Guus Tamminga

Influence of Departure Time Spans and Corresponding Network Performance on Evacuation Time

Estimation of the time needed to evacuate a population from a threatened area in case of a disaster is one of the main issues in the design of an evacuation plan. The challenge is to develop a strategy that optimally uses the network capacity to minimize the total evacuation time. In this paper, the impacts of various departure time spans on evacuation time and network performance are investigated with a microscopic traffic simulation model. The network performance has been analyzed with the use of the macroscopic fundamental diagram (MFD). Although the MFD usually shows a decrease in travel production after a peak is reached, this is not the case in the simulation of evacuation scenarios. The outflow of the network remains constant because it depends on the capacity of local bottlenecks upstream of the limited number of destinations, but the number of vehicles in the network increases because of an increase in congestion. Although the overall network performance is insensitive to the evacuation time spans, it is observed that for shorter evacuation time spans, internal gridlock effects cause lengthy delays for specific groups of drivers, who, it turns out, are unable to leave the network in time. The use of a simulation study in combination with an MFD can therefore identify the routes used and the bottlenecks on these routes leading to the destinations, while the maximum production level (defined as the number of arrivals, determined in the MFD) indicates the optimal level of demand.

Longitudinal driving behavior under adverse weather conditions: adaptation effects, model performance and freeway capacity in case of fog

Adverse weather conditions have been shown to have a substantial impact on traffic flow operations. It is however unclear which adaptation effects in actual longitudinal driving behavior underlie this impact, how these adaptation effects relate to freeway capacity as well as to what extent current mathematical models of car-following behavior are adequate in incorporating these adaptation effects. In this regard a driving simulator experiment with a repeated measures design was performed in order to examine the influence of fog on adaptation effects, freeway capacity and parameter value changes and model performance of the Helly model and Intelligent Driver Model. From the results followed that fog led to a decrease in speed as well as in acceleration. Furthermore a substantial increase in distance to the lead vehicle was observed. These effects were implemented and simulated in a traffic simulation model. A substantial reduction in freeway capacity was found. This stresses the need to possess models of driving behavior, which are adequate in describing and predicting these adaptation effects. From the estimation results of the Helly model and IDM using a calibration approach for joint estimation followed that sensitivity factors, maximum acceleration and deceleration decreased substantially after the start of the adverse weather condition. Parameters representing headway increased significantly. Furthermore it followed from the results that the estimated models decreased in performance after the start of the adverse weather conditions

A modular approach for exchangeable driving task models in a microscopic simulation framework

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.

Design of open source framework for traffic and travel simulation

For the evaluation, design, and planning of traffic facilities and measures, traffic simulation packages are the de facto tools for consultants, policy makers, and researchers. However, the available commercial simulation packages do not always offer the desired work flow and flexibility for academic research. In many cases, researchers resort to designing and building their own dedicated models, without an intrinsic incentive (or the practical means) to make the results available in the public domain. To make matters worse, a substantial part of these efforts pertains to rebuilding basic functionality and, in many respects, reinventing the wheel. This problem not only affects the research community but adversely affects the entire traffic simulation community and frustrates the development of traffic simulation in general. For this problem to be addressed, this paper describes an open source approach, OpenTraffic, which is being developed as a collaborative effort between the Queensland University of Technology, Australia; the National Institute of Informatics, Tokyo; and the Technical University of Delft, the Netherlands. The OpenTraffic simulation framework enables academics from geographic areas and disciplines within the traffic domain to work together and contribute to a specific topic of interest, ranging from travel choice behavior to car following, and from response to intelligent transportation systems to activity planning. The modular approach enables users of the software to focus on their area of interest, whereas other functional modules can be regarded as black boxes. Specific attention is paid to a standardization of data inputs and outputs for traffic simulations. Such standardization will allow the sharing of data with many existing commercial simulation packages.

Sensitivity analysis on heterogeneity of driving behavior for evacuation studies and its impacts on traffic safety

The driving behavior of travelers has been found to be different in case of emergency conditions compared to normal traffic conditions. In this paper, we show how this different driving behavior, as well as the heterogeneity among drivers, has an impact on traffic safety. We do so by performing a sensitivity analysis on the model parameters representing the different (heterogeneous) driving behavior and investigating the impact of these variations on traffic safety. The analysis is conducted applying an evacuation simulation framework using S-Paramics. The results show that reductions in mean time headway and minimum gap substantially increase the number of potential safety conflicts. Also, it is found that variation in driving behavior plays a smaller, yet still important, role in traffic safety.

Getting the Human Factor into Traffic Flow Models: New Open-Source Design to Simulate Next Generation of Traffic Operations

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.

Open Traffic: A Toolbox for Traffic Research

Open Traffic is an open source software project that provides a transport modeling software environment. While most transport model packages offer ready-to-use modules for end-users, Open Traffic provides open access to a modelling environment for the (further) development of methods and algorithms and enables the sharing, distribution and further development of the implied knowledge. The Open Traffic platform is designed as a modular system which enables users to utilize existing modules and extend the system with new ones. The system supports the development of multi modal and multi scale models by providing a collection of objects that enable the creation of a transport infrastructure and its environment at multiple levels of detail. The definition of the geographical objects aligns to the principles of CityGML, an open standard for geo data that is internationally accepted by the Open Geospatial Consortium. Additional utilities such as a graphical editor and visualizer, in addition with facilities to import data from external sources like Open Streetmap and Esri shape files, enable users to quickly create and demonstrate their use-cases. In this article we present the high level architecture of Open Traffic, its current status, and a first application with the implementation of the micro simulation model MOTUS. Also, the possibilities and requirements to adhere Open Traffic to agent based modelling approaches are explored.