Various types of techniques have been so far used for travel demand forecast in transport planning. In the actual practical transport projects, thechoice of the technique often depends on the analyst's preference or his/her knowledge and experience. However, even if the same data set is used, the estimated results may vary among various types of demand forecast techniques. This could cause the inadequate analysis and it may result into the wrong decision-making. This paper analyzes empirically to what extent the choice of travel demand forecast techniques impact on the estimated results. The urban rail demand forecast in the Tokyo Metropolitan Area will beused for the empirical analysis. The paper focused on the following threefactors that should be considered in the urban rail demand analysis particularly in the mega cities such as London, Paris and Tokyo: the in-vehiclecongestion; the stochastic route choice; and the route choice set. Firstly, it is often observed that the rail-use commuters suffer from the serious in-vehicle congestion in the morning peak hours. As the in-vehicle congestion varies among the rail routes, the rail-use commuters can choose the route by considering not only the travel time and the travel cost but alsothe in-vehicle congestion. When considering the in-vehicle congestion explicitly, the users equilibrium under which any route from an origin to a destination has the same utility level is important. Whether the equilibrium is considered or not may impact on the estimated demand. Secondly, the urban rail network in the mega cities is so complicated that it may make itdifficult for rail users to understand the network well. If it is assumedthat they have incomplete information, it may be better to apply the stochastic approach. Thirdly, the urban rail network in the mega cities is so dense that it enables rail users to choose various alternative routes. When using the probabilistic choice-based technique for demand analysis, the individual choice set should be defined. The following four methods were selected as the rail route choice models: the all-or-nothing (AON) assignment model, the multinominal logit (MNL) model, the user equilibrium (UE) model, and the stochastic user equilibrium (SUE) model. The data used for the empirical analysis is the Tokyo Metropolitan Travel Census 2000. The rail network including the 1,877 zones, 4,850 nodes and 9,796 links in the Tokyo Metropolitan Area is used. The empirical analysis showed that the SUE has the best fitness. However, although the computation time of the SUE ismore than ten times longer than the UE, the difference in fitness betweenSUE and UE is not so significant. The comparison of model fitness betweenthe AON and the MNL shows that the influence of in-vehicle congestion is significant. The way of defining the rail choice set has also considerableeffects the estimated demand. For the covering abstract see ITRD E137145.
Abstract