Vehicular Mobile Data Collection Platform to Support the Development of Intelligent Transportation Systems (original) (raw)

SmartCityBus - A Platform for Smart Transportation Systems

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

With the growth of the Internet of Things (IoT), Smart(er) Cities have been a research goal of researchers, businesses and local authorities willing to adopt IoT technologies to improve their services. Among them, Smart Transportation [7, 8], the integrated application of modern technologies and management strategies in transportation systems, refers to the adoption of new IoT solutions to improve urban mobility. These technologies aim to provide innovative solutions related to different modes of transport and traffic management and enable users to be better informed and make safer and 'smarter' use of transport networks. This talk presents SmartCityBus, a data-driven intelligent transportation system (ITS) whose main objective is to use online and offline data in order to provide accurate statistics and predictions and improve public transportation services in the short and medium/long term.

A smart city framework for intelligent traffic system using VANET

2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013

The total number of vehicles in the world has experienced a remarkable growth, increasing traffic density which results in more and more accidents. Therefore the manufactures, researchers and government is shifting focus towards improving the on road safety rather than improving the quality of the roads.

SMaRTCaR: An Integrated Smartphone-based Platform to Support Traffic Management Applications

Workshop on Vehicular Traffic Management for Smart Cities 2012 (VTM), in IFIP Wireless Days, Dublin, Ireland, 2012

A massive research effort is recently going in the direction of interfacing cars with smartphones to offer valueadded services to driver and passengers. Such an interest is motivated by the large popularity of smartphones and by the observation that vehicles can act as effective collectors of information from the surrounding environment. Indeed, modern cars are endowed with several sensors forming an in-vehicle network, which provides kinematics information, automotive diagnostic services, etc. Cars can be further equipped with external sensing devices to monitor specific physical parameters, such as pollution, humidity, temperature, etc. If properly collected and delivered, such data can contribute to make the road transport greener, smarter, and safer.

Data management issues for intelligent transportation systems

In this paper we discuss the technical challenges of devising a Data Stream Management System (DSMS) in the intelligent transportation scenario considered in the PEGASUS project, where the final aim is to provide reliable and timely information to improve the safety and the efficiency of vehicles' and goods' flows. The system should collect and integrate the large amounts of geo-located stream items coming from On Board Units (OBUs) installed on vehicles, with the aim of producing real-time maps including traffic and Points Of Interest (POIs) information to be then distributed to OBUs. OBUs' smart navigation engines will exploit these maps to enhance mobility and provide user-targeted information. We propose a two-tiered GIS DSMS architecture where stream items are pulled from the source input stream, processed and stored in a result container to be further pulled by other operators. The system reduces the data acquisition costs by adopting communication-saving policies, supports ad-hoc strategies for reducing the storage management costs (lowering response times and memory consumption), and provides the required data access functionalities through an SQL-like query language enhanced with stream, event, spatial and temporal operators. OBU stream items are also exploited to detect Events Of Interest (EOIs) such as jams and accidents and to support a collaborative mechanism for user-powered POI management and rating. EOIs and POIs are modeled through specific ontologies which allow for a flexible and extensible data management and guarantee data independence from the raw streams.

Data Management Issues for Intelligent Transportation Systems (Position Paper)⋆

SmartMobility, an Application for Multiple Integrated Transportation Services in a Smart City

Proceedings of the 15th International Conference on Web Information Systems and Technologies, 2019

In this paper, we present SmartMobility, an application for multimobility information services in a smart city, exploiting an ecosystem of IoT devices. Such application, designed for a real case study, is extremely heterogeneous in terms of IoT devices and implements a wide range of services for citizens. The application aims at contributing to reducing traffic generated by private vehicles in the city besides helping drivers going towards high traffic areas by presenting real-time mobility data from different sources. The experiments carried out in this study have evaluated some behaviors of the application in front of different configurations, allowing understanding how the experience varies under a wide number of devices and services, particularly in terms of mobility alternatives offered to the final user. The research findings showed that the bus transportation service is the most common one, while carsharing and bikesharing are not widespread and must be improved.

Smart Urban Mobility: When Mobility Systems Meet Smart Data

IEEE Transactions on Intelligent Transportation Systems, 2021

Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the United States. These figures indicate the rapid rise of industrial cities and the urgent need to move from traditional cities to smart cities. This article provides a survey of different approaches and technologies such as intelligent transportation systems (ITS) that leverage communication technologies to help maintain road users safe while driving, as well as support autonomous mobility through the optimization of control systems. The role of ITS is strengthened when combined with accurate artificial intelligence models that are built to optimize urban planning, analyze crowd behavior and predict traffic conditions. AI-driven ITS is becoming possible thanks to the existence of a large volume of mobility data generated by billions of users through their use of new technologies and online social media. The optimization of urban planning enhances vehicle routing capabilities and solves traffic congestion problems, as discussed in this paper. From an ecological perspective, we discuss the measures and incentives provided to foster the use of mobility systems. We also underline the role of the political will in promoting open data in the transport sector, considered as an essential ingredient for developing technological solutions necessary for cities to become healthier and more sustainable.

A vehicular network–based intelligent transport system for smart cities

International Journal of Distributed Sensor Networks

Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. In this article, we propose an intelligent vehicular network framework for smart cities that enables route selection based on real-time data received from neighboring vehicles in an ad hoc fashion. We used Wi-Fi Direct–enabled Android-based smartphones as embedded devices in vehicles. We used a vehicular ad hoc network to implement an in...

Connected Vehicular Transportation: Data Analytics and Traffic-Dependent Networking

IEEE Vehicular Technology Magazine, 2017

With onboard operating systems becoming increasingly common in vehicles, the real-time broadband infotainment and Intelligent Transportation System (ITS) service applications in fast-motion vehicles become ever demanding, which are highly expected to significantly improve the efficiency and safety of our daily on-road lives. The emerging ITS and vehicular applications, e.g., trip planning, however, require substantial efforts on the real-time pervasive information collection and big data processing so as to provide quick decision making and feedbacks to the fast moving vehicles, which thus impose the significant challenges on the development of an efficient vehicular communication platform. In this article, we present TrasoNET, an integrated network framework to provide realtime intelligent transportation services to connected vehicles by exploring the data analytics and networking techniques. TrasoNET is built upon two key components. The first one guides vehicles to the appropriate access networks by exploring the information of realtime traffic status, specific user preferences, service applications and network conditions. The second component mainly involves a distributed automatic access engine, which enables individual vehicles to make distributed access decisions based on access recommender, local observation and historic information. We showcase the application of TrasoNET in a case study on real-time traffic sensing based on real traces of taxis. I. INTRODUCTION Our earth is facing the unstoppable increasing trend of vehicles. In United States, there are on average 812 cars for every 1, 000 people. In China, the amount of vehicles is estimated to be 250 million in 2020. The massive increase in vehicles has brought a series of social and environmental issues to our cities and daily lives such as frequent traffic jams, vehicle crashes, throat-choking air pollution, etc. A sustainable, intelligent and green transportation system is thus of crucial importance. Towards this goal, one practical solution is to use the cutting-edge wireless information and communication technologies to provide real-time transport-related information services to road administrators and vehicles, namely Connected Vehicular Transportation System (CVTS) [1-3]. As a result, the transportation efficiency can be significantly improved with more smooth traffic flows and travellers can get informed for more wise route selections and enhanced travel experience. Furthermore, both Google and Apple released their mobile operating systems for autos in 2014. It is estimated that the global Connected Car Market will reach 30.2 billion in

Vehicular Mobile Data Collection Platform to Support the Development of Intelligent Transportation Systems (original) (raw)

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