THREE ESSAYS ON URBAN TRANSPORTATION STUDIES IN WASHINGTON D.C.: SAFETY EFFECT OF ALL-WAY STOP CONTROL, SAFETY EFFECT OF REVERSIBLE LANE AND LOADING ZONE ALLOCATION

Systematic data-driven and evidence-based urban transportation policy making and evaluation become increasingly important for public agencies to ensure transparent and efficient services. This dissertation, consisting of three essays on urban transportation studies, focuses on two issues (safety and asset management) that are broadly related with urban transportation policy making and evaluation in Washington D.C. In Chapter One, I evaluate the safety effect of All Way Stop Control (AWSC) conversion with an observational treatment group and a randomly selected control group from stratified samples. Selection bias and time trend are controlled using empirical strategies such as Multiway ANOVA and Difference-in-Differences analysis. The study reveals statistically significant reductions of right angle crashes upon AWSC conversions. However, for all the other collision types, including right turn, left turn, rear end, sideswipes and bicycle crashes, none of the estimated coefficients were statistically significant. In addition, the study quantified a statistically significant increase of straight hit pedestrian crashes upon AWSC conversion. In Chapter Two, I study the safety effect of removing reversible lane operations along urban arterials. Taking advantage of the termination of three reversible lane arterials in 2010, the evaluation is performed using the Before-After (BA) study with a control group and the Empirical Bayes (EB) method, respectively. I estimate Crash Modification Factors (CMF) for all crashes, fatal/injury crashes, property damage only (PDO) crashes, rear-end crashes, left turn crashes and sideswipe crashes. My findings suggest a clear tradeoff between safety and the gain of peak direction capacity by operating reversible lanes along urban arterials. In Chapter Three, I propose an innovative procedure for allocating scarce curbside space for loading zones in an equitable, quantifiable and repeatable manner. Freight Trip Generation (FTG) models are used to estimate the delivery needs for business establishments at a block face level. The current numbers of loading zones per block face are regressed against the Gross FTG (GFTG) per block face and other block face characteristic variables using zero-truncated Negative Binomial models to establish a baseline. Curbside spaces are then assigned as loading zones in an iterative process

A Procedure For Allocating Zonal Attributes To A Link Network In A GIS Environment

This paper presents a procedure for assigning zone data to links in the context of a Geographic Information System (GIS) environment. This may be used for several applications; the one motivating this paper is the need to approximate how much secondary traffic is accessing the main street network from adjacent zones. This will then be used in models to predict the number of accidents on the main road network. The full procedure consists of three sub-procedures: 1) The splitting of links into shorter segments that either are fully located within one zone or act as a border between the same two zones for their entire length, 2) Identification of all link segments either adjacent to or interior to each zone, and 3) Allocation of the zone attributes to the links associated with each zone, according to attributes of the zones and the links, as well as other information describing the area

Network-Based Highway Crash Prediction Using Geographic Information Systems

The objectives of this project were to estimate network-based crash prediction models that will predict the expected crash experience in any given geographic area as a function of the highway link, intersection and land use features observed in the area. The result is a system of GIS programs that permit a polygon to be drawn on a map, or a set of links and intersections to be selected, and then predict the number of crashes expected to occur on the selected traffic facilities. These expected values can then be compared with observed values to identify locations with higher than usual crash incidence and may require attention to improve the safety of the location. Alternatively, this tool could be used to estimate the safety impacts of proposed changes in highway facilities or in different land development scenarios. A network approach was chosen to solve this problem, in which separate models were estimated for crashes at major intersections, and intersection-related and segment-related crashes on road segments. All three sets of models can then be used to predict the number of crashes for an entire highway facility delineated as the user desires – including all intersections. These models also consider all relevant road features, in particular the intensity of traffic at intersections and driveways resulting from the surrounding land use. Gathering traffic volumes at every intersection and driveway on the road network would preclude the feasibility of such an approach, both for estimation and in practice. Instead, the link between land development and trip generation was exploited to estimate the driveway and minor road volumes. Land development intensity variables were generated from land use inventories organized using Geographic Information Systems (GIS), permitting virtually automatic preparation of the required data sets for model estimation and application and prediction of crash counts on roads. Specifically, population and retail and non-retail employment counts were associated with each analysis segment to represent vehicle exposure to intersection-related crashes. GIS was used for two purposes in this project: 1) distributing population and employment counts in a traffic analysis zone (TAZ) among all the links in that zone. 2) Visually comparing the predicted and observed accident counts in order to identify higher than usual crash locations