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.     

The competitive environment of a service industry: the examples of the U.K.-Continent passenger sea ferry services

The paper examines an international service sector (passenger sea ferry services between the U.K. and continental Europe) from the perspective of its competitive environment. Porter's five forces model forms the basis of the analysis of the market illustrated by recent develoments. Within this context the barriers to entry into and exit from the market are considered in detail. Recently, there have been many changes in market structure, and with increased competition, including that from the Channel Tunnel, it may be necessary to pool services. This will improve ferry competition with other forms of transport, but will require the brand awareness of ferries to shift from the ferry company to the ferry service offer. The service offer will need to be more clearly defined.     

The Mixed Logit model: The state of practice

The mixed logit model is considered to be the most promising state of the art discrete choice model currently available. Increasingly researchers and practitioners are estimating mixed logit models of various degrees of sophistication with mixtures of revealed preference and stated choice data. It is timely to review progress in model estimation since the learning curve is steep and the unwary are likely to fall into a chasm if not careful. These chasms are very deep indeed given the complexity of the mixed logit model. Although the theory is relatively clear, estimation and data issues are far from clear. Indeed there is a great deal of potential mis-inference consequent on trying to extract increased behavioural realism from data that are often not able to comply with the demands of mixed logit models. Possibly for the first time we now have an estimation method that requires extremely high quality data if the analyst wishes to take advantage of the extended behavioural capabilities of such models. This paper focuses on the new opportunities offered by mixed logit models and some issues to be aware of to avoid misuse of such advanced discrete choice methods by the practitioner.     

The Wheel Shimmy Problem: Its Relationship to Wheel and Road Irregularities

The equations of motion are derived for a single wheel steerable pneumatic tire system. Included in this system are a built-in wheel wobble and wheel-tire irregularities which produce oscillation of the normal load. Special emphasis is placed on the dynamic characterization of the tire cornering force and aligning torque. The results show that the built-in wheel wobble causes a steady shimmy which is large when the wheel rotation frequency is close to the natural shimmy frequency. The results also show that a normal load oscillation which has a frequency approximately twice the natural shimmy frequency causes a decrease in shimmy stability.