Heinz Spiess
Centre de recherche sur les transports, Université de Montréal, Canada
and EMME/2 Support Center, Aegerten, Switzerland

and

Dieter Suter
Rapp AG Ingenieure und Planer, Basel, Switzerland

September 1990

Abstract:

As our society becomes more concerned with the importance of environmental issues and the dangers of unlimited growth, the purpose of transportation planning is changing, too. It is no longer limited to its traditional role of locating and dimensioning new facilities. While the widely-used peak-hour models are well suited for the latter type of problems, they fail when trying to address any questions regarding the environmental aspects, such as energy consumption, emissions and noise. In an urban setting, the 24-hour model cannot be used for this purpose either, since it does not take into account the varying level of congestion during the day, which influences not only the route choice, but is also crucial in view of the strong non-linearity of some of the impacts to be studied. The use of dynamic assignment models, finally, is still much too complex and costly for most cities. In our paper, we describe a new approach to model the variations of the flows within the day, without leaving the steady-state framework. A small set of basic states, each represented by a demand matrix, is defined and each hour of the day is represented by a characteristic combination of these states. Given hourly traffic counts on a subset of links, the set of coefficients for each hour is determined by combining the equilibrium assignment model with a multiple linear regression. The result is a practical model for forecasting the traffic volumes and speeds on the entire network for each hour of the day. With this new degree of refinement, it becomes possible to assess much more precisely the environmental aspects of the road network. This model has been successfully applied for the city of Basel, Switzerland, using a network of 330 traffic zones, 2200 street links and 220 countposts with hourly counts. Starting with an outdated AM-peak O-D matrix only, we were able to define three basic states which resulted in an all-day model with an overall of 94%, when comparing predicted vs observed hourly volumes. The model forms the base for the computation of the emissions of the various air pollutants.

Introduction

Most of the transportation planning methods which are applied in practice today use steady-state assignments which are based on a 24-hour period, the morning and/or the evening peak hour.

The 24-hour models try to model the average daily traffic on the network links by assigning a daily trip matrix. Since the entire day is modelled by a single state, these models cannot take into account variations of the travel patterns during the day, such as those resulting from temporary congestion during peak periods. Thus, their use is essentially limited to non-urban contexts, in which congestion effects are neglectable.

In peak-hour models, congestion effects can better be taken into account, since the assumed steady state extends to the considered peak period only. Depending on the aim of the study, either the morning peak, the evening peak or both are considered. With this type of model, the planner tries to simulate the "worst case", i.e. the periods in which the network will be most congested. For planning new facilities, this approach is a valid one, since it allows the planner to adjust the dimensions of the proposed new facilities and also to compare the peak performance of different proposed scenarios. This type of planning was most typical for the sixties and seventies, when the main challenge of the planning efforts was to decide how and where to build new roads to cope with the rapid growth.

However, the goal of transportation planning has drastically shifted away from the mere planning of new facilities. More and more, the transportation planner's primary task is to study the impacts of the existing traffic in a more global way, and to assess the effects of changes to the transportation infrastructure with respect to environmental issues, such as emissions of the various pollutants, noise levels and energy consumption. Since these questions are concerned with the total impacts during the entire day, such as computing the total amount of emitted pollutants per day, the peak-hour models are not suitable for this type of problem. On the other hand, the congestion effects are not neglectable in an urban setting, and the impacts to be computed are, in general, very sensitive to the speed of the cars, thus 24-hour models are not suitable either in this context.

Recently, a lot of research has been carried out to replace the steady-state models by true dynamic models, which take into account explicitly the time dependence of the transportation demand. Instead of modelling the demand in the form of a simple O-D matrix, these models also need a departure or arrival time for every trip. Knowing that in real applications, obtaining even a reasonably good static trip matrix is a major challenge, it is clear that providing arrival or departure times at the individual trip level is an almost impossible task to realize in practice. Thus, while the dynamic assignment approach is very promising in the context we consider here, its possibilities for practical applications today are severely limited by the non-availability of the required data and, as well, by a lack of application software which implement these models.

The aim of this paper is to present a practical method to enable the planner to assess this new type of question. It does not require specialized data sets to define the model, nor the use of specialized software to implement.

As will be shown with the results of a study carried out for the city of Basel, Switzerland, the model can (and has been) successfully applied with a minimal set of input data consisting of a coded node/link network with calibrated volume delay functions, an outdated AM peak matrix and observed hourly volumes on a subset of links. The model was implemented by using only a standard version of the EMME/2 transportation planning software (see Spiess [