Abstract
This paper develops a probabilistic, competing-destinations, assignment model that predicts changes in the spatial pattern of the working population as a result of transport improvements. The choice of residence is explained by a new non-parametric model, which represents an alternative to the popular multinominal logit model. Travel times between zones are approximated by a normal distribution function with different mean and variance for each pair of zones, whereas previous models only use average travel times. The model’s forecast error of the spatial distribution of the Dutch working population is 7% when tested on 1998 base-year data. To incorporate endogenous changes in its causal variables, an almost ideal demand system is estimated to explain the choice of transport mode, and a new economic geography inter-industry model (RAEM) is estimated to explain the spatial distribution of employment. In the application, the model is used to forecast the impact of six mutually exclusive Dutch core-periphery railway proposals in the projection year 2020.
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Notes
Recent examples are Naevdal et al. (1996) and Thorsen et al. (1999). A Dutch example is the LMS model developed by the Hague Consultancy Group (see Daly 2000). Although the HCG has build an extremely large transport model of the entire Netherlands, even this model assumes that the spatial distributions of population and employment are given.
Our software program is available on request.
A provider of location, mapping, and routing information (http://www.and.nl).
For each mode of transport the time needed to travel 1 km is found to decrease with the commuting trip time. For the slow transport mode it starts with about 8 min for a short trip and decreases to 5 min for a long trip. For public transport it starts with about 4 min and decreases to 1.5 min and for car transport it starts with about 3 min and also decreases to 1.5 min.
In this respect it should be noted that a typical commuting model of modal choice contains personal characteristics and attributes of the residential environment, but not prices of the different transport modes. See Dieleman et al. (2002) for a recent example based on the same data set being used in this study.
This implies that the probability of choosing a particular mode of transport may be regressed on the time needed to travel from i to j by that mode using micro data. On the other hand, when the travel times of the alternative modes are not taken into account, the resulting model is not identical to an almost ideal demand system.
Transport cost at the firm level consists of freight cost and passenger transport cost, i.e., personal business travel and shopping travel by the customers of the firm. It has been assumed that the transport cost mark-up on f.o.b. prices for freight depends on distance and for passengers on travel time (during off-peak hours).
Multiplying by about 2.2 gives the comparable numbers for the total population.
Readers interested in the results of an integral cost-benefit evaluation of some of the Maglev variants discussed here are referred to Elhorst et al. (2004).
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Acknowledgements
The authors thank Ward Romp and Dirk Stelder for helping with the idea, the data and running the model. They furthermore thank several anonymous referees, and the participants of the 43rd European Congress and the 40th North American Meetings of RSAI in 2003, for useful comments on earlier versions of this paper.
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Elhorst, J.P., Oosterhaven, J. Forecasting the impact of transport improvements on commuting and residential choice. J Geograph Syst 8, 39–59 (2006). https://doi.org/10.1007/s10109-005-0015-4
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DOI: https://doi.org/10.1007/s10109-005-0015-4