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Delft, Netherlands

Snelder M.,TNO | Snelder M.,Technical University of Delft | Snelder M.,TRAIL Research School | van Zuylen H.J.,Technical University of Delft | And 4 more authors.
Transportation Research Part A: Policy and Practice | Year: 2012

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. © 2012 Elsevier Ltd. Source


Zheng F.,Technical University of Delft | Zheng F.,TRAIL Research School | Van Zuylen H.,Technical University of Delft | Van Zuylen H.,Hunan University
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC | Year: 2010

In an urban road network, travel times are not uniquely determined by the traffic states due to stochastic properties of traffic flow, stochastic arrivals and departures at intersections and traffic signal control. As a result, for a given traffic state, a range of travel times (delays) is found. This can be represented by a distribution of travel times (delays). Calibrating a model for the travel time only for the expectation value gives a large 'noise' such that the model will have little value for the prediction purpose. In this paper, the delay distribution function as derived from the analytical model under different circumstances is introduced. The overflow queue distribution which is the parameter in the delay distribution function is estimated based on traffic measurements, e.g., the measured delays, flows and cycle time. The Least Squares (LS) and Maximum Likelihood (ML) Estimation are used to perform the parameter estimation in the delay distribution. The Genetic Algorithm (GA) is applied to find the optimal solution for the objective functions in terms of minimizing square error and maximizing the likelihood function. Based on the estimated model parameters, the delay distribution is reconstructed. The estimated delay distribution is compared with that obtained from VISSIM simulation. Results show that both ML and LS estimation methods perform well in the undersaturated condition. While in the oversaturated condition, the ML method performs considerably better than the LS method. ©2010 IEEE. Source


Jie L.,Technical University of Delft | Jie L.,TRAIL Research School | Sen C.Y.,TNO | Hao L.,Technical University of Delft | And 3 more authors.
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC | Year: 2010

Cost issues have been an important concern in the development of Personal Rapid Transit (PRT) since the concept was developed several decades ago. The lightweight, computer-guided electric vehicles operating the PRT system are generally a major part of the capital cost of the system, especially in larger network with high demand. A sufficient number of empty vehicles are needed to be moved to the stations where passengers are waiting or demand is expected. Generally a larger fleet size leads to a reduction in waiting time of passengers and thus a higher level of service given a specific demand, but an increased investment cost including capital cost per vehicle and additional operation and maintenance. So it requires a compromise between user cost (in terms of passenger waiting times) and operator cost (in terms of fleet sizedependent capital cost and operating/maintenance costs). There should be an optimal fleet size so that the sum of these two costs can be minimized while an expected level of service is achieved. This paper presents first the way to obtain the PRT demand, and then a prescription to determine the optimal fleet size using a cost-effectiveness analysis with traffic simulation. This prescription identifies the set of activities that are necessary to perform the optimization task. Each activity is regarded as a component in our general framework and this framework is illustrated by a case study in the Waal/ Eemshaven harbor area in the Port of Rotterdam, The Netherlands. Source


Van Hinsbergen C.P.I.J.,Technical University of Delft | Van Hinsbergen C.P.I.J.,TRAIL Research School | Schreiter T.,Technical University of Delft | Zuurbier F.S.,Technical University of Delft | And 3 more authors.
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC | Year: 2010

Traffic state estimation is important input to traffic information and traffic management systems. A wide variety of traffic state estimation methods exist, either data-driven or model-driven. In this paper a model-driven approach is used: the LWR model solved by the Godunov scheme. The most widely applied method to combine this model with real-time data is the Extended Kalman Filter (EKF). A large disadvantage of the EKF is that it is too slow to perform in real-time on large networks. In this paper the novel Localized EKF (L-EKF) is proposed that sequentially makes many local corrections instead of one large global correction. The L-EKF does not use all information available to correct the state of the network, but in an experiment it is shown that the resulting loss of accuracy is negligible in case the radius of the local filters is taken sufficiently large. The L-EKF hence is a highly scalable solution to the state estimation problem that results in equally accurate state estimates. ©2010 IEEE. Source


Van Hinsbergen C.P.I.J.,Fileradar B.V. | Schreiter T.,Technical University of Delft | Schreiter T.,TRAIL Research School | Zuurbier F.S.,Fileradar B.V. | And 3 more authors.
IEEE Transactions on Intelligent Transportation Systems | Year: 2012

Current or historic traffic states are essential input to advanced traveler information, dynamic traffic management, and model predictive control systems. As traffic states are usually not perfectly measured and are everywhere, they need to be estimated from local and noisy sensor data. One of the most widely applied estimation methods is the Lighthill-Whitham and Richards (LWR) model with an extended Kalman filter (EKF). A large disadvantage of the EKF is that it is too slow to perform in real time on large networks. To overcome this problem, the novel localized EKF (L-EKF) is proposed in this paper. The logic of the traffic network is used to correct only the state in the vicinity of a detector. The L-EKF does not use all information available to correct the state of the network; the resulting accuracy is equal, however, if the radius of the local filters is sufficiently large. In two experiments, it is shown that the L-EKF is much faster than the traditional Global EKF (G-EKF), that it scales much better with the network size, and that it leads to estimates with nearly the same accuracy as the G-EKF and when the spacing between detectors is varied somewhere between 0.7 and 5.1 km. Compared with the G-EKF, the L-EKF is a highly scalable solution to the state estimation problem. © 2011 IEEE. Source

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