In many tram networks multiple lines share tracks and stations, thus requiring robust schedules which prevent inevitable delays from spreading through the network. Feasible schedules also have to fulfill various planning requirements originating from political and economical reasons.In this paper we present a tool set designed to generate schedules optimized for robustness, which also satisfy given sets of planning requirements. These tools allow us to compare time tables with respect to their applicability and evaluate them prior to their implementation in the field.This paper begins with a description of the tool set focusing on optimization and simulation modules. These software utilities are then employed to generate schedules for our hometown Cologne's tram network, and to subsequently compare them for their applicability.

In public transit planning, regularity of timetables is seen as an important means to improve capacity efficiency by assuring an even trip distribution, as well as to improve product attractiveness and appreciation. This paper focuses on examining whether a regular timetable can also help to reduce network delay, especially resulting from inevitable small disturbances. Following the formulation of a mathematical optimization approach, we propose a number of conditions a network has to fulfill for timetable regularity to have a delay reducing impact. A set of three network properties is identified, which consists of (a) the sharing of resources between tram lines, (b) a low variability of driving times, and (c) the non-redundancy of the network's central resources. To test the impact of these properties, a series of optimization and simulation experiments is conducted on models of the tram network of the cities of Cologne, Germany, and Montpellier, France. Small disturbances are introduced to the simulated operations to check whether the presence of all three properties is necessary for a network to benefit from a regular timetable. The results show that while with all properties present a regular timetable can indeed help to reduce delays resulting from small disturbances, the non-compliance with any one of the conditions nullifies the impact of regularity on the result.

Shifting from effect- towards cause-oriented and systemic approaches in sustainable climate change adaptation requires a solid understanding of the climate-related and societal causes behind climate risks. Thus, capturing, systemizing, and prioritizing factors contributing to climate risks are essential for developing cause-oriented climate risk and vulnerability assessments (CRVA). Impact Chains (IC) are conceptual models used to capture hazard, vulnerability and exposure factors that lead to a specific risk. IC modeling includes a participatory stakeholder phase and an operational quantification phase. While ICs are widely implemented to systematically capture risk processes, they still show methodological gaps concerning, e.g., the integration of dynamic feedback or balanced stakeholder involvement. Such gaps usually only become apparent in practical applications, and there is currently no systematic perspective on common challenges and methodological needs. Therefore, we reviewed 47 articles applying IC and similar CRVA methods that consider the cause-effect dynamics governing risk. We provide an overview of common challenges and opportunities as a roadmap for future improvements. We conclude that IC should move from a linear-, to an impact web-like representation of risk to integrate cause-effect dynamics. Qualitative approaches are based on significant stakeholder involvement to capture expert-, place-, and context-specific knowledge. The integration of IC into quantifiable, executable models is still highly underexplored due to a limited understanding of systems, data, evaluation options, and other uncertainties. Ultimately, using IC to capture the underlying, complex processes behind risk supports effective, long-term, and sustainable climate change adaptation.

In mid-sized cities, tram networks are a major component of the public service infrastructure. In those networks with their typically dense schedules multiple lines share tracks and stations, resulting in a dynamic system behavior and mounting delays following even small disruptions. Robustness is an important factor to keep delays from spreading through the network and to minimize average delays.As part of a project on simulation and optimization of robust schedules, this paper describes the development, implementation and application of a simulation model representing a tram network and its assigned time table. We begin by describing the components of a tram network, which consist of physical and logical entities. These concepts are then integrated into a model of time table based tram traffic. We apply the resulting simulation software to our hometown Cologne's tram network and present some experimental results. IntroductionTram networks are important parts of the public transport infrastructure, in our hometown Cologne's tram network for example 745,000 passengers are transported every day [6]. Especially mid-sized cities often have mixed tram networks, i.e. networks where trams travel on street level (and are thus subject to individual traffic and corresponding traffic regulation strategies) and on underground tracks. In such dense networks robustness is an important factor to minimize average delays. Robustness measures the degree on which inevitable small disturbances, e.g. obstructed tracks due to parked cars, have impact on the whole network. With robust time tables delays are kept at a local level, whereas with non robust time tables they spread through the network and might subsequently cause delays of other vehicles [3,4].In this paper we present parts of a larger project to generate and evaluate robust time tables in order to minimize the impact of small delays, as written in [5]. We develop a model and implement an application to simulate time tables of mixed tram networks in order to evaluate given time tables before their implementation in the field and to compare time tables generated by optimization methods (as in [2]) in respect to their applicability. In addition we want to show that the adopted simulation engine can be applied to real world problems.We begin the remainder of this paper by describing the basics of time table based tram traffic (section 2), followed by a short discussion of our model representing the physical

Typically, public transit modeling requires the availability of an extensive data basis to enable detailed modeling, calibration, and validation. Sometimes such