Transit assignment procedures need to reflect the constraints imposed by line routes and timetables. They require specific search algorithms that consider transfers between transit lines with their precise transfer times. Such an assignment procedure is presented for transit networks using a timetable-based search algorithm. In contrast to existing timetable-based search methods employing a shortest-path algorithm, the described procedure constructs connections using branch and bound techniques. This approach significantly reduces computing time, thus facilitating the use of timetable-based assignment for large networks. At the same time, it produces better results in cases where slow but cheap or direct connections compete with fast connections that are more expensive or require transfers.

The importance of critical infrastructures and strategic planning in the context of extreme events, climate change and urbanization has been underscored recently in international policy frameworks, such as the Sustainable Development Goals (SDGs), the Sendai -HABITAT 2016). This paper outlines key research challenges in addressing the nexus between extreme weather events, critical infrastructure resilience, human vulnerability and strategic planning. Using a structured expert dialogue approach (particularly based on a roundtable discussion funded by the German National Science Foundation (DFG)), the paper outlines emerging research issues in the context of extreme events, critical infrastructures, human vulnerability and strategic planning, providing perspectives for inter-and transdisciplinary research on this important nexus. The main contribution of the paper is a compilation of identified research gaps and needs from an interdisciplinary perspective including the lack of integration across subjects and mismatches between different concepts and schools of thought.

This paper presents a method for generating origin–destination (O-D) matrices with the use of floating phone data, that is, data generated from mobile phones moving through a study area. Mobile phone signals recorded in the cellular phone network are used to derive time–space trajectories of moving mobile phone devices. The start and end points of each trajectory determine the origin and destination zone. Link counts are used to project the sample of mobile phone movements to the broader movement of cars, trucks, and rail passengers. With results of a clustering process of traffic counts, O-D matrices for typical traffic days are computed. The resulting O-D matrices can be used for a long-term traffic state forecast.

The introduction of automated vehicles is expected to affect traffic performance. Microscopic traffic simulation offers good possibilities to investigate the potential effects of the introduction of automated vehicles. However, current microscopic traffic simulation models are designed for modelling human-driven vehicles. Thus, modelling the behaviour of automated vehicles requires further development. There are several possible ways to extend the models, but independent of approach a large problem is that the information available on how automated vehicles will behave is limited to today’s partly automated vehicles. How future generations of automated vehicles will behave will be unknown for some time. There are also large uncertainties related to what automation functions are technically feasible, allowed, and actually activated by the users, for different road environments and at different stages of the transition from 0 to 100% of automated vehicles. This article presents an approach for handling several of these uncertainties by introducing conceptual descriptions of four different types of driving behaviour of automated vehicles (Rail-safe, Cautious, Normal, and All-knowing) and presents how these driving logics can be implemented in a commonly used traffic simulation program. The driving logics are also linked to assumptions on which logic that could operate in which environment at which part of the transition period. Simulation results for four different types of road facilities are also presented to illustrate potential effects on traffic performance of the driving logics. The simulation results show large variations in throughput, from large decreases to large increases, depending on driving logic and penetration rate.

State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes). The paper presents a matching algorithm, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with noninteger demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up. The travel demand model of the Stuttgart Region serves as an application example. The study estimates that the entire travel demand of all motorized modes in the Stuttgart Region could be transported by 7% of the current car fleet with 65% of the current vehicle distance traveled, if all travelers were willing to either use ridesharing vehicles with 6 seats or traditional rail.