Unmet travel needs can be defined as trips and activities that people need or would like to do more, but for a variety of reasons they are prevented from doing so. This paper provides a critical evaluation of the literature focused on unmet travel needs, with the aim of assessing the scope of existing studies on this topic and better understanding the full context of older people’s mobility. This narrative review identifies how travel needs in later life have been assessed, and the barriers that affect the ability of older people to fulfil these needs. Due to the heterogeneity of older people and differences in research approaches, the analysis of the literature is not conclusive in terms of identifying the real impact of the analysed variables and measures on unrealised mobility. Nevertheless, of the studies analysed, on average at least one-third of older people report unmet travel needs. This situation was found to worsen with age, and women were reported to be more affected than men. The pursuit of leisure, and in particular visiting friends and family, was found to be the activity most associated with unmet travel needs.

One of the primary functions of the layers that make up the trackbed in a conventional railway track system is to distribute wheel/rail contact forces so that the stresses in the subgrade are of an acceptable level. To ensure that the trackbed layers perform this task adequately, there are a number of design procedures that may be used to calculate an appropriate thickness of the layers. This paper describes and compares five such design procedures from the USA, UK, Europe, and Japan. The comparisons show that the design procedures give large differences in the calculated layer thicknesses which are due to the design methodologies used in each procedure. Consequently, to enable an appropriate design procedure to be adopted for a given set of conditions, it is necessary to have a thorough knowledge of the methodologies employed together with their inherent assumptions.

The measurement of road roughness is important for the management of economic road maintenance. Not only is it an indicator of road condition and ride quality, but it also is used to determine road use costs, including travel time, fuel consumption, and vehicle maintenance. Because of the importance of roughness for road asset management decision-making, road agencies spend considerable resources trying to measure road roughness in a repeatable and reproducible manner. However, many road agencies with large road networks are unable to record the condition of the entire network on a sufficiently frequent basis to determine adequately road condition to make informed preventative maintenance decisions. To address this, research has been carried out to develop low cost smartphone based technologies fitted inside vehicles to measure road condition. The trial of these systems has met with varying degrees of success. This paper presents an in-depth parametric study carried out using state-of-the-art vehicle dynamics software, informed by a review of the literature, to appreciate how and to what degree various influencing variables might affect roughness measurements using a smartphone fitted to a moving vehicle. These variables included the type and position of the smartphone; the type, speed, mass, dynamic response, suspension system, and tire pressure of the vehicle in which the smartphone is fitted; and the longitudinal road profile. The results of the parametric analysis were used to build multivariate linear regression and machine learning algorithms which predict road roughness from a measure of a vehicle’s vertical acceleration taking into account the predominant influencing variables. The multivariate linear regression equations can be used to predict road roughness with a similar degree of accuracy that is expected from a visual inspection. On the other hand, the machine learning algorithms, when suitably trained, were able to estimate reliably the road roughness on an integer-based rating scale at a level of detail which is suitable for strategic road asset management, provided that the vehicle type and speed and the type of smartphone are taken into account.

This paper describes the development of a model which can be used to evaluate the reliability of a sleeper system when the sleeper condition is known. Two cases that are of particular interest to the railway industry are investigated. These are the failure of a sleeper system caused by at least two consecutive failed sleepers and by at least three consecutive failed ones, respectively. A model to optimize sleeper maintenance is proposed, which minimizes the number of sleepers restored during immediate maintenance subject to meeting the requirements of reliable and safe sleeper operation. Finally, an example is given to illustrate the model.