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There is a growing awareness that road networks, are becoming more and more vulnerable to unforeseen disturbances like incidents and that measures need to be taken in order to make road networks more robust. In order to do this the following questions need to be addressed: How is robustness defined? Against which disturbances should the network be made robust? Which factors determine the robustness of a road network? What is the relationship between robustness, travel times and travel time reliability? Which indicators can be used to quantify robustness? How can these indicators be computed? This paper addresses these questions by developing a consistent framework for robustness in which a definition, terms related to robustness, indicators and an evaluation method are included. By doing this, policy makers and transportation analyst are offered a framework to discuss issues that are related to road network robustness and vulnerability which goes beyond the disconnected definitions, indicators and evaluation methods used so far in literature. Furthermore, the evaluation method that is presented for evaluating the robustness of the road network against short term variations in supply (like incidents) contributes to the problem of designing robust road networks because it has a relatively short computation time and it takes spillback effects and alternative routes into account.

There is a growing awareness that road networks, especially in major urban areas, are becoming more and more vulnerable to unforeseen disturbances like incidents because of the fact that the level of congestion keeps growing. Therefore, robustness measures have to be taken. In this paper the robust road network design problem is presented as well as a solution algorithm for solving the problem. The solution algorithm combines expert knowledge with advanced modeling techniques. In this way the method can be applied to large scale networks. This paper shows the quality of the algorithm for a test network.

Much of the delay in transport networks is caused by incidents. Many indicators are developed to determine vulnerable parts of a network without simulating the network flows with an incident on each of the links. This paper lists indicators proposed in literature and cross compares them. Their values for all links on three networks of different sizes are computed. Among others, the order and the cross correlation of the indicators is compared. For one network the effects are also fully computed, running one simulation per blocked link. Different vulnerability indicators rank the links differently. None of the indicators produces a result similar to the full computation. We conclude that the listed indicators are complementary.

Rapport in opdracht van het Ministerie van Verkeer en Waterstaat, Adviesdienst Verkeer en Vervoer (AVV). De toenemende complexiteit van de regeltaak vereist de ontwikkeling van geavanceerde methodes voor het ondersteunen van de beheerstaak van operators in verkeerscentrales. Deze ondersteuning beoogt de operator in staat te stellen, het verkeersnetwerk op een meer efficiënte manier te regelen, bijvoorbeeld door coördinatie tussen de verschillende beschikbare beheersingsmaatregelen te realiseren. Gezien de perspectieven van vage logica (fuzzy logic) in beslissingsondersteunende systemen, wordt door de Technische Universiteit Delft (TUD) in opdracht van de Adviesdienst Verkeer en Vervoer van het ministerie van Verkeer en Waterstaat (AW-RWS) een 'fuzzy decision support systeem' (FDSS) ontwikkeld. Dit project vormt een onderdeel van het BOSS project. In dit document wordt het functioneel ontwerp van dit FDSS beschreven. Hierbij is uitgegaan van een logische structuur, gebaseerd op vage kennis- en beslisregels. De functies van het beslissingsondersteunend systeem zijn onderverdeeld in verschillende (deel-) taken. Bij het verrichten van een functie, worden één of meer van deze (deel-) taken uitgevoerd. De taken zijn onder meer het stellen van de diagnose en het doen van aanbevelingen, het geven van uitleg, het verwerven van informatie, het ontwerpen van maatregelen en het optimaliseren ervan, en het maken van een synthese van de mogelijke maatregelen. Voor deze taken worden verschillende taakstructuren ontworpen en toegelicht, met name voor de taak systeemanalyse (probleemidentificatie, simulatie en besturen van het systeem) en de deeltaak synthese van maatregelen, ofwel het maken van een maatregelscenario (specificatie, ontwerp, en samenstellen). De logische structuren gebruikt voor het vervatten van de beschikbare kennis (d.i. domeinkennis en kennisregels), worden in dit ontwerp zowel voor de systeemanalyse als het maken van maatregelscenarios, vage kennis- en beslisregels toegepast. Deze regels beschrijven zowel de problemen in het systeem en de daaruit volgende diagnose, als de aanbevolen maatregelscenario's die volgend uit het geïdentificeerde probleem en de betreffende diagnose. Ten aanzien van implementatiestructuren is gekozen voor vage sjablonen of fuzzy frames (kennis-, probleem- en regelsjablonen). De resulterende fuzzy frames kunnen op verschillende manieren worden gebruikt in het beslisproces: een regelframe kan worden gebruikt voor het bepalen van een regelscenario en het voorspellen van de verkeersafwikkeling in het netwerk, gegeven dit regelscenario.

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.

The Dutch national road network has been developed over several decades. In the past, roads were constructed according to the then current spatial and transportation planning philosophies. Because the existing road network is a result of a long process of successive developments, the question can be asked whether this network is the most appropriate from the current point of view, especially taking in consideration the current socio-economic structure of the Netherlands. To answer this question an optimization algorithm for designing road networks has been developed. With this algorithm the Dutch road network has been redesigned based on minimization of the travel and infrastructure costs and by taking into account the socio-economic structure of the Netherlands. A comparison between the existing network and the new design shows that the redesigned Dutch national road network has significantly lower total costs than the existing road network. It is found that the construction of less roads with more lanes on different locations leads to a reduction of the total travel time and the total vehicles kilometers traveled.

It is computationally expensive to find out where vulnerable parts in a network are. In literature a variety of methods were introduced that use relatively simple selection criteria (measured in real-life or calculated in a traffic simulator) to pre-determine the seriousness of the delays caused by the blocking of that link and thereafter perform a more detailed analysis. This paper reviews the selection criteria proposed in the literature and assesses the quality of these criteria. Furthermore, a multi linear fit of the criteria is made to find a better, combined, criterion to rank the links according to their vulnerability. The paper shows that different criteria indicate different links to be vulnerable. Also combined they cannot well predict the vulnerability of a link. Therefore, it is concluded that to find vulnerable links, one has to look further than link-based indicators.

Recent research has revealed that there exist large inter-driver differences in car-following behavior such that different car-following models may apply to different drivers. This study applies Bayesian techniques to the calibration of car-following models, where prior distributions on each model parameter are converted to posterior distributions. The priors and posteriors are then used to calculate the so-called ‘evidence’, which can be used to quantitatively assess how well different models explain one driver’s car-following behavior. When considered over multiple drivers, the evidence represents probabilities for different models as a whole. These model probabilities can be used in a micro simulation, where for each driver first a model is drawn according to these probabilities, after which parameters are drawn from the posterior distribution for each parameter of that model that were obtained when calibrating the model. In a test case on actual data the Bayesian evidence indeed reveals inter-driver differences and it is shown how these differences can quantitatively be assessed