Systematic comparison of trip distribution laws and models

TL;DR

This paper systematically compares gravity and intervening opportunities trip distribution laws, finding gravity laws generally outperform in estimating commuting flows across different scales and countries, with implications for urban planning models.

Contribution

It provides a comprehensive empirical comparison of trip distribution laws, including recent models like the radiation law, highlighting the effectiveness of gravity laws in various contexts.

Findings

Gravity law better estimates commuting flows than intervening opportunities laws.

Gravity law preserves network structure and fits distance distribution well.

Approaches can be used without detailed calibration data, relying on scale-dependent parameters.

Abstract

Trip distribution laws are basic for the travel demand characterization needed in transport and urban planning. Several approaches have been considered in the last years. One of them is the so-called gravity law, in which the number of trips is assumed to be related to the population at origin and destination and to decrease with the distance. The mathematical expression of this law resembles Newton's law of gravity, which explains its name. Another popular approach is inspired by the theory of intervening opportunities which argues that the distance has no effect on the destination choice, playing only the role of a surrogate for the number of intervening opportunities between them. In this paper, we perform a thorough comparison between these two approaches in their ability at estimating commuting flows by testing them against empirical trip data at different scales and coming from different countries. Different versions of the gravity and the intervening opportunities laws, including the recently proposed radiation law, are used to estimate the probability that an individual has to commute from one unit to another, called trip distribution law. Based on these probability distribution laws, the commuting networks are simulated with different trip distribution models. We show that the gravity law performs better than the intervening opportunities laws to estimate the commuting flows, to preserve the structure of the network and to fit the commuting distance distribution although it fails at predicting commuting flows at large distances. Finally, we show that the different approaches can be used in the absence of detailed data for calibration since their only parameter depends only on the scale of the geographic unit.