Beyond 5G URLLC Evolution: New Service Modes and Practical Considerations

Ultra-reliable low latency communications (URLLC) arose to serve industrial IoT (IIoT) use cases within the 5G. Currently, it has inherent limitations to support future services. Based on state-of-the-art research and practical deployment experience, in this article, we introduce and advocate for three variants: broadband, scalable and extreme URLLC. We discuss use cases and key performance indicators and identify technology enablers for the new service modes. We bring practical considerations from the IIoT testbed and provide an outlook toward some new research directions.Comment: Submitted to IEEE Wireless Commun. Ma

SenSys: A Smartphone-Based Framework for ITS applications

Intelligent transportation systems (ITS) use different methods to collect and process traffic data. Conventional techniques suffer from different challenges, like the high installation and maintenance cost, connectivity and communication problems, and the limited set of data. The recent massive spread of smartphones among drivers encouraged the ITS community to use them to solve ITS challenges. Using smartphones in ITS is gaining an increasing interest among researchers and developers. Typically, the set of sensors that comes with smartphones is utilized to develop tools and services in order to enhance safety and driving experience. GPS, cameras, Bluetooth, inertial sensors and other embedded sensors are used to detect and analyze drivers\u27 behavior and vehicles\u27 motion. The use of smartphones made the data collection process easier because of their availability among drivers, the set of different sensors, the computation ability, and the low installation and maintenance cost. On the other hand, different smartphones sensors have diverse characteristics and accuracy and each one of them needs special fusion, processing, and filtration methods to generate more stable and accurate data. Using smartphones in ITS faces different challenges like inaccurate readings, weak GPS reception, noisy sensors and unaligned readings.These challenges waste researchers and developers time and effort, and they prevent them from building accurate ITS applications. This work proposes SenSys a smartphone framework that collects and processes traffic data and then analyzes and extracts vehicle dynamics and vehicle activities which can be used by developers and researchers to create their navigation, communication, and safety ITS applications. SenSys framework fuses and filters smartphone\u27s sensors readings which result in enhancing the accuracy of tracking and analyzing various vehicle dynamics such as vehicle\u27s stops, lane changes, turn detection, and accurate vehicle speed calculation that, in turn, will enable development of new ITS applications and services

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

On Intelligent Transportation Systems and Road Congestion

Despite substantial investments in transportation infrastructures, road congestion in urban areas has not abated. While there is a growing interest among policymakers in intelligent transportation systems (ITS), the role of ITS in road congestion has not been established. To investigate the effect of ITS on road congestion, we utilized a unique dataset on traffic and ITS adoption from 99 U.S. urban areas in 2001-2008. The results from fixed-effects estimations show that ITS adoption reduces road congestion, saving an average driver 98 minutes of driving time and $38 per year. We also obtained preliminary evidence that ITS reduces carbon emissions by alleviating road congestion. Our findings extend the emerging IS literature on IT value in the public sector and the societal impacts of IT. Our study also contributes to the transportation economics literature and informs transportation policymakers by showing that ITS could be a cost-effective alternative to tackle road congestion

AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities

Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in different regions across the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems to detect and respond to traffic accidents spontaneously. Integrating the proposed framework with emergency services will harness the power of traffic cameras and machine learning algorithms to create an efficient solution for responding to traffic accidents and reducing human errors. Advanced intelligence technologies, such as the proposed accident detection systems in smart cities, will improve traffic management and traffic accident severity. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution to enhance the safety and efficiency of transportation systems.Comment: 8,

Mobile Network Data Analytics for Intelligent Transportation Systems

In this dissertation, we explore how the interplay between transportation and mobile networks manifests itself in mobile network billing and signaling data, and we show how to use this data to estimate different transportation supply and demand models. To perform the necessary simulation studies for this dissertation, we present a simula- tion scenario of Luxembourg, which allows the simulation of vehicular Long-Term Evolu- tion (LTE) connectivity with realistic mobility. We first focus on modeling travel time from Cell Dwell Time (CDT), and show – on a synthetic data set– that we can achieve a prediction Mean Absolute Percentage Error (MAPE) below 12%. We also encounter proportionality between the square of the mean CDT and the number of handovers in the system, which we confirmed in the aforementioned simulation scenario. This motivated our later studies of traffic state models generated from mobile network data. We also consider mobile network data for supporting synthetic population generation and demand estimation. In a study on Call Detail Records (CDR) data from Senegal, we estimate CDT distributions to allow generating the duration of user activities, and validate them at a large scale against a data set from China. In a different study, we show how mobile network signaling data can be used for initializing the seed Origin- Destination (O-D) matrix in demand estimation schemes, and show that it increases the rate of convergence. Finally, we address the traffic state estimation problem, by showing how handovers can be used as a proxy metric for flows in the underlying urban road network. Using a traffic flow theory model, we show that clusters of mobile network cells behave characteristically, and with this model we reach a MAPE of 11.1% with respect to floating-car data as ground truth. The presented model can be used in regions without traffic counting infrastructure, or complement existing traffic state estimation systems

Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving

Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving