Variation Based Online Travel Time Prediction Using Clustered Neural Networks

Yu, Jie; Chang, Gang‐Len; Ho, H.W.; Liu, Yue

doi:10.1109/itsc.2008.4732594

Cited by 27 publications

(11 citation statements)

References 8 publications

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“…A dynamic neural network model was proposed by Shen for freeway travel time estimation. Yu et al proposed a variation‐based online travel time prediction approach using clustered neural networks. Zou et al developed a multi‐topology ANN model with a supplemental component of an enhanced k‐nearest neighbour (k‐NN) model for travel time prediction when the detectors were widely spaced.…”

Section: Literature Reviewmentioning

confidence: 99%

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Badhrudeen

Raj

Vanajakshi

2016

J of Advced Transportation

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Summary The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Literature Reviewmentioning

confidence: 99%

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Badhrudeen

Raj

Vanajakshi

2016

J of Advced Transportation

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“…In review of literature, researchers have used parametric models in order to forecast the travel time, such as regression models or time series and nonparametric models that include ANN models [39,40,41]. Studies have shown that ANNs (including modular neural network model and statespace neural network model) are a powerful tool to predict travel time on freeways [41,40].…”

Section: Time Series Modelsmentioning

confidence: 99%

“…al. [39] proposed a travel time prediction model which comprised two parts: a base travel time and a travel time variation. The rst term is computed by using a fuzzy membership value average of the clustered historical data that reects the trac pattern.…”

Section: Time Series Modelsmentioning

confidence: 99%

Personal Advanced Traveler Assistant

Radu¹

2011

Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics

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In spite of numerous road management schemes and developed infrastructure the society nowadays still faces the problem of highly congested roads due to the increasing trac demand.The focus of this thesis is to develop a complex and integrated system that addresses the challenges of dynamic trac assignment in modern times. To reach this goal we had to study the theory behind such systems, come with new ideas and implement them into a real world human-centered traveler information system. Taking into account state of the art systems in this eld we built a design for a Personal Advanced Traveler Assistant (PTA). The main purpose of PTA is to give routing advices depending on the users preferences and the available capacity in the network. The system incorporates a dynamic trac algorithm that employs a prediction model. The prediction model should use current trac updates and the routes of the guided cars in order to achieve an accurate prediction of the future traveling times.Next step was to implement a prototype. This is available through multiple interfaces like smart phones or desktop applications.The prediction model that the algorithm uses is based on historical data. Given the time constraints the prototype uses now only historical data but it can be easily extended to include live updates. This allowed us to build a prediction model that considers typical trac dynamics in order to estimate the traveling time.The performance measure of the trac assignment algorithm is the shortest traveling time. The algorithm that we implemented is a time dimensional-extended version of the Dijkstra algorithm. The algorithm is tested on real data collected from the highways in The Netherlands. We evaluate the performance of the algorithm by comparing it to two versions of the static Dijkstra algorithm.To conclude, we successfully implemented a working prototype that uses various technologies such as Java, the Open Street Map API for rendering the map or J2ME for the mobile phone client. The prototype that we have built represents a working proof of concept for a dynamic routing assistant. One advantage of the structure that we chose to implement is that each component can be further on extended independently. In this way we showed that such a system is feasible but we also left the possibility for dierent parts of the system to be extended into more advanced applications. The master thesis that you are reading represents my graduation project as a Master student at Delft University of Technology. I would like to take this opportunity and thank those who helped and supported me during the last 2 years. I thank especially to Prof. L.J.M. Rothkrantz for guiding my steps to successfully accomplish this master thesis. I am grateful to him for all the support he oered me, the eorts he invested in this research project, for giving me new ideas whenever I though I reached a dead end and not only. I would also like to thank him for trusting me and for making me feel more condent. I consider myself lucky to have met him and e...

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“…A wide variety of techniques have been used to develop traffic condition prediction models. These techniques include time series model [1]- [3], K nearest neighbors nonparametric model [4], [5], Bayesian network theory [6], Kalman filtering algorithm [7], neural network based models [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian regularized neural network approach to short-term traffic speed prediction

Qiu

Wang

Zuo

et al. 2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Short term traffic speed prediction is very important in intelligent transportation systems. Neural networks have been widely used for traffic speed prediction. However, the classical neural network usually lacks satisfactory generalization ability, which usually results in an imprecise prediction of traffic speed. Regularization is an essential technique to improve the generalization ability of neural network. Regularization is realized by adding a weight decay function to the energy function of the neural network. One of the key problems of the regularization technique is how to decide the parameter of the weight decay function. In this paper, the Bayesian technique is used to optimize these regularization parameters and a Bayesian regularized neural network (BRNN) used for traffic speed prediction is proposed. The speed prediction model was validated by the real-world traffic speeds of the Hangzhou city collected from the floating car system. The experimental results show that the proposed method is able to improve the generalization ability of neural networks, and can achieve better prediction results than several traditional prediction models.

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Variation Based Online Travel Time Prediction Using Clustered Neural Networks

Cited by 27 publications

References 8 publications

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Personal Advanced Traveler Assistant

A Bayesian regularized neural network approach to short-term traffic speed prediction

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