The optimal spacing of bus-stops along a local bus-route with nonuniform many to many travel demand is determined. Weak assumptions are made regarding the local street network. The daily demand functions are assumed to vary slowly within a spacing. The analysis is based on continuum approximations and methods of calculus. The probability of a null demand for boarding and alighting a given bus at a given bus-stop is exploited to space stops closer than otherwise.

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The optimum zone structure for a planned urban commuter rail line with one to many or many to one type demand during peak periods is analyzed. The objective is to minimize the passenger time costs as well as the relevant system operating and capital costs. The analysis is based on the interaction of a range of variables such as the number of zones, zone boundaries, uniform station spacings, fleet size and train headway. The approximately optimum values of the decision variables are determined mostly in closed form. A numerical application and sensitivity analyses are presented.

The use of disaggregate models in modelling intercity passengers mode choice behaviour has emerged over the past 20 years. In an attempt to encourage this use, the present paper addresses the advantages and disadvantages of the disaggregate approach as opposed to the conventional aggregate techniques. The results of a literature review in this regard indicate that disaggregation is statistically and behaviourally necessary to model human travel behaviour while being sensitive in selecting the unit of analysis. The paper also compares the logit analysis with other modelling techniques available for application in order to identify the most suitable one. A critical review of previous modelling efforts in the U.S. and Canada, based on the disaggregate logit analysis is presented to demonstrate the applicability of this technique to modelling intercity passengers mode choice behaviour. Some modelling drawbacks and the general findings of the studies are emphasized to provide useful insight for future modelling considerations.

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