Systematic comparison of trip distribution laws and models

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.

15 pages, 10 figures

References in corpus (6)

• Gravity model in the Korean highway
• Predicting commuter flows in spatial networks using a radiation model based on temporal ranges
• The Distance-Decay Function of Geographical Gravity Model: Power Law or Exponential Law?
• Limits of Predictability in Commuting Flows in the Absence of Data for Calibration
• Human Mobility in a Continuum Approach
• Supersampling and network reconstruction of urban mobility

Cited by in corpus (26)

• Human Mobility: Models and Applications
• The statistical physics of Cities
• Data-driven generation of spatio-temporal routines in human mobility
• Field theory for recurrent mobility
• Learning Geo-Contextual Embeddings for Commuting Flow Prediction
• An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques
• Generating Synthetic Mobility Networks with Generative Adversarial Networks
• Human mobility is well described by closed-form gravity-like models learned automatically from data
• Gravity model explained by the radiation model on a population landscape
• Nonlocal biased random walks and fractional transport on directed networks
• Some recent advances in urban system science: models and data
• Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models
• Urban Computing in the Era of Large Language Models
• Customer mobility and congestion in supermarkets
• Predictive limitations of spatial interaction models: a non-Gaussian analysis
• Mobility signatures: a tool for characterizing cities using intercity mobility flows
• Mobility constraints in segregation models
• Assessing Refugees' Integration via Spatio-temporal Similarities of Mobility and Calling Behaviors
• Highway freight transportation diversity of cities based on radiation models
• A modeling framework to support the electrification of private transport in African cities: a case study of Addis Ababa
• Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
• Unified Mobility Estimation Mode
• Is spatial information in ICT data reliable?
• A Machine Learning Approach to Modeling Human Migration
• Transportation Scenario Planning with Graph Neural Networks
• Understanding following patterns among high-performance athletes