Problem, research strategy, and findings: It is commonly asserted that so-called compact development is the urban form most able to sustainably accommodate growth by reducing travel distances and conserving land, but credible supportive evidence remains limited. This study rigorously and realistically tested the relative performance of spatial options over the next 30 years for three distinct kinds of English city regions. Statistical models first forecast the behavior of people within interacting markets for land and transport. These outputs were then fed to established simulation models to generate 26 indicators measuring the economic efficiency, resource use, social impact, and environmental impact of the spatial options. This permitted an explicit comparison of the costs and benefits of compact against sprawling urban forms for these regions. While the prototypes (i.e., compaction, sprawl, edge expansion, and new towns) were indeed found to differ in their sustainability, no one form was clearly superior. Rather, the change to “white collar” lifestyles and associated population growth dominates the impacts on the natural environment and resources, far overwhelming those attributable to spatial urban form. Takeaway for practice: Urban form policies can have important impacts on local environmental quality, economy, crowding, and social equity, but their influence on energy consumption and land use is very modest; compact development should not automatically be associated with the preferred spatial growth strategy. Research support: The research was funded by the UK Engineering and Physical Science Research Council.

Driving is important for older people to maintain mobility. To reduce age‐related functional decline, older drivers may adjust their driving by avoiding difficult situations. One of these situations is driving in adverse weather conditions such as in the rain, snow and fog which reduce the visual clarity of the road ahead. The upcoming highly automated vehicle (HAV) has the potential of supporting older people. However, only limited work has been done to study older drivers’ interaction with HAV, especially in adverse weather conditions. This study investigates the effect of age and weather on takeover control performance among drivers from HAV. A driving simulation study with 76 drivers has been implemented. The participants took over the vehicle control from HAV under four weather conditions clear weather, rain, snow and fog, where the time and quality of the takeover control are quantified and measured. Results show age did affect the takeover time (TOT) and quality. Moreover, adverse weather conditions, especially snow and fog, lead to a longer TOT and worst takeover quality. The results highlighted that a user‐centred design of human–machine interaction would have the potential to facilitate a safe interaction with HAV under the adverse weather for older drivers.

This paper illustrates the early results of ongoing research developing novel methods to analyse and simulate the relationship between trasport-related air pollutant concentrations and easily accessible explanatory variables. The final scope is to integrate the new models in traditional traffic management support systems for a sustainable mobility of road vehicles in urban areas.This first stage concerns the relationship between the hourly mean concentration of nitrogen dioxide (NO2) and explanatory factors reflecting the NO2 mean level one hour back, along with traffic and weather conditions. Particular attention is given to the prediction of pollution peaks, defined as exceedances of normative concentration limits. Two model frameworks are explored: the Artificial Neural Network approach and the ARIMAX model. Furthermore, the benefit of a synergic use of both models for air quality forecasting is investigated.The analysis of findings points out that the prediction of extreme concentrations is best performed by integrating the two models into an ensemble. The neural network is outperformed by the ARIMAX model in foreseeing peaks, but gives a more realistic representation of the concentration's dependency upon wind characteristics. So, the Neural Network can be exploited to highlight the involved functional forms and improve the ARIMAX model specification. In the end, the study shows that the ability to forecast exceedances of legal pollution limits can be enhanced by requiring traffic management actions when the predicted concentration exceeds a lower threshold than the normative one

Driving is closely linked to older people’s mobility and independence. However, age-related functional decline reduces their safe driving abilities and thereby their wellbeing may decline. The rapid development of vehicle automation has the potential to enhance the mobility of older drivers by enabling them to continue driving safer for longer. So far only limited work has been carried out to study older drivers’ interaction with highly automated vehicles (HAV). This study investigates the effect of age and level of driving disengagement on the takeover control performance in HAV. A driving simulation study with 76 drivers has been conducted. Results showed that 20 s was sufficient for drivers to take over control from HAV. Older drivers take longer to respond and make decisions than younger drivers. The age effect on some aspects of takeover quality, in terms of operating steering wheel and pedals, is still pronounced. In addition, complete disengagement in driving in HAV leads to a longer takeover time and worse takeover quality, and it affects older drivers more seriously than younger drivers. The results highlight that an age-friendly design of human-machine interaction is important for enhancing the safety and comfort of older drivers when interacting with HAVs.