On Identifying Dynamic Intersections in Large Cities

Ivanchev, Jordan; Aydt, Heiko; Knoll, Alois

doi:10.1109/itsc.2015.466

Cited by 4 publications

(1 citation statement)

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“…Importance of road segments using entropy measures of nodes has been defined in [12] and used for optimal sensor positioning. The temporal flow variation has been examined [13] identifying nodes, which exhibit highly dynamic traffic conditions and are therefore critical to manage. Another factor, which should be taken into consideration for determining road criticality is the mismatch between the demand and capacity of the road [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Fast identification of critical roads by neural networks using system optimum assignment information

Ivanchev¹,

Zehe²,

Nair³

et al. 2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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Identification of critical segments in a road network is a crucial task for transportation system planners as it allows for in depth analysis of the robustness of the city's infrastructure. The current techniques require a considerable amount of computation, which does not scale well with the size of the system. With recent advances in machine learning, especially classification techniques, there are methods, which can prove to be more efficient replacements of current approaches. In this paper we propose a neural network (NN) based approach for classification of critical roads under user equilibrium traffic (UE) assignment. We, furthermore, introduce a novel predictor attribute, which captures the contrast between UE and system optimum (SO) assignment on the network. Our results demonstrate that the neural network can achieve considerable identification precision of critical road segments and that the SO related attributes significantly increase the classification power. We, furthermore, demonstrate that the NN approach outperforms the commonly used approach of linear regression (LR) and another popular classification approach from the field of machine learning, namely support vector machines (SVM).

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Section: Literature Reviewmentioning

confidence: 99%