Transportation System Applications Research Articles

Dual‐feed CSRR‐loaded switchable multiband microstrip patch antenna for ITS applications

A novel complementary split ring resonator (CSRR)-loaded switchable multiband antenna with circular and linear polarisation switching is proposed for intelligent transportation system (ITS) application. It has one C-shaped square patch with coaxial feeding and another direct coupled square-ring patch with short-ended microstrip feeding. In order to achieve switchable frequency response, a PIN-diode is used. The proposed antenna is designed, fabricated, and experimentally tested. The measured data at the centre frequency 1.93, 3.73, 4.35, 5.9, 6.35, 7.25, and 8.75 GHz have impedance bandwidths of 50 MHz (1.90-1.95 GHz), 50 MHz (3.70-3.75 GHz), 500 MHz (4.10-4.60 GHz), 200 MHz (6.25-6.45 GHz), 300 MHz (7.10-7.40 GHz), and 600 MHz (8.45-9.05 GHz). The maximum bandwidth of the proposed antenna is 13%. The simulated and measured values of return loss of the antenna are in very good uniformity.

Adaptive beaconing for RSU-based intersection assistance systems: Protocols analysis and enhancement

Current envisaged cooperative vehicular applications require moderate to severe requirements of reliability and latency according to their purpose. Dedicated Short Range Communications (DSRC)-based applications mainly rely on the periodic exchange of information that under certain circumstances may cause congestion problems on the communication channel obtaining unreliable and outdated information at application level. Adaptive beaconing protocols adapt transmission parameters to different criteria such as the channel load and application requirements to improve the overall performance of the vehicle network. Nevertheless, it has not been determined yet if the information disseminated by these protocols is suitable enough for the implementation of specific applications, e.g., Road Side Unit (RSU)-based Intersection Assistance Systems (IAS) like Intersection Collision Risk Warning (ICRW). In this context, we first analyze the network behavior in a realistic simulated intersection area where probability of packet reception becomes difficult to predict and models become highly complex. In that scenario, we present a critical analysis on the performance of current EU and US decentralized congestion control protocols while their performance is evaluated with respect to tracking accuracies required by Intelligent Transportation System (ITS) applications. Results obtained lead us to conclude that adaptation criteria of beaconing protocols is not able to support different safety applications at the same time, that is, there is a tradeoff in the selection of such criteria between enhancing applications supporting vehicles or infrastructure. In that sense, we discuss and provide novel adaptation criteria (Intersection Assistance State Machine, IASM) to improve the performance of beaconing protocols towards assisting safety RSU-based IAS. Finally, we propose and validate through simulations a novel beaconing protocol (Intersection Assistance Protocol, IAP) that improves performance over studied protocols.

المدن الذکية ودورهل في علاج مشکلات النقل الحضري- حالة المدينة المنورة-دراسة جغرافية

يعالج هذا البحث الدور الذي تؤديه تطبيقات المدن الذکية لإيجاد حلول لمشکلات النقل الحضري, والتي نتجت عن النمو المستمر والمتزايد في أعداد السکان والمرکبات, وذلک بالاعتماد على آليات نظم النقل الذکية مثل نظم تحديد المواقع العالمي GPS, ونظم الموقع الأوتوماتيکي للمرکبات AVL, بالإضافة الى ادارة حرکة المرور. ويناقش البحث تطبيقات نظم النقل الذکية والمتمثلة في: النظم المتقدمة للتحکم والسيطرة وإدارة المرور، النقل العام و معلومات المتنقلين، التحکم بالمرکبة وسلامتها, نظم عمليات أساطيل المرکبات التجارية، و متطلبات تطبيق نظم النقل الذکية في المدينة المنورة, و أهمية کل منها, ومدى ملاءمتها لواقع المدينة وظروفها الخاصة. مع الترکيز على أهمية تطوير خريطة هيکلية لنظم النقل الذکية، والخيارات المتاحة للمدينة لتطوير خارطتها الهيکلية, ودورها في ايجاد حلول لمشکلات النقل الحضري کتخفيض الضغط المرورى على شوارعها, وتحسين الحرکة المرورية, ورفع مستوى السلامة المرورية, والتقليل من الوقت المهدر في رحلات النقل الداخلي, والحد من التلوث البيئي واستهلاک الوقود والحوادث المرورية التي تمثل تکلفة اجتماعية واقتصادية, بالإضافة إلى زيادة التحکم والسيطرة على النقل وإدارته بفاعلية کبيرة. ومن هنا تأتي أهمية هذه الدراسة والتي تعد بمثابة تحليل جغرافي ديناميکي يسلط الضوء على هذه المشکلات وکيفية علاجها في المدينة المنورة. Abstract This research addresses the role played by Smart Cities applications to find solutions to urban transport problems resulting from the continuous and growing population and vehicle growth, based on intelligent transport systems such as GPS, AVL automatic location systems, the traffic. The research discusses the applications of intelligent transport systems: advanced systems for control, control and traffic management, public transport and mobile information, vehicle control and safety, fleet systems of commercial vehicles, and the requirements of the application of intelligent transport systems in Medina, the importance of each, The city has its own conditions. With emphasis on the importance of developing a structural map of intelligent transport systems, options for the city to develop its structural plan, and its role in finding solutions to urban transport problems such as reducing traffic pressure on its streets, improving traffic, improving traffic safety, From environmental pollution, fuel consumption and traffic accidents, which represent a social and economic cost, as well as greater control, control and management of transport. Hence the importance of this study, which is a dynamic geographical analysis that highlights these problems and how to treat them in Medina.

Intelligent Traffic Decision Analysis System Based on Big Data Mining

The urban intelligent traffic decision analysis system is a system designed to solve urban road traffic problems, improve traffic safety and comfort, and improve the level of traffic services. The application of big data mining technology can improve the ability of various types of road traffic information analysis and processing, expand the scope of application, and provide decision-making reference for the application of intelligent transportation systems, so as to ensure the smoothness and safety of roads. This article will introduce the concept of big data mining technology and intelligent transportation system, and analyze the architecture of urban traffic intelligent system based on big data technology and the design and implementation of mining system.

A Quality-Oriented Data Collection Scheme in Vehicular Sensor Networks

Considerable research attention has been dedicated to vehicular sensor networks (VSNs) because of its great potential in traffic monitoring. By taking advantage of sensors embedded in vehicles, a VSN harvests data while vehicles are traveling along the roads and then updates the collected data to the infrastructure to support the intelligent transportation system (ITS) applications. To meet the data collection requirements of different ITS applications, a huge number of update packets are generated, which may exhaust the available wireless communication bandwidth. To improve the efficiency of utilization of wireless bandwidth, in this study, we propose a quality-oriented data collection scheme, which aims to effectively support both the accuracy and real-time requirements stipulated by ITS applications while reducing communication overhead. We formulate a minimized communication overhead (MCO) problem and propose two algorithms, mixed-integer linear programming (MILP) and deviation-detection (DD) , to solve the MCO problem. MILP can obtain the optimal solution by having all the data collected by every vehicle, while DD could achieve an efficient solution without this impractical assumption. We conducted extensive experiments by using SUMO to simulate vehicle traces in freeway and downtown environments. The experimental results have demonstrated the effectiveness of the proposed solutions.

Current Transport Systems and Clinical Applications for Small Interfering RNA (siRNA) Drugs.

Small interfering RNAs (siRNAs) are an attractive new agent with potential as a therapeutic tool because of its ability to inhibit specific genes for many conditions, including viral infections and cancers. However, despite this potential, many challenges remain, including off-target effects, difficulties with delivery, immune responses, and toxicity. Traditional genetic vectors do not guarantee that siRNAs will silence genes in vivo. Rational design strategies, such as chemical modification, viral vectors, and non-viral vectors, including cationic liposomes, polymers, nanocarriers, and bioconjugated siRNAs, provide important opportunities to overcome these challenges. We summarize the results of research into vector delivery of siRNAs as a therapeutic agent from their design to clinical trials in ophthalmic diseases, cancers, respiratory diseases, and liver virus infections. Finally, we discuss the current state of siRNA delivery methods and the need for greater understanding of the requirements.

Maintenance Optimization of Continuous State Systems Based on Performance Improvement

The continuous state system is a special kind of a system in which the states of the system and its components have continuous values, ranging from perfect functioning to complete failure. This paper introduces the performance improvement for a continuous state system, which can be used to measure the improvement of systems performance comparing pre- and postmaintenance time. The probabilistic characteristics of performance improvement are discussed in detail. Then, the performance improvement for multicomponent maintenance and corresponding calculation method are also put forward to establish the objective function for maintenance optimization. Third, a maintenance optimization model for such a system is studied, and corresponding performance improvement based genetic algorithm is provided to search a near global optimal solution. Finally, two numerical examples and an oil transportation system application case study are implemented to verify the effectiveness of the proposed method.

Compact Circularly Polarized Wide-Beamwidth Fern-Fractal-Shaped Microstrip Antenna for Vehicular Communication

A compact circularly polarized wide-beamwidth patch antenna is presented here. The proposed antenna can be used for blind spot detection in the smart vehicle. Miniaturization and beamwidth enhancement of the antenna are the main areas of discussion. The size of the patch becomes 44% smaller than the conventional patch by folding the edge of the radiating patch with loading slots. Due to the small size, it can be placed in the compact aerodynamic wireless devices. The half power beamwidth is enhanced by 100 $ ^{\circ }$ , and the axial ratio beamwidth is maximized by 187 $ ^{\circ }$ with the help of a surrounding dielectric substrate and the metallic block as necessitated by intelligent transportation system applications. The proposed antenna can capture signals from a wide elevation angle and a complete azimuth range to cover the entire blind spot zone. The impedance bandwidth of the antenna is 410 MHz with 90-MHz 3-dB axial ratio bandwidth.

Traffic Scene Segmentation Based on RGB-D Image and Deep Learning

Semantic segmentation of traffic scenes has potential applications in intelligent transportation systems. Deep learning techniques can improve segmentation accuracy, especially when the information from depth maps is introduced. However, little research has been done on the application of depth maps to the segmentation of traffic scene. In this paper, we propose a method for semantic segmentation of traffic scenes based on RGB-D images and deep learning. The semi-global stereo matching algorithm and the fast global image smoothing method are employed to obtain a smooth disparity map. We present a new deep fully convolutional neural network architecture for semantic pixel-wise segmentation. We test the performance of the proposed network architecture using RGB-D images as input and compare the results with the method that only takes RGB images as input. The experimental results show that the introduction of the disparity map can help to improve the semantic segmentation accuracy and that our proposed network architecture achieves good real-time performance and competitive segmentation accuracy.

Dual-Band Full-Duplex Tx/Rx Antennas for Vehicular Communications

This paper proposes a novel dual-band full-duplex antenna/array for intelligent transport systems applications. Different from traditional single-port single-band antennas, the two ports of the antenna are highly isolated and designed to operate at different frequency bands simultaneously. Such a property could support the full-duplex operation-mode, which significantly simplifies the complexity of the RF frontend subsystem. The other contribution of this work is that multiple functions such as filtering, duplexing, and radiation are combined into one single device, resulting in a simplified RF frontend. This co-design multifunctional device could also remove the separate filters, duplexers, and interfaces between them, resulting in the reduction of the size, weight, and cost. In addition, cross-coupling is investigated and employed to generate additional transmission zeros so as to improve the channel isolation and out-of-band interference. To verify the concept, an antenna element and two 2 × 2 arrays at C-band are designed, prototyped, and tested. All the measurements agree well with the simulations, showing two full-duplex channels of 4.58–4.83 GHz and 5.86–6.2 GHz for transmitting and receiving, respectively. The proposed antennas also exhibit excellent performance in terms of channel isolations, frequency selectivity, out-of-band rejections, and gains.

Design and analysis of a wideband circularly polarised perturbed psi‐shaped antenna

The goal of this study is to propose a wideband circularly polarised (CP) psi-shaped antenna of high gain and wide bandwidth as necessitated by intelligent transportation system applications. A wide impedance bandwidth of 2 GHz (40%) centred at 5.3 GHz is achieved through the combination of modified TM 01 , TM 20 , and TM 21 modes with three slots on either radiating edge in the simple rectangular geometry of the antenna. The stacked patch configuration helps to achieve a minimum gain of 7 dBic in the entire bandwidth. The CP psi-shaped antenna has an impressive axial ratio bandwidth of 1 GHz, which is >19% of its centre frequency 5.3 GHz. The proposed antenna can be used for detection of the blind spots of a vehicle at 5.88 GHz (DSRC frequency band), vehicle-to-vehicle communication at 5.2 GHz (WLAN IEEE 802.11a frequency band), and vehicle to roadside communication at 5.5 GHz (WiMAX IEEE 802.16 frequency band).

Spatiotemporal Local-Remote Senor Fusion (ST-LRSF) for Cooperative Vehicle Positioning.

Vehicle positioning plays an important role in the design of protocols, algorithms, and applications in the intelligent transport systems. In this paper, we present a new framework of spatiotemporal local-remote sensor fusion (ST-LRSF) that cooperatively improves the accuracy of absolute vehicle positioning based on two state estimates of a vehicle in the vicinity: a local sensing estimate, measured by the on-board exteroceptive sensors, and a remote sensing estimate, received from neighbor vehicles via vehicle-to-everything communications. Given both estimates of vehicle state, the ST-LRSF scheme identifies the set of vehicles in the vicinity, determines the reference vehicle state, proposes a spatiotemporal dissimilarity metric between two reference vehicle states, and presents a greedy algorithm to compute a minimal weighted matching (MWM) between them. Given the outcome of MWM, the theoretical position uncertainty of the proposed refinement algorithm is proven to be inversely proportional to the square root of matching size. To further reduce the positioning uncertainty, we also develop an extended Kalman filter model with the refined position of ST-LRSF as one of the measurement inputs. The numerical results demonstrate that the proposed ST-LRSF framework can achieve high positioning accuracy for many different scenarios of cooperative vehicle positioning.

Urban link travel time estimation using traffic states‐based data fusion

Estimated travel time is a key input for many intelligent transport systems (ITS) applications and traffic management functions. There are numerous studies that show that fusing data from different sources such as global positioning system (GPS), Bluetooth, mobile phone network (MPN), and inductive loop detector (ILD) can result in more accurate travel time estimation. However, to date, there has been little research investigating the contribution of individual data sources to the quality of the final estimate or how this varies according to source-specific data quality under different traffic states. Here, three different data sources, namely bus-based GPS (bGPS) data, ILD data, and MPN data, of varying quality are combined using three different data fusion techniques of varying complexity. In order to quantify the accuracy of travel time estimation, travel time calculated using automatic number plate recognition (ANPR) data are used as the `ground truth'. The final results indicate that fusing multiple data together does not necessarily enhance the accuracy of travel time estimation. The results also show that even in dense urban areas, bGPS data, when combined with ILD data, can provide reasonable travel time estimates of general traffic stream under different traffic states.

Enhancement of safety and comfort of cyclists at intersections

Cyclist safety is increasingly becoming a societal problem in Europe, as shown by road safety statistics. Frequent stops for red traffic lights at intersections are experienced by cyclists as a major inconvenience. This study introduces a green wave concept for cyclists, with focus on the traffic management and control aspects under cooperative intelligent transport systems applications. It especially addresses increasing stability of the adaptive control system, to overcome the drawbacks of both actuated and traditional adaptive control (which are too unpredictable for a green wave speed advice). In addition, solutions for avoiding increased delays for other traffic are investigated, as generally result from a classic green wave approach (with only fixed-time control) and traditional adaptive control. This study introduces an adaptive control algorithm for a real-time model-predictive controller and implements a plan-deviation cost function to address stabilisation. Simulation results show that the developed method increases stability of the adaptive control system, limits extra delays for other traffic and yields a high success rate for the green wave concept.

An Intelligent Transportation System Application for Smartphones Based on Vehicle Position Advertising and Route Sharing in Vehicular Ad-Hoc Networks

Alerting drivers about incoming emergency vehicles and their routes can greatly improve their travel time in congested cities, while reducing the risk of accidents due to distractions. This paper contributes to this goal by proposing Messiah, an Android application capable of informing regular vehicles about incoming emergency vehicles like ambulances, police cars and fire brigades. This is made possible by creating a network of vehicles capable of directly communicating between them. The user can, therefore, take driving decisions in a timely manner by considering incoming alerts. Using the support of our GRCBox hardware, the application can rely on vehicular ad-hoc network communications in the 5 GHz band, being V2V (vehicle-to-vehicle) communication provided through a combination of Android-based smartphone and our GRCBox device. The application was tested in three different scenarios with different levels of obstruction, showing that it is capable of providing alerts up to 300 meters, and notifying vehicles within less than one second.

Evaluation Method based on a Tracing Mechanism for Adaptive User Interfaces: Application in Intelligent Transport Systems

Evaluation Method based on a Tracing Mechanism for Adaptive User Interfaces: Application in Intelligent Transport Systems

Information Patterns in the Modeling and Design of Mobility Management Services

The development of sustainable transportation infrastructure for people and goods, using new technology and business models, can prove beneficial or detrimental for mobility, depending on its design and use. The focus of this paper is on the increasing impact new mobility services have on traffic patterns and transportation efficiency in general. Over the last decade, the rise of the mobile internet and the usage of mobile devices have enabled ubiquitous traffic information. With the increased adoption of specific smartphone applications, the number of users of routing applications has become large enough to disrupt traffic flow patterns in a significant manner. Similarly, but at a slightly slower pace, novel services for freight transportation and city logistics improve the efficiency of goods transportation and change the use of road infrastructure. This paper provides a general four-layer framework for modeling these new trends. The main motivation behind the development is to provide a unifying formal system description that can at the same time encompass system physics (flow and motion of vehicles) as well as coordination strategies under various information and cooperation structures. To showcase the framework, we apply it to the specific challenge of modeling and analyzing the integration of routing applications in today's transportation systems. In this framework, at the lowest layer (flow dynamics), we distinguish routed users from nonrouted users. A distributed parameter model based on a nonlocal partial differential equation is introduced and analyzed. The second layer incorporates connected services (e.g., routing) and other applications used to optimize the local performance of the system. As inputs to those applications, we propose a third layer introducing the incentive design and global objectives, which are typically varying over the day depending on road and weather conditions, external events, etc. The high-level planning is handled on the fourth layer taking social longterm objectives into account. We illustrate the framework by considering its ability to model at two different levels. Specific to vehicular traffic, numerical examples enable us to demonstrate the links between the traffic network layer and the routing decision layer. With a second example on optimized freight transport, we then discuss the links between the cooperative control layer and the lower layers. The congestion pricing in Stockholm is used to illustrate how also the social planning layer can be incorporated in future mobility services.

Needs, restrictions and priorities for the development and installation of a multifunctional C-ITS solution on existing road pavement

Abstract The scope of this paper is to recognize the needs, restrictions and priorities in developing effort of a breaking through technology that will achieve to enable Cooperative Intelligent Transportation System (C-ITS) applications in existing road infrastructure, to make roads self-explanatory and forgiving for all road users and all vehicle generations with reduced maintenance cost, full recyclability and added value services, as well as supporting real-time predictive road maintenance functions. The enhanced broad functionality of the system on multiple levels imposes emerging needs and demands that will constitute the groundwork of the Use Cases for the implementation of the project, which are extracted through an iterative user-centered methodology approach and correspond to the target applications of the system. More particularly, the needs, views and priorities of all the relevant stakeholders have been captured through the outputs of 431 respondents participated from 10 countries in on-line and in-depth surveys. From the infrastructure point of view, the complementary investigation of relevant accident/incident and gaps/priorities was based on literature while the legal/operational limitations have been based on the study of relevant Directives and the view of experts. These sources provided important qualitative as well as quantitative outcomes, the aggregation of which led to the prioritisation of the target applications, two of which (last in ranking) are considered to be secondary in the sense that can be implemented with some flexibility.

Harnessing the Power of HPC in Simulation and Optimization of Large Transportation Networks: Spatio-Temporal Traffic Management in the Greater Toronto Area

The significant growth of many urban areas comes at a cost of increasing demand for mobility and traffic congestion in large-scale urban environments. As congestion levels soar to unprecedented levels, and researchers and governments are challenged addressing the basic needs for transportation and mobility; solutions are becoming more complex and untraditional, creating a strong potential for optimization and simulation of large-scale transportation networks. In this paper, we present a generic traffic management framework for solving large-scale constraint optimization problems in advanced Intelligent Transportation systems (ITS) applications. The framework employs a distributed computing approach to enable replicating analysis of large-scale traffic simulation networks, providing a practical mechanism for solving complex optimization problems in transportation applications. We discuss employing the framework in two use cases, congestion pricing and emergency evacuation, targeting optimization of spatial and temporal traffic management.

Predicting the travel time of arterial traffic using particle filter with speed matrix

Travel time prediction is an essential part of intelligent transportation system applications. However, the existing travel time prediction methods mainly focus on the freeway due to its simplicity and the high coverage of sensors and few researches have been conducted for the urban arterial road. Consequently, a travel time prediction algorithm based on particle filter is proposed in this paper to predict short-term travel time of the arterial traffic with historical floating car data and the concept of speed matrix is developed to illustrate the spatiotemporal properties of the arterial traffic. Unlike previous travel time prediction methods, the proposed algorithm uses particles with corresponding weights to model the traffic trend in the historical data instead of state-transition function and the weight for each particle are calculated with similarities between the speed matrix of the particle and the current traffic pattern. Moreover, a resampling process is developed to solve the degeneracy problem of the particles by replacing the low-weight particles with historical data. A real floating car dataset of 10357 taxis over a period of 3 months within Beijing is utilized to evaluate the performances of the algorithms. The proposed algorithm has the least errors by comparing with other three algorithms.