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More than ever before, public transit must compete in the transport market. This competition is, on the one hand, against steadily increasing car traffic; and on the other hand, between public transit operators. This, in turn, leads to new demands regarding the type, content and quality of data needed for planning and management. Frequently, traditional travel behaviour surveys do not provide sufficiently accurate and detailed information about public transit demand. To plan public transit, frequently a precise description of all trip stages, including the first and the last mile, is necessary. To achieve this, an adaptation of the traditional survey methods is necessary. In many countries, public transit associations have been established to integrate services offered by individual public transit operators with the help of through-ticketing and a coordination of lines and timetables into what looks, to the user, like a single system. To distribute revenue among the operators involved, detailed surveys of passengers are needed. Measuring the quality of public transit service and surveying customer satisfaction are new tasks. Such data are the basis for quality assurance and are essential for gaining and keeping customers of the public transit system. New technologies such as the Global Positioning System, automated passenger counts and Smart Card Payment Systems offer new possibilities to collect data more efficiently and cost-effectively. This article covers essential aspects of surveys and the collection of data that are crucial for the planning and management of public transit; it points to state-of-the-art methods and offers potential solutions.

This chapter presents a novel visualisation tool, known as Flexible Integrated Transport Services (FITS) that transport commissioners, providers and administrators could employ to specify and edit the operating constraints as they redesign transport services.

The context of rural transport planning is discussed noting that where resources are fewer, effective co-ordination is required to provide passengers with efficient transport services. An overview of the FITS visualisation tool and its different sub-systems (e.g. general information regarding services, operating area, passenger eligibility, fare structure and surcharge structure) is given. Additionally, some key computational details of the system are discussed. Preliminary results of a sample case study that trialled the FITS tool in a specific test run, using simulated transport to health data in the Morayshire and North-West Aberdeenshire area of Scotland are presented. The concluding discussion considers the potential impact of employing tools like FITS in planning transport services in rural and low-demand settings.

Results from the case study show how these effects could be quantified in terms of changes in costs incurred by transport providers, the level of potential demand that could be covered and the associated revenues (fares and subsidies) which could be generated by providers.

The FITS visualisation tool has the potential to act as a planning tool to help transport commissioners, providers and administrators visualise the effects of shifting operating boundaries of flexible transport services.

This chapter examines the airline performance effect arising from collaboration between airlines and high speed railway (HSR). The analysis presents scenario simulations using a bi-level model, which takes into account the effect of competition among airlines and HSR. Using real data, we examine the Japanese domestic market and the Japan-based international market: the markets consist of Tokyo Metropolitan Area, Osaka Metropolitan Area, Seoul/Korea, Frankfurt/Germany, Paris/France, London/United Kingdom, and Los Angeles/United States. Analysis of the domestic market assumes airlines and HSR compete against each other, and analysis of the international market assumes airlines only compete with each other. Initially, we conduct performance analysis using a simulation that mimics the current relationship between airlines and HSR. Then we present three scenarios for different combinations of collaboration between airline and HSR based on airline alliances. The results from this exercise are then used to examine the impact of the collaboration on the profits of airlines and HSR, passenger’s utility, and the network design of airlines. Last, we show the potential benefit to airlines – profitability, market share, and demand growth – from the airline-HSR collaboration. Our model shows that in Japan: (1) Airlines can improve their profitability in international operations by the collaboration with HSR when airlines set their hubs so they can connect to HSR; (2) The airline which has a lower unit operating cost than rivals and sets its hubs to connect to HSR can improve its joint profit with HSR through collaboration; (3) Airlines that don’t operate domestic flights and don’t set their hubs to connect to HSR encourage increased fare competition by coordinating with HSR, but their profit decreases. Whether these results are generalizable to other regions should be the subject of future study.

It is demonstrated how an analysis of airports’ cost structures and the calculation of long-run marginal costs (MCs) of serving passengers and airplanes can be used as a basis for setting airport charges according to the principles of welfare economics. Based on Norwegian data, the MC for an extra passenger (PAX) and extra air traffic movement (ATM) are used to set airport charges under the assumption that the charges should be equal for all airports in the country. When adjusting the estimates to meet revenue restrictions and comparing the estimates to current charges, we observe that PAX should be charged more and ATM less. This finding is in line with recommendations from the International Air Transport Association (IATA). When allowing charges to vary between airports, we demonstrate how a Ramsey pricing approach can be applied to set differentiated PAX and ATM charges, considering both the supply side (the competitive conditions between the airlines operating at the airports) and the demand side (the passengers’ price elasticity of demand).