Docit: An Integrated System for Risk-Averse Multi-Modal Journey Advising

Current systems for multi-modal journey planning assume a deterministic environment. However, in reality, transportation networks feature many types of uncertainty, such as variations in the arrival times of public transport vehicles. Slight errors in the deterministic assumptions can result in lost connections, with a corresponding delay at the arrival.

We present Docit, the first multi-modal journey advising system that reasons about uncertainty in the network knowledge, creating journey plans optimized on the likelihood of arriving on time. We describe its main functions, created both for travellers and network operators. We discuss our solutions to integration challenges, including the integration, as part of the same system, of two different uncertainty-aware planning engines. Our system has been integrated with two commercial products, to gain access to dynamically updated network data, and to provide network operators with network awareness information computed by our system.

By: Adi Botea, Michele Berlingerio, Stefano Braghin, Eric Bouillet, Francesco Calabrese, Bei Chen, Yiannis Gkoufas, Rahul Nair, Tim Nonner, Marco Laummans

Published in: RC25547 in 2015


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