Abstract

This thesis focuses on exploring the emerging automated technologies for last-mile on-demand food delivery as a new means of transportation to reduce congestion in urban areas. In order to achieve that 4 systems are designed and evaluated: Robot delivery system, drone delivery system and two hybrid delivery systems. Both hybrid systems are based on hub-spoke networks, Hybrid System 1.0 uses robots for phase one of the delivery and drones for phase two Hybrid System 2.0 uses drones for phase one and robots for phase two. To evaluate the efficiency of these systems, an in-house agent-based simulation model in MATLAB is developed for the City of Mississauga. 30 scenarios are tested differing in terms of demand and fleet size. The results show that Hybrid system 2.0 is the most efficient system of all four proposed due to the use of hub, customer waiting time and landing zones for drones.

Highlights

  • Travel demand in urban areas continues to grow resulting in increased congestion on the existing networks

  • To test the efficiency of the three proposed systems, they were applied to city of Mississauga in an in-house developed simulation platform during evening peak period

  • The results obtained from simulation showed that for the robot system, a large fleet size is needed to accommodate the applied demand, since the robots operate on the sidewalks and on a lower speed

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Summary

Introduction

Travel demand in urban areas continues to grow resulting in increased congestion on the existing networks. In Canada the number of vehicles registered increased by 1.8% from 2015 to 2016 and continued to increase by 1.6% from 2016 reaching 34.3 million vehicles in 2017 [31][32] With this rate of increase, the existing infrastructure will not be able to keep up with the rise in demand and it is neither sustainable nor realistic to build enough roads and infrastructure to comfortably accommodate this increase. It was found that on-demand transportation companies such as Uber and Lyft are primary factors in contributing to the congestion. The effect of these services varies through the day, having the highest affect during evening peak hours, accounting for 70% of the increase in vehicle delay

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