scholarly journals Computing Multi-Modal Journey Plans under Uncertainty

2019 ◽  
Vol 65 ◽  
pp. 633-674
Author(s):  
Adi Botea ◽  
Akihiro Kishimoto ◽  
Evdokia Nikolova ◽  
Stefano Braghin ◽  
Michele Berlingerio ◽  
...  
Keyword(s):  
State Space ◽  
Travel Time ◽  
Empirical Analysis ◽  
Real Life ◽  
Practical Interest ◽  
Transport Networks ◽  
Hybrid Search ◽  
Admissible Heuristics ◽  
Search Approach ◽  
Np Hardness

Multi-modal journey planning, which allows multiple types of transport within a single trip, is becoming increasingly popular, due to a strong practical interest and an increasing availability of data. In real life, transport networks feature uncertainty. Yet, most approaches assume a deterministic environment, making plans more prone to failures such as missed connections and major delays in the arrival. This paper presents an approach to computing optimal contingent plans in multi-modal journey planning. The problem is modeled as a search in an and/or state space. We describe search enhancements used on top of the AO* algorithm. Enhancements include admissible heuristics, multiple types of pruning that preserve the completeness and the optimality, and a hybrid search approach with a deterministic and a nondeterministic search. We demonstrate an NP-hardness result, with the hardness stemming from the dynamically changing distributions of the travel time random variables. We perform a detailed empirical analysis on realistic transport networks from cities such as Montpellier, Rome and Dublin. The results demonstrate the effectiveness of our algorithmic contributions, and the benefits of contingent plans as compared to standard sequential plans, when the arrival and departure times of buses are characterized by uncertainty.

Hybrid state-space self-tuning control using dual-rate sampling

1991 ◽  
Vol 138 (1) ◽  
pp. 50 ◽  
Author(s):  
Leang S. Shieh ◽  
Xiao M. Zhao ◽  
John W. Sunkel
Keyword(s):  
State Space ◽  
Hybrid State ◽  
Self Tuning
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