A Study of Spatial Distribution Characteristics of Probe Vehicles on Signalized Arterials Travel Time Estimation
碩士 === 淡江大學 === 運輸管理學系碩士班 === 96 === This research addresses the relationship between the needed amount and spatial distributions of Probe Vehicle, and the travel time estimation errors, when practically using Probe Vehicles on signalized arterials to estimate travel time. Tools of travel time’s d...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2008
|
Online Access: | http://ndltd.ncl.edu.tw/handle/04184065874947787780 |
id |
ndltd-TW-096TKU05425002 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-096TKU054250022016-05-18T04:13:37Z http://ndltd.ncl.edu.tw/handle/04184065874947787780 A Study of Spatial Distribution Characteristics of Probe Vehicles on Signalized Arterials Travel Time Estimation 利用探針車空間分佈特性推估號誌化道路旅行時間之研究 Chih-Liang Chien 簡誌良 碩士 淡江大學 運輸管理學系碩士班 96 This research addresses the relationship between the needed amount and spatial distributions of Probe Vehicle, and the travel time estimation errors, when practically using Probe Vehicles on signalized arterials to estimate travel time. Tools of travel time’s data-collection can be categorized as Vehicle Detector and Probe Vehicle. Probe Vehicle is becoming widely used in Taiwan because of its lower setup cost. However, Probe Vehicle researches at present all focus on highway, non-signalized arterials, resulting in the lack of discussion in how traffic signals affect travel time in urban districts. Therefore, the purpose of this research contains two main points: first, under limited amount of probe vehicles, how to efficiently use their positions to correct estimation errors of travel time on signalized arterials; second, if the number of Probe Vehicle becomes very popular in the future, how to select Probe Vehicles’ data in which locations to narrow down the errors within limited sampling. This research uses experimental design and ANOVA (Analysis of Variance) to explore the relationship between Probe Vehicles’ spatial distribution and Space Mean Speed, and furthermore, uses Probe Vehicles’ spatial distribution as Artificial Neural Network’s variables to correct estimation errors. The research results show that the relationship between Probe Vehicles’ spatial distribution and Space Mean Speed is not obvious for the short link under 200 meters, but it is significant for the link longer than 200 meters. When Probe Vehicles’ spatial distribution is used as variables in Artificial Neural Network, travel time estimation errors can be efficiently corrected and more accurate signalized arterials travel time can be obtained. Shiaw- Shyan Luo 羅孝賢 2008 學位論文 ; thesis 135 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 淡江大學 === 運輸管理學系碩士班 === 96 === This research addresses the relationship between the needed amount and spatial distributions of Probe Vehicle, and the travel time estimation errors, when practically using Probe Vehicles on signalized arterials to estimate travel time.
Tools of travel time’s data-collection can be categorized as Vehicle Detector and Probe Vehicle. Probe Vehicle is becoming widely used in Taiwan because of its lower setup cost. However, Probe Vehicle researches at present all focus on highway, non-signalized arterials, resulting in the lack of discussion in how traffic signals affect travel time in urban districts.
Therefore, the purpose of this research contains two main points: first, under limited amount of probe vehicles, how to efficiently use their positions to correct estimation errors of travel time on signalized arterials; second, if the number of Probe Vehicle becomes very popular in the future, how to select Probe Vehicles’ data in which locations to narrow down the errors within limited sampling.
This research uses experimental design and ANOVA (Analysis of Variance) to explore the relationship between Probe Vehicles’ spatial distribution and Space Mean Speed, and furthermore, uses Probe Vehicles’ spatial distribution as Artificial Neural Network’s variables to correct estimation errors. The research results show that the relationship between Probe Vehicles’ spatial distribution and Space Mean Speed is not obvious for the short link under 200 meters, but it is significant for the link longer than 200 meters. When Probe Vehicles’ spatial distribution is used as variables in Artificial Neural Network, travel time estimation errors can be efficiently corrected and more accurate signalized arterials travel time can be obtained.
|
author2 |
Shiaw- Shyan Luo |
author_facet |
Shiaw- Shyan Luo Chih-Liang Chien 簡誌良 |
author |
Chih-Liang Chien 簡誌良 |
spellingShingle |
Chih-Liang Chien 簡誌良 A Study of Spatial Distribution Characteristics of Probe Vehicles on Signalized Arterials Travel Time Estimation |
author_sort |
Chih-Liang Chien |
title |
A Study of Spatial Distribution Characteristics of Probe Vehicles on Signalized Arterials Travel Time Estimation |
title_short |
A Study of Spatial Distribution Characteristics of Probe Vehicles on Signalized Arterials Travel Time Estimation |
title_full |
A Study of Spatial Distribution Characteristics of Probe Vehicles on Signalized Arterials Travel Time Estimation |
title_fullStr |
A Study of Spatial Distribution Characteristics of Probe Vehicles on Signalized Arterials Travel Time Estimation |
title_full_unstemmed |
A Study of Spatial Distribution Characteristics of Probe Vehicles on Signalized Arterials Travel Time Estimation |
title_sort |
study of spatial distribution characteristics of probe vehicles on signalized arterials travel time estimation |
publishDate |
2008 |
url |
http://ndltd.ncl.edu.tw/handle/04184065874947787780 |
work_keys_str_mv |
AT chihliangchien astudyofspatialdistributioncharacteristicsofprobevehiclesonsignalizedarterialstraveltimeestimation AT jiǎnzhìliáng astudyofspatialdistributioncharacteristicsofprobevehiclesonsignalizedarterialstraveltimeestimation AT chihliangchien lìyòngtànzhēnchēkōngjiānfēnbùtèxìngtuīgūhàozhìhuàdàolùlǚxíngshíjiānzhīyánjiū AT jiǎnzhìliáng lìyòngtànzhēnchēkōngjiānfēnbùtèxìngtuīgūhàozhìhuàdàolùlǚxíngshíjiānzhīyánjiū AT chihliangchien studyofspatialdistributioncharacteristicsofprobevehiclesonsignalizedarterialstraveltimeestimation AT jiǎnzhìliáng studyofspatialdistributioncharacteristicsofprobevehiclesonsignalizedarterialstraveltimeestimation |
_version_ |
1718271559729152000 |