citydistR

citydistR provides modular R utilities for distribution-aware and city-adaptive analysis of learning-based road-network distance estimates.

The package was prepared as a software dissemination output of the research project New Optimization Procedures in Machine Learning Algorithms.

Design principle

The package is not tied to one fixed loss function, optimizer, or neural-network architecture. The road-network manuscript evaluates one specific configuration, but the software exposes reusable pieces so that alternative robust losses, weights, structural indicators, and objective combinations can be investigated.

The current release includes:

• detour-factor calculation;
• Topological Predictability Index (TPI);
• Tail95 detour-heaviness index;
• automatic robust-loss activation;
• MSE, MAE, Huber and log-cosh point losses;
• median-based detour regularization;
• modular weighted objective composition;
• a manuscript-aligned hybrid objective;
• city-adaptive validation weights and validation score;
• MAE, RMSE, P95, bias and Spearman evaluation utilities;
• multi-model comparison helpers.

Installation from a local folder

install.packages("path/to/citydistR", repos = NULL, type = "source")

Or, from the repository root:

R CMD INSTALL citydistR

Installation from GitHub

After the repository is created and YOUR-GITHUB-USERNAME is replaced in DESCRIPTION:

install.packages("remotes")
remotes::install_github("YOUR-GITHUB-USERNAME/citydistR")

Quick example

library(citydistR)

network <- c(12, 18, 31, 45, 70)
euclid  <- c(10, 15, 25, 35, 40)
pred    <- c(11, 20, 29, 48, 63)

df <- detour_factor(network, euclid)
city_indices(detour = df)

hybrid_objective(
  true_distance = network,
  pred_distance = pred,
  euclidean_distance = euclid,
  loss = "auto",
  tail95_value = tail95(df)
)

adaptive_validation_score(
  true_distance = network,
  pred_distance = pred,
  tail95_value = tail95(df),
  tpi_value = tpi(df)
)

evaluate_distance_model(network, pred)

Alternative loss combinations

The manuscript-aligned objective is only one configuration. For example:

parts <- c(
  distance = point_loss(c(-1, 0.5, 3), method = "huber"),
  geometry = point_loss(c(0.1, -0.2, 0.4), method = "logcosh"),
  stability = 0.15
)

combine_objectives(
  parts,
  weights = c(distance = 1, geometry = 0.7, stability = 0.2)
)

Tested road-network configuration

The research manuscript uses a city-adaptive configuration in which training-set Tail95 and TPI values inform robustness and validation. The reported robust-loss activation threshold is Tail95 >= 1.35. This default is included for reproducibility, but users can provide another threshold.

Development status

Version 0.1.0 is a source release prepared for GitHub dissemination and subsequent R/CRAN checking. The maintainer email and GitHub username in DESCRIPTION are placeholders and must be replaced before public CRAN submission.

Funding context

Developed in the context of the TÜBİTAK 2219 International Postdoctoral Research Fellowship project.