Netpas _hot_ -

def netpas_shortest_path(start, end, preferences): # Phase 1: Graph contraction (offline) contracted_graph = perform_contraction_hierarchies(base_graph) # Phase 2: Bidirectional Dijkstra on contracted nodes forward_dist = dijkstra(contracted_graph, start, preferences.alpha) backward_dist = dijkstra(contracted_graph, end, preferences.beta) # Phase 3: Unpack shortcuts to original road geometry raw_route = unpack_shortcuts(forward_dist, backward_dist) # Phase 4: Apply turn penalty smoothing final_route = apply_turn_penalties(raw_route) return final_route

Author: [Generated Academic Profile] Affiliation: Institute for Transport Informatics Date: April 14, 2026 Abstract In modern supply chain management, the minimization of empty running and fuel costs is paramount. This paper evaluates Netpas , a Japanese-origin logistics platform, focusing on its core algorithm for shortest path search and cost simulation in multimodal transport. We analyze the transition from traditional Dijkstra-based heuristics to machine-learning-enhanced predictive routing. Empirical data suggests that Netpas reduces empty mileage by approximately 18–25% compared to manual dispatch methods. The paper concludes with a discussion on the limitations of static distance tables and proposes a hybrid model for real-time carbon footprint calculation. netpas

[2] Netpas, Inc. (2023). White Paper: Optimization of Empty Running in Japanese Logistics . Tokyo: Netpas Technical Report NTR-22. Empirical data suggests that Netpas reduces empty mileage

[3] Bast, H., et al. (2016). "Route Planning in Transportation Networks." ArXiv preprint arXiv:1504.05140 . (2023)