Multi Agent Simulation has increasingly been used for transportation simulation in recent years. With current techniques, it is possible to simulate systems consisting of several million agents. Such Multi Agent Simulations have been applied to whole cities and even large regions. In this paper it is demonstrated how to adapt an existing multi agent transportation simulation framework to large-scale pedestrian evacuation simulation. The underlying flow model simulates the traffic based on a simple queue model where only free speed, bottleneck capacities, and space constraints are taken into account. The queue simulation, albeit simple, captures the most important aspects of evacuations such as the congestion effects of bottlenecks and the time needed to evacuate the endangered area. In the case of an evacuation simulation the network has time dependent attributes. For instance, large-scale inundations or conflagrations do not cover all the endangered area at once. These time dependent attributes are modeled as network change events. Network change events are modifying link parameters at predefined points in time. The simulation framework is demonstrated through a case study for the Indonesian city of Padang, which faces a high risk of being inundated by a tsunami.
It had been shown previously that so-called agent-based traffic micro-simulations could be used for dynamic traffic assignment, that is, iterative route adjustment, until either a Nash equilibrium or some steady state distribution between alternatives had been found. It was also shown that the same approach could be extended to (departure) time adjustment; that is, time adjustment and route adjustment could exist in the same iterative approach. In this paper it is shown that the approach can be extended to mode choice by forcing every synthetic traveler to consider every available mode. The implementation is verified with a test case for which an approximate solution can be analytically derived and for which it is shown that simulation and theory are consistent. It is then applied to a large-scale real-world example, the metropolitan Zurich, Switzerland, area, with about 1 million inhabitants. For this example, it is shown that the adaptive scheme, albeit seemingly simple, can outperform a more traditional approach that first computes mode choice on the basis of aggregate data and then runs the assignment for car traffic only. Sensitivity tests show that the model reacts in meaningful ways, in particular concerning the interaction between the time structure of activities and mode choice.
Standard economic policy evaluation allows the realization of projects if the aggregated economic benefit outweighs their costs. The use of one single aggregated welfare measure for evaluating and ranking projects has often been criticized for many reasons. A major issue is that differentiated effects on individuals or subgroups of the population are not taken into consideration. This leads to the need for transport planning tools that provide additional information for politicians and decision makers. The microscopic multi-agent simulation approach presented in this paper is capable of helping to design better solutions in such situations. In particular, it is shown that the inclusion of individual income in utility calculations allows a better understanding of problems linked to public acceptance. First, individual income-contingent utility functions are estimated based on survey data in order to describe human mobility behavior. Subsequently, using the MATSim framework, the implementation is tested in a test scenario. Furthermore, and going beyond Franklin (2006), it is shown that the approach works in a large-scale real world example. Based on a hypothetical speed increase of public transit, effects on the welfare distribution of the population are discussed. It is shown that the identification of winners and losers seems to be quite robust. However, results indicate that a conversion or aggregation of individual utility changes for welfare analysis is highly dependent on the functional form of the utility functions as well as on the choice of the aggregation procedure.
Tra c signals ensure security of travelers at junctions and regulate right of way. Furthermore, by assigning green times to the di erent approaches of a junction, they determine and evaluate junctions' performance. There are di erent strategies for tra c signal control: xed-time trafc signal control, for example, periodically repeats the same schedule for signalization, while tra c-responsive signal control reacts dynamically to the prevailing tra c patterns to improve the junction or system performance. Tra c signal control can improve the tra c conditions at a single junction, but the whole system can be worse if a single junction is improved. Hu and Mahmassani (1997) argue that second order or network e ects should be taken into account when e ects of signal control strategies are tested. Network e ects include drivers' reactions: not only route choice, but also scheduling. Thus, tra c control, especially tra c-responsive signals, need certain constraints. Otherwise, tra c may become unstable: rapidly at two nearby junctions, or at the network level (Lämmer and Helbing, 2010). MATSim can capture most of these e ects. This chapter reviews How to cite this book chapter:
In democratically organized societies, the implementation of measures with regressive effects on the welfare distribution tends to be complicated due to low public acceptance. The microscopic multi-agent simulation approach presented in this paper is capable to help designing better solutions in such situations. It is shown that income can be included in utility calculations for a better understanding of problems linked to acceptability. This paper shows how the approach can be used in policy evaluation when including income in the user preferences. Using the MATSim framework, the implementation is tested in a simple scenario. Furthermore, and going beyond (1), it is shown that the approach works in a large-scale real world example. Based on a hypothetical price and speed increase of public transit, effects on the welfare distribution of the population are discussed. It is shown that this approach, in contrast to applied economic policy analysis, allows choice modeling and economic evaluation to be realised in a consistent way.
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