Detection of incidents and events in urban networks

Events and incidents are relatively rare, but they often have a negative impact on traffic. Reliable travel demand predictions during events and incident detection algorithms are thus essential. The authors study link flows that were collected throughout the Dutch city of Almelo. We show that reliable, event-related demand forecasting is possible, but predictions can be improved if exact start and end times of events are known, and demand variations are monitored conscientiously. For incident detection, we adopt a method that is based on the detection of outliers. Our algorithm detects most outliers, while the fraction of detections due to noisy data is only a few percent. Although our method is less suitable for automatic incident detection, it can be used in an urban warning system that alerts managers in case of a possible incident. It also enables us to study incidents off-line. In doing so, we find that a significant fraction of traffic changes route during an incident

Performance of a Genetic Algorithm for Solving the Multi-Objective, Multimodal Transportation Network Design Problem

The optimization of infrastructure planning in a multimodal network is defined as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train stations and the frequency of public transport lines. For a case study the Pareto set is estimated by the Non-dominated Sorting Genetic Algorithm (NSGA-II). Such a Pareto set is one specific outcome of the optimization process, for a specific value of the parameters generation size, number of generations and mutation rate and for a specific outcome of the Monte Carlo simulation within NSGA-II. Similar issues exist for many other metaheuristics. However, when applied in practice, a policy maker desires a result that is robust for these unknown aspects of the method. In this paper Pareto sets from various runs of the NSGA-II algorithm are analyzed and compared. To compare the values of the decision variables in the Pareto sets, new methods are necessary, so these are defined and applied. The results show that the differences concerning decision variables are considerably larger than the differences concerning objectives. This indicates that the randomness of the algorithm may be a problem when determining the decisions to be made. Furthermore, it is concluded that variations caused by different parameters are comparable with the variations caused by randomness within the algorithm