Testing and Evaluation of Low-Cost Sensors for Developing Open Smart Campus Systems Based on IoT

Urbanization has led to the need for the intelligent management of various urban challenges, from traffic to energy. In this context, smart campuses and buildings emerge as microcosms of smart cities, offering both opportunities and challenges in technology and communication integration. This study sets itself apart by prioritizing sustainable, adaptable, and reusable solutions through an open-source framework and open data protocols. We utilized the Internet of Things (IoT) and cost-effective sensors to capture real-time data for three different use cases: real-time monitoring of visitor counts, room and parking occupancy, and the collection of environment and climate data. Our analysis revealed that the implementation of the utilized hardware and software combination significantly improved the implementation of open smart campus systems, providing a usable visitor information system for students. Moreover, our focus on data privacy and technological versatility offers valuable insights into real-world applicability and limitations. This study contributes a novel framework that not only drives technological advancements but is also readily adaptable, improvable, and reusable across diverse settings, thereby showcasing the untapped potential of smart, sustainable systems.

Smoothing regression and impact measures for accidents of traffic flows

Traffic pattern identification and accident evaluation are essential for improving traffic planning, road safety, and traffic management. In this paper, we establish classification and regression models to characterize the relationship between traffic flows and different time points and identify different patterns of traffic flows by a negative binomial model with smoothing splines. It provides mean response curves and Bayesian credible bands for traffic flows, a single index, and the log-likelihood difference, for traffic flow pattern recognition. We further propose an impact measure for evaluating the influence of accidents on traffic flows based on the fitted negative binomial model. The proposed method has been successfully applied to real-world traffic flows, and it can be used for improving traffic management.

Understanding traffic capacity of urban networks

Traffic in an urban network becomes congested once there is a critical number of vehicles in the network. To improve traffic operations, develop new congestion mitigation strategies, and reduce negative traffic externalities, understanding the basic laws governing the network’s critical number of vehicles and the network’s traffic capacity is necessary. However, until now, a holistic understanding of this critical point and an empirical quantification of its driving factors has been missing. Here we show with billions of vehicle observations from more than 40 cities, how road and bus network topology explains around 90% of the empirically observed critical point variation, making it therefore predictable. Importantly, we find a sublinear relationship between network size and critical accumulation emphasizing decreasing marginal returns of infrastructure investment. As transportation networks are the lifeline of our cities, our findings have profound implications on how to build and operate our cities more efficiently.

Modeling and optimization of road transport facility operations

Purpose

– This paper aims to present a linear mathematical framework for modeling and optimizing road transport infrastructure. The framework assesses and optimizes performance of existing transport facility rather than relying on building new roads for the ever-increasing travel demand.

Design/methodology/approach

– The mathematical framework is built upon a traffic model called Cell Transmission Model (CTM). CTM describes the relationship and evolution of traffic flow and concentration over space and time. The model is parsimonious and accurate in predicting traffic dynamics. More importantly, the traffic flow model is piecewise linear with which the corresponding transport facility optimization problem can be formulated as a Linear Programming (LP) problem and solved by established solution algorithm for global optimality.

Findings

– We select a section on England Motorway M25 as a case study. With traffic data, we first calibrate the CTM, and we are able to produce traffic estimation with a reasonable error rate of 12 per cent. The corresponding LP then seeks an optimal ramp metering strategy that minimizes the delay on the motorway. It is shown that an optimal and practical strategy can be derived which reduces the motorway delay by 10 per cent without significantly hurting the surrounding connectors.

Originality/value

– Instead of the tedious microscopic models used by many traditional tools, the underlying CTM is parsimonious and reliable. The tools developed herein are based upon plausible traffic theory and will be accessible for a wide range of users. The LP formulation can be easily implemented and solved for optimal and practical control strategies for real-world transport networks by using existing computer software (CPLEX) within reasonable computational time. The present work will certainly contribute to the sustainable development of transport facility.