A dataset of logistics sites in England and Wales: Location, size, type and loading bays

Data on the location and size of logistics sites is essential for the accurate system-level modelling of transport and logistics operations. This is becoming increasingly important to support governments and industry transition to a net zero future which will feature new operating models and vehicle technologies, particularly for electric vehicle operations. In this work we present a dataset of logistics sites across England and Wales categorised into warehouses, retail sites, and factories. There are 47,683 rows of data in total, comprising 27,691 warehouse sites, 6,441 retail sites, and 13,551 factory sites. Each row contains the site's category, location (latitude and longitude), size (in square meters), and modelled number of heavy goods vehicle loading bays. Raw data on non-domestic properties in England and Wales were sourced from the UK's Valuation Agency Office database. Addresses were geocoded to determine the coordinates of each site, floor area was determined for each site via a web crawler script, and the type of site was derived using a keyword-based categorisation process. The size of the site gives an indication of the expected transport activity (i.e. volume of goods handled) and is a useful proxy to estimate the number of loading bays which, in turn, is a useful proxy for the number of electric heavy goods vehicle charging points the site may have to accommodate to support electric vehicle operations. Models relating the floor area to the number of loading bays were developed using satellite imagery of a sample of data from each category. Uncertainty in the geolocation, category and floor area data is deemed to be very low<1%), while the models to predict loading bay data are based on a sample of the overall dataset and subject to higher uncertainty (<20 %). Larger sample datasets and alternative models may be explored in future work to suit other applications.

Impacts of speed variations on freeway crashes by severity and vehicle type

Speed variations are identified as potentially important predictors of freeway crash rates; however, their impacts on crashes are not entirely known. Existing findings tend to be inconsistent possibly because of the different definitions for speed variations, different crash type consideration or different modelling and data aggregation approaches. This study explores the relationships of speed variations with crashes on a freeway section in the UK. Crashes split by vehicle type (heavy and light vehicles) and by severity mode (killed/serious injury and slight injury crashes) are aggregated based on the similarities of the conditions just before their occurrence (condition-based approach) and modelled using Multivariate Poisson lognormal regression. The models control for speed variations along with other traffic and weather variables as well as their interactions. Speed variations are expressed as two separate variables namely the standard deviations of speed within the same lane and between-lanes over a five-minute interval. The results, similar for all crash types (by coefficient significance and sign), suggest that crash rates increase as the within lane speed variations raise, especially at higher traffic volumes. Higher speeds coupled with greater volume and high between-lanes speed variation also increase crash likelihood. Overall, the results suggest that specific combinations of traffic characteristics increase the likelihood of crash occurrences rather than their individual effects. Identification of these specific crash prone conditions could improve our understanding of crash risk and would support the development of more efficient safety countermeasures.

Looking forward to safer HGVs: The impact of mirrors on driver reaction times

Heavy Goods Vehicle (HGV) collisions are responsible for a disproportionate number of urban vulnerable road user casualties (VRU - cyclists and pedestrians). Blind-spots to the front and side of HGVs can make it difficult (sometimes impossible) to detect close proximity VRUs and may be the cause of some collisions. The current solution to this problem is to provide additional mirrors that can allow the driver to see into the blind-spots. However, keeping track of many mirrors requires frequent off-road glances which can be difficult to execute during demanding driving situations. One suggestion is that driving safety could be improved by redesigning cabs in order to reduce/remove blind-spot regions, with the aim of reducing the need for mirrors, and increasing detection rates (and thereby reducing collisions). To examine whether mirrors delay driver responses we created a series of simulated driving tasks and tested regular car drivers and expert HGV drivers. First we measured baseline reaction times to objects appearing when not driving ('Parked'). Participants then repeated the task whilst driving through a simulated town (primary driving tasks were steering, braking, and following directional signs): driving slowed reaction times to objects visible in mirrors but not to objects visible through the front windscreen. In a second experiment cognitive load was increased, this slowed RTs overall but did not alter the pattern of responses across windows and mirrors. Crucially, we demonstrate that the distribution of mirror RTs can be captured simply by the mirror's spatial position (eccentricity). These findings provide robust evidence that drivers are slower reacting to objects only visible in eccentric mirrors compared to direct viewing through the front windscreen.

21st century trucking: A trajectory for ergonomics and road freight

Over the past decade there has been significant pressure to minimise emissions and safety risks related to commercial driving. This pressure to meet the triple bottom line of cost, environment, and society has often resulted in the rapid application of vehicle technologies designed to mitigate undesired effects. Often the cognitive and behavioural effects of technologies on the commercial driver have not received in-depth analysis to determine comprehensive viability. As such, this paper aims to identify a timescale for implementation for future technologies for UK road freight, and likely associated human factors issues, improving upon the currently employed 'trial-and-error' approach to implementation which may carry high economic, environmental, safety-related risk. Thought experiments are carried out to broadly explore these future systems. Furthermore, this work aims to examine whether technology alone will be enough to meet future CO2 reduction targets, and assess the role of behavioural and systems interventions for future research.