Joachim Wahle

Decision dynamics in a traffic scenario

Information is a key commodity in many socio-economic systems like stock markets or traffic systems. In this paper the influence of dynamic information on the stability of traffic patterns is investigated using a very simple route choice scenario. The basis of the route decisions is dynamic information generated by traffic flow simulations. A correlation analysis yields that the system can be destabilized by introducing information. It is found that the overall performance of the system is reduced, although the information should help to distribute traffic more efficiently.

The impact of real-time information in a two-route scenario using agent-based simulation

Abstract Since advanced traveler information systems (ATIS) have been introduced, their potential benefits as well as their drawbacks have been discussed controversially. This will continue as long as the drivers’ reactions upon current or even predictive information about the traffic situation are not known. Thus, traffic models that also consider this feedback are necessary. In this paper, we address a basic two-route scenario with different types of information and study the impact of it using simulations. The road users are modeled as agents, a natural and promising approach to describe them. Different ways of generating current information are tested. It is pointed out that the nature of the information very much influences the potential benefits of the ATIS.

Different methods of traffic forecast based on real data

Abstract Different methods to forecast traffic are analysed and discussed. An elementary approach is to develop heuristics based on the statistical analysis of historical data. Daily traffic demand data from 350 inductive loops of the inner city of Duisburg over a period of 2 years served as input. The sets of data are organized into four basic classes and a matching process that assigns these sets into their class automatically is proposed. Furthermore, two models for short-term forecast are examined: the constant and the linear model. These are compared with a prediction based on heuristics. The results show that the constant model provides a good prediction for short horizons whereas the heuristics is better for longer times. The results can be improved with a model that combines the short- and long-term methods.

Microscopic Simulation of Evacuation Processes on Passenger Ships

The analysis of evacuation processes on-board passenger ships has attracted increasing interest over the last years. Most of the approaches utilise so called flow models. Cellular automaton models, widely used for traffic simulations, on the other hand provide a more natural approach towards pedestrian dynamics. Two major difficulties are intrinsical to the problem: two-dimensional movement of pedestrians and the complexity of psychological and social influences. In this paper a simple CA-model for the description of crowd motion is presented and its implementation in a simulation software outlined. The validity of the assumptions and the scope of the applications will have to be scrutinised by comparison with empirical data from actual evacuations or drills.

Agents in Traffic Modelling - From Reactive to Social Behaviour

In modern societies the demand for mobility is increasing daily. One challenge to researchers dealing with transportation is to find efficient ways to model and predict traffic flow, even if the behaviour of people in traffic is not a trivial problem. The social nature of traffic (e.g. coordinated decisions) seems to be a key question, not well explored. We aim at creating a model of drivers as social agents, thus allowing their behaviour to be predicted and considered in the simulation. This may, on its turn, improve the accuracy of the existing Advanced Travel Information Systems (ATIS).

A dynamic route guidance system based on real traffic data

Abstract Mobility is one of the vital goods of modern societies. One way to alleviate congestion and to utilise the existing infrastructure more efficiently are Advanced Traveller Information Systems (ATIS). To provide the road user with optimal travel routes, we propose a procedure in two steps. First on-line simulations supplemented by real traffic data are performed to calculate actual travel times and traffic loads. Afterwards these data are processed in a route guidance system which allows the road user an optimisation with regard to individual preferences. To solve this multiple criteria optimisation problem fuzzy set theory is applied to the dynamic routing problem.

Traffic forecast using simulations of large scale networks

In this contribution an approach to traffic forecast using a microsimulator is presented. In order to provide network-wide information about the current traffic state a cellular automaton traffic flow model is combined with measured data. The framework is applied to the freeway network of North Rhine-Westphalia (NRW), where data from about 3,500 inductive loops are available and provided online minute by minute. Technical aspects of the simulation like the network structure are illustrated. Furthermore, heuristics are developed based on the statistical analysis of historical data.

Simulation of traffic in large road networks

Abstract The modeling and simulation of traffic flow nowadays is one of the most promising strategies to generate traffic information. The efficiency of even microscopic simulation models is high enough to allow for reproducing the flow of whole networks in multiple real time. The details of the car motion have to be defined very carefully in order to yield the correct traffic states in various situations, e.g., bottlenecks, intersections and on- and off-ramps. In this paper the current status of the work in this field is reported and the application to the road network of the downtown area of Duisburg is discussed.

Anticipatory Traffic Forecast Using Multi-Agent Techniques

In this contribution, intelligent transportation systems (ITS) and their impact on traffic systems are discussed. Although traffic forecast offers the possibility to rearrange the temporal distribution of traffic patterns, it suffers from a fundamental problem because the reaction of the driver to the forecast is a priori unknown. On the other hand the behaviour of drivers can have a serious impact on the quality of a traffic forecast since it can result in a feedback - an anticipatory forecast is needed. To include such effects we propose a two-layered agent architecture for modelling drivers’ behaviour in more detail. The layers distinguish different tasks of road users.

A multi-agent system for on-line simulations based on real-world traffic data

Presents and reviews a framework for online simulations and predictions which are based on the combination of real-world traffic data and a multi-agent traffic flow model. The agent architecture consists of two layers which distinguish the different tasks that road users have to perform. The framework is applied to the urban road network of Duisburg and the freeway network of North Rhine-Westphalia. On the basis of historical data, heuristics are derived, which can be combined with the dynamic data of the simulations to provide a short-term traffic forecast. The necessity for an anticipatory traffic forecast, which includes decision-making and route choice behavior of the road users, is discussed.