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In the last two decades, different policy initiatives have been set up to increase the share of intermodal freight transport through a modal shift. In the design of these policies, often critical break-even distances are set, showing the cost or price competitiveness of intermodal transport to delineate transport routes that qualify for such a modal shift. In this chapter, we discuss to which extent such break-even distances can be generalized on a larger scale and how they are calculated.

We use two price-based models to calculate break-even distances for an intermodal rail and an intermodal barge transport case. General break-even values do not show the price variation in the transport market and vagueness in the calculation of these values adds to this problem.

We find that for the inland waterway case, intermodal barge transport shows potential on shorter distances as well. In addition, different ways to lower the break-even distance are discussed and a framework for calculating break-even distances is suggested.

The research elaborates on break-even distances in a European context using price data which are fluctuating over time, location specific and often not publicly available.

Policy initiatives promoting intermodal transport should not focus solely on long distance transport. Moreover, evaluating the competitiveness of the intermodal sector solely on a price comparison dishonours its true potential.

This chapter challenges the current European policy on intermodal transport by showing the price competitiveness of intermodal transport in two cases.

This study analyses the effect of fuel efficiency increase on travel demand in the city of Berlin. Vehicle technologies such as advanced driver assistance systems can help drivers to save fuel and thus lower exhaust emissions on a network level. In order to obtain high political endorsement among different stakeholders, the analysis of such effects which have an impact on overall fuel and emission savings are highly relevant. Recent testing of so called advanced driver assistance systems showed their ability to reduce fuel consumption and lower traffic emissions by giving driving recommendations to drivers.

Two effects on driving were simulated using a travel demand model: the increase in fuel prices which will take place in the coming years and a possible increase in vehicle fuel efficiency. Comparing these scenarios allowed us to calculate the effect of price change and the rebound effect of fuel efficiency gains using standard methods for transport elasticities. The simulation was run with the travel demand model TAPAS and the city of Berlin was the network used as a case study.

As fuel prices increase over time, driving tends to decrease. Driving increases, however, if vehicles become more fuel efficient and the result is the observed rebound effect. On a city network level, this also translates to lower emission savings than expected from the vehicle fuel efficiency gains. The rebound effect which we estimated matches similar findings in the literature, specifically in terms of their magnitude.

We used a simulation to compare scenarios of city travel demand. The result allowed us to estimate changes to the desired variables of fuel efficiency and fuel prices. For those interested in the effects of vehicle efficiency gains on city level these results are highly recommended for consideration.

The proposed framework for analysing rebound effects helped to assess the impacts of energy efficiency technologies on a city level.