Bayesian Approach on Quantifying the Safety Effects of Pedestrian Countdown Signals to Drivers

Pedestrian countdown signals (PCSs) are viable traffic control devices that assist pedestrians in crossing intersections safely. Despite the fact that PCSs are meant for pedestrians, they also have an impact on drivers’ behavior at intersections. This study focuses on the evaluation of the safety effectiveness of PCSs to drivers in the cities of Jacksonville and Gainesville, Florida. The study employs two Bayesian approaches, before-and-after empirical Bayes (EB) and full Bayes (FB) with a comparison group, to quantify the safety impacts of PCSs to drivers. Specifically, crash modification factors (CMFs), which are estimated using the aforementioned two methods, were used to evaluate the safety effects of PCSs to drivers. Apart from establishing CMFs, crash modification functions (CMFunctions) were also developed to observe the relationship between CMFs and traffic volume. The CMFs were established for distinctive categories of crashes based on crash type (rear-end and angle collisions) and severity level (total, fatal and injury (FI), and property damage only (PDO) collisions). The CMFs findings, using the EB approach indicated that installing PCSs result in a significant improvement of driver’s safety, at a 95% confidence interval (CI), by a 8.8% reduction in total crashes, a 8.0% reduction in rear-end crashes, and a 7.1% reduction in PDO crashes. In addition, FI crashes and angle crashes were observed to be reduced by 4.8%, whereas a 4.6% reduction in angle crashes was observed. In the case of the FB approach, PCSs were observed to be effective and significant, at a 95% Bayesian credible interval (BCI), for a total (Mean = 0.894, 95% BCI (0.828, 0.911)), PDO (Mean = 0.908, 95% BCI (0.838, 0.953)), and rear-end (Mean = 0.920, 95% BCI (0.842, 0.942)) crashes. The results of two crash categories such as FI (Mean = 0.957, 95% BCI (0.886, 1. 020)) and angle (Mean = 0.969, 95% BCI (0.931, 1.022)) crashes are less than one but are not significant at the 95 % BCI. Also, discussed in this study are the CMFunctions, showing the relationship between the developed CMFs and total entering traffic volume, obtained by combining the total traffic on the major and the minor approaches. In addition, the CMFunctions developed using the FB indicated the relationship between the estimated CMFs with the post-treatment year. The CMFunctions developed in this study clearly show that the treatment effectiveness varies considerably with post-treatment time and traffic volume. Moreover, using the FB methodology, the results suggest the treatment effectiveness increased over time in the post-treatment years for the crash categories with two important indicators of effectiveness, i.e., total and PDO, and rear-end crashes. Nevertheless, the treatment effectiveness on rear-end crashes is observed to decline with post-treatment time, although the base value is still less than one for all the three years. In summary, the results suggest the usefulness of PCSs for drivers

Performance Evaluation of Connected Vehicle (CV) and Transportation Systems Management and Operations (TSM&O) Projects in Florida

BDV29-977-64Connected vehicle (CV) technologies and Transportation Systems Management and Operations (TSM&O) strategies are increasingly being considered by transportation agencies to improve the safety and mobility of the transportation network. To fully understand the potential benefits of CV and TSM&O initiatives, it is crucial to not only identify the performance measures used to evaluate the progress of each initiative, but also to estimate the benefit-to-cost (B/C) ratios to justify the funding requests associated with implementing these technologies and strategies. The primary goal of this research was to assist the Florida Department of Transportation (FDOT) in developing approaches to evaluate the performance of CV projects and current TSM&O strategies being deployed, including the Rapid Incident Scene Clearance (RISC) program, the Road Ranger Service Patrol (RRSP) program, and the Smart Work Zone (SWZ) TSM&O strategies. A comprehensive review of the existing body of literature was conducted to identify the quantitative and qualitative performance measures and metrics that are being considered in evaluating the performance of CV deployments and TSM&O strategies. B/C analyses were conducted to quantify the mobility and safety benefits associated with implementing the RISC and RRSP programs. Results indicate that for every dollar spent on the RISC program, $5.78 is returned in secondary crash savings, and$1.20 is returned in incident-related traffic delay savings. For every dollar spent on the RRSP program, $5.15 is returned in secondary crash savings, and$7.44 is returned in incident-related traffic delay savings. The study also discussed the potential safety and mobility benefits of Smart Work Zone (SWZ) technologies. Performance criteria and evaluation metrics were also developed for the different stages of the CV project development process (i.e., pre-project phase, planning phase, design-deploy-test phase, and the operations & maintenance phase). The performance criteria of two CV deployments in Florida (Gainesville Signal Phase and Timing (SPaT) Project and I-4 Florida\u2019s Regional Advanced Mobility Elements (I-4 FRAME) Project) were also reviewed. Findings from this research offer guidance in evaluating the effectiveness of CV and TSM&O initiatives. Evaluation criteria and approaches presented in this report can better prepare FDOT for deployments

A full Bayesian approach to appraise the safety effects of pedestrian countdown signals to drivers

Although they are meant for pedestrians, pedestrian countdown signals (PCSs) give cues to drivers about the length of the remaining green phase, hence affecting drivers’ behavior at intersections. This study focuses on the evaluation of the safety effectiveness of PCSs to drivers, in the cities of Jacksonville and Gainesville, Florida, using crash modification factors (CMFs) and crash modification functions (CMFunctions). A full Bayes (FB) before-and-after with comparison group method was used to quantify the safety impacts of PCSs to drivers. The CMFs were established for distinctive categories of crashes based on crash type (rear-end and angle collisions) and severity level (total, fatal and injury (FI), and property damage only (PDO) collisions). The CMFs findings indicated that installing PCSs result in a significant improvement of drivers’ safety, at a 95% Bayesian credible interval (BCI), for total, PDO, and rear-end collisions. The results of FI and angle crashes were not significant. The CMFunctions indicate that the treatment effectiveness varies considerably with post-treatment time and traffic volume. Nevertheless, the CMFs on rear-end crashes are observed to decline with post-treatment time. In summary, the results suggest the usefulness of PCSs for drivers. The findings of this study may prompt a need for a broader research to investigate the need to design PCSs that will serve the purpose not only of pedestrians, but drivers as well

Examining the Influence of Alternative Fuels\u27 Regulations and Incentives on Electric-Vehicle Acquisition

States and federal administrations in America provide regulations and incentives to promote the utilization of alternative fuels. The contents and effects of such regulations and incentives have yet been explored to a great extent. This study evaluates the content and impact of the incentives and regulations on electric vehicle (EV) acquisitions using text mining and the negative binomial (NB) regression. Findings indicate that western states have a relatively higher number of EVs per million residents. Moreover, the NB results show that rebates and grants are associated with more EVs. On the other hand, exemptions and tax incentives are associated with lower EVs acquired. Loan incentives are associated with an increase in the acquisition of EVs but are statistically insignificant. Furthermore, air quality and emissions-related regulations are associated with the increased acquisition of EVs. The findings may assist agencies in identifying best practices and policies to promote alternative fuels

Impacts of COVID-19 on the Operational Performance of Express Lanes and General-Purpose Lanes

The COVID-19 pandemic outbreak brought significant changes in the travel behavior and operational characteristics of transportation systems. Express lanes (ELs) are among the transportation facilities that are affected by this pandemic. These facilities are built adjacent to existing general-purpose lanes (GPLs), providing drivers additional lanes that are dynamically priced in response to changing traffic conditions. This research investigated the impacts of COVID-19 on the operational performance of ELs and GPLs based on field data from a 5.5 mi corridor on I-95 in Miami, Florida, U.S. The traffic flow parameters, which include speed, traffic flow, and occupancy, were used to describe the traffic conditions before and during COVID-19 (i.e., March-June 2019 and March-June 2020, respectively). The travel time reliability measures, coefficient of variation of travel time, and planning time index, were used to measure user satisfaction. These metrics were derived from a multivariate Bayesian additive regression model that was developed to calibrate the traffic conditions on the study corridor. Overall, the model results indicated that both ELs and GPLs have less variation in travel time, thus making the travel time more reliable during COVID-19 than before. This may be attributed to the decline in the traffic volume observed during the pandemic. The results further showed that COVID-19 had more impact on the GPLs compared with the ELs. The results from this research could assist transportation agencies in understanding the impacts of the COVID-19 pandemic on ELs and GPLs in relation to traffic operations

Investigating proximity of crash locations to aging pedestrian residences

Many campaigns promote walking for recreation, work, and general-purpose trips for health and environmental benefits. This study investigated factors that influence the occurrence of crashes involving elderly pedestrians in relation to where they reside. Using actual pedestrian residential addresses, a Google integrated GIS-based method was developed for estimating distances from crash locations to pedestrian residences. A generalized linear mixed model (GLMM) was used to evaluate the effect of factors associated with residences, such as age group, roadway features, and demographic characteristics on the proximity of crash locations. Results indicated that the proximity of crash locations to pedestrian residences is influenced by the pedestrian age, gender, roadway traffic volume, seasons of the year, and pedestrian residence demographic characteristics. The findings of this study can be used by transportation agencies to develop plans that enhance aging pedestrian safety and improve livability

Evaluating the impact of Road Rangers in preventing secondary crashes

Many transportation agencies utilize freeway service patrols (FSPs) to quickly identify and respond to incidents. The objectives of FSP are to minimize the incident duration and increase safety at the incident scene. The current research explored the safety benefits of Florida\u27s FSP program known as Road Rangers – harnessed from lowering the likelihood of secondary crashes (SCs) – compared to other responding agencies. The analysis was done on 6088 incidents that occurred on freeways in Jacksonville, Florida, from 2015 through 2017. Since SCs were not explicitly identified in the SunGuide® incident database, the study adopted a data-driven technique that used BlueToad® speed data to identify them. Once SCs were identified, a model was developed to identify factors influencing their occurrence. Factors such as an increase in equivalent hourly traffic volume, incident impact duration, and the percent of lanes closed significantly increased the likelihood of a SC. Besides, moderate/severe incidents, crash events, weekdays, peak hours, shoulder blockage, and incidents involving towing showed a high likelihood of resulting in a SC. Of practical importance, the model results revealed that a minute increase in incident impact duration increased the SC probability by 1.2 percent, with other factors held constant. Based on a 16-minutes decrease in incident impact duration, the Road Rangers program could lessen the probability of SCs by 21 percent, compared to other agencies. These findings could be beneficial to incident managers, responders, and researchers in evaluating the program\u27s performance

Using crash modification factors to appraise the safety effects of pedestrian countdown signals for drivers

Although pedestrian countdown signals (PCSs) are meant for pedestrians, they give cues to drivers on the remaining amount of green as the timer counts down. This study focuses on the evaluation of safety effectiveness of PCSs to drivers in the cities of Jacksonville and Gainesville, Florida, using the before-after study with the empirical Bayes method. This analysis explored 110 intersections with PCSs and their respective 93 comparison sites. The findings indicate that PCSs significantly improve driver safety by 8.8% reduction in total crashes, 8.0% in rear-end and 7.1% in property-damage-only crashes, where both of these results were significant at the 95% confidence level. Results for angle crashes as well as fatal and injury crashes were not significant at the 95% confidence level. Also discussed in this study are the crash modification functions developed to show the relationship between the estimated crash modification factors and total entering traffic volume at the intersection. In summary, the results suggest the usefulness of PCSs for drivers

Are older drivers safe on interchanges? Analyzing driving errors causing crashes

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters