مقاله انگلیسی رایگان در مورد بررسی پیش بینی ترافیک در شهرهای هوشمند – الزویر 2018

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.