An Investigation of Higher Capacity Urban Freight Vehicles
type: CreativeWork
description: Studies have shown that increasing the capacity of Heavy Goods Vehicles is one of the most effective ways of reducing fuel consumption per tonne-kilometre of freight moved, with consequent reductions in greenhouse and noxious emissions. Some of the disadvantages of larger vehicles are more pronounced in urban environments, including safety of other road users, and reduced manoeuvrability. This thesis discusses technologies for improving safety of vulnerable road users, and frameworks for assessing the maximum size of urban freight vehicles. An overview of the freight industry is provided in Chapter 1, with a focus on maximising capacity as a method for reducing emissions. Chapter 2 focusses on the safety of vulnerable road users, through development of a camera-based detection system for cyclists, which is essential for a predictive collision avoidance system. The proposed system is accurate to within 10 cm at distances of greater than 1 m from the vehicle, but suffers from loss of accuracy at close range, and in poor lighting conditions. The logistics of urban freight operations are analysed in Chapter 3, including a comparison between two supermarket home delivery operations, and an analysis of refuse collection schedules. A framework is proposed for selecting an optimum vehicle size for a multi-drop operation, given reductions in driving distance and time spent on other procedures. A potential capacity increase of 80% is demonstrated, requiring a 50% reduction in driving distance, and automation of certain procedures. Chapters 4 to 6 propose a novel framework for assessing the optimum size of Heavy Goods Vehicles, according to the limits of their manoeuvrability. This method is based on simulation of vehicles attempting a library of real-world manoeuvres. Simulation models are described in Chapter 4, and path planning algorithms in Chapter 5. The framework is evaluated on three case studies: a 4.25 t grocery delivery vehicle, a 44 t articulated refuse collection …
abstract: Studies have shown that increasing the capacity of Heavy Goods Vehicles is one of the most effective ways of reducing fuel consumption per tonne-kilometre of freight moved, with consequent reductions in greenhouse and noxious emissions. Some of the disadvantages of larger vehicles are more pronounced in urban environments, including safety of other road users, and reduced manoeuvrability. This thesis discusses technologies for improving safety of vulnerable road users, and frameworks for assessing the maximum size of urban freight vehicles. An overview of the freight industry is provided in Chapter 1, with a focus on maximising capacity as a method for reducing emissions. Chapter 2 focusses on the safety of vulnerable road users, through development of a camera-based detection system for cyclists, which is essential for a predictive collision avoidance system. The proposed system is accurate to within 10 cm at distances of greater than 1 m from the vehicle, but suffers from loss of accuracy at close range, and in poor lighting conditions. The logistics of urban freight operations are analysed in Chapter 3, including a comparison between two supermarket home delivery operations, and an analysis of refuse collection schedules. A framework is proposed for selecting an optimum vehicle size for a multi-drop operation, given reductions in driving distance and time spent on other procedures. A potential capacity increase of 80% is demonstrated, requiring a 50% reduction in driving distance, and automation of certain procedures. Chapters 4 to 6 propose a novel framework for assessing the optimum size of Heavy Goods Vehicles, according to the limits of their manoeuvrability. This method is based on simulation of vehicles attempting a library of real-world manoeuvres. Simulation models are described in Chapter 4, and path planning algorithms in Chapter 5. The framework is evaluated on three case studies: a 4.25 t grocery delivery vehicle, a 44 t articulated refuse collection …
author: Eddy, Christopher
language: en
keywords: Higher Capacity Vehicles, HGVs, Computer Vision, Performance-Based Standards, Manoeuvrability, Modelling, Simulation, Urban Freight Vehicles, Green Logistics, Home Delivery, Refuse Collection, Grocery Delivery, Urban Freight, Sustainable Freight, Road Freight
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/f826c97c-f75b-4d6c-b528-4e30347212ea/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2020-03-14
date modified: 2026-07-22T22:24:30Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.48820