Travel cost demand model based river recreation benefit estimates with on-site and household surveys: Comparative results and a correction procedure

2003 ◽  
Vol 39 (4) ◽  
Author(s):  
John Loomis
Keyword(s):  
Travel Cost ◽  
Household Surveys ◽  
Correction Procedure ◽  
Demand Model ◽  
Model Based ◽  
River Recreation ◽  
Comparative Results

Comparing Recreation Benefits from On-Site versus Household Surveys in Count Data Travel Cost Demand Models with Overdispersion

2008 ◽  
Vol 14 (3) ◽  
pp. 567-576 ◽  
Author(s):  
Roberto Martínez-Espiñeira ◽  
John B. Loomis ◽  
Joe Amoako-Tuffour ◽  
Joseph M. Hilbe
Keyword(s):  
Count Data ◽  
Travel Cost ◽  
Household Surveys ◽  
Demand Models ◽  
Recreation Benefits ◽  
Site Versus

scholarly journals Testing Significance of Multi-Destination and Multi-Purpose Trip Effects in a Travel Cost Method Demand Model for Whale Watching Trips

2000 ◽  
Vol 29 (2) ◽  
pp. 183-191 ◽  
Author(s):  
John Loomis ◽  
Shizuka Yorizane ◽  
Douglas Larson
Keyword(s):  
Willingness To Pay ◽  
Count Data ◽  
Cost Model ◽  
Consumer Surplus ◽  
Travel Cost ◽  
Travel Cost Method ◽  
Demand Model ◽  
Generalized Model ◽  
Travel Cost Model ◽  
Recreation Site

Inclusion of multi-destination and multi-purpose visitors has an appreciable influence on a standard count data travel cost model derived estimate of willingness to pay but the differences are not statistically significant. We adapt a more general travel cost model (TCM) of Parsons and Wilson (1997) that allows for inclusion of multi-destination visitors as incidental demand to allow estimation of an unbiased measure of single and multi-destination willingness to pay for whale viewing using a single pooled equation. The primary purpose trip values from the standard TCM and simple generalized TCM model are identical at $43 per person per day and neither are significantly different from the $50 day value from a generalized model that distinguishes between joint and incidental trips. The general models avoid underestimation of total recreation site benefits that would result from omitting the consumer surplus of multi-destination visitors.

Research on Parking Demand Model of Colleges and Universities Based on Shared Parking

2014 ◽  
Vol 505-506 ◽  
pp. 415-421 ◽  
Author(s):  
Kai Xie ◽  
Jun Chen ◽  
Jing Heng Zheng
Keyword(s):  
Colleges And Universities ◽  
Demand Model ◽  
Traditional Methods ◽  
Main Building ◽  
Model Based ◽  
Regional Features ◽  
Parking Demand

With purpose of quantifying university parking demand more accurately, and making up the limitation of traditional methods, university parking demand model based on behavior of shared parking was established. This paper classified university parking demand as main building and non-main building parking demand. First, the parking demand of the main building was calculated through parking survey which determined the number of shared structures and shared area of the main building. Second, according to the regional features of universities, dividing campus into several big shared parking areas and calculating the parking demand of non-main building in it .An example was prepared to verify the correctness of the model. The deviation of this model was 7%, significantly less than that of traditional university parking demand model.

Model of Improving Searching Precision For Interactive Users with Searching Requirements

2013 ◽  
Vol 756-759 ◽  
pp. 833-836
Author(s):  
Tian Chong ◽  
Yan Ling Shao ◽  
Jian Jun Wang
Keyword(s):  
Data Mining ◽  
Search Engine ◽  
User Feedback ◽  
Demand Model ◽  
Model Based ◽  
Search Precision ◽  
The One ◽  
Feedback Data

This paper presents a model based on analysis on interactive users search needs. This model changes from the analysis on user's behavior to the one on search content, which reduces to violate users privacy, and enables users to actively participate in determining the needs of search from the client end in the search engine, in addition to establishment of search demand model based on a large number of user feedback data mining in the search engine subsystem, as a result of higher search precision with less client spending and higher user s participation.

Sign in / Sign up

Export Citation Format

Share Document