Analysing flows in a transportation network is a complex task, especially when the system is congested. The underlying reason is that the traffic flows result from the interactions of all participants in the network. In this paper a simple simulation model for a congested transportation network is shortly described. In the model the individual interactions between the participants play a major role. The simplicity of the model makes it possible to focus attention on the effects of different kinds of information mechanisms on the resulting traffic flows. In the presented model each individual travels several periods in the network. The route and departure time choice are made by using individual stochastic utility functions. A learning mechanism is used in these functions to model the individuals' experience of the situation in the network in the past. This implies that every next period the route and departure time choice are based on a better knowledge of the congested network. The learning mechanism in the utility function can be changed to model more advanced kinds of information acquisition. Four different kinds of information mechanisms will be presented. One of these is a real-time information system (RTI) which has been in the centre of interest recently (e.g. the DRIVE-project). Finally some simulations with the model are carried out. This is done with a program written in the language C. The congested network, used for the simulations, represents the major roads around Amsterdam in 1989. The simulations will lead to some interesting results. It will be shown among others that, depending on the way information is obtained in a congested network, following the shortest route in time will not always lead to the shortest travel time in the whole network. 1 Information as a Tool for Changing Travel Behaviour Transport networks are increasingly faced with the problem of negative externalities (congestion, pollution) which tend to reduce the overall performance of networks. Apart from market solutions (e.g. charges), regulatory measures (e.g. car pooling stimuli), transport related instruments (e.g. parking policies) and technological options (e.g. low emission cars), information provision is increasingly regarded as a vehicle for improving the efficiency of networks. The implementation and application of infonnation systems in transport networks is likely to offer new promising possibilities for tackling the congestion problem (and hence indirectly the environmental problem). Developers of route guidance systems strongly believe that information systems can reduce travel time and mileage in road transport. The main idea is that without information on road situations most drivers base their choices on inferior (biased or incomplete) knowledge on the situation in the network, which leads to poor route and departure time choices (Ben-Akiva et al, 1991). By proper information provision to (potential) drivers 'better' choices will be made due to an improvement of drivers' perceptions and knowledge, and hence road congestion will decrease as well as the travel times of individual drivers. Other researchers hold some reservations and argue that drivers, once provided with (estimated or real-time) information, will possibly face a situation of oversaturation, overreaction or overconcentration, with the consequence that congestion will remain unchanged or even increase. See for arguments amongst others Araott et al. (1991), Ben-Akiva et al. (1991) and Mahmassani and Jayaknshnan (1991). Thus it is not a priori evident that information will lead to a reduction in congestion. There is a need for a more rigorous analysis of the impacts of infonnation on drivers' choice, based on a formal behavioural model describing the attributes and consequences of choices in case of presence and use of road information systems for drivers. By 1 Oversaturation means that a driver is not able to process the information on overall road situations in order to select the optimal individual route. Overreaction occurs when drivers'reactions to infonnation (e.g. route choice) lead to a shift in congestion from one road to another. Information will lead to concentration if a great number of drivers choose the same best altemative. These three phenomena may offset the overall benefits of infonnation provision and acceptance.
[1]
Y. She.
Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods
,
1985
.
[2]
André de Palma,et al.
Does providing information to drivers reduce traffic congestion
,
1991
.
[3]
Dietrich Braess,et al.
Über ein Paradoxon aus der Verkehrsplanung
,
1968,
Unternehmensforschung.
[4]
E. Lehmann.
Testing Statistical Hypotheses
,
1960
.
[5]
André de Palma,et al.
Departure Time and Route Choice
,
1986
.
[6]
David E. Boyce,et al.
ROUTE GUIDANCE SYSTEMS FOR IMPROVING URBAN TRAVEL AND LOCATION CHOICES. IN: TRANSPORT AND INFORMATION SYSTEMS
,
1988
.
[7]
Moshe Ben-Akiva,et al.
Discrete Choice Analysis: Theory and Application to Travel Demand
,
1985
.
[8]
Hani S. Mahmassani,et al.
Dynamic models of commuter behavior: Experimental investigation and application to the analysis of planned traffic disruptions
,
1990
.
[9]
J. Horowitz.
The stability of stochastic equilibrium in a two-link transportation network
,
1984
.
[10]
Moshe Ben-Akiva,et al.
Dynamic network models and driver information systems
,
1991
.
[11]
Hani S. Mahmassani,et al.
System performance and user response under real-time information in a congested traffic corridor
,
1991
.
[12]
P. Bovy,et al.
ROUTE CHOICE: WAYFINDING IN TRANSPORT NETWORKS
,
1990
.