Chris Eddy has successfully completed and defended his PhD thesis entitled ‘An investigation of higher capacity urban freight vehicles’, (University of Cambridge, July, 2019). The abstract is provided below.
Click here for a copy of the thesis.
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 multidrop 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 vehicle, and a 44 t general urban vehicle with rear axle steering. A range of potential higher capacity vehicles are proposed in Chapter 6 for those applications.
The impact of rear axle steering on manoeuvrability is also considered in detail in Chapter 6. It is shown that the use of rear axle steering does not always allow the use of a longer vehicle, because a rear axle steered vehicle cannot compromise between cut-in and tailswing in the way a conventional vehicle can. However, the use of rear axle steering allows reduction in both tyre wear and rear axle load limits, which permits greater vehicle fill before rear axle loads are exceeded.
These results are compared, in Chapter 7, to an alternative method for modelling manoeuvrability (Performance Based Standards). Finally, Chapter 8 presents some concluding remarks and recommendations for future work, including investigation of an improved cyclist detection system fusing cameras and ultrasonic sensors, and increased development of the manoeuvrability models to more accurately reflect real driving.