Associate Professor Oregon State University Corvallis, OR, United States
Abstract: Traditional food delivery services predominantly rely on cars, which, despite their speed, incur high maintenance and insurance costs, and significantly impact the environment. Bicycles and electric bikes present a sustainable alternative. However, there is a notable lack of practical methods for assessing the viability of bike and e-bike delivery services in the food industry. Addressing this gap, our study develops an algorithm to evaluate the environmental impact, economic effects, and operational feasibility of bike and e-bike delivery services for fast-food restaurants. Simulating delivery orders, and considering population factors for potential order locations, the study extracts travel information for bikes, e-bikes, and cars from OpenStreetMap API. This data enables comparisons of emissions, travel times, and the economic viability of delivery services. It also informs the identification of high-usage bike routes for potential infrastructure enhancements. The application of our algorithm to two Domino's Pizza branches in Logan suggests that a mixed fleet of vehicles and e-bikes is optimal. Bike or e-bike deliveries are economically feasible, especially for distances of around 3.6 kilometers. A bike delivery rider at North Domino's could manage up to 13 orders daily, generating around $103 and reducing CO2 emissions by 84.2% compared to car delivery. E-bikes, with their increased speed and lower emissions, emerged as a promising alternative. The study concludes that the North Logan Domino's branch is more suitable for establishing a bike delivery service than its southern counterpart. Policymakers are urged to provide incentives to businesses implementing bike delivery services and invest in infrastructure improvements, particularly for frequently used bike routes, thereby promoting the benefits of sustainable transport in food delivery services.
Learning Objectives:
Attendees can expect to learn the following from this session:
Participants will learn to use Google Maps API to calculate travel time, distance, and route efficiency for optimizing delivery services.
Participants will learn to assess the environmental and economic impacts of using bikes and e-bikes versus cars for food delivery, focusing on travel times, emissions, and costs.
Attendees will explore how to use geographic data to determine high-traffic bike routes for potential infrastructure enhancements that support sustainable food delivery systems in urban settings