A Relaxation Approach for Estimating Origin–Destination Trip Tables

Nie, Yu; Zhang, H. M.

doi:10.1007/s11067-007-9059-y

Cited by 29 publications

(15 citation statements)

References 18 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, few researchers consider regional aggregation and spatial differentiation for both source and sink regions. This is mainly limited by the fact that it was difficult to obtain detailed nationwide tourist flow statistics in the past, and many advanced regional division methods have not been introduced into the analysis of tourist flows [49]. Some scholars analyze tourism flow from the perspective of spatial structure and influencing mechanisms by using GIS technology and complex network analysis models.…”

Section: Introductionmentioning

confidence: 99%

Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

Zhang

Chen

et al. 2019

IJGI

View full text Add to dashboard Cite

At present, population mobility for the purpose of tourism has become a popular phenomenon. As it becomes easier to capture big data on the tourist digital footprint, it is possible to analyze the respective regional features and driving forces for both tourism sources and destination regions at a macro level. Based on the data of tourist flows to Nanjing on five short-period national holidays in China, this study first calculated the travel rate of tourist source regions (315 cities) and the geographical concentration index of the visited attractions (51 scenic spots). Then, the spatial autocorrelation metrics index was used to analyze the global autocorrelation of the travel rates of tourist source regions and the geographical concentration index of the tourist destinations on five short-term national holidays. Finally, a heuristic unsupervised machine-learning method was used to analyze and map tourist sources and visited attractions by adopting the travel rate and the geographical concentration index accordingly as regionalized variables. The results indicate that both source and sink regions expressed distinctive regional differentiation patterns in the corresponding regional variables. This study method provides a practical tool for analyzing regionalization of big data in tourist flows, and it can also be applied to other origin-destination (OD) studies.

show abstract

Section: Introductionmentioning

confidence: 99%

Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

Zhang

Chen

et al. 2019

IJGI

View full text Add to dashboard Cite

show abstract

“…A sensitivity-based algorithm by Josefsson and Patriksson [38] and a heuristic algorithm by Yang [91] are commonly used to solve for it. In addition, Nie and Zhang [58] relaxes the UE-based ODE to a one-level optimization problem, enhanced by Shen and Wynter [72] with a convex relaxation program on a one-level optimization problem. Remark 6.…”

Section: Remark 5 Formulation 22 Is Non-convexmentioning

confidence: 99%

Statistical inference of probabilistic origin-destination demand using day-to-day traffic data

Qian

2018

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

Recent transportation network studies on uncertainty and reliability call for modeling the probabilistic O-D demand and probabilistic network flow. Making the best use of day-to-day traffic data collected over many years, this paper develops a novel theoretical framework for estimating the mean and variance/covariance matrix of O-D demand considering the day-to-day variation induced by travelers' independent route choices. It also estimates the probability distributions of link/path flow and their travel cost where the variance stems from three sources, O-D demand, route choice and unknown errors. The framework estimates O-D demand mean and variance/covariance matrix iteratively, also known as iterative generalized least squares (IGLS) in statistics. Lasso regularization is employed to obtain sparse covariance matrix for better interpretation and computational efficiency. Though the probabilistic O-D estimation (ODE) works with a much larger solution space than the deterministic ODE, we show that its estimator for O-D demand mean is no worse than the best possible estimator by an error that reduces with the increase in sample size. The probabilistic ODE is examined on two small networks and two real-world large-scale networks. The solution converges quickly under the IGLS framework. In all those experiments, the results of the probabilistic ODE are compelling, satisfactory and computationally plausible. Lasso regularization on the covariance matrix estimation leans to underestimate most of variance/covariance entries. A proper Lasso penalty ensures a good trade-off between bias and variance of the estimation.

show abstract

“…In principle, such a model can be viewed as a reduced version of combined trip distribution and traffic assignment models because it in fact estimates origin-destination flows and link flows simultaneously. In particular, the network equilibrium implied in those existing models of this type is specified by two types of mathematical forms: (a) nonlinear forms of link flows, typically written as a CO or VI (41)(42)(43)(44)(45)(46)(47), and (b) linear forms of origin-destination costs (48)(49)(50)(51), in which origin-destination costs can be conveniently calculated from the given link counts in concert with link cost functions.…”

Section: Trip Matrix Estimationmentioning

confidence: 99%

Two-Stage Equilibrium Travel Demand Model for Sketch Planning

Xie

Kockelman

2012

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

1This paper describes a two-stage equilibrium travel demand model. The unique feature of this model is 2 that it takes time-of-day traffic counts instead of land use and demographic data as inputs to derive spatial 3 and temporal travel demand patterns. The first stage of the model is a traffic count-based trip matrix 4 estimator; the second stage is an elastic-demand network flow estimator, which recognizes latent demand 5 shifts while performing mode split, time-of-day split, and traffic assignment in a multi-level equilibration. 6The main purpose of this paper is to share with readers our model development, algorithm design, and 7 software implementation experiences. An example application illustrates how the model is used to 8 evaluate multi-class, multi-mode and multi-period network flow patterns for sketch planning purposes. 9

show abstract

A Relaxation Approach for Estimating Origin–Destination Trip Tables

Abstract: Static O–D estimation problem, Bi-level program, Relaxation, Column generation,

Cited by 29 publications

References 18 publications

Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

Statistical inference of probabilistic origin-destination demand using day-to-day traffic data

Two-Stage Equilibrium Travel Demand Model for Sketch Planning

Contact Info

Product

Resources

About