Historical Volume Data Research Articles

The first mile problem in the circular economy supply chains – Collecting recyclable textiles from consumers

This paper presents the first mile problem of the circular economy supply chains: organizing the collection of used materials from individual consumers. Efficient solving of this problem is a prerequisite for recycling, as it ensures that adequate volumes of used materials, in this study end-of-life textiles, can be transformed into high-quality raw materials. Textile features affect the collection system design and make used textiles differ from other household waste fractions, such as glass or plastic. The aim is to investigate how to collect textile waste from consumers in a way that addresses the specific features of textiles, and the scarce resources of the collector organizations, but results in the largest collected yields to eventually drive up the recycling rates of used textiles. The study takes a supply chain view to reverse flows of textiles. Key experts from a sample of textile organizations were interviewed to understand their collection practices and possible future solutions, and a data analysis model was constructed to provide accurate prediction of end-of-life textile volumes. This study proposes how the first mile problem of end-of-life textiles, the collection from households, can be modelled in a situation where no historical volume data is available. The practical contribution lies in the urgent need for textile collection solutions, as municipal actors and waste companies in Europe are searching for efficient solutions to respond to the requirement of separate used textile collection by 2025. The study demonstrates how a circular economy problem with multiple unknows and uncertainties can be modelled quantitatively.

Subtractive Clustering-Based K-means Technique for Determining Optimum Time-of-Day Breakpoints

In many countries, the most widely used method for timing plan selection and implementation is the time-of-day (TOD) method. In TOD mode, a few traffic patterns that exist in the historical volume data are recognized and used to find the signal timing plans needed to achieve optimum performance of the intersections during the day. Traffic engineers usually determine TOD breakpoints by analyzing 1 or 2 days worth of traffic data and relying on their engineering judgment. The current statistical methods, such as hierarchical and K-means clustering methods, determine TOD breakpoints but introduce a large number of transitions. This paper proposes adopting the Z-score of the traffic flow and time variable in the K-means clustering to reduce the number of transitions. The numbers of optimum breakpoints are chosen based on a microscopic simulation model considering a set of performance measures. By using simulation and the K-means algorithm, it was found that five clusters are the optimum for a major arterial in Al-Khobar, Saudi Arabia. As an alternative to the simulation-based approach, a subtractive algorithm-based K-means technique is introduced to determine the optimum number of TODs. Through simulation, it was found that both approaches results in almost the same values of measure of effectiveness (MOE). The proposed two approaches seem promising for similar studies in other regions, and both of them can be extended for different types of roads. The paper also suggests a procedure for considering the cyclic nature of the daily traffic in the clustering effort.

A Robust Model for Optimal Time-of-Day Speed Control at Highway Work Zones

This paper proposes a new speed control strategy, named time-of-day speed limit (TOD SL) control, for highway work-zone operations. The main purposes of the TOD SL control are to overcome the difficulty in setting the optimal real-time speed limit due to the lack of detectors and to maximize the use of available data such as the historical volume data on the target work zone. Its core logic is to divide the entire day of operations into a number of control periods and to accommodate the time-varying traffic conditions within each control period. The measure of effectiveness (MOE) selected in the TOD SL model takes into account both the operational efficiency and traffic safety. To encompass all possible traffic conditions during each control period, the control model employs traffic flow relations calibrated from historical data to estimate the speed and density data with available volume under possible traffic scenarios. The performance of the proposed TOD SL control has been evaluated with the simulation experiments and compared with the other speed control strategies based on the selected measures of effectiveness