Hans van Lint | Delft University of Technology (original) (raw)
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National Research University “Higher School of Economics” (HSE), Moscow, Russia
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Papers by Hans van Lint
Transportation Research Record, 2008
This paper presents an algorithm for the off-line estimation of route-level travel times for unin... more This paper presents an algorithm for the off-line estimation of route-level travel times for uninterrupted traffic flow facilities, such as motorway corridors, based on time-series of traffic speed observations taken from the sections that constitute a route. The proposed method is an extension of an existing and widely used method known as the trajectory method. The novelty of the new method is the fact that trajectories are constructed based on the assumption of piecewise linear (and continuous at section boundaries) vehicle speeds rather than piecewise constant (and discontinuous at section boundaries) speeds.
In this paper we present a new approach to freeway travel time prediction, based on recurrent neu... more In this paper we present a new approach to freeway travel time prediction, based on recurrent neural networks. Travel time prediction requires a modeling approach that is capable of dealing with complex non-linear spatio-temporal relationships between flows, speeds and densities. Based on literature, feedforward neural networks are a class of mathematical models that are well suited to solve this problem. A drawback of the feed-forward approach is that size and composition of the input time-series are inherently design choices and thus fixed for all input. This may lead to unnecessary large models. Moreover, for different traffic conditions different sizes and compositions of input time-series may be required, a requirement that is not satisfied by any feed forward data-driven method.
Accuracy and robustness with respect to missing or corrupt input data are two key characteristics... more Accuracy and robustness with respect to missing or corrupt input data are two key characteristics for any travel time prediction model that is to be applied in a real-time environment (e.g. for display on variable message signs on freeways). This article proposes a freeway travel time prediction framework that exhibits both qualities. The framework exploits a recurrent neural network topology, the so-called statespace neural network (SSNN), with preprocessing strategies based on imputation. Although the SSNN model is a neural network, its design (in terms of input-and model selection) is not ''black box'' nor location-specific. Instead, it is based on the lay-out of the freeway stretch of interest. In this sense, the SSNN model combines the generality of neural network approaches, with traffic related (''white-box'') design. Robustness to missing data is tackled by means of simple imputation (data replacement) schemes, such as exponential forecasts and spatial interpolation. Although there are clear theoretical shortcomings to ''simple'' imputation schemes to remedy input failure, our results indicate that their use is justified in this particular application. The SSNN model appears to be robust to the ''damage'' done by these imputation schemes. This is true for both incidental (random) and structural input failure. We demonstrate that the SSNN travel time prediction framework yields good accurate and robust travel time predictions on both synthetic and real data.
A hybrid model for predicting urban arterial travel time on the basis of so-called state-space ne... more A hybrid model for predicting urban arterial travel time on the basis of so-called state-space neural networks (SSNNs) and the extended Kalman filter (EKF) is presented. Previous research demonstrated that SSNNs can address complex nonlinear spatiotemporal problems. However, SSNN ...
Transportation Research Record, 2008
This paper presents an algorithm for the off-line estimation of route-level travel times for unin... more This paper presents an algorithm for the off-line estimation of route-level travel times for uninterrupted traffic flow facilities, such as motorway corridors, based on time-series of traffic speed observations taken from the sections that constitute a route. The proposed method is an extension of an existing and widely used method known as the trajectory method. The novelty of the new method is the fact that trajectories are constructed based on the assumption of piecewise linear (and continuous at section boundaries) vehicle speeds rather than piecewise constant (and discontinuous at section boundaries) speeds.
In this paper we present a new approach to freeway travel time prediction, based on recurrent neu... more In this paper we present a new approach to freeway travel time prediction, based on recurrent neural networks. Travel time prediction requires a modeling approach that is capable of dealing with complex non-linear spatio-temporal relationships between flows, speeds and densities. Based on literature, feedforward neural networks are a class of mathematical models that are well suited to solve this problem. A drawback of the feed-forward approach is that size and composition of the input time-series are inherently design choices and thus fixed for all input. This may lead to unnecessary large models. Moreover, for different traffic conditions different sizes and compositions of input time-series may be required, a requirement that is not satisfied by any feed forward data-driven method.
Accuracy and robustness with respect to missing or corrupt input data are two key characteristics... more Accuracy and robustness with respect to missing or corrupt input data are two key characteristics for any travel time prediction model that is to be applied in a real-time environment (e.g. for display on variable message signs on freeways). This article proposes a freeway travel time prediction framework that exhibits both qualities. The framework exploits a recurrent neural network topology, the so-called statespace neural network (SSNN), with preprocessing strategies based on imputation. Although the SSNN model is a neural network, its design (in terms of input-and model selection) is not ''black box'' nor location-specific. Instead, it is based on the lay-out of the freeway stretch of interest. In this sense, the SSNN model combines the generality of neural network approaches, with traffic related (''white-box'') design. Robustness to missing data is tackled by means of simple imputation (data replacement) schemes, such as exponential forecasts and spatial interpolation. Although there are clear theoretical shortcomings to ''simple'' imputation schemes to remedy input failure, our results indicate that their use is justified in this particular application. The SSNN model appears to be robust to the ''damage'' done by these imputation schemes. This is true for both incidental (random) and structural input failure. We demonstrate that the SSNN travel time prediction framework yields good accurate and robust travel time predictions on both synthetic and real data.
A hybrid model for predicting urban arterial travel time on the basis of so-called state-space ne... more A hybrid model for predicting urban arterial travel time on the basis of so-called state-space neural networks (SSNNs) and the extended Kalman filter (EKF) is presented. Previous research demonstrated that SSNNs can address complex nonlinear spatiotemporal problems. However, SSNN ...