TY - GEN
T1 - A critical-time-point approach to all-start-time Lagrangian shortest paths
T2 - 12th International Symposium on Advances in Spatial and Temporal Databases, SSTD 2011
AU - Gunturi, Venkata M V
AU - Nunes, Ernesto
AU - Yang, KwangSoo
AU - Shekhar, Shashi
PY - 2011
Y1 - 2011
N2 - Given a spatio-temporal network, a source, a destination, and a start-time interval, the All-start-time Lagrangian Shortest Paths (ALSP) problem determines a path set which includes the shortest path for every start time in the given interval. ALSP is important for critical societal applications related to air travel, road travel, and other spatio-temporal networks. However, ALSP is computationally challenging due to the non-stationary ranking of the candidate paths, meaning that a candidate path which is optimal for one start time may not be optimal for others. Determining a shortest path for each start-time leads to redundant computations across consecutive start times sharing a common solution. The proposed approach reduces this redundancy by determining the critical time points at which an optimal path may change. Theoretical analysis and experimental results show that this approach performs better than naive approaches particularly when there are few critical time points.
AB - Given a spatio-temporal network, a source, a destination, and a start-time interval, the All-start-time Lagrangian Shortest Paths (ALSP) problem determines a path set which includes the shortest path for every start time in the given interval. ALSP is important for critical societal applications related to air travel, road travel, and other spatio-temporal networks. However, ALSP is computationally challenging due to the non-stationary ranking of the candidate paths, meaning that a candidate path which is optimal for one start time may not be optimal for others. Determining a shortest path for each start-time leads to redundant computations across consecutive start times sharing a common solution. The proposed approach reduces this redundancy by determining the critical time points at which an optimal path may change. Theoretical analysis and experimental results show that this approach performs better than naive approaches particularly when there are few critical time points.
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U2 - 10.1007/978-3-642-22922-0_6
DO - 10.1007/978-3-642-22922-0_6
M3 - Conference contribution
AN - SCOPUS:80052734222
SN - 9783642229213
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 74
EP - 91
BT - Advances in Spatial and Temporal Databases - 12th International Symposium, SSTD 2011, Proceedings
Y2 - 24 August 2011 through 26 August 2011
ER -