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.

A case study in identifying acceptable bitrates for human face recognition tasks

Face recognition from images or video footage requires a certain level of recorded image quality. This paper derives acceptable bitrates (relating to levels of compression and consequently quality) of footage with human faces, using an industry implementation of the standard H.264/MPEG-4 AVC and the Closed-Circuit Television (CCTV) recording systems on London buses. The London buses application is utilized as a case study for setting up a methodology and implementing suitable data analysis for face recognition from recorded footage, which has been degraded by compression. The majority of CCTV recorders on buses use a proprietary format based on the H.264/MPEG-4 AVC video coding standard, exploiting both spatial and temporal redundancy. Low bitrates are favored in the CCTV industry for saving storage and transmission bandwidth, but they compromise the image usefulness of the recorded imagery. In this context, usefulness is determined by the presence of enough facial information remaining in the compressed image to allow a specialist to recognize a person. The investigation includes four steps: (1) Development of a video dataset representative of typical CCTV bus scenarios. (2) Selection and grouping of video scenes based on local (facial) and global (entire scene) content properties. (3) Psychophysical investigations to identify the key scenes, which are most affected by compression, using an industry implementation of H.264/MPEG-4 AVC. (4) Testing of CCTV recording systems on buses with the key scenes and further psychophysical investigations. The results showed a dependency upon scene content properties. Very dark scenes and scenes with high levels of spatial–temporal busyness were the most challenging to compress, requiring higher bitrates to maintain useful information.