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A data-driven method to automate the detection of traffic control systems that do not perform as intended

Brouwer, Victor (2019) A data-driven method to automate the detection of traffic control systems that do not perform as intended.

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Abstract:Due to the limited available resources and the sheer number of Tra�c Control Systems (TCS) used in contemporary cities, the frequency of updating TCS'timing program is often low or sporadic. An outdated timing program means a less than optimal performance of the TCS, resulting in longer travel times and unnecessary travel costs. Past literature has investigated how the TCS can be improved through retiming, but there are limited studies on determining for which TCS retiming is most valuable. This study �lls this research gap by investigating the performance of machine learning methods for identifying TCS that needs retiming. The performance indicators that monitor the performance of a TCS are often in uenced by the policy of the road authority or geographical characteristics of the TCS. To enable the unbiased comparison of di�erent TCS, this study uses policy- and geographically- neutral performance indicators, such as double stops and red-light runners. Then, we test the performance of unsupervised learning methods (Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Isolation Forest) on a six-month case study in three regions of the Netherlands (province of North-holland, the city of the Hague, the city of Deventer). This case study demonstrates the bene�t that the di�erences in the TCS performance helps by providing targeted maintenance. All the 11 TCS which are detected as anomalous by the DBSCAN have at least one performance indicator with a statistically extreme value. The Isolation Forest detects 17 TCS as anomalous, where 2 anomalies do not have a statically extreme value for one of the performance indicators. In total 38 of the 125 TCS had at least one performance indicator with a statistically extreme value. This work supports the introduction of automated methods for identifying problematic TCS by providing the �rst step in this direction.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:56 civil engineering
Programme:Civil Engineering and Management MSc (60026)
Link to this item:http://purl.utwente.nl/essays/80085
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