Abstract
This research investigates the modal split of passengers’ transport in a new approach different from the dominant methodology of four-step demand model, using accidents data in calculation, assuming that the more accidents for a mode (cars, public transport, and bicycles), the more passengers who are using it. More than 100,000 road accidents are analyzed for consecutive four years in Hungary. Drivers, front-seat and rear-seat passengers are categorized through age intervals. Age distribution profiles for each mode of private cars, public transport, cycling, motors are developed after normalized the intervals with their corresponding population. Hypothesis of using the approach of finding passenger modal split through road accident data is tested as overall percentages in macro level as Hungary scale and selected cities in micro level for cities of Budapest, Miskolc, Pecs, and Szeged. Forecasting of the passenger cars split ratio is developed for four years ahead depending on time-series decomposition method. Results show that modes of passenger cars, and walking and cycling percentages are acceptable with limitations in reality, unlike public transport which is rejected at all times to be estimated through road accidents data, while forecasting passenger cars is convenient.
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Jaber, A., Juhász, J. (2022). Measuring and Forecasting of Passengers Modal Split Through Road Accidents Statistical Data. In: Sierpiński, G. (eds) Intelligent Solutions for Cities and Mobility of the Future. TSTP 2021. Lecture Notes in Networks and Systems, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-030-91156-0_2
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