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Efficient Transport Modeling

Source: R/_PACKAGE.R
flownet-package.Rd

flownet provides efficient tools for transportation modeling in R, supporting network processing, route enumeration, and traffic assignment tasks. It implements the path-sized logit (PSL) model for traffic assignment and provides powerful utilities for network processing/preparation.

Network Processing

linestrings_to_graph() — Convert LINESTRING geometries to graph
create_undirected_graph() — Convert directed graph to undirected
consolidate_graph() — Consolidate graph by removing intermediate nodes
simplify_network() — Simplify network graph

Traffic Assignment

run_assignment() — Run traffic assignment using path-sized logit model

Graph Utilities

normalize_graph() — Normalize node IDs to consecutive integers
nodes_from_graph() — Extract unique nodes from graph
linestrings_from_graph() — Convert graph to LINESTRING geometries
distances_from_graph() — Compute distance matrix from graph

OD Matrix Utilities

melt_od_matrix() — Convert origin-destination matrix to long format

Data

africa_trade — Average BACI HS96 2012-22 trade flows by section between 47 continental African countries
africa_cities_ports — The 453 largest (port-)cities in continental Africa within a 70km radius - from Krantz (2024), doi:10.1596/1813-9450-10893
africa_network — African continental road network + extensions to optimally connect the 453 cities - from Krantz (2024), doi:10.1596/1813-9450-10893
africa_segments — Primary segments derived from OpenStreetMap routes between the 453 cities - from Krantz (2024), doi:10.1596/1813-9450-10893

Replication materials: https://github.com/SebKrantz/OptimalAfricanRoads

Details

The package uses efficient C implementations for critical path operations and leverages:

  • collapse - Fast data transformations

  • geodist - Fast geodesic distance computations

  • igraph - Graph operations and shortest path algorithms

  • leaderCluster - Fast spatial clustering for network simplification

References

Ben-Akiva, M., & Bierlaire, M. (1999). Discrete choice methods and their applications to short term travel decisions. In R. W. Hall (Ed.), Handbook of Transportation Science (pp. 5-33). Springer US. doi:10.1007/978-1-4615-5203-1_2

Cascetta, E. (2001). Transportation systems engineering: Theory and methods. Springer.

Ben-Akiva, M., & Lerman, S. R. (1985). Discrete choice analysis: Theory and application to travel demand. The MIT Press.

Ramming, M. S. (2002). Network knowledge and route choice (Doctoral dissertation). Massachusetts Institute of Technology.

Prato, C. G. (2009). Route choice modeling: Past, present and future research directions. Journal of Choice Modelling, 2(1), 65-100. doi:10.1016/S1755-5345(13)70005-8

AequilibiaE Python Documentation: https://www.aequilibrae.com/develop/python/route_choice/path_size_logit.html

Author

Sebastian Krantz sebastian.krantz@graduateinstitute.ch and Kamol Roy kamol.roy08@gmail.com