How do I install Java for r5r on Windows?

Use the {rJavaEnv} R package to install JDK 21 automatically, or manually download from Adoptium. Ensure Java is configured and allocate RAM with options(java.parameters = '-Xmx2G') before loading r5r.

r5r vs opentripplanner in R: which is better for multimodal routing?

r5r is faster with parallel computing and focused on detailed multimodal analysis, while opentripplanner might offer more server-based flexibility. Choose r5r for performance in R-based transport research.

Can r5r handle real-time GTFS updates?

No, r5r uses static GTFS data from downloaded files. For real-time updates, you'd need to manually refresh the GTFS feeds, as it doesn't integrate live data streams.

How to calculate accessibility with step decay in r5r?

Use the accessibility() function with decay_function = 'step', specifying cutoffs and opportunities column. The package vignette provides a step-by-step demonstration with sample data.

What are the memory requirements for large travel time matrices?

r5r requires significant RAM allocation to Java; use options(java.parameters = '-Xmx') to set memory. Performance scales with parallel computing, but very large networks may need high memory.

Is r5r compatible with sf objects for spatial analysis?

Yes, functions like detailed_itineraries() return sf LINESTRINGs, and isochrone() outputs sf polygons, allowing integration with R's spatial packages for mapping.

r5r — Multimodal Transport Routing in R

What is r5r?

r5r is an R package that provides an interface to the Conveyal R5 routing engine for rapid, realistic multimodal transport network analysis. It allows users to calculate travel times, generate detailed itineraries, and compute accessibility metrics for walking, biking, public transport, and car modes. The package solves the problem of performing high-performance routing and accessibility calculations within the R environment, leveraging parallel computing for efficiency.

Target Audience

Transportation planners, researchers, and data scientists working in R who need to analyze multimodal transport networks, calculate travel times, or measure accessibility. It is particularly useful for urban analytics, transport planning, and academic research requiring robust routing capabilities.

Value Proposition

Developers choose r5r because it offers a simple and friendly R interface to one of the fastest and most robust routing engines (R5), enabling complex multimodal transport analysis without leaving the R ecosystem. Its seamless parallel computing and detailed output options provide performance and flexibility not found in other R routing packages.

Overview

r5r is an R package that provides a simple and friendly interface to the Conveyal R5 routing engine for multimodal transport analysis. It enables R users to perform rapid, realistic routing calculations, generate travel time matrices, and compute accessibility metrics using seamless parallel computing, all within a familiar R environment.

Key Features

Multimodal Routing — Calculates travel times and routes combining walking, biking, public transport, and car modes.
Accessibility Analysis — Fast computation of access to opportunities using various decay functions (step, logistic, exponential, linear).
Detailed Itineraries — Returns alternative routes with segment-by-segment details on mode, time, and walk distance.
Travel Time Matrices — Generates origin-destination travel time matrices considering departure or arrival times.
Pareto Frontier Analysis — Evaluates trade-offs between travel time and monetary cost for route alternatives.
Isochrone Mapping — Creates polygons showing reachable areas from an origin within a time limit.
Monetary Cost Integration — Accounts for fares and other costs in routing and accessibility calculations.

Philosophy

r5r aims to make advanced, high-performance multimodal transport routing accessible and easy to use for R practitioners, bridging the gap between robust routing engines and the R ecosystem for transport planning and research.

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