Transport infrastructure owners are moving from reactive toward proactive infrastructure management. This involves computation of costs associated with failure or maintenance, including expected transport delays. These delays are often computed by multiplying additional travel time by the number of travellers. However, this does not reflect the process of decision-making by travellers using the infrastructure asset, such as mode choices, departure time changes and trip cancellations to reduce time wasted in a traffic jam. Therefore, we introduce a multi-modal transport model that simulates travellers’ behaviour after a large-scale infrastructure failure at a critical node in the European TEN-T network. We use a novel approach of modelling the region around the infrastructure disruption in a very detailed manner, whereas the rest of Europe is modelled in a more basic way. This enables us to model impacts of disruptions in high detail, whereas also effects throughout Europe are considered, within reasonable computation time.

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Allowing Level 4 Automated Vehicles (AVs) to drive on highways could potentially have an impact on the road network performance. Although it might probably take a while before AVs are on the road, National Road Authorities (NRAs) are already concerned about understanding what changes would be required on their current infrastructure to make it ready for AVs. In this study, we simulate part of the highway network in the Netherlands, the region of Rotterdam The Hague, to investigate the impact of AVs on the network performance in terms of network travel times and distances travelled. Results allow us to conclude that 50% AVs (Level 4) result in an increase in distance travelled on highways but a decrease in the total network travel times and corresponding delays.@en

Allowing level 4 Automated Vehicles to drive on highways could significantly impact the traffic efficiency and safety. Although it might probably take a while before AVs are on the road, NRAs (National Road Authorities) are already investigating how to adapt their current infrastructure to make it ready for AVs – while optimizing both traffic efficiency and safety. Therefore, in this paper we simulate a highway section in VISSIM including a weaving section calibrated with empirical data. We test different taper lane lengths, different demand levels (0.55 and 0.80 F/C ratios) and different CAV penetration rates (0-100%) and we assess the impact on traffic efficiency and safety. It is concluded that increasing AV penetration rates lead to decreasing travel times for all road users (automated and non-automated). Different taper lane lengths as well as different traffic flow levels do not seem to influence the observed pattern much.@en

Mandatory breaks for truck drivers are nowadays scheduled after the route has been decided. However, in some cases it is beneficial to plan these breaks during waiting time caused by truck driving bans. Optimally planning a single break considering driving bans can be done using Dijkstra’s algorithm with multiple labels. This has large effects on predicted travel times: 17% of the analysed routes having a night rest obtain an earlier arrival time by 5 hours on average. However, the computation times of this algorithm are long. A novel heuristic version of time-dependent contraction hierarchies leads to significant reductions in computation times from several seconds to several milliseconds per route. Experiments show that the solutions are still optimal for a representative test set consisting of 10,000 route queries.@en