computer vision technology based on convolutional neural networks is proposed. The considered method makes it possible to fully automate collection and analysis of data on traffic flows. The article describes the first application of the proposed method when performing transport and economic surveys within the framework of the design of «Northern bypass of the city of Perm». The advantages of the applied method in relation to the traditional ones are described. To implement this project, software was developed for analysing traffic flows using video materials.Further, traffic monitoring is considered, its goals and objectives are described, the necessary functionality of the road traffic monitoring automation system is indicated, the traffic parameters that it should determine are listed. The methodology for implementation of an automated traffic monitoring system based on video materials on a section of the road is considered.A presented project of a traffic monitoring system makes it possible to extend the previously considered approach to the entire road network. Technologies are described that make it possible to implement this system based on video analytics of materials from CCTV cameras. A method for vehicle re-identification is proposed, and the implementation of this method is demonstrated. The method allows building a correspondence matrix of vehicles recorded by CCTV cameras located on different segments of the road network, as well as determining all traffic parameters for the entire street and road network.The conclusions outline the prospects for development of the developed software in terms of application in intelligent transport systems.

Assessing the impact of case mobility: issues and recommendations from Greece

This paper is concerned with the assessment of future applications of CASE (Co-operative, Autonomous, Shared, and Electric) mobility—a term that is also taken to include the more traditionally known applications of ITS (Intelligent Transport Systems). It sets the objective of making such assessments more holistic and horizontal in nature because future CASE mobility applications will include many technologies and service concepts as an integrated whole serving specific mobility objective. Traditional evaluation methodologies will therefore have to be modified to account for this situation, and to this end, the paper focuses on assessing and adapting such “traditional” methodologies. It draws from the experience gained in Greece in the last decade when a substantial number of ITS applications were implemented and assessed, especially in the second largest urban area of the country, the city of Thessaloniki (part of the EU’s European Network of Living Labs). Four basic methodologies are selected: the use of KPIs (Key Performance Indicators), focused interviews, the CMME (CASE Mobility Matrix Evaluation), and the use of safety audits before and after the CASE mobility application. For the first three, the paper suggests specific indicators and/or content. It also gives an example of the use of CMME based on a use case from Thessaloniki. The contents and recommendations of this paper provide a better understanding of the emerging situation as regards CASE mobility applications and point to the need for establishing a timely and comprehensive CASE mobility evaluation framework at both national and European levels, for future implementations.

Design of an Efficient Vehicle-Actuated Signal Control Logic for Signalized Intersections

The effectiveness of fixed-time management systems dramatically decreases in case of fluctuated traffic demands at signalized intersection approaches. This leads to the waste of time in traffic and may cause material, psychological and ecological problems. Especially in recent years, to minimize the negative impacts of these problems, many researchers focus on Intelligent Transportation Systems (ITS). As it is known, one of the Intelligent Transportation Systems applications is called vehicle-actuated (traffic-actuated) management systems. Because the success of these types of applications is directly related to the created control logic, the selection of the most proper control parameters for the created control logic is an important issue. This study aims to create an effective control logic and flow chart for the vehicle-actuated management system. At the end of the analyses, it is seen that the created vehicle-actuated management system can adapt to fluctuations in traffic demands at signalized intersection approaches. Average vehicle delays, fuel consumptions and exhaust emissions can be reduced significantly by the created system. Especially, in case of fluctuations in traffic demands at intersection approaches exist, it is concluded that the performance of the intersection can increase enormously with the created vehicle-actuated management system.

Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data

AbstractThe accurate forecasting of traffic states is an essential application of intelligent transportation system. Due to the periodic signal control at intersections, the traffic flow in an urban road network is often disturbed and expresses intermittent features. This study proposes a forecasting framework named the spatiotemporal gated graph attention network (STGGAT) model to achieve accurate predictions for network‐scale traffic flows on urban roads. Based on license plate recognition (LPR) records, the average travel times and volume transition relationships are estimated to construct weighted directed graphs. The proposed STGGAT model integrates a gated recurrent unit layer, a graph attention network layer with edge features, a gated mechanism based on the bidirectional long short‐term memory and a residual structure to extract the spatiotemporal dependencies of the approach‐ and lane‐level traffic volumes. Validated on the LPR system in Changsha, China, STGGAT demonstrates superior accuracy and stability to those of the baselines and reveals its inductive learning and fault tolerance capabilities.

Utilizing UAVs Technology on Microscopic Traffic Naturalistic Data Acquirement

Research into collecting and measuring reliable, accurate, and naturalistic microscopic traffic data is a fundamental aspect in road network planning scientific literature. The vehicle trajectory is one of the main variables in traffic flow theory that allows to extract information regarding microscopic traffic flow characteristics. Several methods and techniques have been applied regarding the acquisition of vehicle trajectory. The forthcoming applications of intelligent transport systems on vehicles and infrastructure require sufficient and innovative tools to calibrate existing models on more complex situations. Unmanned aerial vehicles (UAVs) are one of the most emerging technologies being used recently in the transportation field to monitor and analyze the traffic flow. The aim of this paper is to examine the use of UAVs as a tool for microscopic traffic data collection and analysis. A comprehensive guiding framework for accurate and cost-effective naturalistic traffic surveys and analysis using UAVs is proposed and presented in detail. Field experiments of acquiring vehicle trajectories on two multilane roundabouts were carried out following the proposed framework. Results of the experiment indicate the usefulness of the UAVs technology on various traffic analysis studies. The results of this study provide a practical guide regarding vehicle trajectory acquirement using UAVs.

Assessment of Queue Warning Application on Signalized Intersections for Connected Freight Vehicles

Connected vehicle (CV) systems are at the core of intelligent transportation systems (ITS) for their capability to support a variety of ITS applications and to unite vehicles and infrastructure elements into a well-integrated transportation system. CV refers to vehicles that exchange information with each other and with the infrastructure. The queue warning application (Q-WARN) uses CV technologies to allow vehicles within the queue to broadcast their queued status information automatically to upstream vehicles and to infrastructure. Queue warnings are sent to oncoming vehicles to prevent rear-end or other secondary collisions. This paper focuses on Q-WARN applications for freight vehicles at signalized intersections adjacent to I-80 in Wyoming, which are characterized by heavy truck traffic. The algorithms use the latitude/longitude coordinates of freight CVs and intersections to form a communication link and to share information. Tests were performed in VISSIM microsimulation with Econolite ASC/3 software-in-the-loop controller emulator for different CV market penetration rates. Three locations in Wyoming were used as test-bed cases. The developed Q-WARN algorithms are successful in reducing vehicle delays by an average of 2% to 5%. Time to collision (TTC) significantly increased with an increase in CV rates, by two to five times. The abundance of information obtained from CVs can be used further to enhance signal control algorithms. The developed algorithms can easily be implemented in the field, since they use existing CV communication protocols and signal control logic.

Detection of GNSS no-line of sight signals using LiDAR sensors for intelligent transportation systems

The reliability and robustness of positioning systems in urban and suburban environments are intrinsic. This is obvious following the continuous increase of Intelligent Transportation Systems (ITS) applications in such challenging environments. Global Navigation Satellite Systems (GNSS) represent the primary positioning technique used for navigation purposes in these applications, which can be satisfying in open-sky areas. However, GNSS cannot provide the same level of navigation performance in urban environments. One of the main reasons for this is the No-Line of Sight (NLOS) signals. In this study, the integration of GNSS and Light Detection and Ranging (LiDAR) sensors is exploited, and a new algorithm is proposed for the detection of NLOS signals. Real field data are used to test and validate the proposed strategy and algorithm. Phase-smoothed code observations are employed to evaluate the accuracy improvement after excluding the NLOS observations. The results show that the horizontal direction's positional accuracy can be improved significantly after applying the proposed algorithm. This improvement reaches 10.403 m with a mean value of 2.162 m (62.2% improvement) over all epochs with detected NLOS signals. After analysing this improvement in the Cross-Track (CT) and Along-Track (AT) directions, it is found that the accuracy improvement reaches 8.641 m with a mean value of 1.699 m in the CT direction and 6.879 m with a mean value of 1.303 m in the AT direction.

Design and implementation of microstrip array antenna for intelligent transportation systems application

Abstract This article proposes a design and implementation of array Microstrip Patch antenna of configuration 2 × 2 at an operating frequency of 3.5 GHz. The proposed design takes a dimension of 80 mm × 92 mm × 1.6 mm with four radiating elements arranged in rectangular form with an optimized separation between the patches. All the radiating elements were connected through a corporate series network with an inset feed to have better impedance matching. The model gives an efficiency of 90.99% with a bandwidth of 510 MHz and with fractal configuration, the bandwidth further enhances to 1.12 GHz. The maximum gain measured was found as 11.01 dBi at θ = 10° and ɸ = 360° and 10.45 dBi with fractal configuration. The designed antenna is proposed to be used in RADAR which will be used in the intelligent transportation system for the detection of nearby (short-range) vehicles in the blind zone. This kind of Radar also finds its application in collision avoidance and activating airbags/break boosting and thus helping mankind by saving lives. The article gives an idea of the use of an array antenna in intelligent transportation system for better gain and efficient results.

Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

With the rapid advancement of Intelligent Transportation Systems (ITS) and vehicular communications, Vehicular Edge Computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.

Hierarchical Graph Convolution Network for Traffic Forecasting

Traffic forecasting is attracting considerable interest due to its widespread application in intelligent transportation systems. Given the complex and dynamic traffic data, many methods focus on how to establish a spatial-temporal model to express the non-stationary traffic patterns. Recently, the latest Graph Convolution Network (GCN) has been introduced to learn spatial features while the time neural networks are used to learn temporal features. These GCN based methods obtain state-of-the-art performance. However, the current GCN based methods ignore the natural hierarchical structure of traffic systems which is composed of the micro layers of road networks and the macro layers of region networks, in which the nodes are obtained through pooling method and could include some hot traffic regions such as downtown and CBD etc., while the current GCN is only applied on the micro graph of road networks. In this paper, we propose a novel Hierarchical Graph Convolution Networks (HGCN) for traffic forecasting by operating on both the micro and macro traffic graphs. The proposed method is evaluated on two complex city traffic speed datasets. Compared to the latest GCN based methods like Graph WaveNet, the proposed HGCN gets higher traffic forecasting precision with lower computational cost.The website of the code is https://github.com/guokan987/HGCN.git.

Multi mode Resonator based Concurrent Triple band Band pass Filter with Six Transmission Zeros for Defence Intelligent Transportation Systems Application

A compact and highly selective triple-band bandpass filter (BPF) is designed and presented in this paper. Proposed filter offers low insertion loss, and passband characteristics is achieved by using two coupled MMR multi-mode resonators (MMR1 and MMR2) and an inverted T and circular shape MMRs. The filter operates at frequency 2.43 GHz (Vehicular Communication), 5.91 GHz (ITS band), and 8.86 GHz (satellite communication band). The simulation and measurement results show a minimum insertion loss of 1.6 dB, 0.73 dB, and 2.8 dB for triple-band BPF. The return loss is found to be greater than 13.06 dB, 28.6 dB, and 21.55 dB. It is noted that measurement results are in accordance with the result of electromagnetic simulation. Desired triple-band multi-mode resonators (MMRs) filter characteristics are achieved with six transmission zeroes (TZs). The filter comprises of MMRs which provide small size and control over the spurious frequency. By using a parallel-coupled microstrip line, the first and third passbands are realised. Whereas by using an end-coupled microstrip line, the second passband is recognised. At the input and output ports, the resonator coupling technique is used. By using the anti-parallel microstrip line arrangement, the transmission zero is acquired. The dimensions of the designed filter are 25×16 mm 2.

On-demand multi-hop forwarding for sustainable vehicular data transfer network

Smart cities play a vital role to develop a sustainable infrastructure with efficient management of the Internet of things devices. The infrastructure is used to support various applications for smart hospitals, smart factories, and intelligent transportation systems. With the extensive deployment of Internet of things devices, unprecedented growth in data has lead to capacity and transfer issues. In this article, we proposed an efficient data transfer mechanism based on self-sustainable networks over the vehicular environment. Depending on whether the network is connected with vehicles available to support direct connection from the source to destination, we propose end-to-end and hop-by-hop forwarding for vehicular networks that are inherently disconnected. The evaluation results demonstrate that the lifetime of the discovered paths depends on the coverage area, vehicle mobility, and vehicle speed. Therefore, at times redundant disjoint paths are selected for communication. In the proposed work, selected vehicles are used to reach the destination.

Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems

Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of transmission map plays a vital role in image dehazing. In this work, the coarse transmission map is firstly estimated using a robust fusion-based strategy. A unified optimization framework is then proposed to estimate the refined transmission map and latent sharp image simultaneously. The resulting constrained minimization model is solved using a two-step optimization algorithm. To further enhance dehazing performance, the solutions of subproblems obtained in this optimization algorithm are equivalent to deep learning-based image denoising. Due to the powerful representation ability, the proposed method can accurately and robustly estimate the transmission map and latent sharp image. Numerous experiments on both synthetic and realistic datasets have been performed to compare our method with several state-of-the-art dehazing methods. Dehazing results have demonstrated the proposed method’s superior imaging performance in terms of both quantitative and qualitative evaluations. The enhanced imaging quality is beneficial for practical applications in maritime ITS, for example, vessel detection, recognition, and tracking.

Performance analysis of vehicle-to-vehicle communications based on organic light emitting diodes and discrete multitone modulation

We provide a numerical model to investigate the performance of a visible light communication-based vehicle-to-vehicle (V2V) communication system that incorporates organic light emitting diode (OLED) transmitters and discrete multitone modulation to overcome the bandwidth limitations of the organic devices. Different application scenarios using vehicle’s headlights and taillights as optical transmitters are considered. The results indicate the potential of using OLEDs for cooperative intelligent transportation system applications to achieve transmission of low-to-moderate data rates, providing evidence that the proposed OLED-based V2V system can achieve data communication of several Mbps. In the scenario that involves transmitting data to the leading car using the OLED headlights as transmitters, we show that the data rate can exceed 5 Mbps, despite the low modulation bandwidth of the OLED. When transmitting data to the trailing car using the taillights, we show that the transmission rate can reach 2 Mbps. We also discuss the trade-off between vehicle distance and communication performance and show that, even for distances up to 40 m, data rates on the order of several hundred kbps can be supported.

Enhanced Protective System For Children In School Transportation

The higher number of vehicles on the roads increase in the number of road accidents worldwide. And these problems are considered as major public safety problem. A major part of providing a quality education to every children is ensuring their safety during transportation from their home to school. Thus, we have developed a smart and effective school bus system for the safety of children while transportation. This paper discusses about the hardware-based smart and effective school bus system for ensuring and monitoring the safety of school-going children through school bus. In this project, vehicles are considered as the main key enabling technology to improve children safety and the emergence of next-generation cooperative intelligent transport systems (ITS). In ITS, network services and applications (e.g., safety messages) will require event location information. Faster and efficient routing protocols could play a vital role in the applications of ITS, safe guarding both the driver and the children and thus maintaining a safe on-road environment. In this project, an intelligent transportation system (ITS) is designed to monitor real evacuation tests where an emergency event has been simulated and experimented and a general methodology is reported with the description of the main models and procedures. Models are calibrated and validated employing observed data obtained from ITS.

Improvisation of optimization technique and AODV routing protocol in VANET

The vehicular ad-hoc network (VANET) is a dynamic network with various applications in intelligent transportation systems. There are several routing protocols for setting up a path between sender node and receiver node. AODV is a reactive routing protocol which broadcast the route request to all neighbouring node and then those nodes will forward the request to their neighbouring nodes. This process helps in selecting the path from sender to receiver. AODV protocol is the best performing routing protocol when number of nodes for communication is more in number. In this research, AODV and Ant Colony Optimization (ACO) technique is applied and data is transmitted. Ant colony optimization algorithm is applied which takes into the initial coordinates of each node. The path which is established using reactive routing protocol has high chances of congestion in the network and also consume large amount of network bandwidth. This research is based on to establish path from source to destination using multicasting approach and also reduce the chances of congestion in the network. It will generate a path with shortest distance and leads to improve throughput, reduce packet loss and delay when compared with the existing protocol.

Optimal Distribution of Workloads in Cloud-Fog Architecture in Intelligent Vehicular Networks

With the fast growth in network-connected vehicular devices, the Internet of Vehicles (IoV) has many advances in terms of size and speed for Intelligent Transportation System (ITS) applications. As a result, the amount of produced data and computational loads has increased intensely. A solution to handle the vast volume of workload has been traditionally cloud computing such that a substantial delay is encountered in the processing of workload, and this has made a serious challenge in the ITS management and workload distribution. Processing a part of workloads at the edge-systems of the vehicular network can reduce the processing delay while striking energy restrictions by migrating the mission of handling workloads from powerful servers of the cloud to the edge systems with limited computing resources at the same time. Therefore, a fair distribution method is required that can evenly distribute the workloads between the powerful data centers and the light computing systems at the edge of the vehicular network. In this paper, a kind of Genetic Algorithm (GA) is exploited to optimize the power consumption of edge systems and reduce delays in the processing of workloads simultaneously. By considering the battery depreciation, the supporting power supply, and the delay, the proposed method can distribute the workloads more evenly between cloud and fog servers so that the processing delay decreases significantly. Also, in comparison with the existing methods, the proposed algorithm performs significantly better in both using green energy for recharging the fog server batteries and reducing the delay in processing data.

Car Following and Microscopic Traffic Simulation Under Distracted Driving

Microscopic simulation models have been widely used as tools to investigate the operation of traffic systems and different intelligent transportation systems applications. The fidelity of microscopic simulation tools depends on the driving behavior models that they implement. However, current models commonly do not consider human-related factors, such as distraction. The potential for distraction while driving has increased rapidly with the availability of smartphones and other connected and infotainment devices. Thus, an understanding of the impact of distraction on driving behavior is essential to improve the realism of microscopic traffic tools and support safety and other applications that are sensitive to it. This study focuses on car-following behavior in the context of distracting activities. The parameters of the well-known GM and intelligent driver models are estimated under various distraction scenarios using data collected with an experiment conducted in a driving simulator. The estimation results show that drivers are less sensitive to their leaders while talking on the phone and especially while texting. The estimated models are implemented in a microscopic traffic simulation model. The average speed, coefficient of variation of speed, acceleration noise and acceleration and deceleration time fractions were used as measures of performance indicating traffic flow and safety implications. The simulation results show deterioration of traffic flow with texting and to some extent talking on the phone: average speeds are lower and the coefficient of variation of speeds are higher. Further experimentation with varying fractions of texting drivers showed similar trends.