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Demand for sustainable mobility is particularly high in urban areas. Hence, there is a growing need to predict when people will decide to use different travel modes with an emphasis on environmentally friendly travel modes. As travel mode choice (TMC) is influenced by multiple factors, in a growing number of cases machine learning methods are used to predict travel mode choices given respondent and journey features. Typically, travel diaries are used to provide core relevant data. However, other features such as attributes of mode alternatives including, but not limited to travel times, and, in the case of public transport (PT), also walking distances have a major impact on whether a person decides to use a travel mode of interest. Hence, in this work, we propose an architecture of a software platform performing the data fusion combining data documenting journeys with the features calculated to summarise transport options available for these journeys, built environment and environmental factors such as weather conditions possibly influencing travel mode decisions. Furthermore, we propose various novel features, many of which we show to be among the most important for TMC prediction. We propose how stream processing engines and other Big Data systems can be used for their calculation. The data processed by the platform is used to develop machine learning models predicting travel mode choices. To validate the platform, we propose ablation studies investigating the importance of individual feature subsets calculated by it and their impact on the TMC models built with them. In our experiments, we combine survey data, GPS traces, weather and pollution time series, transport model data, and spatial data of the built environment. The growth in the accuracy of TMC models built with the additional features is up to 18.2% compared to the use of core survey data only., Comment: 34 pages, 5 figures

Public transport systems are expected to reduce pollution and contribute to sustainable development. However, disruptions in public transport such as delays may negatively affect mobility choices. To quantify delays, aggregated data from vehicle locations systems are frequently used. However, delays observed at individual stops are caused inter alia by fluctuations in running times and propagation of delays occurring in other locations. Hence, in this work, we propose both the method detecting significant delays and reference architecture, relying on stream processing engines, in which the method is implemented. The method can complement the calculation of delays defined as deviation from schedules. This provides both online rather than batch identification of significant and repetitive delays, and resilience to the limited quality of location data. The method we propose can be used with different change detectors, such as ADWIN, applied to location data stream shuffled to individual edges of a transport graph. It can detect in an online manner at which edges statistically significant delays are observed and at which edges delays arise and are reduced. Detections can be used to model mobility choices and quantify the impact of repetitive rather than random disruptions on feasible trips with multimodal trip modelling engines. The evaluation performed with the public transport data of over 2000 vehicles confirms the merits of the method and reveals that a limited-size subgraph of a transport system graph causes statistically significant delays, Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in Computational Science - ICCS 2022. Lecture Notes in Computer Science, vol 13353. Springer, Cham, and is available online at https://doi.org/10.1007/978-3-031-08760-8_41

We consider the applicability of the data from operators of cellular systems to modelling demand for transportation. While individual-level data may contain precise paths of movement, stringent privacy rules prohibit their use without consent. Presence data aggregate the individual-level data to information on the numbers of transactions at each base transceiver station (BTS) per each time period. Our work is aimed at demonstrating value of such aggregate data for mobility management while maintaining privacy of users. In particular, given mobile subscriber activity aggregated to short time intervals for a zone, a convex optimisation problem estimates most likely transitions between zones. We demonstrate the method on presence data from Warsaw, Poland, and compare with official demand estimates obtained with classical econometric methods.