What are the best statistical programming languages for transportation planning?

R is a popular statistical language for transportation planning, particularly due to its flexibility, powerful GIS capabilities, and free accessibility. The stplanr package, for example, has been developed to provide a toolbox for transport planning, emphasizing spatial data and active modes[2]. R's proficiency at handling spatial, temporal, and travel survey data, as well as its ability to integrate with other software systems, makes it a well-suited environment for transport planning research[2]. While R is not the only language used for transportation planning, it is a prominent choice due to its open-source nature, which aligns with the growing trend of open and democratic processes in the field.

Python ranked ?? in several scripting lang popularity indices, including the TIOBE Index and the PYPL Index, the popularity of Python has boomed in recent years and it remains the most popular programming language. Python is an open-source, general-purpose programming language with broad applicability not only in the data science industry, but also in other domains, like web dev & game dev. Any data science tasks you can think of can be done with Python. This is mainly thanks to its rich ecosystem of libraries. With 1000’s of powerful packages backed by its huge community of users, Python can perform all kinds of operations, from data preprocessing, visualization, and statistical analysis, to the deployment of ML and deep learning models.

Python is a versatile programming language that can be used for transportation planning. Python has a wide range of libraries and tools that can be used for transportation planning, such as stplanpy, which is a sustainable transportation planner that uses American community survey (ACS) origin-destination data to analyze bicycle commute patterns. Python can also be used for optimization problems in transportation, such as routing, scheduling, network design, and logistics. Optimization models are mathematical representations of the objectives, constraints, and variables of a decision problem, and they can help find the best or most efficient solutions.

Julia is an emerging contender for transportation planning, offering a flexible and high-performance environment for statistical analysis and optimization problems. Julia's multiple dispatch and type system provide greater flexibility compared to some other languages, and its performance can be an order of magnitude faster than languages like R. Julia's optimization capabilities, particularly with the JuMP library, make it a strong choice for transportation planning applications that involve optimization problems. However, Julia's adoption in transportation planning is not as widespread as R or Python, which have more established libraries and tools for this domain.