The rapid growth of food delivery services has increased safety concerns for delivery riders who frequently rely on smartphones for navigation while driving. This study proposes a smart helmet system designed to enhance rider safety and operational efficiency by providing hands-free navigation assistance. The helmet integrates GPS and IMU sensors whose measurements are fused using the Unscented Kalman Filter (UKF) to improve positioning accuracy and maintain short-term motion estimation during temporary GPS signal loss. Navigation instructions are delivered through a bone conduction audio system, allowing riders to receive guidance without blocking environmental sounds. To support system sustainability, a lightweight CIGS photovoltaic panel is integrated on the helmet to harvest solar energy and power the embedded electronics. Photovoltaic performance was evaluated using PVsyst based on solar irradiation data for Jeddah, Saudi Arabia, considering three tilt angles (0°, 15°, and 30°). The results demonstrate the feasibility of integrating wearable photovoltaic systems with intelligent sensing technologies for safer urban food delivery operations.

Drone-based delivery services have emerged as a promising innovation for enhancing last-mile logistics through faster deliveries, improved accessibility, and reduced environmental impacts. However, the successful implementation of this technology depends largely on consumer acceptance. Existing studies examining drone delivery adoption remain fragmented, often focusing on a limited set of determinants or relying on a single theoretical perspective. Accordingly, this study reviews and synthesizes the existing literature to identify the key factors influencing consumers’ attitudes toward drone-based delivery services. Drawing on insights from the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, Diffusion of Innovation Theory, Task–Technology Fit Theory, and Privacy Calculus Theory, an integrated conceptual framework is developed. The review identifies perceived usefulness, perceived ease of use, task–technology fit, relative advantage, speed expectancy, hedonic motivation, environmental friendliness expectancy, social influence, privacy risk, and safety risk as the principal determinants of consumer attitudes toward drone delivery services.

This paper studies a sustainable last-mile delivery problem, which is one of the most complex vehicle routing problems. The problem consists of customer parcel allocation and the routing of an electric vehicle and a heterogeneous fleet of drones to distribute parcels to customers. To optimize this problem, a Mixed-Integer Linear Programming (MILP) model is formulated with the objective of minimizing the total weighted tardiness. The model is solved using an exact approach with the CPLEX optimization solver on nine realistic problem instances ranging from 5 to 15 customers. Numerical results show that parcel delivery planning with multiple heterogeneous drones and a single EV provides better solutions than single drone-EV combinations.