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Urban mobility is facing many challenges, such as energy consumption, pollution, and safety. Therefore, it is necessary to analyze the mobility of users through the transportation network using data containing information regarding the used transport mode. This data article describes a dataset from mobile devices collected by users as they move through the transportation network. Each sample in this dataset is labelled with a corresponding transport mode. Eight transport modes are present in the dataset: Car, Bus, Walking, Bicycle, Train, Tram, Running and Electric Scooter. The basic breakdown of the raw data according to users, transport modes and multimodal routes is presented. During data collection, data from the accelerometer, magnetometer, and gyroscope sensors mounted within the mobile device were stored. The data were collected using a mobile application from mobile devices with an embedded Android operating system. The structure of the text files in which the data were stored and the structure of the application used to collect the data are presented in the paper. The collected data provides a highly relevant basis for mobility analysis and planning, analysis of road conditions, clustering of user behaviour, and comparison of transport mode classification methods., (© 2023 The Author(s).)

Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms.