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.

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.

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.

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 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.

scholarly journals Determining population trends and conservation status of the common quail (Coturnix coturnix) in Western Europe

2012 ◽  
Vol 35 (2) ◽  
pp. 343-352
Author(s):  
M. Puigcerver ◽  
◽  
F. Sardà–Palomera ◽  
J. D. Rodriguez-Teijeiro ◽  
◽  
...  
Keyword(s):  
Western Europe ◽  
Conservation Status ◽  
Population Trends ◽  
Distribution Model ◽  
Breeding Period ◽  
Distribution Models ◽  
Winter Cereal ◽  
Coturnix Coturnix ◽  
The Common ◽  
Common Quail

In this paper we review the conservation status and population trends of the common quail (Coturnix coturnix) from 1900 to the present. Data are sometimes contradictory with regard to the status of this species as it has some features that make it difficult to produce reliable population estimates. Recent data clearly suggest, either at a local scale or at a trans–national scale, that the Atlantic common quail populations have remained stable in the last two decades, and that restocking practices with farm–reared quails (hybrids with the Japanese quail, Coturnix japonica) do not affect our estimates. The complex movement patterns showed by this species require special attention. Analysis of ring recoveries can give important information, especially about the nomadic movement of quails in search of suitable habitats after the destruction of winter cereal crops due to harvesting. Thus, when developing a breeding distribution model for this species, continuously updated information on seasonal habitat and weather must be included for optimal prediction. Including fortnightly data of vegetation indices in distribution models, for example, has shown good results. Obtaining reliable predictions about changes in species distribution and movements during the breeding period could provide useful knowledge about the conservation status and population trends and would help in the design of future management measures.

scholarly journals Different distributions of gold nanoparticles on the tumor and calculation of dose enhancement factor by Monte Carlo simulation

2019 ◽  
Vol 5 (4) ◽  
pp. 361-371 ◽  
Author(s):  
Sajad Keshavarz ◽  
Dariush Sardari
Keyword(s):  
Gold Nanoparticles ◽  
Uniform Distribution ◽  
Enhancement Factor ◽  
Radiation Energy ◽  
Distribution Model ◽  
Distribution Models ◽  
Dose Enhancement ◽  
X Ray ◽  
Nanoparticle Distribution ◽  
Nanoparticle Sizes

Gold nanoparticles can be used to increase the dose of the tumor due to its high atomic number as well as being free from apparent toxicity. The aim of this study is to evaluate the effect of distribution of gold nanoparticles models, as well as changes in nanoparticle sizes and spectrum of radiation energy along with the effects of nanoparticle penetration into surrounding tissues in dose enhancement factor DEF. Three mathematical models were considered for distribution of gold nanoparticles in the tumor, such as 1-uniform, 2- non-uniform distribution with no penetration margin and 3- non-uniform distribution with penetration margin of 2.7 mm of gold nanoparticles. For this purpose, a cube-shaped water phantom of 50 cm size in each side and a cube with 1 cm side placed at depth of 2 cm below the upper surface of the cubic phantom as the tumor was defined, and then 3 models of nanoparticle distribution were modeled. MCNPX code was used to simulate 3 distribution models. DEF was evaluated for sizes of 20, 25, 30, 50, 70, 90 and 100 nm of gold nanoparticles, and 50, 95, 250 keV and 4 MeV photon energies. In uniform distribution model the maximum DEF was observed at 100 nm and 50 keV being equal to 2.90, in non-uniform distribution with no penetration margin, the maximum DEF was measured at 100 nm and 50 keV being 1.69, and in non-uniform distribution with penetration margin of 2.7 mm, the maximum DEF was measured at 100 nm and 50 keV as 1.38, and the results have been showed that the dose was increased by injecting nanoparticles into the tumor. It is concluded that the highest DEF could be achieved in low energy photons and larger sizes of nanoparticles. Non-uniform distribution of gold nanoparticles can increase the dose and also decrease the DEF in comparison with the uniform distribution. The non-uniform distribution of nanoparticles with penetration margin showed a lower DEF than the non-uniform distribution without any margin and uniform distribution. Meanwhile, utilization of the real X-ray spectrum brought about a smaller DEF in comparison to mono-energetic X-ray photons.

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