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# Route Choice

As argued in the literature [1], the route choice problem does not have a closed solution, and the selection of one of the many modelling frameworks [2] depends on many factors. A common modelling framework in practice is consists of two steps: Choice set generation and the choice selection process.

AequilibraE is the first modeling package with full support for route choice, from the creation of choice sets through multiple algorithms to the assignment of trips to the network using the traditional Path-Size logit.

## Costs, utilities and signs

AequilibraE’s path computation procedures require all link costs to be positive. For that reason, link utilities (or disutilities) must be positive, while its obvious minus sign is handled internally. This mechanism prevents the possibility of links with actual positive utility, but those cases are arguably not reasonable to exist in practice.

## Choice set generation algorithms available

All algorithms have been implemented as a single software class

Algorithm | Brief description
--- | ---
Link-Penalisation | Classical link penalisation.
Breadth-First Search with Link Removal | As described in [2].
Breadth-First Search with Link Removal + Link-Penalisation | A combination of BFS-LE and LP See RouteChoice documentation

## Imports

    from aequilibrae.paths.route_choice_set import RouteChoiceSet
    from aequilibrae import Project

    proj = Project()
    proj.load('path/to/project/folder')

    proj.network.build_graphs()
    graph = proj.network.graphs['c']

    # Measure that will be used to compute paths
    theta = 0.0014
    graph.network = graph.network.assign(utility=graph.network.distance * theta)
    graph.set_graph('utility')

    graph.prepare_graph(centroids=[list_of_all_nodes_network_centroids])

    rc = RouteChoice(graph, mat)
    rc.set_choice_set_generation("bfsle", max_routes=5)
    rc.execute(perform_assignment=True)

## Full process overview

The estimation of route choice models based on vehicle GPS data can be explored on a family of papers scheduled to be presented at the ATRF 2024 [1] [3] [4].

Dive deep into route choice

- Route choice models
- Choice set generation algorithms

[1] (1,2)

Zill, J. C., and P. V. de Camargo. State-Wide Route Choice Models (Submitted). Presented at the ATRF, Melbourne, Australia, 2024.

[2] (1,2)

Rieser-Schüssler, N., Balmer, M., & Axhausen, K. W. (2012). Route choice sets for very high-resolution data. Transportmetrica A: Transport Science, 9(9), 825–845. DOI: https://doi.org/10.1080/18128602.2012.671383

[3]

Camargo, P. V. de, and R. Imai. Map-Matching Large Streams of Vehicle GPS Data into Bespoke Networks. Presented at the ATRF, Melbourne, 2024.

[4]

Moss, J., P. V. de Camargo, C. de Freitas, and R. Imai. High-Performance Route Choice Set Generation on Large Networks. Presented at the ATRF, Melbourne, 2024.