Understanding urban freight activity: key issues for freight planning

Using information gathered from some 30 UK surveys undertaken over the last 15 years, this paper provides planners with an understanding of road-based urban retail freight transport activity. The findings suggest that the average High Street business could expect up to 10 core goods and 7.6 service visits per week, in non-peak trading periods with 25% additional activity during the build up to Christmas. Vans (‘light goods vehicles’) were the dominant mode, responsible for 42% of delivery activity with a mean dwell time of 10 min. Where possible, load consolidation should be encouraged by methods such as Delivery and Servicing Plans and using out-of-town freight consolidation centres to bring in goods over the last mile in shared vehicles. Where this is not possible, loading bay monitoring and control, and preferred lorry routes can help manage the movement of vehicles in and out of dense urban areas. Service vehicle activity is a significant contributor to urban freight movements and often requires vehicles to be parked close to the premises being served. Centrally coordinating elements of service provision (e.g. for cleaning, equipment maintenance, recyclate collection), or providing improved, more flexible parking provision for service vehicles could be as or more beneficial in reducing overall freight impacts than focusing on core goods deliveries. In the case of the latter, ‘pay-as-you-leave’ car park charging systems could encourage short-stay service vehicles to park off-street.

Genetic programming for the minimum time swing up and balance control acrobot problem

This work describes how genetic programming is applied to evolving controllers for the minimum time swing up and inverted balance tasks of the continuous state and action: limited torque acrobot. The best swing-up controller is able to swing the acrobot up to a position very close to the inverted ‘handstand’ position in a very short time, shorter than that of Coulom (2004), who applied the same constraints on the applied torque values, and to take only slightly longer than the approach by Lai et al. (2009) where far larger torque values were allowed. The best balance controller is able to balance the acrobot in the inverted position when starting from the balance position for the length of time used in the fitness function in all runs; furthermore, 47 out of 50 of the runs evolve controllers able to maintain the balance position for an extended period, an improvement on the balance controllers generated by Dracopoulos and Nichols (2012), which this paper is extended from. The most successful balance controller is also able to balance the acrobot when starting from a small offset from the balance position for this extended period.