· · · Institutional Repository

Incidents cause a large part of the delays in road networks. This is caused by a decrease of the capacity at the incident site. A detailed knowledge of the queue discharge rate can improve for instance the traffic prediction and thereby improve delay information or routing advice. Therefore, this study determines the queue discharge rate for many incident locations during an incident situation and these are compared with the queue discharge rate at the same location in normal conditions. Ninety incidents meet the requirements to apply the proposed methodology. It is found in case a driving lane is blocked, the queue discharge rate for each available lane is reduced by 50%. In case the driving lanes are open but there is a distraction of an incident at the emergency lane or on the roadway for the opposite direction, the queue discharge rate is reduced by 30%.

Incidents cause a large part of the congestion on the road. This PhD study describes how people change their behaviour when facing an incident situation. It is found that car-following behaviour changes and drivers react slower on their predecessors. Furthermore, it is found that drivers change their route when facing unexpected delay caused by an incident. The route choice if the queue is caused by an incident is different from the situation with a similar queue which is not caused by an incident. Also the queuing patterns in the network are studied. It is found that so called “spillback” effects are important. This is a queue with cars heading to a direction with a bottleneck which blocks the cars to another direction, which do not need to pass the bottleneck. Due to these effects, it is essential to use an accurate representation of traffic when calculating the total delays of an incident. The findings of this thesis can be used for creating more robust road networks, causing less delay in case of incidents.

Transport networks in major urban areas are becoming more and more vulnerable to unforeseen disturbances in transport networks, like incidents. For the near future, we expect an increasing number of incidents with a large impact due to the overall increase of the traffic load. In this paper the hypothesis is tested that, if no measures are taken, the impact of incidents increases in the future and, therefore, the vulnerability of the road network increases. It is shown that the current network of the area The Hague-Rotterdam in the Netherlands is already vulnerable. If the demand increases, the increase in total travel time is more than linear with the increase in demand in the situation without an incident. The impact of incidents also increases when the level of demand increases. This results in the overall conclusion that it is necessary to make the road network more robust.

This thesis explores to build a Safety Management System for infectious disease outbreak control in the Netherlands. Q-fever and Salmonella were two major disease outbreaks in the past decade. The outbreak management were criticised for ineffective implementation of control measures, and the magnitude of the outbreaks increased by years. Although they were not as contagious and lethal as pandemics such as SARS, but the well-being of the people were threatened. Moreover, the two outbreaks did not end well: relevant actors had fallen victim to last-minute remedies which cost a large proportion of their properties. RIVM (National Institute of Public Health and Environment) has initiated a project to investigate the past outbreaks, aiming at a clearer understanding of the complexity of the situation back in time during the outbreaks, and calling for strategies for improving the outbreak management system in the country. The research project started with studying four evaluation reports on Q-fever and Salmonella. Phase I of the research focused on reconstructing chains of events, which led to root causes analysis of unwanted events. Fact reconstruction tool Event and Conditional Factors Analysis+ (ECFA+) was used to analyse the two cases. Significant events were picked from the resulted ECF chart, and underwent Cause Change Control Analysis (3CA), out of which work controls/protective barriers and root causes of the significant events were obtained. Results of ECFA+ and 3CA were revised within the project team with the attendance of an expert from RIVM. Phase II of the research focused on system building and discussion. A Risk Management System (RMS) for outbreak control and a companion Business Process Model (BPM) were constructed to address the controls or protective barriers identified in the 3CA analysis. Organisational Learning (OL), as an embedded process in a risk management system, was mapped to the RMS, and then barriers to organisational learning were discussed. After a focus group discussion, recommendations were given in terms of a guideline of gap analysis for the safety management system, as well as how to tackle the barriers to organisational learning in the system for outbreak control. This exploratory study attempted to combine incidents investigation tools (ECFA+3CA), using abductive reasoning as a base to formulate explanations for the occurrence of unwanted events and to build a safety management system preventing such occurrence. The resulted RMS steps and business process model added systemic perspectives to the infectious disease outbreak control management in the Netherlands.

Reliability of travel times is an important indicator of the performance of a traffic system. The congestion caused by incidents is an important cause of the unreliability of travel times. Travel time reliability should be incorporated in social cost benefit analyses for infrastructure investments. With an accurate forecast of travel time reliability a well-educated decision of the consequences of infrastructure investments on travel time reliability, can be made. A large number of incidents (car accidents and breakdowns) are simulated in marginal traffic models MIC (marginal incident computation) and MaC (marginal computation). The research shows that it is possible to forecast travel time variability from door to door in case of an incident, with explicit simulation of incidents in a dynamic traffic model within reasonable calculation time. This could be done because of the usage of marginal traffic models, MIC and MaC, reducing the number of calculations needed and therefor the calculation time.