Benjamin Kickhöfer

Heterogeneous Tolls and Values of Time in Multi-agent Transport Simulation

In evolutionary algorithms, agents’ genotypes are often generated by more or less random mutation, followed by selection based on the fitness of their phenotypes. This paper shows that elements of this principle can be applied in multi-agent transport simulations, in the sense that a router, when faced with complex interactions between heterogeneous toll levels and heterogeneous values of time, can resort to some amount of randomness rather than being able to compute the exact best solution in every situation. The computational illustrations are based on a real world case study in the province of Gauteng, South Africa.

Rising car user costs: comparing aggregated and geo-spatial impacts on travel demand and air pollutant emissions: Market Structure, Sustainability and Decision Making

Transport is debated by many, and liberalization processes, transport policy, transport and climate change and increased competition between transport modes are the subject of heated discussion. Smart Transport Networks illustrates that whether concerning road, water, rail or air, knowledge on the structure of transport markets is crucial in order to tackle transport issues. The book therefore explores key factors concerning the structure of transport markets, their environmental impact, and questions why decision makers often fail to tackle transport-related problems.

Agent-based Simultaneous Optimization of Congestion and Air Pollution: A Real-World Case Study

Abstract The exclusion of external costs from the behavioral decision making process of individuals yields travel demand beyond the system optimum which implies inefficiencies in the transport system. The present study investigates the effect of congestion optimization on emissions levels and vice versa while considering heterogeneity in individual attributes and choice behavior. In consequence, the resulting correction terms (tolls) are highly differentiated. Furthermore, and going beyond existing literature, the present study proposes a joint optimization of vehicular congestion and emissions. The proposed model uses a microscopic agent-based simulation framework which is applied to a real-world scenario of the Munich metropolitan area in Germany. The combined pricing scheme accounts for both external effects and in an iterative process, agents learn how to adapt their route and mode choice decisions in presence of this combined toll. The results indicate that the combined pricing strategy moves the car transport system towards the optimum, measured by a strong decrease of congestion and emission costs. Furthermore, it is found that pricing emissions only pushes users on routes with shorter distances, whereas pricing congestion only steers users on routes with shorter travel times, and potentially longer distances. That is, the two pricing strategies influence behavior by tendency into opposite directions.

Policy Evaluation in Multiagent Transport Simulations

In democratically organized societies, the implementation of measures with regressive effects on welfare distribution tends to be complicated because of low public acceptance. The microscopic multiagent simulation approach presented in this paper can help to design better solutions in such situations. 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 income is included in user preferences. With the MATSim framework, the implementation is tested in a simple scenario. Furthermore the approach works in a large-scale, real-world example. On the basis of a hypothetical price and speed increase of public transit, effects on the welfare distribution of the population are discussed. This approach, in contrast with applied economic policy analysis, allows choice modeling and economic evaluation to be realized consistently.

Mind the price gap: How optimal emission pricing relates to the EU CO2 reduction targets

Abstract From the transport economic literature, it is known that optimal pricing of (environmental) externalities improves the urban system. In contrast to theory-based optimal pricing strategies, real-world policy setting often follows so-called “backcasting” approaches where certain targets are set, and policy measures are implemented in order to reach those targets. An example for the latter approach is the EU goal to reduce global greenhouse gas emissions in the transport sector by 20% until 2020 with respect to 1990 levels. This article aims to (i) compare optimal pricing and backcasting approaches for a specific case study in a simulation environment by identifying the contribution of each approach in EU’s 2020 emission reduction target, and (ii) to determine the costs required to reach the desired targets. For this purpose, an optimal emission pricing strategy is applied to a real-world scenario of the Munich metropolitan area in Germany. The highly differentiated tolls relate to individual exhaust emissions, i.e. they are calculated using damage cost estimates from the literature and vary over time of day, with traffic situation, and with vehicle type. The results indicate that the desired reduction in CO2 emissions is not reached for optimal pricing approach, and that the initial damage costs estimates need to be multiplied by a factor of 5 in order to reach the target, yielding a price of 350 CO2. When aiming at a decrease of the overall emission costs by 20% (CO2 and local pollutants), the initial cost estimates need to be multiplied by a factor of 10. Furthermore, it is shown that the major contribution to the overall emission reduction stems from behavioral changes of commuters and reverse commuters rather than from urban travelers; under some circumstances, urban travelers even increase their CO2 emission levels. Hence, the study rises awareness that conflicting trends for different types of pollutants and different types of individuals are very likely: an increase in non-methane hydrocarbon levels for urban travelers and freight depicts that pricing emissions does not necessarily result in a reduction of all pollutants or of the emissions levels of all travelers. It is shown how agent-based simulations can be used to provide valuable insights and decision support in such possibly counter-intuitive situations.