Is marginal emission cost pricing enough to comply with the EU CO2 reduction targets?

From transport economic literature it is known that pricing (environmental) externalities can improve the efficiency of a transport system. However, in real-world politics, policy setting often follows so-called `backcasting' approaches where predefined goals are set, and policy measures are implemented to reach those goals. This study presents, for a specific case study, an parametric approach to identify the gap between toll levels derived from environmental damage cost internalization and toll levels from the goal to reduce global greenhouse gas emissions in the transport sector until 2020 by 20% (avoidance cost approach). For this purpose, the damage costs internalization is applied to a real-world scenario of Munich metropolitan area. The results indicate that the desired reduction in CO2 emissions is not reached. This parametric internalization approach with damage cost estimates from the literature yields toll levels that are by a factor of 5 too low in order to reach the predefined goal. When aiming at overall emission cost reductions by 20%, the damage cost estimates are even by a factor of 10 too low. Furthermore, it is shown that the major contribution to the overall emission reduction stems from behavioral changes of (reverse) commuters rather than from urban travelers; under some circumstances, the latter even increase their CO2 emission levels. Finally, the study indicates that there might be conicting trends for different types of pollutants, i.e. pricing emissions does not necessarily result in a reduction of all pollutant types

The correlation of externalities in marginal cost pricing: lessons learned from a real-world case study

Negative externalities cause inefficiencies in the allocation of capacities and resources in a transport system. Marginal social cost pricing allows to correct for these inefficiencies in a simulation environment and to derive real-world policy recommendations. In this context, it has been shown for analytical models considering more than one externality, that the correlation between the externalities needs to be taken into account. Typically, in order to avoid overpricing, this is performed by introducing correction factors which capture the correlation effect. However, the correlation structure between, say, emission and congestion externalities changes for every congested facility over time of day. This makes it close to impossible to calculate the factors analytically for large-scale systems. Hence, this paper presents a simulation-based approach to calculate and internalize the correct dynamic price levels for both externalities simultaneously. For a real-world case study, it is shown that the iterative calculation of prices based on cost estimates from the literature allows to identify the amplitude of the correlation between the two externalities under consideration: for the urban travelers of the case study, emission toll levels—without pricing congestion—turn out to be 4.0% too high in peak hours and 2.8% too high in off-peak hours. In contrary, congestion toll levels—without pricing emissions—are overestimated by 3.0% in peak hours and by 7.2% in off-peak hours. With a joint pricing policy of both externalities, the paper shows that the approach is capable to determine the amplitude of the necessary correction factors for large-scale systems. It also provides the corrected average toll levels per vehicle kilometer for peak and off-peak hours for the case study under consideration: again, for urban travelers, the correct price level for emission and congestion externalities amounts approximately to 38 EURct/km in peak hours and to 30 EURct/km in off-peak hours. These toll levels can be used to derive real-world pricing schemes. Finally, the economic assessment indicators for the joint pricing policy provided in the paper allow to compare other policies to this benchmark state of the transport system

Preliminary Results of a Multiagent Traffic Simulation for Berlin

This paper provides an introduction to multi-agent traffic simulation. Metropolitan regions can consist of several million inhabitants, implying the simulation of several million travelers, which represents a considerable computational challenge. We reports on our recent case study of a real-world Berlin scenario. The paper explains computational techniques necessary to achieve results. It turns out that the difficulties there, because of data availability and because of the special situation of Berlin after the re-unification, are considerably larger than in previous scenarios that we have treated

Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems

We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multiagent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level