In this study, the procedures for the estimation of intrazonal travel time by using the OD survey is developed with more than 13000 participants conducted in Denizli city in Turkey. The city area was subdivided several times as to provide 214 traffic analysis zones (TAZ). For home-based school trips, the intrazonal travel time of three different categorize of modes are studied including walking, public transport, and all trip modes together. The all trip modes model contains walking, public transport, private vehicle, and subscription bus. The correlation between the average intrazonal travel time and the most related factors, which have the important effect on it, is designated from the survey. Some geographic and especially socio-economic characteristics are investigated which received the less attention in the past studies. The results show that the population density of the TAZ has the highest effect on the intrazonal travel time of walking mode. The number of links in the network has the highest influence on the intrazonal travel time of the public transport, and this factor for all trip modes together is the population of the TAZ. Regarding these results and by using the regression model, the equation for each category is obtained. The finding shows that the best-fit trend for walking mode is logarithmic, for public transport and all trip modes models are polynomial. By testing the different values for transition points, the transition point for each category is provided which are 10000 people/area for walking mode, 800 link in the network for public transport and 15000 people for all trip modes.
Estimation of the Intrazonal Travel Time of Different Modes for the Home-Based School Trips Using Regression Model / Delice, Yavuz; Özen, Halit; Amirnazmiafshar, Ehsan. - In: EUROPEAN JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY. - ISSN 2538-9181. - 2:issue:1(2019), pp. 49-58. [10.33422/EJEST.2019.01.50]
|Titolo:||Estimation of the Intrazonal Travel Time of Different Modes for the Home-Based School Trips Using Regression Model|
|Data di pubblicazione:||2019|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.33422/EJEST.2019.01.50|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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