This paper assesses the contribution of the Ecological Footprint as a method for estimating the environmental impact of festivals. It responds to calls for more rigorous methods to assess the environmental impacts of festivals, and contributes towards providing festival organisers and policy-makers with a more balanced evaluation of their outcomes. This paper focuses on the 2012 Hay Festival of Literature and Arts (Wales, United Kingdom), and describes how the Ecological Footprint was used to calculate the environmental impact of its visitors. It also considers the potential value of the Ecological Footprint as a method for evaluating alternative strategies designed to improve the environmental sustainability of festivals. The paper demonstrates that Ecological Footprint analysis can provide valuable information for festival organisers and policy-makers on factors influencing the scale of a festivals’ environmental impact, and the types of strategies needed to reduce the effect of visitor travel.

Predicting how changes to the urban environment layout will affect the spatial distribution of pedestrian flows is important for environmental, social and economic sustainability. We present longitudinal evaluation of a model of the effect of urban environmental layout change in a city centre (Cardiff 2007–2010), on pedestrian flows. Our model can be classed as regression based direct demand using Multiple Hybrid Spatial Design Network Analysis (MH-sDNA) assignment, which bridges the gap between direct demand models, facility-based activity estimation and spatial network analysis (which can also be conceived as a pedestrian route assignment based direct demand model). Multiple theoretical flows are computed based on retail floor area: everywhere to shops, shop to shop, railway stations to shops and parking to shops. Route assignment, in contrast to the usual approach of shortest path only, is based on a hybrid of shortest path and least directional change (most direct) with a degree of randomization. The calibration process determines a suitable balance of theoretical flows to best match observed pedestrian flows, using generalized cross-validation to prevent overfit. Validation shows that the model successfully predicts the effect of layout change on flows of up to approx. 8000 pedestrians per hour based on counts spanning a 1 km²city centre, calibrated on 2007 data and validated to 2010 and 2011. This is the first time, to our knowledge, that a pedestrian flow model with assignment has been evaluated for its ability to forecast the effect of urban layout changes over time.

The idea that quantum effects could be harnessed to allow faster computation was first proposed by Feynman. As of 2020 we appear to have achieved `quantum supremacy', that is, a quantum computer that performs a given task faster than its classical counterpart. This paper examines some possibilities opened up by potential future application of quantum computing to transportation simulation and planning. To date, no such research was found to exist, therefore we begin with an introduction to quantum computing for the programmers of transport models. We discuss existing quantum computing research relevant to transportation, finding developments in network analysis, shortest path computation, multi-objective routing, optimization and calibration - of which the latter three appear to offer the greater promise in future research. Two examples are developed in greater detail, (1) an application of Grover's quantum algorithm for extracting the mean, which has general applicability towards summarizing distributions which are expensive to compute classically, is applied to an assignment or betweenness model - quantum speedup is elusive in the general case but achievable when trading speed for accuracy for limited outputs; (2) quantum optimization is applied to an activity-based model, giving a theoretically quadratic speedup. Recent developments notwithstanding, implementation of quantum transportation algorithms will for the foreseeable future remain a challenge due to space overheads imposed by the requirement for reversible computation.

Betweenness is a measure long used in spatial network analysis (SpNA) to predict flows of pedestrians and vehicles, and more recently in public health research. We improve on this approach with a methodology for combining multiple betweenness computations using cross-validated ridge regression to create wide-scale, high-resolution transport models. This enables computationally efficient calibration of distance decay, agglomeration effects, and multiple trip purposes. Together with minimization of the Geoffrey E. Havers (GEH) statistic commonly used to evaluate transport models, this bridges a gap between SpNA and mainstream transport modeling practice. The methodology is demonstrated using models of bicycle transport, where the higher resolution of the SpNA models compared to mainstream (four-step) models is of particular use. Additional models are developed incorporating heterogeneous user preferences (cyclist aversion to motor traffic). Based on network shape and flow data alone the best model gives reasonable correlation against cyclist flows on individual links, weighted to optimize GEH (r² = 0.78, GEH = 1.9). As SpNA models use a single step rather than four, and can be based on flow data alone rather than demographics and surveys, the cost of calibration is lower, ensuring suitability for small-scale infrastructure projects as well as large-scale studies.

Recent years have seen renewed policy interest in urban cycling due to the negative impacts of motorized traffic, obesity and emissions. Simulating bicycle mode share and flows can help decide where to build new infrastructure for maximum impact, though modelling budgets are limited. The four step model used for vehicles is not typically used for this task as, aside from the expense of use, it is designed around too-large zone sizes and a simplified network. Alternative approaches are based on aggregate statistics or spatial network analysis, the latter being necessary to create a model sufficiently sensitive to infrastructure location, although still requiring considerable modelling effort due to the need to simulate motor vehicle flows in order to account for the effect of motorized traffic in disincentivising cycling. The model presented uses an existing spatial network analysis methodology on an unsimplified network, but simplifies the analysis by substituting explicit prediction of motorized traffic flow with an alternative based on road classification. The method offers a large reduction in modelling effort, but nonetheless gives model correlation with actual cycling flows (R² = 0.85) broadly comparable to a previous model with motorized traffic fully simulated (R² = 0.78).

This paper develops spatial network metrics that contribute to analysis of regional development. We use the sDNA software to derive longitudinal road network density and efficiency measures based on the network within a 1‐hr travel time buffer. We estimate this travel time itself from network shape and show it to be comparable to Google Maps travel time data. Economic analysis of 374 Local Administrative Units in the UK mainland shows cross‐sectional association between our network density and efficiency measures and Gross Value Added per capita (GVApc), whether measured in bivariate correlation or in multiple regression controlling for population, education, economic activity rate and rail stations. This is however both mediated and moderated by the proportion of knowledge‐based businesses; regions lacking a strong knowledge‐based sector show only weak correspondence between GVApc and accessibility. Looking at change over time, increase in network accessibility is linked to growth in the knowledge‐based sector, but inversely linked to economic performance during the 8‐year period studied, a finding which remains unexplained. Although further substantiation is needed, results suggest that the policy of transport investment as a driver of UK economic growth may be less effective in areas lacking potential to develop a strong knowledge‐based sector.