{'fraction_non_alphanumeric': 0.01833976833976834, 'fraction_numerical': 0.0, 'mean_word_length': 5.1, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Towards building an uncertainty-aware personal journey planner

Public transit, specially the bus, are usually considered unreliable as arrival times do not often match with the scheduled times. Current journey planners do not take this uncertainty into account, for example do not account for the risk of missing a transfer and of delays due to traffic conditions. This paper deals with analytics for building a multi modal journey planner, by estimating arrival and travel times for a certain trip involving transfers, and taking into account the delay of buses and risk of missing connections. We develop a system to compute probability distributions of end-to-end travel times, taking into account uncertain departure, travel and transfer times. We tested the system using real historical data for the whole bus system in Dublin, and showed the uncertainty of scheduled travel times with respect to the stochasticity of real traffic conditions. This is a first step towards building an uncertainty-aware multi-modal journey planner.