The objective of this research is to develop and assess bus transit ridership models at a bus-stop level using two spatial modeling methods: spatial proximity method (SPM) and spatial weight method (SWM). Data for the Charlotte (North Carolina) area are used to illustrate 1) the working of the methods and 2) development and assessment of the models. Features available in Geographic Information System (GIS) software were explored to capture spatial attributes such as demographic, socioeconomic, and land use characteristics around each selected bus stop. These, along with on-network characteristics surrounding the bus stop, were used as explanatory variables. Models were then developed, using the generalized estimating equations (GEE) framework, to estimate riders boarding (dependent variable) at the bus stop as a function of selected explanatory variables that are not correlated to each other. Results obtained indicate that Negative Binomial with log-link distribution better fits the data to estimate ridership at the bus-stop level (for both SPM and SWM) than when compared to linear, Poisson with log-link and Gamma with log-link distributions. Although SPM models demonstrated distance decay behavior, statistical parameters indicate that SWM (based on functions 1/D, 1/D2, and 1/D3) does not yield better or more meaningful estimates than when compared to SPM using 0.25- mile buffer width data.

Objective: Violation of traffic rules is a major contributing factor in both crashes and fatalities in the United States. This study aims at quantifying risk that drivers pose to themselves and other drivers by violating traffic rules. Method: Crash data from 2010 to 2013 were gathered for the state of North Carolina. Descriptive analysis was carried out to identify frequent traffic violations and who were committing the traffic violations that resulted in crashes. A multinomial logit model was then developed to examine the relation between different traffic violations and driver injury severity. Additionally, odds ratios were estimated to identify the likelihood (probability) of severe or moderate injury to the driver and other drivers due to a driver violating a traffic rule that led to a crash. Results: Exceeding the speed limit is more likely to result in severe injury compared to disregarding traffic signals. However, going the wrong way is more likely to result in severe injury to other drivers when compared to any other traffic violation. Driving under the influence of alcohol is 2 times more likely to result in severe injury than driving under the influence of drugs. These 2 traffic violations by a driver are almost equally likely to result in severe injury to other drivers. Conclusions: Drivers often perceive that violating traffic rules will not result in a crash or severe injury. However, the results from this study show that a majority of the traffic violations lead to severe injury to the violator as well as to other drivers. The findings from this study serve as documented evidence to educate drivers about the risk they pose to themselves and to other drivers by violating traffic rules and encourage the adaptation of safe driving behavior in order to contribute toward reaching the “zero traffic deaths” vision. They also help make policy changes pertaining to penalty points and fines for violating a traffic rule.