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Community design, road safety & tran...

Marshall, Wesley Earl.

Community design, road safety & transportation sustainability.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Community design, road safety & transportation sustainability.
作者:	Marshall, Wesley Earl.
面頁冊數:	167 p.
附註:	Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: 5688.
附註:	Adviser: Norman W. Garrick.
Contained By:	Dissertation Abstracts International70-09B.
標題:	Engineering, Civil.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3376843
ISBN:	9781109374483

Community design, road safety & transportation sustainability.
Marshall, Wesley Earl.

Community design, road safety & transportation sustainability. - 167 p.

Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: 5688.

Thesis (Ph.D.)--University of Connecticut, 2009.

This research explores the relationship between street network design and key indicators of sustainability such as road safety and mode choice. The basis for this analysis was 230,000 individual crash records geo-coded into a GIS database for over 1,000 Census Block Groups in 24 medium-sized California cities selected from an initial database of over 150 cities. Street network measures were combined with street characteristics, journey to work numbers, socioeconomic data, traffic flow information, and the crash records from eleven years of crash data for five crash severity levels. The fundamental street network characteristics that were considered in this analysis included: street network density, street connectivity, and street network patterns.

ISBN: 9781109374483Subjects--Topical Terms:

212394
Engineering, Civil.

Community design, road safety & transportation sustainability.
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500 $a Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: 5688.
500 $a Adviser: Norman W. Garrick.
502 $a Thesis (Ph.D.)--University of Connecticut, 2009.
520 $a This research explores the relationship between street network design and key indicators of sustainability such as road safety and mode choice. The basis for this analysis was 230,000 individual crash records geo-coded into a GIS database for over 1,000 Census Block Groups in 24 medium-sized California cities selected from an initial database of over 150 cities. Street network measures were combined with street characteristics, journey to work numbers, socioeconomic data, traffic flow information, and the crash records from eleven years of crash data for five crash severity levels. The fundamental street network characteristics that were considered in this analysis included: street network density, street connectivity, and street network patterns.
520 $a Overall, we found that high street network densities were correlated with more walking and biking as well as fewer crashes across all severity levels. In fact, the effect of increased street network density was much more pronounced for fatalities than for less severe injuries. This suggests that lower vehicle speeds or differences in travel patterns might be some of the factors contributing to fewer fatalities in denser, more urban environments. Increased street connectivity was significantly associated with a decrease in driving and an increase in crashes. The negative effect of street connectivity may be due to increased traffic conflicts associated with more connectivity; however, it is important to keep in mind that the places in our study with highly connected street networks also tended to have complementary design features that were found to increase safety.
520 $a For all types of street patterns, both street network characteristics and street design factors played a major role in how people use the transportation system and their traffic safety outcomes. Across the board, this research showed that denser, gridded street networks with more urban street features were associated with much more walking, biking, and transit use and a safer overall transportation system. These results suggest that both the design of the street network and the streets themselves work together to influence travel decisions, driver behavior, and vehicle speeds---all of which are factors that affect the total number of crashes and the severity of those crashes.
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