<p> The main objective of this research is to examine the effect of city-level urban characteristic, such as urban form and trip generation factors, on traffic safety in general and pedestrian safety in particular. For this purpose, the information for 100 major Urban Areas (UAs) in the United States in 2010 is studied. Factor analysis is applied to construct latent variables from multiple observed variables to measure and describe urban form, macro-level trip generation, citywide transportation network features and traffic safety. Structural Equation Modeling (SEM) is then used to investigate how city-level urban form and trip generation affect traffic safety directly and indirectly (through mediators of transportation network features).</p><p> Based on the statistical analysis, it is found that encouraging the use of non-driving transportation modes and controlling traffic congestion, as significant mediators, are effective policies to increase overall traffic safety and pedestrian safety, respectively. In this regard, urban areas with a more even spatial distribution of job-housing balance (more polycentricity), more uniform spatial distribution of different social classes, higher urban density (less sprawl), and more connectivity in their transportation network (more accessibility) have the safest urban form designs.</p><p> Moreover, mixed land-use designs with provided local access to services and amenities, food and beverage centers, and religious organizations, followed by strict pedestrian safety standards for neighborhoods are the safest type of land use designs in urban areas. In addition, regulating the off-peak hours allowed time for heavy vehicles and changing the work schedule of workers who do not reside in the urban area can also help city planners to increase traffic safety.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10245444 |
| Date | 05 May 2017 |
| Creators | Najaf, Pooya |
| Publisher | The University of North Carolina at Charlotte |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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