Grocery stores are an important amenity in neighborhoods and access to grocery stores is important for health and well-being. While grocery store accessibility is a popular topic of research, studies measuring access for pedestrians, bicyclists, and transit riders are extremely rare. When a new store opened in the Excelsior/Outer Mission districts of San Francisco on a street lacking basic infrastructure for pedestrians, bicyclists, and transit riders, the importance of this study became apparent. The Excelsior/Outer Mission neighborhood has a shocking number of collisions (over 1,100 between 2015 and 2019), elevated levels of walking, biking, and transit ridership, and minimal safe infrastructure for these modes compared to other residential areas in San Francisco. To account for the effects of these conditions on accessibility, a rating system to measure infrastructure for users was used in addition to the more traditional gravity model. Combining results into a composite accessibility score highlights how using only a gravity model to measure accessibility may conceal some of the nuances of accessibility as perceived by pedestrians, bicyclists, and transit riders. While it appears from gravity indices that stores in the Excelsior/Outer Mission are only slightly less accessible, the sensitivity analysis shows that infrastructure can have a large effect on overall accessibility. Specifically in the Excelsior/Outer Mission neighborhood, with its rather low infrastructure scores, the higher the weight attributed to the importance of infrastructure the lower is composite accessibility. Rather than measuring accessibility using only travel time via a gravity model or other spatial model, this study shows the importance of combining physical proximity measurements with infrastructure information to provide a more complete picture. This is particularly important for those walking, biking, or riding transit where safety is an important consideration. This study provides one such way to include the unique considerations of pedestrians, bicyclists, and transit riders by including an infrastructure scoring system. Not only does this highlight the importance of including infrastructure measures, but it provides a framework for future infrastructure improvements around stores.

The Flashing Yellow Arrow Left Turn signal is quickly becoming prominent in Central Florida as a new method of handling left turns at traffic signals. While the concept of a protected-permitted left turn is not groundbreaking, the departure from the typical display of a five-section signal head is, for this type of operation. The signal head introduced is a four-section head with a flashing yellow arrow between the yellow and green arrows. With this signal head quickly becoming the standard, there is a need to re-evaluate the operational characteristics of the left turning vehicle and advance the knowledge of the significant parameters that may affect the ability for a driver to make a left turn at a signalized intersection. With previous research into the behavioral and operational characteristics of the flashing yellow arrow conducted, there is more information becoming available about the differences between this signal and the previously accepted method of allowing left turns at an intersection. The protected-permitted signal is typically displayed at an intersection with up to two through lanes and generally a protected signal is installed when the number of through lanes increases above two unless specific criteria is met. With the advent of larger arterials and more traffic on the highway networks, the push to operate these intersections at their maximum efficiency has resulted in more of these protected-permitted signals being present at these larger intersections, including the flashing yellow arrow. The core of the research that follows is a comparative analysis of the operation and parameters that affect the left turn movement of the intersection with larger geometry to that of the smaller geometry. The significant parameters of the left turn movement were examined through means of collecting, organizing and analyzing just over 68 hours of field data. This research details the determining of the significant parameters based on the generation of a simulation model of the protected left turn using Synchro, a traffic simulation package, and regression models using field driven data to determine the significant parameters for predicting the number of left turns that can be made in the permitted phase under specific operating conditions. Intuitively, there is an expectation that a larger intersection will not allow for as many permitted lefts as a smaller intersection with all conditions remaining the same. The conclusions drawn from this analysis provide the framework to understanding the similarities and the differences that are encountered when the intersection geometry differs and help to more efficiently manage traffic at signalized intersections. The work of this field promises to enhance the operations of the left turning movement for traffic control devices. With an understanding of the statistical models generated, a broader base of knowledge is gained as to the significant parameters that affect a driver's ability to make the left turn. A discussion of the statistical differences and between the models generated from the small and large geometry intersections is critical to drive further research into standards being developed for the highway transportation network and the treatment of these large signalized intersections. The exploration of specific parameters to predict the number of permitted left turns will yield results as to if there is more to be considered with larger intersections moving forward as they become a standard sight on the roadway network.

The standard method for assessing traffic safety is to use the predictive method outlined in the Highway Safety Manual (HSM). This method is site-level, data-intensive, and does not account for interactions between sites, making it difficult to assess larger areas. This dissertation develops a corridor-level approach to traffic safety which uses less data than the HSM predictive method and views roadways holistically rather than combinations of individual, independent sites. First, a corridor definition is developed and applied to 10 urban Florida counties with a history of many crashes, resulting in the identification of 1,048 corridors. These corridors were primarily defined using context classification and lane count, with additional considerations for data availability and minimum length. From 2017–2021, these corridors experienced 459,603 unique crashes. After preliminary modeling and scope refinement, 559 corridors received supplemental data collection. Between the two datasets, a total of 11 models were developed using either negative binomial (NB) or random forest (RF) regression. NB models can be used for network screening purposes or identifying the impacts of potential safety improvements, while RF models can be used to identify variables important to the accuracy of the prediction. Potential safety improvements identified from the NB models include increasing proactive law enforcement patrols for dangerous driving behaviors and installing corridor lighting in corridors without lighting. While both NB and RF models were accurate, NB models were recommended due to resulting in a definite equation and overdispersion parameter that could be used with the empirical Bayes (EB) method to improve prediction accuracy. Overall, the corridor-level NB models outperformed the HSM models in terms of accuracy and statistical reliability. Using a corridor-level approach can help agencies quickly network screen their systems to identify high-risk corridors in need of safety improvements or supplement site-level analyses.

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Highway roughness is a concern for both the motoring public and highway authorities. Roughness may even increase the risk of crashes. Rail-highway grade crossings are particularly problematic. Roughness may be due to deterioration or simply due to the way the crossing was built to accommodate grade change, local utilities, or rail elevation. With over 216,000 crossings in the US, maintenance is a vast undertaking. While methods are available to quantify highway roughness, no method exists to quantitatively assess the condition of rail crossings. Conventional inspection relies on a labor-intensive process of qualitative judgment. A quantifiable, objective and extensible procedure for rating and prioritizing improvement of crossings is thus desired. In this dissertation, a 3D infrastructure condition assessment model is developed for evaluating the condition and performance of rail highway grade crossings. Various scanning techniques and devices are developed or used to obtain the 3D “point cloud” or surface as the first step towards quantifying crossing roughness. Next, a technique for repeatable field measurement of acceleration is presented and tested to provide a condition index. Acceleration-based metrics are developed, and these can be used to rate and compare crossings for improvement programs to mitigate potential vehicle damage and provide passenger comfort. A vehicle dynamic model is next customized to use surface models to estimate vertical accelerations eliminating the need for field data collection. Following, crossing roughness and rideability is estimated directly from 3D point clouds. This allows isolation of acceleration components derived from the surface condition and original design profile. Finally, a practice ready application of the 3D point cloud is developed and presented to address hump crossing safety. In conclusion, the dissertation presents several methods to assess the condition and performance of rail crossings. It provides quantitative metrics that can be used to evaluate designs and construction methods, and efficiently implement cost effective improvement programs. The metrics provide a technique to measure and monitor system assets over time, and can be extended to other infrastructure components such as pavements and bridges.

Merging from on-ramps to mainline traffic is one of the most challenging driving maneuvers on freeways. The challenges are further heightened for older drivers, as they are known to have longer perception-reaction times, larger acceptance gaps, and slower acceleration rates. In this research, VISSIM, a microscopic traffic simulation software, was used to evaluate the influence of the aging drivers on the operations of a typical diamond interchange. First, drivers were recorded on video cameras as they negotiated joining the mainline traffic from an on-ramp acceleration lane at two sites along I-75 in Southwest Florida. Several measures of effectiveness were collected including speeds, gaps, and location of entry to the mainline lanes. This information was used as either model input or for verification purposes. Two VISSIM models were developed for each site – one for the existing conditions and verification, and another for a sensitivity analysis, varying the percentage of older drivers and Level of Service (from A to E), to determine their influence on ramp operational characteristics. According to the results, there was a significant difference in driving behavior between older, middle-aged, and younger drivers, based on the measures of effectiveness analyzed in this study. Additionally, as the level of service and percentage of older adult motorists increased, longer queues were observed with slower speeds on the acceleration lanes and the right-most travel lane of the mainline traffic.