The formulation of marketing strategy in urban public transport

This paper prescribes the parameters of marketing strategy formulation in the context of urban public transport and emphasises in particular the usefulness of the marketing mix and segmentation concepts. A marketing mix comprising the two primary dimensions of service attributes and promotion is advocated, the service attributes to include price, in-vehicle time, mesh density, frequency, reliability and comfort. Three primary trip purpose segments are identified viz. journey-to-work, shopping, and leisure/social trip segments, but these may be expanded into a twenty-four cell matrix once origin-destination and car access factors are included. The sensitivity of demand in these segments is examined mostly by reference to demonstration and experimental project data drawn from the United States and the United Kingdom. For the three primary trip purpose segments demand elasticity is shown to be greater for non-price than for price features; for both price and non-price service features elasticity is shown to be a function of journey purpose being least elastic for journey-to-work trips and most elastic for social/leisure trips. The role of promotion is examined and its importance to the successful marketing of public transport clearly demonstrated.This paper is a condensation of material appearing in The Management of Urban Public Transport: a marketing approach, by P. J. Hovell, W. H. Jones, and A. J. Moran, to be published by Saxon House and Lexington Books in 1975.     

Assessing the impact of the built environment on travel behavior: a case study of Buffalo,New York

Assessing the impact of characteristics of the built environment on travel behavior can yield valuable tools for land use and transportation planning. Of particular interest are planning models that can estimate the effects of ‘smart growth’ planning. In this paper, a post-processor method of quantifying and searching for relationships among many aspects of travel behavior and the built environment is developed and applied to the Buffalo, NY area. A wide scope of travel behavior is examined, and over 50 variables, many of which are based on high-detail data sources, are examined for potentially quantifying the built environment. Linear modeling is then used to relate travel behavior and the built environment, and the resulting models may be applied in a post-processor fashion to travel models to provide some measure of sensitivity to built environment modifications. The study’s findings demonstrate that mode choice is highly correlated to measures of the built environment, and that many of the principles of smart growth appear to be a valid way to encourage non-vehicle travel. Home-based VHT and VMT appear to be affected by the built environment to a lesser degree.     

Identifying variations and co-variations in discrete choice models

This study introduces an extended version of a standard multilevel cross-classified logit model which takes co-variations into account, i.e., variations jointly caused by two or more unobserved factors. Whilst focusing on mode choice behavior, this study deals with four different types of variation: spatial variations, inter-individual variations, intra-individual variations and co-variations between inter-individual and spatial variations. Such co-variations represent individual-specific spatial effects, reflecting different responses to the same space among individuals, which may for example be due to differences in their spatial perceptions. In our empirical analysis, we use data from Mobidrive (a continuous six-week travel survey) to clarify the existence of co-variation effects by comparing two models with and without co-variation terms. The results of this analysis indicate that co-variations certainly exist, especially for utility differences in bicycle and public transport use in comparison with car use. We then sequentially introduce four further sets of explanatory variables, examine the sources of behavioral variations and determine what types of influential factors are dominant in mode choice behavior.     

Exploring the impacts of road surface texture on tyre/road noise - A case study in Hong Kong

The impacts of road surface texture on tyre/road noise are explored by analysing field data applying fast Fourier transform technique to tyre/road noise spectrum analysis. The results indicate that the tyre/road noise can be higher at lower frequency bands but lower at higher bands. Cluster analysis on surface texture in terms of wavelength identified three cluster groups that centred at 1.32 mm, 3.65 mm, and 5.11 mm have different impacts on noise. The shorter wavelength group suppressed tyre/road noise, medium group aggravated it and the high wavelength group were outliers.     

A genetic algorithm-based method for improving quality of travel time prediction intervals

The transportation literature is rich in the application of neural networks for travel time prediction. The uncertainty prevailing in operation of transportation systems, however, highly degrades prediction performance of neural networks. Prediction intervals for neural network outcomes can properly represent the uncertainty associated with the predictions. This paper studies an application of the delta technique for the construction of prediction intervals for bus and freeway travel times. The quality of these intervals strongly depends on the neural network structure and a training hyperparameter. A genetic algorithm–based method is developed that automates the neural network model selection and adjustment of the hyperparameter. Model selection and parameter adjustment is carried out through minimization of a prediction interval-based cost function, which depends on the width and coverage probability of constructed prediction intervals. Experiments conducted using the bus and freeway travel time datasets demonstrate the suitability of the proposed method for improving the quality of constructed prediction intervals in terms of their length and coverage probability.     

How much disruption to activities could fuel shortages cause? – The British fuel crisis of September 2000

In September of 2000 the UK experienced a blockade of oil refineries in response to rising fuel prices. These protests resulted in severe fuel supply disruptions that intensified over the course of about one week. During the peak of the crisis, travel activity by car was curtailed. This paper analyzes survey data collected about two months after the crisis utilizing the recent memory of respondents as to how they would expect this sort of disruption to affect their participation in daily activities. Specifically, we focused on a variety of non-discretionary and discretionary activities and examined what factors are associated with respondents expecting disruption to those activities. Statistical models were developed to analyze how demographic factors, commute mode selection, vehicle characteristics, and various other factors can explain how individuals expect disruption to their activities. Results suggest that the majority of individuals do not expect major disruptions, although for more car-dependent individuals, disruption was expected to be substantial, especially for work-related trips. These results have implications for the potential success and benefits of an integrated transport policy.     

A fuzzy method for predicting the demand for rail freight transportation

The demand for rail freight transportation is a continuously changing process over space and time and is affected by many quantitative and qualitative factors. In order to develop a more rational transport planning process to be followed by railway organizations, there is a need to accurately forecast freight demand under a dynamic and uncertain environment. In conventional linear regression analysis, the deviations between the observed and the estimated values are supposed to be due to observation errors. In this paper, taking a different perspective, these deviations are regarded as the fuzziness of the system's structure. The details of fuzzy linear regression method are put forward and discussed in the paper. Based on an analyzes of the characteristics of the rail transportation problem, the proposed model was successfully applied to a real example from China. The results of that application are also presented here.     

Modelling passenger demand for parkway rail stations

Interest in Parkway stations emerged in the 1980s. These act as convenient out-of-town stations for inter-urban rail journeys. There were 13 so-called Parkway stations in Great Britain in 1999 and two have subsequently been opened. This paper reports the development and application of a new Parkway forecasting model which was conducted for the Association of Train Operating Companies (ATOC), undertaken as part of an extensive update to the Passenger Demand Forecasting Handbook, which recommends demand forecasting frameworks and associated parameters that are widely used in the railway industry in Great Britain. The objective was to develop a model that had more desirable properties and was more straightforward to apply than the previously recommended procedure. The focus is entirely upon inter-urban journeys of over 80 km.The model forecasts the demand for Parkway stations based solely on rail ticket sales data and its properties are illustrated with two case study applications. The nature of Parkway stations forces consideration of competition, and it is demonstrated that the inclusion of a station choice component leads to a somewhat improved explanatory power and a more plausible generalised cost elasticity.In addition to the methodological developments, the model has provided generally reasonable elasticities and forecasts and shown that Parkway users have different preferences to rail travellers in general. In a test based around a newly opened Parkway station, its forecasts are more accurate than the procedure it replaces.