Trip Distribution and Disaggregation

1975 ◽  
Vol 7 (1) ◽  
pp. 71-97 ◽  
Author(s):  
P J Hathaway
Keyword(s):  
Distribution Model ◽  
Standard Industrial Classification ◽  
Model Parameters ◽  
Socioeconomic Group ◽  
Distribution Models ◽  
Trip Distribution ◽  
Occupational Classification ◽  
Calibration Methods ◽  
Distinct Category ◽  
Better Than

This paper describes an investigation into the gain in the predictive and descriptive abilities of a trip-distribution model which takes into account the differences shown to exist between various categories of trip maker. Four classifications of trip makers are considered—age, sex and marital status, socioeconomic group, occupational classification, and standard industrial classification. A trip-distribution model is calibrated for each of the categories mentioned above. Calibration methods are discussed and a new method whereby the significance of the differences in the values in the trip-distribution model parameters between categories may be examined. It is concluded that it is not worth running separate distribution models for each distinct category of trip maker since the improvement, at least in descriptive ability, is only marginally better than the fit shown by a single trip-distribution model.

scholarly journals Estimation of Contaminants for Direct Imaging of Exoplanets: Constraint on the Stellar Distribution Model with both NIR and Deep Imaging Data

2013 ◽  
Vol 8 (S299) ◽  
pp. 42-43
Author(s):  
Mihoko Konishi ◽  
Hiroshi Shibai ◽  
Taro Matsuo ◽  
Kodai Yamamoto ◽  
Jun Sudo ◽  
...  
Keyword(s):  
Point Sources ◽  
Distribution Model ◽  
Model Parameters ◽  
Imaging Data ◽  
Direct Imaging ◽  
Distribution Models ◽  
New Model ◽  
Deep Imaging

AbstractThere are faint contaminants near primary stars in the direct imaging of exoplanets. Our goal is to estimate statistically the ratio of exoplanets in the detected batch of point sources by calculating the fraction of contamination. In this study, we compared the detected number of stars with the number of contaminants predicted by our model. We found that the observed number of faint stars were fewer than the predicted results towards the Pleiades and GOODS-South field when the parameters of the conventional stellar distribution models were employed. We thus estimated new model parameters in correspondence to the results of the observations.

Explanatory and forecasting capabilities of trip distribution models

1986 ◽  
Vol 13 (6) ◽  
pp. 666-673 ◽  
Author(s):  
P. Volet ◽  
B. G. Hutchinson
Keyword(s):  
Spatial Structure ◽  
Gravity Model ◽  
Distribution Model ◽  
Distribution Models ◽  
Trip Distribution ◽  
Socioeconomic Groups ◽  
Commuting Flows ◽  
Journey To Work

Trip distribution models attempt to capture two effects and these are changes in the overall scale of travel between some base year and forecast year as well as fundamental changes in commuting structure. The paper begins with a very brief discussion of observed commuting changes in the Toronto region between 1971 and 1981 using the census journey-to-work data. The abilities of a doubly constrained gravity model to emulate interzonal commuting flows in 1971 and 1981 are examined as well as its ability to forecast 1981 flows. These explanatory and forecasting capabilities are compared with those of a Fratar-type trip distribution model. The trip interchange residuals for both model types are isolated and interpreted in terms of the changes in spatial structure that have occurred in the Toronto region over the analysis period. It is concluded that the forecasts provided by the Fratar model are much superior to those of the aggregate doubly constrained gravity model. Both model types have difficulties in emulating shifts in commuting structure that are due to fundamental changes in living and working patterns by the various socioeconomic groups.

Exact Methods for Gravity Trip-Distribution Models

1989 ◽  
Vol 21 (1) ◽  
pp. 81-97 ◽  
Author(s):  
K Holmberg ◽  
K Jörnsten
Keyword(s):  
Computational Effort ◽  
Distribution Model ◽  
Interval Methods ◽  
Integer Programming Problem ◽  
Exact Methods ◽  
Distribution Models ◽  
Step Method ◽  
Separable Programming ◽  
Trip Distribution ◽  
Solution Methods

Gravity-type trip-distribution models are widely used to predict trip matrices. One of the reasons for the popularity of the gravity-type models is that simple and fast methods for computation of the trip matrices exist. These solution methods will not, however, solve the original trip-distribution problem, but an approximate problem in which the discrete and combinatorial nature of the problem is not taken into account. In this paper the solution methods for the ‘exact gravity trip-distribution model’, which is an integer programming problem, will be presented. It will be shown that with a certain amount of extra computational effort it is possible to derive the trip matrix that is the exact solution to the model and not just an asymptotic estimate of it. This also eliminates the infeasibility that will most probably occur as a result of rounding the solution to the continuous model. The solution methods presented herein are based on separable programming techniques. A one-step method is presented as well as the iterative shrinking-interval and moving-interval methods. Results that show the difference between the trip matrices produced by means of the exact method and the continuous approximation are also presented.

scholarly journals Not so Normal Normals: Species Distribution Model Results are Sensitive to Choice of Climate Normals and Model Type

Climate ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 37 ◽  
Author(s):  
Catherine Jarnevich ◽  
Nicholas Young
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Model Parameters ◽  
Climate Data ◽  
Distribution Models ◽  
Model Type ◽  
Time Periods ◽  
The Impact

Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development.

MODEL DISTRIBUSI PERJALANAN PENDUDUK UNTUK PENENTUAN KORIDOR ‘LRT’ DI PULAU BATAM

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Djoko Prijo Utomo
Keyword(s):  
Network Performance ◽  
Travel Demand ◽  
Mass Transit ◽  
Distribution Model ◽  
Economic Activities ◽  
Transit System ◽  
Trip Distribution ◽  
Home Interview ◽  
Interview Survey ◽  
Private Car

In consequence of the increasing of regional economic activities in Pulau Batam, a reliable transportation system is required. Decreasing road network performance as a result of increasing traffic volume needs a strategic planning to anticipate the worsening condition in the future. One of the solutions is by providing mass transit system which is expected to attract private car users. Therefore, determination of potential corridor of mass transit system need to be identified so that the system provide better accessibility. Trip pattern in Pulau Batam must be known by developing trip distribution model. The trip distribution model is calibrated using origin-destination (O-D) data that is based on home interview survey. The validated model will be used to forecast and simulate travel demand onto transport network. Result of model calibration process shows mean trip length difference between model and survey is equal 0.141 %. From simulation of trip assignment is obtained that potential corridor for mass transit system using LRT is Batu Ampar – Batu Aji via Muka Kuning. Passenger forecast in the year 2030 is 193,990 passenger/day (2 directions).

scholarly journals A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 388
Author(s):  
Riccardo De Blasis ◽  
Giovanni Batista Masala ◽  
Filippo Petroni
Keyword(s):  
Markov Model ◽  
Electricity Market ◽  
Wind Farm ◽  
Central Italy ◽  
High Order ◽  
Distribution Model ◽  
Spot Price ◽  
Model Parameters ◽  
Price Series ◽  
Cross Correlations

The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

scholarly journals Application Natura 2000 Data For The Invasive Plants Spread Prediction

2015 ◽  
Vol 46 (4) ◽  
pp. 159-166 ◽  
Author(s):  
J. Pěknicová ◽  
D. Petrus ◽  
K. Berchová-Bímová
Keyword(s):  
Environmental Factors ◽  
Invasive Plants ◽  
Species Distribution ◽  
Natura 2000 ◽  
Distribution Model ◽  
Distribution Data ◽  
Habitat Types ◽  
Distribution Models ◽  
Occurrence Data ◽  
Heracleum Mantegazzianum

AbstractThe distribution of invasive plants depends on several environmental factors, e.g. on the distance from the vector of spreading, invaded community composition, land-use, etc. The species distribution models, a research tool for invasive plants spread prediction, involve the combination of environmental factors, occurrence data, and statistical approach. For the construction of the presented distribution model, the occurrence data on invasive plants (Solidagosp.,Fallopiasp.,Robinia pseudoaccacia,andHeracleum mantegazzianum) and Natura 2000 habitat types from the Protected Landscape Area Kokořínsko have been intersected in ArcGIS and statistically analyzed. The data analysis was focused on (1) verification of the accuracy of the Natura 2000 habitat map layer, and the accordance with the habitats occupied by invasive species and (2) identification of a suitable scale of intersection between the habitat and species distribution. Data suitability was evaluated for the construction of the model on local scale. Based on the data, the invaded habitat types were described and the optimal scale grid was evaluated. The results show the suitability of Natura 2000 habitat types for modelling, however more input data (e.g. on soil types, elevation) are needed.

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