Studies have shown that increasing the capacity of Heavy Goods Vehicles is one of the most effective ways of reducing fuel consumption per tonne-kilometer 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 the safety of other road users, and reduced maneuverability.
A simple approach to reducing the negative externalities associated with urban road use is to reduce the number of vehicles on the roads. A thesis by Christopher Eddy from the University of Cambridge discusses technologies for improving the safety of vulnerable road users, and frameworks for assessing the maximum size of urban freight vehicles. In the UK, Heavy Goods Vehicles (HGVs) accounted for 23% of cyclist deaths, despite representing only 5% of total road traffic.
Camera-based detection systems
The thesis looks at the safety of vulnerable road users, through the 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. Urban freight operations are analyzed, including a comparison between two supermarket home delivery operations, and analysis of refuse collection schedules.
The system described is promising but not sufficiently robust for all conditions, due to the loss of accuracy at close range, and the impracticality in low-light conditions. It is likely that the most suitable solution would be a hybrid system fusing image and ultrasonic data.
Vehicle size
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. The thesis proposes a novel framework for assessing the optimum size of Heavy Goods Vehicles, according to the limits of their maneuverability. This method is based on simulation of vehicles attempting a library of real-world maneuvers. It was shown that in order to assess the operational impact of higher capacity vehicles used to restock city center stores, modeling of an entire distribution network would be required.
Modeling
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 is proposed for those applications The impact of rear-axle steering on maneuverability is also considered in detail. 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 tail swing in the way a conventional vehicle can. However, the use of rear-axle steering allows a reduction in both tire wear and rear axle load limits, which permits greater vehicle fill before rear axle loads are exceeded.
The thesis 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 maneuverability models to more accurately reflect real driving.