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You searched for +publisher:"Delft University of Technology" +contributor:("Verhoeven, Eddy"). Showing records 1 – 2 of 2 total matches.

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Delft University of Technology

1. van Senden, JanCees (author). DIRECTOR: Enabling advanced driver assistance systems with predictive signalized intersection control using LSTM networks: An AI approach to signalized intersection control.

Degree: 2018, Delft University of Technology

Traffic congestion at signalized intersections is a big economical and ecological problem. Handcrafted traffic light controllers (TLCs) are currently used to minimize the impact, but they are expensive to design and maintain and their performance degrades over time. Predictive TLCs and advanced driver assistance systems (ADAS) form a potential solution but are still unfeasible in practice today because of their computational complexity and unpredictability. The distributed predictive TLC developed in this thesis, called DIRECTOR, is feasible and enables time to green/red and green light optimal speed advice (GLOSA) systems. DIRECTOR utilizes predictions of the arriving traffic flows and a model of the current queue length to optimize the traffic light schedule. It can operate in two modes; Ad-hoc mode, where the schedule is generated and applied right away, and fixed-ahead mode, where the schedule is fixed in advance to enable ADAS. DIRECTOR's design makes it scalable and suitable for live learning, eliminating the need for expensive (re)calibrations and improving its performance with more and better data, which will become available in the near future. A long short-term memory recurrent neural network is developed to predict the arriving traffic flows. On a case study this network proves to be on average 4.7% more accurate than the current state-of-the-art model, which is significant for a controller's performance. Simulations of the same case study intersection, which is currently equipped with a state-of-the-art actuated controller with green wave coordination, show that in ad-hoc mode DIRECTOR performs similar to the current controller. DIRECTOR reduces the average delay per vehicle by 1% (from 10.4s to 10.3s) at the cost of an increase of 15% in the average number of stops per vehicle (from 0.40 to 0.46) compared to the current controller. Simulations with ideal predictions show that, in ad-hoc mode, DIRECTOR has the potential to improve the average delay by 8.7% (from 10.4s to 9.5s) while keeping the number of stops equal (at 0.40). Simulations with GLOSA show a 30% reduction in the average number of stops at the cost of a 13% increase of the travel time compared to the ad-hoc mode. Combining this with ideal predictions shows that DIRECTOR in fixed-ahead mode has the potential to keep the average delay equal compared to the current controller, which will greatly improve traffic flow. Compared to a more typical Dutch actuated controller, DIRECTOR achieves a delay reduction of 39% in ad-hoc mode and 23% in fixed-ahead mode. Overall, DIRECTOR is a new data-driven traffic light controller that is relatively easy to set up, reduces costs, can enable advanced driver assistance systems, is futureproof and has the potential to greatly improve traffic flow.

Embedded Systems

Advisors/Committee Members: Langendoen, Koen (mentor), Verhoeven, Eddy (graduation committee), Spaan, Matthijs (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: traffic flow; advanced driver assistance systems; traffic light control; intersection control; long short-term memory networks; traffic flow prediction; model predictive control; green light optimal speed advise; time to green/red; GLOSA; T2G/R; ADAS; signalized intersections; distributed control

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

van Senden, J. (. (2018). DIRECTOR: Enabling advanced driver assistance systems with predictive signalized intersection control using LSTM networks: An AI approach to signalized intersection control. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:57a30ffc-1f16-477e-93cc-90bd7220ad09

Chicago Manual of Style (16th Edition):

van Senden, JanCees (author). “DIRECTOR: Enabling advanced driver assistance systems with predictive signalized intersection control using LSTM networks: An AI approach to signalized intersection control.” 2018. Masters Thesis, Delft University of Technology. Accessed February 26, 2021. http://resolver.tudelft.nl/uuid:57a30ffc-1f16-477e-93cc-90bd7220ad09.

MLA Handbook (7th Edition):

van Senden, JanCees (author). “DIRECTOR: Enabling advanced driver assistance systems with predictive signalized intersection control using LSTM networks: An AI approach to signalized intersection control.” 2018. Web. 26 Feb 2021.

Vancouver:

van Senden J(. DIRECTOR: Enabling advanced driver assistance systems with predictive signalized intersection control using LSTM networks: An AI approach to signalized intersection control. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:57a30ffc-1f16-477e-93cc-90bd7220ad09.

Council of Science Editors:

van Senden J(. DIRECTOR: Enabling advanced driver assistance systems with predictive signalized intersection control using LSTM networks: An AI approach to signalized intersection control. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:57a30ffc-1f16-477e-93cc-90bd7220ad09


Delft University of Technology

2. Helmy, Noorhan (author). An Intelligent Traffic Flow Progression Model for Predictive Control Applications: A Proposed Approach for Making Short-term Traffic Flow Predictions, Using a Recurrent, Multi-task Learning Neural Network Model, to Transform Traffic Light Controllers from Adaptive to Predictive with Minimal Hardware Changes.

Degree: 2017, Delft University of Technology

This study explores the possibility of developing a short-term traffic flow prediction model that can be used to convert installed adaptive controllers to predictive controllers with minimal hardware changes. By using the prediction model’s outputs to virtually trigger vehicle loop detectors, the outputs of an adaptive controller can be extracted in advance of actual vehicle arrivals. This will enable service providers to send out time to green/red (T2G/R) information or green light optimal speed advice (GLOSA), which are driver assistance use cases that aim to efficiently guide vehicles through intersections, in anticipation of known upcoming signal states. The main requirements for developing the prediction model are that it should be scalable to different intersection configurations, adaptable to different traffic conditions and should encompass the nonlinearity of traffic flow behavior. Since it fits these criteria, the developed model is a multi-task learning recurrent neural network with exogenous inputs (NARX), which is designed to match the traffic flow simulation abilities of a well adopted analytical traffic flow progression model. Both models were tested for a corridor in Delft that experiences almost consistent free flow conditions, and on an intersection in Haarlem with varying traffic conditions. For both case studies the neural network outperformed the analytical model. Most notably, it was better adaptable to long queues at intersections, had lower average error values, made fewer large errors, and better recognized the effect of a source/sink. When interfaced with an adaptive controller to test the predictive control methodology proposed, the superiority of the designed neural network model over the analytical model became more prominent. At no point did the neural network’s prediction errors result in queue spill-back, which was not true for the analytical model. However, the overall accuracy of the NARX model was still not yet satisfactory enough for practical application (especially for highly under-saturated traffic conditions) without the use of corrective measures. Nonetheless, due to its significant superiority over the widely used analytical model, with regards to both accuracy and adaptability, this model can be considered as a new starting point for traffic flow progression modeling.

Transport, Infrastructure and Logistics

Advisors/Committee Members: van Arem, Bart (mentor), Wang, Meng (mentor), Happee, Riender (mentor), Verhoeven, Eddy (mentor), Vermeer, John (mentor), Delft University of Technology (degree granting institution).

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Helmy, N. (. (2017). An Intelligent Traffic Flow Progression Model for Predictive Control Applications: A Proposed Approach for Making Short-term Traffic Flow Predictions, Using a Recurrent, Multi-task Learning Neural Network Model, to Transform Traffic Light Controllers from Adaptive to Predictive with Minimal Hardware Changes. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d1abdee5-301a-45d8-ae95-c204a2000bbd

Chicago Manual of Style (16th Edition):

Helmy, Noorhan (author). “An Intelligent Traffic Flow Progression Model for Predictive Control Applications: A Proposed Approach for Making Short-term Traffic Flow Predictions, Using a Recurrent, Multi-task Learning Neural Network Model, to Transform Traffic Light Controllers from Adaptive to Predictive with Minimal Hardware Changes.” 2017. Masters Thesis, Delft University of Technology. Accessed February 26, 2021. http://resolver.tudelft.nl/uuid:d1abdee5-301a-45d8-ae95-c204a2000bbd.

MLA Handbook (7th Edition):

Helmy, Noorhan (author). “An Intelligent Traffic Flow Progression Model for Predictive Control Applications: A Proposed Approach for Making Short-term Traffic Flow Predictions, Using a Recurrent, Multi-task Learning Neural Network Model, to Transform Traffic Light Controllers from Adaptive to Predictive with Minimal Hardware Changes.” 2017. Web. 26 Feb 2021.

Vancouver:

Helmy N(. An Intelligent Traffic Flow Progression Model for Predictive Control Applications: A Proposed Approach for Making Short-term Traffic Flow Predictions, Using a Recurrent, Multi-task Learning Neural Network Model, to Transform Traffic Light Controllers from Adaptive to Predictive with Minimal Hardware Changes. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:d1abdee5-301a-45d8-ae95-c204a2000bbd.

Council of Science Editors:

Helmy N(. An Intelligent Traffic Flow Progression Model for Predictive Control Applications: A Proposed Approach for Making Short-term Traffic Flow Predictions, Using a Recurrent, Multi-task Learning Neural Network Model, to Transform Traffic Light Controllers from Adaptive to Predictive with Minimal Hardware Changes. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:d1abdee5-301a-45d8-ae95-c204a2000bbd

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