Kevin GingerichAdonai Manace Garcia Santana2024-07-182024-07-182024-04-192024-07-18https://hdl.handle.net/10315/42183This thesis explores the optimization of electric cargo cycles (ECCs) for urban logistics, focusing on battery performance and routing efficiency. By analyzing two ECCs under different operational scenarios, a novel Electric Cargo Cycle Battery (ECCB) model is developed for battery performance forecasting. This extends to the creation of the Electric Cargo Cycle Routing Problem (ECCRP), an adaptation of the capacitated vehicle routing problem, and its Enhanced version (E-ECCRP), incorporating battery constraints and the flexibility of battery swapping. Comparative analysis of these models reveals the benefits of integrating battery performance into routing strategies, underscoring ECCs' role in eco-friendly urban logistics. This research offers valuable insights into electric mobility utilization for sustainable city transport solutions, providing a foundation for optimizing ECC usage in urban environments.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Civil engineeringTransportation planningUrban planningElectronic Thesis or Dissertation2024-07-18electric cargo cyclecargo bikeelectric bikelogisticslast milecity logisticsbatteryrouting problemoptimization routing modelbattery modelmodellingrandom forestmachine learningdata-driven