scholarly journals Trip distribution model using trip length distribution.

1986 ◽  
pp. 41-49
Author(s):  
Toshikazu SHIMAZAKI
Keyword(s):  
Length Distribution ◽  
Distribution Model ◽  
Trip Distribution ◽  
Trip Length

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).

Multiple Uses of Big Data for Model Validation and Express Lanes Traffic Forecasts

2020 ◽  
Vol 2674 (11) ◽  
pp. 87-100
Author(s):  
Amar Sarvepalli ◽  
Barbara Davis
Keyword(s):  
Big Data ◽  
Model Validation ◽  
Dynamic Traffic Assignment ◽  
Length Distribution ◽  
Distribution Model ◽  
Tourist Attractions ◽  
Matrix Estimation ◽  
Seasonal Flow ◽  
Investment Grade ◽  
Express Lanes

This paper highlights a variety of uses for Big Data when developing project forecasts and model validations. In most travel models, validation often refers to estimating model volumes close to the observed highway counts. While this is an established practice for producing reasonable confidence in the model, these statistics are often not sufficient to build confidence in the project forecast. This is especially true for investment-grade level traffic and revenue forecasts for projects involving congestion pricing. This paper explores the application of Big Data to validate subarea models in multiple dimensions: subarea district-to-district origin-destination (O-D) flows; corridor segment-to-segment O-D flows; and trip length distribution by O-D types for the I-4 Ultimate Express Lanes Study. Additionally, the paper reviews historical O-D flows to determine the peak seasonal flow and appropriate O-D data to use in model validation and seed tables for Origin-Destination Matrix Estimation (ODME). In addition to model validation, the expanded Big Data O-D trips were assigned to multiple paths for each O-D pair built via Google Directions API to study the eligible corridor trips and the alternative corridors competing with the project. Furthermore, spatiotemporal distributions from Big Data were used to develop time-dependent trip tables for the Dynamic Traffic Assignment (DTA) model. Several international tourist attractions located along the I-4 Ultimate Corridor serve high visitor and weekend traffic, and Big Data was used to analyze and develop a weekend distribution model. Each of these modules involves some form of observed data, all coming from one source, “Big Data.”

Intraprovincial trip distribution model

1986 ◽  
Vol 13 (4) ◽  
pp. 438-444 ◽  
Author(s):  
Bala Ashtakala ◽  
A. S. Narasimha Murthy
Keyword(s):  
Regression Analysis ◽  
Functional Form ◽  
Distribution Model ◽  
Trip Distribution ◽  
Individual Model ◽  
Independent Variables ◽  
Statistical Measures ◽  
Power Transformations ◽  
Independent Variable ◽  
Validity Tests

A town (or city) in a province generates external trips to other towns and cities for various purposes. A cordon origin–destination (O – D) survey shows the origin and destinations of the external trips of a town. An individual external trip distribution model is developed for a town using the cordon O – D survey data. The external trips are recognized as intra provincial trips and therefore the model is called intraprovincial trip distribution model. The data used in this study is taken from cordon O – D surveys done in Alberta.In the formulation of the model, a functional form that uses a linear relationship between the dependent and the independent variables and power transformations on the independent variable is considered. The parameters in the model are obtained by regression analysis. The models are validated by statistical measures and tests. In this paper, an individual model is developed for each of the seven cities and towns chosen for this study. The formulation and development of the model, regression analysis, and validity tests are described in detail. Key words: cordon survey, intraprovincial trips, model, power transformations, regression analysis, trip distribution.

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