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.Comment: 15 pages, 10 figure

Field theory for recurrent mobility

“…This definition allows us to apply a coherent treatment to all our databases and it is an approximation for the total workforce living in every cell. As shown in [46], the mass defined in this way yields better flow estimates than the actual cell population for both gravity and radiation models. If instead of home to work, we consider the returning trip from work to home the picture does not change significantly.…”

“…22 [49]). A recent brute-force comparison between models (gravity, radiation and intervening opportunities with different constrain levels) and empirical commuting flows was carried out in [46]. The performance indicators at single flow level were favoring the exponential gravity model but the metrics were not able to capture big differences across models.…”

“…Moreover, the model can be developed further by taking into account the distinguishability of the trips [35][36][37]. Early after the gravity model introduction, the possibility of defining a potential was discussed [38] but the lack of reliable data prevented ulterior research in this direction.Several works have focused on the comparison between the two families of models and their performance when compared with empirical data [39][40][41][42][43][44][45][46][47]. It is worth mentioning that a fair comparison requires to be carried out over the same type of mobility data (daily or sporadic trips behave differently) and with the same constraints.…”

Accounting for the Boundary Problem at Subnational Level: The Supply–Demand Balance of Biomass Cooking Fuels in Kitui County, Kenya

“…The distance decay used in the above formula describes how the distant demand of one sublocation on another sublocation decreases as a function of the travel time distance between these two sublocations. We used a negative, exponential function [90][91][92][93][94] defined as follows:…”

Debye–Hückel theory for refugees’ migration

“…These two competing models, with variants [26], have been shown to properly predict human mobility and their relative likelihood has been explored [23,27]. However, their validity to model mobility under stressful conditions beyond the daily commute or travel has not been examined.…”