The Roles of ‘Conventional’ and Demand-Responsive Bus Services

Purpose - The roles of ‘conventional’ (fixed-route and fixed-timetable) bus services is examined and compared to demand-responsive services, taking rural areas in England as the basis for comparison. It adopts a ‘rural’ definition of settlements under a population of 10,000. Design/methodology/approach - Evidence from the National Travel Survey, technical press reports and academic work is brought together to examine the overall picture. Findings - Inter-urban services between towns can provide a cost-effective way of serving rural areas where smaller settlements are suitably located. The cost structures of both fixed-route and demand-responsive services indicate that staff time and cost associated with vehicle provision are the main elements. Demand-responsive services may enable larger areas to be covered, to meet planning objectives of ensuring a minimum of level of service, but experience often shows high unit cost and public expenditure per passenger trip. Economic evaluation indicates user benefits per passenger trip of similar magnitude to existing average public expenditure per trip on fixed-route services. Considerable scope exists for improvements to conventional services through better marketing and service reliability. Practical implications - The main issue in England is the level of funding for rural services in general, and the importance attached to serving those without access to cars in such areas. Social implications - The boundary between fixed-route and demand-responsive operation may lie at relatively low population densities. Originality/value - The chapter uses statistical data, academic research and operator experience of enhanced conventional bus services to provide a synthesis of outcomes in rural areas.

Secure and Scalable Statistical Computation of Questionnaire Data in R

Collecting data via a questionnaire and analyzing them while preserving respondents’ privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy-preserving method for collecting and analyzing survey responses using secure multi-party computation (SMC). The method is secure under the semi-honest adversarial model. The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks. The method was evaluated on a questionnaire dataset of 3,158 respondents sampled for a medical study and simulated questionnaire datasets of up to 50,000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.