A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies ill Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper

Towards Smarter Management of Overtourism in Historic Centres Through Visitor-Flow Monitoring

Historic centres are highly regarded destinations for watching and even participating in diverse and unique forms of cultural expression. Cultural tourism, according to the World Tourism Organization (UNWTO), is an important and consolidated tourism sector and its strong growth is expected to continue over the coming years. Tourism, the much dreamt of redeemer for historic centres, also represents one of the main threats to heritage conservation: visitors can dynamize an economy, yet the rapid growth of tourism often has negative effects on both built heritage and the lives of local inhabitants. Knowledge of occupancy levels and flows of visiting tourists is key to the efficient management of tourism; the new technologies—the Internet of Things (IoT), big data, and geographic information systems (GIS)—when combined in interconnected networks represent a qualitative leap forward, compared to traditional methods of estimating locations and flows. A methodology is described in this paper for the management of tourism flows that is designed to promote sustainable tourism in historic centres through intelligent support mechanisms. As part of the Smart Heritage City (SHCITY) project, a collection system for visitors is developed. Following data collection via monitoring equipment, the analysis of a set of quantitative indicators yields information that can then be used to analyse visitor flows; enabling city managers to make management decisions when the tourism-carrying capacity is exceeded and gives way to overtourism.Funded by the Interreg Sudoe Programme of the European Regional Development Funds (ERDF

A neural network based traffic-flow prediction model

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies in Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper. © Association for Scientific Research

Urban lighting project for a small town: comparing citizens and authority benefits

The smart and resilient city evolves by slow procedures of mutation without radical changes, increasing the livability of its territory. The value of the city center in a Smart City can increase through urban lighting systems: its elements on the territory can collect and convey data to increase services to city users; the electrical system becomes the so-called Smart Grid. This paper presents a study of smart lighting for a small town, a touristic location inside a nature reserve on the Italian coast. Three different approaches have been proposed, from minimal to more invasive interventions, and their effect on the territory has been investigated. Based on street typology and its surroundings, the work analyzes the opportunity to introduce smart and useful services for the citizens starting from a retrofitting intervention. Smart city capabilities are examined, showing how it is possible to provide new services to the cities through ICT (Information and Communication Technology) without deep changes and simplifying the control of basic city functions. The results evidence an important impact on annual energy costs, suggesting smart grid planning not only for metropolis applications, but also in smaller towns, such as the examined one