Freight transport is a fast increasing transportation mode due to the economic growth in the world. Heavy-duty vehicles (HDV) have considerably greater fuel consumption, thus making them a suitable target when new policies in road transport emphasize increased energy efficiency and mitigated emission impacts. Intelligent transportation systems, based on emerging V2X communication technology, open new possibilities for developing fuel-efficient driving support functions considering real traffic information. This indicates a large potential of fuel saving and emission reduction for freight transport. This paper studies a dynamic programming-based optimal speed planning considering a maximum acceleration model for HDVs. The optimal speed control is applied for the deceleration case of HDV platoons due to received information on traffic speed reduction ahead. The control can optimize fuel consumption as well as travel time, and theoretical results for the two cases are presented. For maximal fuel saving, a microscopic traffic simulation study is performed for single HDVs and HDV platoons running in real traffic conditions. The results show a decrease in fuel consumption of more than 80% compared to simulations without applying optimal control, while the fuel consumption of other vehicles in the simulation is not significantly affected.

In Sweden and other countries it is not an uncommon practice that freight trains depart more or less on-demand instead of strictly following a pre-planned timetable. However, the systematic effects of freight trains departing late or, in particular, early has long been a contested issue. Although some microscopic simulation tools currently have the capability to evaluate the effect of freight trains departing before schedule, it has yet not been established how macroscopic simulation tools, capable of fast simulation of nation-wide networks, can manage such tasks. This paper uses a case study on a line between two large freight yards in Sweden to investigate how the results of microscopic and macroscopic simulation, represented by two modern simulation tools, differ when it comes to this particular problem. The main findings are that both the microscopic and the macroscopic tools could replicate the empirical punctuality fairly well, with the macroscopic case study results being closer to the empirical data. Furthermore, allowing early departures of freight trains increased overall freight train punctuality without any major impact on passenger train punctuality, as determined by both tools. The results are encouraging, but further studies are needed to determine if macroscopic simulation is on-par with microscopic simulation.

Railway capacity planning aims to determine the amount of traffic that can be operated on a given infrastructure. The timetable compression method described in UIC Code 406 has become one of the standard tools in this area. Motivated by the Swedish Transportation Administration's timetable independent adaptation of the methodology and its need for extension we explore how the compression method can be applied to evaluate the capacity of the underlying infrastructure for strategic planning rather than the occupation ratio of a specific timetable. By performing ensemble averaging of scheduled train sequences we abstract from a single timetable concept and perform a distributional analysis of timetable utilization. To mitigate decomposition-induced underestimation of network effects the compression area is extended and approaches to include interdependencies between stations and lines are investigated. The methodology is applied for capacity assessment of railway stations and line segments in a case study based on the Swedish Southern Main Line rail corridor.

Railway capacity analysis aims to assess the number of trains that can reasonably be operated on a given infrastructure. The UIC timetable compression method is a widely accepted standard for evaluating and comparing the infrastructure utilization of timetable concepts. Originally designed for capacity analysis of railway lines it has been adapted to railway stations in the second version of UIC Code 406. In this paper we discuss further developments of a statistical approach for station capacity analysis using UIC-compression methodology suited for long-term infrastructure planning. It allows to assess the capability of railway station track layouts based on a distributional analysis of different timetable variants derived from a given traffic concept. As a result, the infrastructure’s flexibility to cope with different timetable variants – which is particularly relevant for deregulated transport markets – can be tested. To better account for train-interdependencies, stations are considered in their entirety and a new time-window oriented gap-filling approach is introduced that preserves the overall timetable structure while allowing to perform local optimization of infrastructure occupation. The approach is demonstrated in a case study based on the Southern part of the Swedish railway network.