The important role of simulation in Intelligent Transportation Systems

Dr Michel Bierlaire, DMA, Ecole Polytechnique Fédérale de Lausanne

Intelligent Transportation Systems (ITS) in general and Dynamic Traffic Management Systems (DTMS) in particular are designed to improve general traffic conditions using advanced technologies, managed by real-time intelligent software systems. There are two important functions of such systems. Control systems, often called Advanced Traffic Management Systems (ATMS), impose restrictions and constraints on traffic flows. These systems include traffic lights (based on fixed timing or on pro-active rules), ramp metering, speed limit signs (fixed or variable) and lane use signs. In general, driving laws enforce drivers to comply with these systems. By modifying the capacity of the network, ATMS affect transportation supply. Information systems, or Advanced Traveler Information Systems (ATIS), provide traffic information and travel recommendations and guidance to drivers aimed at helping them make better decisions. These include radio forecast, web-based or on-board navigation systems and variable message signs. Such systems differ from ATMS in that drivers are not obligated to follow the recommendations of the system. ATIS, by influencing drivers travel decisions, are designed to influence transportation demand.

An effective application of these systems must therefore be based on an implicit or explicit simulation of the interaction between demand and supply.

DynaMIT is a simulation-based real-time system designed to estimate the current state of a transportation network, predict future traffic conditions, and provide consistent and unbiased information to travelers. DynaMIT is designed to reside in a Traffic Management Center (TMC). It combines real-time data from a surveillance system (composed, for instance, of loop detectors, probe vehicles, incident detection systems, etc.) with historical data (collected and processed every day) in order to estimate the current state of the network, predict future traffic conditions and provide travel information and guidance through an ATIS.

The degree of sophistication of DTMS motivates an intensive evaluation effort to assess their performance, reliability and robustness. Simulation plays an important role in that phase as well. The integrated evaluation methodology includes both a simulation laboratory and a system analysis tool. The simulation laboratory provides a tool for evaluating and enhancing alternative DTMS designs at the operational level. The system analysis tool ensures that the models and simulation have a robust and reliable connection to the real world.

After a brief introduction on ITS and DTMS, the DynaMIT system will be described in details. Key mathematical, algorithmic and methodological issues will be emphasized. The evaluation methodology will then be sketched based on MITSIM Laboratory. A case study, where DynaMIT has been evaluated with MITSIM Laboratory will conclude the presentation.

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Simulation for the evaluation of optimised operations policies in a container terminal

Dr Andrea Rizzoli, IDSIA, Lugano

A simulation model must be used to evaluate the impact of new operations policies, not only to validate the policies, but also as a tool to convince the decision-makers of the potential advantages in adopting the proposed enhancements in the management. Terminal resource allocation policies and ship loading/unloading policies are obtained by means of Operations Research techniques for the case study of La Spezia Container Terminal (LSCT); a simulation model of the terminal is designed, implemented and validated. The simulation model is used to test the policies and to assess their robustness in front of the inherent stochasticity of the real world.

Several applications of discrete events simulation in the industrial environment
of automated assembly and food packaging

Dr Eric Verdebout, Director of CIMPACT SARL, Yverdon

Although several methods compete when it comes to study the flows of work in process in an industrial installation devoted to automated assembly and food packaging, discrete events simulation is particularly adapted to the situations which involve random effects such as jams and breakdowns and stochastic effects of the flow control policy.

This presentation will first explain the specific aspects of industrial situations such as automated assembly and food packaging, from the flow control point of view.

Then, it will expose the advantages and drawbacks of various approaches and the domain of application for discrete events simulation in the field.

Finally, several case studies will demonstrate the interest of this approach in three actual industrial applications:

• Packaging chocolate sortments (massive amount of events with the effect of the topology on the results),
• Managing the after-sales service of a famous watch-maker (effects of the flow control policy in a manual workshop),
• Managing the assembly of a plastic watch (effect of both the flow control policy and topology).

Capacity optimization of chemical plants using material flow simulation: from concepts to applications

Dr. Philippe Solot, Director of AICOS Technologies AG, Basel

In the process industry where production is partially of continuous nature, the simulation methodology has mainly been applied to technical issues such as the thermodynamic analysis of processes. Nevertheless, in the last five years, logistic simulation has become more and more popular in the chemical-pharmaceutical industry as the need for more flexible (so-called multipurpose, multiproduct) plants and hence the importance of logistic issues were recognized.

We will first highlight the differences that exist between the usual discrete manufacturing situation and that prevailing in the process industry. Using a specialized simulation tool as an example, we will present both the data structure and the process model, showing in particular how the whole operation spectrum from semicontinuous manufacturing to material handling issues can be covered. Results derived from several case studies will illustrate to which extent production capacity can be increased.

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