Wanyang Wu

Clustering-based roadway segment division for the identification of high-crash locations

This article introduces a clustering approach to roadway segment division, in place of the traditional fixed-length and variable-length division methods, to improve the calibration of safety performance functions (SPFs) for the purpose of identifying high-crash locations. The clustering approach helps to reduce crash heterogeneity for within-group elements by grouping roadway segments with similar crash distributions into homogeneous groups. For comparison purpose, all three segment division methods were applied to a 142.6-kilometer (88.6-mile) stretch of freeway on Interstate 95 that spans three counties in southern Florida in the United States. Using 5 years of crash data occurring on segments generated from each of the three division methods, the corresponding SPFs were calibrated using the negative binomial model. The calibrated SPFs were then used in the empirical Bayes approach of identifying high-crash locations. The results showed that clustering method produced a much better-fitted SPF than that produced by using the traditional division methods. Furthermore, the site screening for high-crash locations on segments divided by the clustering method improved upon the shortcomings of that using the existing sliding window method.

Selecting Bus Stops for Accessibility Improvements for Riders with Physical Disabilities

Bus stops are key links in the journeys of riders with disabilities. Inaccessible bus stops prevent people with physical disabilities from using fixed-route bus services, thus limiting their mobility. Due to limited budgets, transit agencies must select bus stops for which their improvements, as part of the effort to comply with the Americas with Disabilities Act (ADA), can maximize the overall benefits to riders with physical disabilities. In this paper, an analytic hierarchy process (AHP) was applied to combine the factors affecting the benefits to riders with physical disabilities, and a binary linear programming model was used to identify bus stops for ADA improvements based on budgetary and construction cost constraints. As an application example, the optimization model was applied to the 5,034 bus stops in Broward County, Florida. Compared to the usual approaches, the optimization model provides a more objective platform on which to identify bus stops for ADA improvements.

Multiobjective Optimization Model for Prioritizing Transit Stops for ADA Improvements

Inaccessible transit stops prevent people with disabilities from using fixed-route transit services, thereby limiting their mobility. The Americans with Disabilities Act (ADA) prescribes the minimum accessibility requirements for transit stops for riders with disabilities. In addition, transit agencies may also choose to implement the “universal-design” paradigm, which involves higher design standards than current ADA requirements and includes amenities that are useful for all riders, such as shelters and lighting. Because of budget limitations, however, transit agencies can select only a limited number of transit stops for ADA improvements each year. To increase the impact of these improvements, it is desirable that transit stops be selected such that they will maximize the overall benefits to patrons with disabilities. This paper describes a multiobjective binary nonlinear programming model for selecting, within a limited annual budget, a priority set of transit stops for improvements. The model aims to...

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besags model and the Lerouxs model, in crash prediction. The Besags models, which differ from the Lerouxs models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies.