Modeling Driver-Pedestrian-Infrastructure Interactions at Signalized Midblock Crosswalks

Cities and metropolitan areas are increasingly facilitating pedestrians’ movement by the provision of pedestrian walking facilities. As pedestrian traffic increases, the risk of crash involvement increases, especially at midblock locations, where pedestrians are exposed to unsafe interactions with vehicular traffic. To improve pedestrians’ safety at midblock locations, various countermeasures are provided, which include signalized crosswalks. Several studies have analyzed driver-pedestrian interactions, as well as pedestrian-infrastructure interactions at signalized midblock crosswalks. However, more in-depth studies are necessary, due to shortfalls of study assumptions, which have led to the application of improper statistical models, as seen in the literature. Improved models are crucial, as they can be used to evaluate the factors affecting the effectiveness of countermeasures at signalized midblock crosswalks. Moreover, there are several aspects of pedestrian-infrastructure interactions that have not been studied in the previous research. This study, therefore, attempts to improve the methodologies for analyzing driver-pedestrian-infrastructure interactions at signalized midblock crosswalks. Specifically, this study is aimed towards: • Developing improved modeling methodology for the yielding compliance of drivers at signalized midblock crosswalks, which considers the time taken to yield right of way, and the transition states undergone during yielding. • Analyzing the risks associated with driver-pedestrian interactions at signalized midblock crosswalks. • Developing the framework for modeling the spatial and temporal crossing compliance of pedestrians at signalized midblock crosswalks. • Evaluating the influence of various crosswalk features, such as signs and markings, traffic-related variables, and pedestrian related factors on the safe utilization of signalized midblock crosswalks; these include factors influencing drivers’ yielding compliance, pedestrians’ crossing compliance, and pedestrians’ utilization of pushbuttons. The study data were collected from a total of twenty signalized midblock crosswalks located in the Las Vegas, Nevada metropolitan area. These crosswalks have varying geometric configurations, signalizations, traffic characteristics, and pedestrian flows. Five types of signalization; Circular Flashing Beacons (CFBs), Circular Rapid Flashing Beacons (CRFBs), Rectangular Rapid Flashing Beacons (RRFBs), Pedestrian Hybrid Beacons (PHBs), and Traffic Control Signals (TCSs) were studied in this research. The observational survey method was applied for data collection, whereby video cameras were used to collect driver-pedestrian interactions. The data extraction was performed by reviewing the videos and recording the information of interest in a spreadsheet, with a total of 2638 pedestrians crossing incidents recorded for analysis. A descriptive analysis was performed, and several statistical models were developed. Multistate hazard-based models are developed to model the yielding compliance of drivers. The transitional states while drivers are yielding right of way to pedestrians are defined as non-yield, “partial-yield” events (partial-yield, scenarios in which driver(s) in one lane yield, while other driver(s) in adjacent lane(s) in the same direction do not), and full-yield. Binary-based models are developed for modeling drivers’ spatial yielding compliance, pedestrians’ spatial crossing compliance, and pedestrians’ temporal crossing compliance. Rare Events Logistic Regression (RELR) is applied to evaluate the occurrence of partial-yield events and near-miss events. In addition to binary models, ordered models and multinomial models are developed and compared to model pedestrians’ spatiotemporal crossing compliance. The results of the multistate models reveal that signal type, number of vehicles within effective crosswalk distance, yield-here sign, and crossing zone factors have similar influence for transition from non-yield to full-yield, non-yield to partial yield, and partial yield to full yield. Thus, the results of the binary models for yielding compliance are only partially comparable to one transition of the multistate model (non-yield to full yield). Through the Rare Event Logistic Regression (RELR) model, this study finds that near crash events are highly associated with a single cross stage, a high number of lanes, and night time. In addition, this study reveals that there is a strong association between partial-yield and near-miss events. Additionally, it is found that for every second that traffic continues to flow while pedestrians are waiting to cross, the probability of a partial-yield event occurring increases by 2.1%, while that of near-crash events increase by about 3%. Moreover, the influence of the crosswalk features and the distance at which drivers yield with respect to the yield line (spatial yielding) was assessed. The logistic regression results for associating drivers’ spatial yielding results shows that the odds for drivers’ spatial yielding are high if the crosswalks are equipped with Rectangular Rapid Flashing Beacons (RRFBs) at the advanced pedestrians crossing signs (APCSs), in the presence of “State Law” and “PED XING” signs. On the other hand, long distances from stripes to the yield lines, multiple cross stages, and high Annual Average Daily Traffic (AADT) are associated with decreased spatial yielding compliance. Regarding pedestrian-infrastructure interactions, the logistic regression results reveal that the arrival sequence to a crosswalk has the highest impact on warning light activation tendencies. This means that the first arriving pedestrians are eight times more likely to press pushbuttons. Moreover, males, the elderly, children, and teens are less likely to press pushbuttons. Furthermore, pedestrians who are involved in secondary activities, such as carrying/holding objects in their hands, have a relatively low odds ratio of pressing the pushbutton, while phone use is a statistically insignificant factor. Several infrastructure and traffic factors, including flash-based signal types (CRFBs, CFBs and RRFBs), a high number of lanes, residential land use, and higher oncoming vehicle speeds are associated with an increase of pushbutton pressing. Among the models applied for spatiotemporal crossing compliance, the logistic regression outperformed the multinomial logit and the ordered logit models. The logistic regression results reveal that the active WALK signal and a crossing incident involving female(s) only are the factors positively associated with pedestrians’ spatiotemporal crossing compliance. On the other hand, wait time, children, and teens, as well as people who cross while using a phone or riding a bike are negatively associated with spatiotemporal crossing compliance. Based on the study’s findings, several recommendations are provided. The findings and recommendations from this study are expected to have academic, industry, and community benefits. Planners and engineers can benefit from this study by learning which countermeasures improve safety for both pedestrians and drivers. The models can be used by academicians and other practitioners to assess the scenarios in question. Improved pedestrian safety due to the selection of appropriate countermeasures, which fit a particular location, is a benefit that directly impacts the community

Towards a Better Understanding of Effectiveness of Bike-share Programs: Exploring Factors Affecting Bikes Idle Duration

Bike-share program is considered effective and reliable if its stations have bikes and empty docks available at any time of a day. Few studies have considered idle bikes in the system and even lesser have glanced on modeling bikes idle duration (BID) in the bike-share system. This study applied descriptive statistics and log-logistic hazard based model on one year Seattle bike-share ridership data to quantify the BID and determine factors associated with the bikes’ idle duration. The findings of the study illustrate that the most and least effective utilized bike were used for 161 hours and 0.19 hours respectively for the entire year. Winter season, especially when raining and snowing was found to increase the likelihood of long BID. On the other end, the bikes located in commercial areas were associated with short BID compared to residential land-use. Moreover, weekend days and evening peak hours (4 p.m. to 6 p.m.) are associated with less likelihood of the BID compared with weekdays and morning peak hours respectively. These findings will facilitate procedures to identify the idle bikes for redistribution strategy and enhancing effective utilization of the bike-share system

Feasibility Study of a Campus-Based Bikesharing Program at UNLV

Bikesharing systems have been deployed worldwide as a transportation demand management strategy to encourage active modes and reduce single-occupant vehicle travel. These systems have been deployed at universities, both as part of a city program or as a stand-alone system, to serve for trips to work, as well as trips on campus. The Regional Transportation Commission of Southern Nevada (RTCSNV) has built a public bikesharing system in downtown Las Vegas, approximately five miles from the University of Nevada, Las Vegas (UNLV). This study analyzes the feasibility of a campus-based bikesharing program at UNLV. Through a review of the literature, survey of UNLV students and staff, and field observations and analysis of potential bikeshare station locations, the authors determined that a bikesharing program is feasible at UNLV

Leveraging autonomous vehicles crash narratives to understand the patterns of parking-related crashes

Autonomous vehicles (AVs) parking has been a subject of interest from various researchers; however, the focus has been on the parking demand, algorithm, and policies, while the safety aspect has received less attention, perhaps due to the lack of AV crash data. This study evaluated the magnitude and pattern of AV parking-related crashes that occurred between January 2017 and August 2022 in California. The study applied descriptive analysis, unsupervised text mining, and supervised text mining (Support Vector Machine, Naïve Bayes, Logitboost, Random Forest, and Neural network) with resampling techniques. It was indicated that parking-related crashes constitute about 16% of all AV crashes, most of them are likely to impact the AV on the rear or left side. The unsupervised text mining results showed that AVs in the conventional mode of operation, reversing, and parallel parking are among the key themes associated with parking-related crashes. The Support Vector Machine, Logitboost, Random Forest, and Neural network showed relatively high prediction accuracy. The important features from these supervised text mining approaches were conventional mode, reservsing, passenger vehicle, parallel parking, which confirm the preliminary findings in the unsupervised text mining. The implications of the findings to operators and policymakers are included in the study. Findings from this paper could be used to introduce measures to reduce AV parking-related crashes

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

Exploring the Shared Use Pathway: A Review of the Design and Demand Estimation Approaches

ABSTRACTShared-use paths (SUPs) are rapidly growing in popularity worldwide as dedicated off-street paths that are shared by non-motorists. SUPS are becoming popular for pedestrians, inline skaters, roller skaters, skateboarders, wheelchair users, and electric bicycle riders, among others. SUPs can be built alongside or far from the roadway, with the former typically separated from vehicular traffic by a buffer. SUPs built off of roadways pose a lower risk of conflict with vehicular traffic. Given the crucial role of safety in SUP utilization, this study provides a targeted review of the existing literature and state of practice focusing on the implementation of the SUPs and design guidelines as well as factors, data, and approaches to estimate SUP user volume. The findings of this study will be helpful for transportation planners and policymakers with the planning, design, implementation, and evaluation of SUPs. Ultimately, this review aims to encourage the development of safer and more accessible SUPs, enabling a broader range of non-motorists to benefit from these essential infrastructure investments

Safety Evaluation of High-Occupancy Toll Facilities Using Bayesian Networks

High-occupancy toll (HOT) lanes have increasingly been adopted as a strategy to reduce congestion. While numerous studies have focused on the operations of HOT facilities, little is known about their safety performance. This study used a Bayesian network model to evaluate the safety performance of HOT facilities by identifying factors contributing to single-vehicle (SV) and multiple-vehicle (MV) crashes at these facilities. The study utilized 3 years (2012-2014) of data from four HOT facilities in California. Concrete barrier separation, wet road surface condition, nighttime condition, and weekend are major contributing factors for SV crashes. MV crashes are associated with pylon separation, weekdays, and daytime conditions. The maximum possible probability (79%) of a SV crash is expected to occur over the weekend, during nighttime, and on a wet road surface located in a rolling/mountainous terrain having double solid white line separation. Meanwhile, the maximum probability (93%) of a MV crash is expected to occur over the weekend, during the daytime, and on a dry road surface located in rolling/mountainous terrain having pylon separation. The study results can assist transportation officials in implementing policies that will improve the safety performance of HOT facilities

Exploring the Need to Model Two- and Multiple-Vehicle Crashes Separately

Single-vehicle crashes have been shown to differ from two-plus vehicle crashes. Several studies have discussed the issues with modeling single-vehicle and two-plus vehicle crashes together. However, none of the empirical studies have attempted to study two-vehicle (2V) and multiple-vehicle (MV), that is, three-plus crash groups, to understand their correlation and influencing factors. This study first investigated whether there is a need to develop separate safety performance functions for 2V and MV crashes, in addition to single-vehicle crashes. Then, the correlation and influencing factors of 2V and MV were evaluated. Three regression models—a correlated bivariate negative binomial regression (BNR) model, an uncorrelated bivariate negative binomial regression (NR) model, and a univariate negative binomial regression (UNR) model, were developed and compared. The analysis was based on the 2011–2015 crashes that occurred on I-4 in Florida. Findings indicated that the BNR model significantly outperformed the NR and the UNR models. The model results suggest that disaggregating 2V and MV crashes while allowing correlation between the groups for the latent effects in the model best describes the data. Traffic volume, posted speed limit, and median type were found significant in contributing to the occurrence of both 2V and MV crashes. Additional contributing factors for 2V crashes included the presence of interchange influence area, and for MV crashes, the presence of a vertical curve and the presence of a horizontal curve. Study findings could assist transportation officials in implementing specific safety countermeasures for road segments identified as hotspots for 2V and MV crashes