Constrained Path Optimization on Time-Dependent Road Networks

Kousik Kumar Dutta, Venkata M. V. Gunturi

Abstract: Time-Dependent Constrained Path Optimization (TD-CPO) takes the following input: (i) time-dependent (TD) road network, (ii) source (s), (iii) destination (d), (iv) departure time (t) and, (v) budget (\(\mathcal {B}\)). In a TD road network, each edge is characterized by a time-dependent arrival time and a score function. TD-CPO aims to determine a loopless s–d path which departs from s at time t and arrives at d on or before time \(t+\mathcal {B}\) while maximizing the score. TD-CPO has applications in urban navigation. TD-CPO is a variant of the Arc Orienteering Problem (AOP) known to be NP-hard in nature The key computational challenge of TD-CPO is that we need to find the “longest path” in terms of score within the given budget constraint in a TD road network. Current algorithms either prune down search space aggressively, leading to low solution quality or are not scalable to large networks. In contrast, our proposed approach \(\mathcal {SCOPE}\) explores a comprehensive search space efficiently. Furthermore, the inherent computational structure of \(\mathcal {SCOPE}\) enables trivial parallelization for improved performance. Our experiments indicate that \(\mathcal {SCOPE}\) achieves both superior quality solutions (nearly 2X) and acceptable runtimes (within 3 secs) when compared to the state-of-the-art algorithm on large road networks. Furthermore, \(\mathcal {SCOPE}\) exhibits almost linear speedup as the number of CPU cores increases.

External IDs:dblp:conf/wise/DuttaG24