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High-quality cold-chain logistics are key to effectively managing the quality of temperature-sensitive foods. Hence, this study investigates the service quality of such logistics, using a real-life case of temperature-sensitive milk delivery.

This study focuses on developing business analytics for quality control in cold-chain perishable-food logistics, grounded in normal accident theory and stakeholder theory, and tests them using real-world data.

The empirical business-analytics results indicate that cargo locations, logistics status and delivery times are the essential factors that influence the quality of temperature-sensitive milk.

This study confirms that a combination of normal accident theory and stakeholder theory can be usefully applied to the development of strategies for managing perishable-food logistics. As such, its proposed business analytics provide a fresh foundation for research on logistics quality management.

The balance between efficiency and service quality in a logistics system should be assessed carefully, and logistics companies should collaborate with their stakeholders and can help to mitigate potential cold-chain risks.

This pioneering analytical study explores the essential quality issues that confront cold chains and demonstrates how to extract vital insights from temperature-sensitive food logistics monitoring data. As such, it represents a noteworthy contribution to the field of logistics quality management.

Urban networks are usually divided into several open or closed sub‐networks. Signal coordination has been recognized as one of the most efficient methods of controlling sub‐networks that have independently optimized timing plans. However, coordinating adjacent intersections in a network is a basic prerequisite to optimizing signal‐timing plans for sub‐networks. This paper aims to develop a linear model to support decisions regarding coordination of adjacent signals.

This paper aims to develop a linear model to support decisions regarding coordination of adjacent signals. The tests of this model which using the field data differ from those for calibration from various roadways, indicating that the model has transferability. Evaluations using microscopic simulation show that the model can objectively determine whether or not to interconnect adjacent signals depending on various traffic demands.

The model was calibrated by stepwise regression analysis with a total of 195 field samples. This model consists of the dependent variable critical block length (CL) between adjacent intersections, and the independent variables original platoon size (OPS) and platoon completeness ratio (PCR). The calibrated model is shown as following: CL = 689.97 + 6.86 OPS−7.15 PCR.

The proposed model appears to be a viable solution for determining whether to coordinate adjacent signals according to various traffic demands for variously configured roadways. The model shows that a larger OPS or a smaller PCR implies a larger CL. The model also indicates that adjacent signals must be interconnected if they are separated by 690 meters or less. The results also suggest that OPS from 10 to 30 fully disperse at about 800 meters downstream of a stop line. The results support the CL for effectively coordinating adjacent signals, similar to that recommended in the Manual on Uniform Traffic Control Devices. These results may be useful for the effective management of traffic signal networks.