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.

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.

Modeling Commuting Flows via Fixed Components

2020 ◽  
Vol 54 (5) ◽  
pp. 1225-1237
Author(s):  
Sahar Babri ◽  
Kurt Jörnsten ◽  
Inge Thorsen ◽  
Jan Ubøe
Keyword(s):  
Goodness Of Fit ◽  
Spatial Interaction ◽  
Interaction Model ◽  
Spatial Separation ◽  
Substantial Improvement ◽  
Distribution Model ◽  
Trip Distribution ◽  
Entropy Maximization ◽  
Commuting Flows ◽  
Job Location

The basic, reasonable hypothesis underlying this paper is that many individuals are comfortable with their current combination of job and residential location and have no intention of changing job location or moving from where they live. This causes autocorrelation in the time series of commuting flows and provides a rationale for introducing fixed components in the trip distribution model. The fixed components are assumed to be constant in time and separated from the observed trip distribution. We next consider the residuals and fit the model by a constrained entropy-maximization procedure. Based on commuting data from Stockholm County in Sweden, the fixed component spatial interaction model is demonstrated to lead to a substantial improvement in goodness of fit compared with conventional spatial interaction models. The identification of fixed components leads to significantly lower estimates of the distance deterrence parameter, and fixed components are further argued to be potentially useful in a prediction perspective. The distance deterrence parameter in the fixed component model reflects the spatial interaction of workers who are actually considering changes in their residential and/or job location. We also compare alternative measures of spatial separation and discuss differences in commuting behaviour by gender.

scholarly journals Permintaan Perjalanan Angkutan Umum Massal Kota Surabaya

2019 ◽  
Vol 8 (2) ◽  
pp. 73-82
Author(s):  
Djoko Prijo Utomo
Keyword(s):  
Gravity Model ◽  
Public Transport ◽  
Travel Demand ◽  
Transport Model ◽  
Urban Transport ◽  
Distribution Model ◽  
Trip Distribution ◽  
The Road ◽  
I Factor ◽  
On The Road

Sebagaimana kota–kota metropolitan di dunia, kemacetan menjadi permasalahan utama dalam bidang transportasi perkotaan. Kemacetan terjadi pada umumnya karena ketidakseimbangan antara penyediaan (supply) dengan permintaan (demand). Usaha untuk menekan jumlah kendaraan di jalan, salah satunya, adalah dengan mempromosikan pengunaan angkutan umum kota. Oleh karena itu diperlukan sebuah perencanaan penyediaan fasilitas angkutan umum. Untuk memperkirakan permintaan angkutan umum dibangun model lalu lintas yang mereplikakan bangkitan, pola dan pembebanan perjalanan. Hasil kalibrasi model distribusi perjalanan menggunakan gravity model diperoleh fungsi impedance yang terbentuk dari fungsi waktu perjalanan dengan faktor  sebesar 0.063. Perbedaan mean trip length model (dengan observed adalah -5,4%. Mean trip length (MTL) model dengan angkutan umum adalah 17,04 menit dan hasil survai 18,02 menit. Model yang terbangun validasinya cukup baik dengan indikator R2 terhadap data observed sebesar 0,88. Hasil dari seluruh tahapan proses pemodelan diperoleh total permintaan perjalanan dengan angkutan umum tahun 2030 diperkirakan mencapai 67.800.434 penumpang/tahun.Kata kunci: Angkutan umum masal, Distribusi perjalanan, Gravity model.AbstractAs metropolitan cities in the world, congestion becomes a major problem in the field of urban transport. Congestion occurs generally due to an imbalance between supplyand demand. Attempts to reduce the number of vehicles on the road, one of which, is to promote the utilization of public transport. Therefore we need a plan for the provision of public transport facilities. To estimate the demand for public transport, it is built transport model which is replicating the trip generation, trip distribution and trip assignment. The results of the trip distribution model calibration using a gravity model obtained impedance function which is formed from travel time functionby a factor of 0.063. Mean trip length difference between model and observed is equal -5,4%.Mean trip length (MTL) model utilizing public transportsis about 17.04 minutes whereasMTL resulted from traffic survey is about 18.02 minutes.The model validation is quite well with observed data by showing the R2 indicatorof about 0.88.The results fromall stages modeling process obtained total travel demand by public transport in the year of 2030 is estimated at about 67,800,434 passengers/year.Keywords: Mass transit, Trip distribution, Gravity model.

Laboratory Testing of Spurious Spatial Structure in Trip Distribution Models

2008 ◽  
Vol 3 (3) ◽  
pp. 361-372 ◽  
Author(s):  
Jan Ubøe ◽  
Jens Petter Gitlesen ◽  
Inge Thorsen
Keyword(s):  
Spatial Structure ◽  
Laboratory Testing ◽  
Distribution Models ◽  
Trip Distribution

Empirical studies of the journey to work in urban Canada

1979 ◽  
Vol 6 (2) ◽  
pp. 308-318 ◽  
Author(s):  
B. G. Hutchinson ◽  
D. P. Smith
Keyword(s):  
Census Tract ◽  
Goodness Of Fit ◽  
Labour Force ◽  
Empirical Studies ◽  
Explanatory Power ◽  
Gravity Models ◽  
Regression Equations ◽  
Distribution Models ◽  
Trip Distribution ◽  
Journey To Work

The 1971 census journey-to-work data supplemented by road-network data for 30 Canadian census areas are analyzed. The data are used to examine the degree to which the commonly used trip-generation and trip-distribution techniques might be used to synthesize journey-to-work patterns and the extent to which the parameters may be generalized across these urban areas.The extent to which intercensus-tract variations in labour force are explained by several separate measures of census-tract residential activity are analyzed by regression analysis. Dwelling-unit composition is isolated as the most effective predictor of census-tract labour force. These multiple regression equations explain from 96–99% of the intercensus-tract variations in labour force and the partial regression coefficients are consistent between census areas.Production-constrained and production-attraction-constrained forms of the gravity model are calibrated. Although conventional goodness-of-fit statistics suggest that these gravity models have a high degree of explanatory power, a detailed examination of the trip-interchange residuals shows that model performances are less than adequate. It is concluded that significant modifications to the traditional gravity trip-distribution models are required if interzonal travel demands are to be estimated with confidence.

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.

Do species distribution models explain spatial structure within tree species ranges?

2009 ◽  
Vol 18 (6) ◽  
pp. 662-673 ◽  
Author(s):  
Daniel Montoya ◽  
Drew W. Purves ◽  
Itziar R. Urbieta ◽  
Miguel A. Zavala
Keyword(s):  
Spatial Structure ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Species Ranges
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