Abstract
Paper present new approach of urban multimodal mobility (UMM) estimation using anonymized data from public mobile network (PMN). The data set is derived from Call Data Records database, and urban multimodal mobility indicators were defined and relativized. Usage of indicators relativized values ensures that they can be applied for mobility estimation in all urban environment regardless of their physical differences, with existing public mobile network as single prerequisite. Travel mode classification is based on Adaptive Neuro Fuzzy Inference System (ANFIS) and trained using set of rules that were determined using method of surveying experts in domain of urban mobility. Accurate estimate creates a foundation for improvement of existing end creation of new services in urban mobility. Also, this approach has potential through implementation within advanced applications of Intelligent Transport Systems with the goal to improve travel modal shift, passenger comfort, efficiency of urban transport etc.
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