I- INTRODUCTION

II- PROBLEM STATEMENT

III- RELATED WORK

IV- THE COLLEGE ADMISSION ALGORITHM

V- THE PROPOSED ALGORITHM

VI- DATASETS

VII- CLUSTERING RESULTS AND DISCUSSION

VIII- CONCLUSION

REFERENCES

Abstract

This paper proposes a novel supervised clustering algorithm to analyze large datasets. The proposed clustering algorithm models the problem as a matching problem between two disjoint sets of agents, namely, centroids and data points. This novel view of the clustering problem allows the proposed algorithm to be multi-objective, where each agent may have its own objective function. The proposed algorithm is used to maximize the purity and similarity in each cluster simultaneously. Our algorithm shows promising performance when tested using two different transportation datasets. The first dataset includes speed measurements along a section of Interstate 64 in the state of Virginia, while the second dataset includes the bike station status of a bike sharing system (BSS) in the San Francisco Bay Area. We clustered each dataset separately to examine how traffic and bike patterns change within clusters and then determined when and where the system would be congested or imbalanced, respectively. Using a spatial analysis of these congestion states or imbalance points, we propose potential solutions for decision makers and agencies to improve the operations of I-64 and the BSS. We demonstrate that the proposed algorithm produces better results than classical kmeans clustering algorithms when applied to our datasets with respect to a time event. The contributions of our paper are: 1) we developed a multi-objective clustering algorithm; 2) the algorithm is scalable (polynomial order), fast, and simple; and 3) the algorithm simultaneously identifies a stable number of clusters and clusters the data.

INTRODUCTION

WITH the growth of new technologies, smart cities and urban areas are adapting advanced devices to control and monitor transportation networks and thus provide better service to the public and private sectors. These devices collect data through many sensors in the city’s infrastructure. Agencies and researchers exploring the massive amounts of collected data often find it challenging to draw meaningful conclusions due the sheer size of the datasets. One way to deal with such data is to use clustering approaches. In the transportation field, operating agencies (such as departments of transportation) have been collecting data to improve the efficiency of the transportation network and provide a better service for all transportation modes. Clustering the travel times or speeds of transportation modes could help operating agencies to better manage the transportation network. In particular, the collected data could be reduced to find the cluster centroids (i.e., the means of the clusters) that represent the entire data with respect to a time event such as time of day, day of month, and month of the year. This could help operating agencies answer several questions related to traffic operations such as, “Can we discriminate between recurrent congestion and outliers?” and “Can we identify how many time periods we need to plan for in terms of resource and congestion management?”

1- Introduction

2- Background and related work

3- 5G-Enabled software-defined ITS

4- Simulation results and discussion

5- Comparative analysis

6- Conclusion and future direction

References

Abstract

With the rapid advancement of technology, an increasing number of devices are being connected to the Internet and getting smart. Such advancement brings new challenges in the field of intelligent transportation system (ITS), including transferring of high data rates, providing rapid response system to users, addition of new devices, and their remote configuration. Thus, mobile information systems, along with intelligent multimodal mobility services, cope with such constraints and take significant benefit of the associated technology from emerging information and communication frameworks. Therefore, recent advancement in the field of telecommunication has witnessed increased interest in ITS, especially vehicular ad-hoc networks (VANETS). Software-defined networks (SDNs) can also bring advantages to ITS due their flexibility and programmability to the network via their logical and centralized control entity. However, the bandwidth and continuous connection between ITS and SDN is still a challenge owing to the highly mobile nature of VANETS. Therefore, to address this issue, this paper presents a novel concept for enhancing the capabilities of ITS via the newly proposed 5G-based SDN architecture for ITS. The proposed system architecture is based on the following three function layers: sensing layer, relay layer, and core network layer. Continuous accessibility, via flexible and programmable features, is achieved through SDN features. In addition, high data rates and bandwidth are provided by the proposed 5G architecture. The simulation results show that the proposed system architecture achieves better results than the ad-hoc on-demand distance vector routing protocol.

Introduction

he future potential of the Internet as a global phenomenon has led to an increasing number of devices becoming internet friendly [1]. Furthermore, traffic management in transportation working with the Internet has become easier as the number of IoT technologies are used for traffic management, i.e., intelligent transportation system (ITS) are envisioned to significantly improve the traffic and safety conditions on road. It is very important to monitor the traffic by using different means, i.e., check speed limit, pollution checks, and emergency response in case of road accidents. Traditionally, to solve such issues, CCTV cameras are used. However, such applications are not satisfactory in cases where several vehicles are moving on the road [1]. To cope with such constraints, IoT technology came up with different methodologies in traffic management; for example, ITS is envisioned to significantly improve the transportation and safety of roads. The concept of ITS is that all vehicles moving on the road are in constant communication with each other, through vehicle-to-vehicle (V2V) or vehicle-toinfrastructure (V2I) communication [2]. The deployment of various technologies needed to accomplish this task has been slow and very expensive [2]. It involves the installation of multiple pieces of large equipment both on the roadway, i.e., Roadside units (RSUs) and inside the vehicle, termed on-board units (OBUs). This suggests that a complete saturation of these units must exist to enable a fully functional ITS [1]. Recently, several research communities have focused on finding quick deployment and lower cost alternative methods for ITS. There already exist many protocols and standards that ensure the deployment of ITS since it is consistent across all states and vehicles. Recently, various aspects of ITS have been researched. ITS can provide several services, but from the quality-ofservice (QoS) viewpoint, the requirements of user satisfaction are not satisfactory [2]. Additionally, the capability of handling a large request is indispensable. Hence, recently, an emerging term, called software-defined network (SDN), has considered the networking paradigm between wired and wireless devices from software perspective. SDN is an emerging network paradigm that separates the control logic from the network device (switch or sensor node), leaving the device with only data forwarding functionality [3]. SDN can not only improve network flexibility and efficiency but also provide a platform for network management. It also enables flexible network management, which is a critical element of IoT. To cope with these limitations and challenges, software-defined networking is the forerunner, forming the backbone of network applications. Thus, SDN appears as a game changer technology that has revolutionized the entire networking mechanism.

1. Introduction

2. Technical Feasibility of Xi ‘an Public Transportation Intelligent Management

3. Establish Intelligent Transportation System Construction Platform Based on Internet of Things

Acknowledgement

References

The field of intelligent transportation has pointed out the goal for the future development of transportation and brought people convenient, scientific and standardized modes of transportation. In recent years, with the emergence of Internet of things, mobile Internet, cloud computing and other new technologies, large and medium-sized cities at home and abroad are developing rapidly in the construction of intelligent transportation and gradually evolving into intelligent transportation system. In this paper, starting from how to build the intelligent transportation system platform, starting from the establishment of the five layers, one main body and three departments of the Internet of things, some solutions and Suggestions are put forward from the technical level. It is also hoped that this will be a theoretical and practical exploration for the construction of intelligent transportation in China’s megacicities (such as xi ‘an).

Introduction

Intelligent transportation is an important part of a smart city. It makes full use of intelligent technologies such as the Internet of things.At the same time, it integrates cloud computing and other information network knowledge speeds up communication transmission speed and improves information processing Ability to systematize the transportation system and expand the scope of space [1]. Intelligent transportation is a new interpretation of intelligent transportation in the context of mobile Internet, Internet of things, 3g / 4g wireless communication network and other new technologies and industrial environment.

With the growth of science and technology and the progress of scientific and technological means, we have entered a new era of “big data”, which influences and quietly changes our lives. In this new era, combined with the characteristics of their own and effective application of the technology and methods of big data, moral education in colleges and universities should seek favorable opportunities in a timely manner, and progressively realize the integration of digital technology and humanistic spirit, the complementary integration of network education and real education, the complementary integration of data thinking and traditional experience, and the complementary integration of situational awareness and ubiquitous-education. In doing so, the effective development of moral education in colleges and universities can be improved constantly.

Introduction

Nowadays, smart city services are becoming more widespread than ever as cities are growing and becoming increasingly crowded as a result of urbanization and world population growth[1]. The term “smart city” in [2] refers to the use of information and communication technologies to sense, analyze and integrate 5 key information from core systems in operating cities. At the same time, smart city services can make intelligent responses to different kinds of needs in terms of daily livelihood, environmental protection, and public safety, as well as the city’s facilities and industrial and commercial activities. As smart city — related technologies develop rapidly (for example, the spread of IoT devices), more things become measurable. As a result, we have more and more usable data about the ecosystem of our cities. 10 Among the various notable goals of smart cities, construction of smart transportation systems and smart urban management systems are two of the key aims, which could significantly influence the lives of residents in future cities. Advanced Traffic Management Systems (ATMSs) and Intelligent Transportation Systems (ITSs) integrate information, communication, and other technologies and apply them in the field of transportation to build an integrated system of people, roads, and vehicles. These systems constitute 15 a large, fully-functioning, real-time, accurate, and efficient transportation management framework [3]. In ATMSs and ITSs, it is a fundamental challenge to predict the next possible states of traffic with high precision, because this information helps to prevent unfortunate events like traffic jams or other anomalies on roads. The literature often refers to traffic as a flow, because it has similar properties to fluids. Thus when we speak about traffic flow prediction, we wish to predict the next state (it can be the 20 volume, speed, density, or behavior) of the traffic flow based on historic and real-time data. The rapid progress of urbanization has modernized many people’s lives, but also brought remarkable challenges like traffic congestion [4] that can lead to increased energy/fuel consumption [5] and enormous emission of pollutants [6]. These phenomena have a great impact on the health and quality of life of citydwellers. According to [7], laboratory studies indicate that transport-related air pollution may increase 25 the risk of developing allergies and can exacerbate symptoms, particularly in susceptible subgroups; cohereas [8] showed that traffic jams increase the risk of heart attack. Intelligent management systems (such as ATMS and ITS) can help overcome or significantly reduce the impact of such negative effects on city-dwellers. Forecasts can also support traffic control centers in managing the road network and allocating resources systematically, such as opening/closing lanes, 30 dynamic parking pricing [9], or adaptive traffic lights [10] with a high level of automation [11]. When driving, knowledge of traffic forecasts for different routes is advantageous [12], and devices will be able to calculate more efficient routes and reduce travel time. Insight into vehicular flows within the smart city could make searching for parking spaces [13] much easier and faster. Moreover, it could be a useful source of information for emerging V2X-based traffic control systems [14], which could play an 35 important role in the route planning of self-driving cars.

مقاله سال 2016

این مقاله به کاوش کمک بالقوه‌ای که نهاد حمل و نقل عمومی می‌تواند برای پارادایم تحرک (ایاب و ذهاب) در حال ظهور  به عنوان یک خدمت (MaaS) از طریق ادغام خدمات حمل و نقل جمعی منظم با طرحواره‌های تکمیلی حمل و نقل انعطاف‌پذیر و سایر شکل‌های حمل و نقل دارای کاربرد مشترک انجام دهد می‌پردازد. جدیدترین پیشرفت‌های ICT، فرصت‌های جدیدی را برای سازماندهی و ارائه‌ی خدمات جمعی و انفرادی ایاب و ذهاب فراهم می‌کنند. در سناریوی در حال تکامل اقتصاد به اشتراک‌گذاری خدمت ، ما  این را با تعدادی از طرحواره‌های جدید ایاب و ذهاب مشاهده می‌کنیم – برای مثال، طرحواره‌های به اشتراک‌گذارنده‌ی خودرو و به اشتراک‌گذارنده‌ی سواری پویا مانند Car2Go، DriveNow، BlaBlaCar، Uber، Lyft. ما یک نهاد ایاب و ذهاب انعطاف‌پذیر و با کاربرد مشترک  (FSUM) را تعریف می‌کنیم؛ یک مرکز مشارکت انفرادی خدمات انعطاف‌پذیر مختلف و طرحواره‌های ایاب و ذهاب مشترک، که نیاز به همکاری و عملیات مشترک در میان فراهم کنندگان مختلف خدمت، ادغام داده‌ها و پلت فرم‌ها، سیستم‌ها و خدمات فنی دارد. استانداردها و فناوری‌های اساسی توانمندسازی توضیح داده می‌شوند و معماری پشتیبانی کننده‌ی ICT مطرح می‌شود. در نهایت، جنبه‌های سازمانی مربوط به عملیات نهاد، با ارجاع به پروژه‌ی PERHT با بودجه‌ی EC مورد بحث قرار می‌گیرند و توضیح داده می‌شوند.