Over the past decades, transport and traffic models have become powerful tools for transport and traffic practitioners and academics all over the world. Most of these commercial model packages provide proprietary and closed source software, which generally implies that users are not enabled to freely edit, modify and extend the code. Specifically for some of the new fields of applications such as the modelling of in-car systems and other ITS systems, and more generally for scientific research, access to the algorithms is often essential. The de facto option then is to code traffic models (or parts thereof) from scratch. To prevent this loss of knowledge and experience, an approach is required where code reuse and proper documentation is stimulated. @en

CityGML, an OGC standard, is an open data model for virtual 3D city models and includes buildings, roads, terrain, water bodies, etc. While many modules are well-developed (eg buildings, bridges, tunnels), the transportation model is, based on our consultations with various government agencies and municipalities, not sufficient for most transportation applications. We propose in this paper several improvements to the CityGML v2. 0 Transportation module, and to the previous efforts for improving it. Our additions are based on the consultations we had, and on the use-cases that were identified. We argue that the following changes are necessary: A) multi-LoD modelling of roads, B) carriageway representation, C) detailed intersection modelling and, D) introducing waterways as a new sub-class.@en

Traffic flow can be modelled at different levels, of which the vehicle level and the link level are mostly used. Recently, traffic relationships at a higher, network level are found, and a dynamic traffic flow model has been introduced. This paper tests such a model, the Network Transmission Model (NTM), for the city of The Hague. In this model, for each zone (i.e., parts of a network, neighbourhoods in this case) the number of vehicles in the network as well as the outflow is determined in each time step. The basic information from the city planners and the network is taken (OD, road lengths and capacities), and used as input to the NTM. Afterwards, the model parameters are adapted with input from practitioners of municipality. The parameters in the model had an understandable effect on the traffic congestion patters. After adaptation, the model predicted a traffic patterns in line with the expectations of experts of the The Hague municipality. Generally the model predicts the congestion in the right zones and the propagation is predicted correctly. Moreover, the parameters have an interpretation so tuning is done. Finally, the model is quick enough to serve as on-line support for traffic management decisions. As face validation two different cases (adapted OD matrix and a modelled incident) were assessed with the model without adapting model parameters. The predicted traffic patterns were assessed as reasonable. The test also revealed further improvements in including traffic waiting for parking spaces and the inabilities to include turning restrictions. @en

Automated driving may lead to much higher road capacities, combined with increased road safety, increased driver comfort, and lower costs. Although this vision may hold ground in the long run, first a transitional period will take place in which increasing percentages of vehicles with many levels of automation will drive on the world’s road networks. This transition poses a fundamental scientific challenge. The models used today to simulate and predict vehicular traffic are not valid to predict emergent properties of traffic flows under increasing amounts of vehicle automation. For example, there is no idea of how drivers of nonautomated vehicles will respond to other drivers reading their morning papers behind the steering wheel or the consequences of such interactions on traffic safety and capacity. In this paper, the authors do not propose a new behavioral theory with which the effects of increasing vehicle automation can be predicted. What the authors propose is an advanced open-source simulation framework, OpenTrafficSim, which makes it possible to extend microscopic models incrementally with explanatory mental models, such that new behavioral theories can be tested and shared within the community. Given the societal importance of predicting the effects on safety and efficiency of vehicle automation, the authors sincerely hope this paper will fuel the discussion on how both open-source and closed-source simulation software can be adapted and made ready for the next generation of traffic simulation models that are needed in the coming decades.@en

We present a structure for driver models regarding different driving tasks in microscopic simulation. This structure is part of an open and extendable simulation framework which facilitates development and research into ITS applications and driver behavior. The structure deals with typical difficulties of providing a high level of flexibility while allowing a high level of overview and user-friendliness. For driver behavior this is not a trivial task as different driving tasks depend on each other and as one requires a consistent driver, e.g. an aggressive driver is usually aggressive regarding all aspects of driving. This results in a high degree of interaction and dependency of driver models where we would like to segregate as much as possible in order to make them individually exchangeable. Our model structure allows a number of ways in which models may interact with varying degrees of generality. Inevitably this leads to concessions where we favor flexibility over user-friendliness. Specifically, the structure does not force appropriate use of model interactions. Appropriate use is thus up to the user.

@en