Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent advancements in artificial intelligence, sensor fusion and algorithms have brought about the introduction of a proactive safety management system closer to reality. The basic prerequisite for developing such a system is to have a reliable crash prediction model that takes real-time traffic data as input and evaluates their association with crash risk. Since the early 21st century, several studies have focused on developing such models. Although the idea has considerably matured over time, the endeavours have been quite discrete and fragmented at best because the fundamental aspects of the overall modelling approach substantially vary. Therefore, a number of transitional challenges have to be identified and subsequently addressed before a ubiquitous proactive safety management system can be formulated, designed and implemented in real-world scenarios. This manuscript conducts a comprehensive review of existing real-time crash prediction models with the aim of illustrating the state-of-the-art and systematically synthesizing the thoughts presented in existing studies in order to facilitate its translation from an idea into a ready to use technology. Towards that journey, it conducts a systematic review by applying various text mining methods and topic modelling. Based on the findings, this paper ascertains the development pathways followed in various studies, formulates the ubiquitous design requirements of such models from existing studies and knowledge of similar systems. Finally, this study evaluates the universality and design compatibility of existing models. This paper is, therefore, expected to serve as a one stop knowledge source for facilitating a faster transition from the idea of real-time crash prediction models to a real-world operational proactive traffic safety management system

Smart PIN: performance and cost-oriented context-aware personal information network

The next generation of networks will involve interconnection of heterogeneous individual networks such as WPAN, WLAN, WMAN and Cellular network, adopting the IP as common infrastructural protocol and providing virtually always-connected network. Furthermore, there are many devices which enable easy acquisition and storage of information as pictures, movies, emails, etc. Therefore, the information overload and divergent content’s characteristics make it difficult for users to handle their data in manual way. Consequently, there is a need for personalised automatic services which would enable data exchange across heterogeneous network and devices. To support these personalised services, user centric approaches for data delivery across the heterogeneous network are also required. In this context, this thesis proposes Smart PIN - a novel performance and cost-oriented context-aware Personal Information Network. Smart PIN's architecture is detailed including its network, service and management components. Within the service component, two novel schemes for efficient delivery of context and content data are proposed: Multimedia Data Replication Scheme (MDRS) and Quality-oriented Algorithm for Multiple-source Multimedia Delivery (QAMMD). MDRS supports efficient data accessibility among distributed devices using data replication which is based on a utility function and a minimum data set. QAMMD employs a buffer underflow avoidance scheme for streaming, which achieves high multimedia quality without content adaptation to network conditions. Simulation models for MDRS and QAMMD were built which are based on various heterogeneous network scenarios. Additionally a multiple-source streaming based on QAMMS was implemented as a prototype and tested in an emulated network environment. Comparative tests show that MDRS and QAMMD perform significantly better than other approaches

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029

LocateMyBus: IoT-Driven Smart Bus Transit

Uncertainty of traffic in cities makes it difficult for metropolitan buses to adhere to predetermined schedules, making it strenuous for commuters to plan travel reliably. The proposed LocateMyBus system leverages Internet of Things(IoT) set-ups at bus stops and buses, and Machine Learning(ML) to assuage this uncertainty by allowing commuters to track live-runningstatus of buses, disseminate tentative and live-status to commuters through Public Announcement(PA) systems at bus-stops and a web-application interface. The schedule prediction module provides a tentative schedule of buses with stop-wise arrival times estimated using ML based on historic and real-time route data. Arrival times of two bus-routes in the Massachusetts Bay Area were collected for a period of four months by periodically querying its real-time General Transit Feed Systems(GTFS). This dataset was used to train and validate the proposed ML methods. The IoT system was modeled on Proteus, and validated with a miniature prototype. LocateMyBus is proposed as a step forward toward minimal intervention algorithmic set-ups to ease the uncertainty associated with bus commute in cities. It enables commuters to track live running status and avail ML-predicted tentative schedules. Furthermore, it eradicates the computation requirements of GPS-based systems, whilst ensuring stop-level tracking granularity. LocateMyBus\u27s ability to log bus arrival times at each stop paves the way to building real-time GTFSs

Internet of things enabled parking management system using long range wide area network for smart city

As the Internet of Things (IoT) evolves, it paves the way for vital smart city applications, with the Smart Parking Management System (SPMS) standing as a prime example. This research introduces a novel IoT-driven SPMS that leverages Long Range Wide Area Network (LoRaWAN) technology, termed as IoT-SPMS-LoRaWAN, to surmount typical restrictions related to communication range, energy usage, and implementation cost seen in traditional systems. IoT-SPMS-LoRaWAN features intelligent sensing nodes that incorporate an Arduino UNO microcontroller and two sensors—a triaxial magnetic sensor and a waterproof ultrasonic sensor. These components collaboratively detect vehicle occupancy and transmit this data to the server via a LoRaWAN gateway. Notably, the integration of LoRa technology enables extensive network coverage and energy efficiency. Users are provided with real-time updates on parking availability via the accessible AllThingsTalk Maker graphical user interface. Additionally, the system operates independently, sustained by a solar-powered rechargeable battery. Practical testing of IoT-SPMS-LoRaWAN under various scenarios validates its merits in terms of functionality, ease of use, reliable data transmission, and precision. Its urban implementation is expected to alleviate traffic congestion, optimize parking utilization, and elevate awareness about available parking spaces among users. Primarily, this study enriches the realm of smart city solutions by enhancing the efficiency of parking management and user experience via IoT

IoT based monitoring of air quality and traffic using regression analysis

Dynamic traffic management (DTM) systems are used to reduce the negative externalities of traffic congestion, such as air pollution in urban areas. They require traffic and environmental monitoring infrastructures. In this paper we present a prototype of a low-cost Internet of Things (IoT) system for monitoring traffic flow and the Air Quality Index (AQI). The computation of the traffic flows is based on processing video in the compressed domain. Only using motion vectors as input, traffic flow is computed in real-time over an embedded architecture. An estimation of the AQI is supported by machine learning regression techniques, using different feature data obtained from the IoT device. These automatic learning techniques overcome the need for complex calibration and other limitations of embedded devices in making the needed measurements of the pollutant gases for the computation of the actual AQI. The experimentation with the data obtained from different cities representing different scenarios with a variety of climate and traffic conditions, allows validating the proposed architecture. As regressors, Linear Regression (LR), Gaussian Process Regression (GPR) and Random Forest (RF) are compared using the performance metrics , , and resulting in a relevant improvement of the AQI estimations of our proposal.Los sistemas de gestión dinámica del tráfico (DTM) se utilizan para reducir las externalidades negativas de la congestión del tráfico, como la contaminación del aire en las zonas urbanas. Requieren infraestructuras de vigilancia del tráfico y del medio ambiente. En este artículo presentamos un prototipo de un sistema de Internet de las cosas (IoT) de bajo costo para monitorear el flujo de tráfico y el índice de calidad del aire (AQI). El cálculo de los flujos de tráfico se basa en el procesamiento de video en el dominio comprimido. Solo utilizando vectores de movimiento como entrada, el flujo de tráfico se calcula en tiempo real sobre una arquitectura integrada. Una estimación del AQI está respaldada por técnicas de regresión de aprendizaje automático , utilizando diferentes datos de características obtenidos del dispositivo IoT . Estas técnicas de aprendizaje automático superan la necesidad de una calibración compleja y otras limitaciones de los dispositivos integrados al realizar las mediciones necesarias de los gases contaminantespara el cálculo del AQI real. La experimentación con los datos obtenidos de diferentes ciudades que representan diferentes escenarios con una variedad de condiciones climáticas y de tráfico, permite validar la arquitectura propuesta. Como regresores , la regresión lineal (LR), la regresión del proceso gaussiano (GPR) y el bosque aleatorio (RF) se comparan utilizando las métricas de rendimiento,,yresultando en una mejora relevante de las estimaciones del AQI de nuestra propuesta

Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets

Presents an overview and analysis of the possible approaches to bringing an active demand side into electricity markets. Part of a series of research reports that examines energy issues facing California