Fatal crashes involving vehicles with driver warning systems: Identifying risk factors using a correlated random parameters ordered logit modeling approach.

Recent advancements in vehicular technology are expected to enhance traffic safety by either warning the drivers or by automating the tasks related to driving to reduce the human driver's involvement. The driver warning systems (DWSs) are designed to warn drivers in unsafe situations such as forward collision, lane departure, or when changing lanes with vehicles in blind spot areas. Although these features are designed to enhance safety, recent crash data shows vehicles with these features are still getting involved in crashes, making it necessary to identify the contributing factors.

Does non-motorists' safety perception of autonomous vehicles vary across opinion change stemming from crash occurrence? Investigating perceptions using fixed and random parameter ordered logit models.

The perception of non-motorists toward autonomous vehicles (AVs) could change or remain the same after hearing about a fatal AV crash. This study aims to discern the differences between non-motorists whose opinions were influenced and those whose views remained unchanged after hearing about the fatal AV crash. Additionally, this study investigates how the operational competence of AV manufacturers affect non-motorist's safety perception.

Exploring the effect of road network, demographic, and land use characteristics on teen crash frequency using geographically weighted negative binomial regression.

Motor vehicle crashes are one of the leading causes of teen deaths worldwide. It is important to assess the environment and identify the risk factors influencing teen crashes for planning strategies and improving their safety. This research, therefore, focuses on exploring the effect of road network, demographic, and land use characteristics to compute teen crash frequency.

Investigating the influence of contributing factors and predicting visibility at road link-level.

Data from weather stations at airports, far away locations or predictions using macro-level data may not be accurate enough to disseminate visibility related information to motorists in advance. Therefore, the objective of this research is to investigate the influence of contributing factors and develop visibility prediction models, at road link-level, by considering data from weather stations located within 1.6 km of state routes, US routes and interstates in the state of North Carolina (NC).

Influence of on-network, traffic, signal, demographic, and land use characteristics by area type on red light violation crashes.

The focus of this research paper is on extraction of predictor variables pertaining to on-network, traffic, signal, demographic, and land use characteristics, by area type, and examining their influence on the number of red light violation crashes. Data for the city of Charlotte, North Carolina was extracted and used for analysis. Three different sets of signalized intersections were selected in the three different area types - Central Business District (CBD), urban, and suburban areas.

Modeling and comparing injury severity of at-fault and not at-fault drivers in crashes.

This paper examines and compares the effect of selected variables on driver injury severity of, both, at-fault and not at-fault drivers. Data from the Highway Safety Information System (HSIS) for the state of North Carolina was used for analysis and modeling. A partial proportional odds model was developed to examine the effect of each variable on injury severity of at-fault driver and not at-fault driver, and, to examine how each variable affects these two drivers' injury severity differently.

Predictability and interpretability of hybrid link-level crash frequency models for urban arterials compared to cluster-based and general negative binomial regression models.

Machine learning (ML) techniques have higher prediction accuracy compared to conventional statistical methods for crash frequency modelling. However, their black-box nature limits the interpretability. The objective of this research is to combine both ML and statistical methods to develop hybrid link-level crash frequency models with high predictability and interpretability.

Modeling crash injury severity by road feature to improve safety.

Objective: The objective of this research is 2-fold: to (a) model and identify critical road features (or locations) based on crash injury severity and compare it with crash frequency and (b) model and identify drivers who are more likely to contribute to crashes by road feature.

Method: Crash data from 2011 to 2013 were obtained from the Highway Safety Information System (HSIS) for the state of North Carolina. Twenty-three different road features were considered, analyzed, and compared with each other as well as no road feature.

Methods to rank traffic rule violations resulting in crashes for allocation of funds.

Education, enforcement and engineering countermeasures are implemented to make road users comply with the traffic rules. Not all the traffic rule violations can be addressed nor countermeasures be implemented at all unsafe locations, at once, due to limited funds. Therefore, this study aims at ranking the traffic rule violations resulting in crashes based on individual ranks, such as 1) frequency (expressed as a function of the number of drivers violating a traffic rule and involved in crashes), 2) crash severity, 3) total crash cost, and, 4) cost severity index, to assist transportation system managers in prioritizing the allocation of funds and improving safety on roads.

Risk drivers pose to themselves and other drivers by violating traffic rules.

Objective: Violation of traffic rules is a major contributing factor in both crashes and fatalities in the United States. This study aims at quantifying risk that drivers pose to themselves and other drivers by violating traffic rules.

Method: Crash data from 2010 to 2013 were gathered for the state of North Carolina.

Evaluating the effectiveness of on-street bicycle lane and assessing risk to bicyclists in Charlotte, North Carolina.

The objectives of this manuscript are (1) to evaluate the effectiveness of on-street bicycle lane in reducing crashes involving bicyclists on urban roads, (2) to quantify and compare risk to bicyclists on road segments with and without on-street bicycle lane, (3) to evaluate the effect of on-street bicycle lane on other road network users (all crashes), and, (4) to assess the role of on-network characteristics (speed limit, the number of lanes, the width of on-street bicycle lane, the width of the right-most travel lane, and, the numbers of driveways, unsignalized approaches and signalized intersections per unit distance) on risk to bicyclists. Data for thirty-six segments with on-street bicycle lane and twenty-six segments without on-street bicycle lane in the city of Charlotte, North Carolina were extracted to compute and compare measures such as the number of bicycle crashes per center-lane mile, the number of bicycle crashes per annual million vehicle miles traveled (MVMT), the number of all crashes per center-lane mile, and the number of all crashes per MVMT. The results obtained from analysis indicate that bicyclists are three to four times at higher risk (based on traffic conditions) on segments without on-street bicycle lane than when compared to segments with on-street bicycle lane.

Effectiveness of red light running camera enforcement program in reducing crashes: evaluation using "before the installation", "after the installation", and "after the termination" data.

The focus of this manuscript is to evaluate and assess the effectiveness of red light running camera (RLC) enforcement program in reducing crashes at signalized intersections. Data from January 1997 to December 2010 for thirty-two signalized intersections in the city of Charlotte, North Carolina, where RLCs were installed between August 1998 and August 2000 and terminated in fall 2006, were gathered, analyzed, and compared for "before the installation", "after the installation", and "after the termination" periods. Descriptive analysis and paired t-tests were performed using rear-end, sideswipe, left-turn, angle, and right-turn crashes as well as the number of total crashes.

Pedestrian and motorists' actions at pedestrian hybrid beacon sites: findings from a pilot study.

This paper focuses on an analysis of pedestrian and motorists' actions at sites with pedestrian hybrid beacons and assesses their effectiveness in improving the safety of pedestrians. Descriptive and statistical analyses (one-tail two-sample T-test and two-proportion Z-test) were conducted using field data collected during morning and evening peak hours at three study sites in the city of Charlotte, NC, before and after the installation of pedestrian hybrid beacons. Further, an analysis was conducted to assess the change in pedestrian and motorists' actions over time (before the installation; 1 month, 3 months, 6 months, and 12 months after the installation).

Assessing the effect of introducing a permitted phase through the use of flashing yellow arrow signal for left-turning vehicles.

This paper evaluates the direct and indirect effects of introducing a permitted phase, through the use of flashing yellow arrow (FYA) signal for left-turning vehicles, in reducing crashes at intersections. Data for 18 study intersections in the city of Charlotte, NC, USA were used to conduct a before-after comparison study through the use of Empirical Bayes (EB) method and examine the effects. The estimated number of left-turn crashes, had the FYA signal not been installed, was compared to the actual number of left-turn crashes to assess the direct effect, while the estimated total number of crashes, had the FYA signal not been installed, was compared to the actual total number of crashes to assess the indirect effect.

Traffic analysis zone level crash estimation models based on land use characteristics.

The objective of this paper is to develop crash estimation models at traffic analysis zone (TAZ) level as a function of land use characteristics. Crash data and land use data for the City of Charlotte, Mecklenburg County, North Carolina were used to illustrate the development of TAZ level crash estimation models. Negative binomial count models (with log-link) were developed as data was observed to be over-dispersed.

Motorist actions at a crosswalk with an in-pavement flashing light system.

Objective: An in-pavement flashing light system is used at crosswalks to alert motorists and pedestrians of possible conflicts and to influence their behavior to enhance safety. The relative behaviors of the drivers and the pedestrians affect safety. An evaluation of motorist behavior at a pedestrian crosswalk with an in-pavement flashing light system is presented in this manuscript.

Are pedestrian countdown signals effective in reducing crashes?

Objective: The time left to cross the street displayed on pedestrian countdown signals can be used by pedestrians as well as drivers of vehicles, though these signals are primarily provided to help pedestrians make better crossing decisions at signalized intersections. This article presents an evaluation of the effect of pedestrian countdown signals in reducing vehicle-pedestrian crashes and all crashes at signalized intersections.

Methods: A before-and-after study approach was adopted to evaluate the effect considering pedestrian countdown signals installed over a 5-month period at 106 signalized intersections in the city of Charlotte, North Carolina.

Pedestrian crash estimation models for signalized intersections.

The focus of this paper is twofold: (1) to examine the non-linear relationship between pedestrian crashes and predictor variables such as demographic characteristics (population and household units), socio-economic characteristics (mean income and total employment), land use characteristics, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume) and accessibility to public transit systems, and (2) to develop generalized linear pedestrian crash estimation models (based on negative binomial distribution to accommodate for over-dispersion of data) by the level of pedestrian activity and spatial proximity to extract site specific data at signalized intersections. Data for 176 randomly selected signalized intersections in the City of Charlotte, North Carolina were used to examine the non-linear relationships and develop pedestrian crash estimation models. The average number of pedestrian crashes per year within 200 feet of each intersection was considered as the dependent variable whereas the demographic characteristics, socio-economic characteristics, land use characteristics, road network characteristics and the number of transit stops were considered as the predictor variables.

Evaluating the role of weaving section characteristics and traffic on crashes in weaving areas.

Objective: The likelihood of being involved in a crash on a freeway, in general, is greater on weaving sections than on basic freeway sections and in ramp influence areas. This is due to possible crossing of entry and/or exit traffic over a short distance while traveling in the same direction without the aid of traffic control devices resulting in potential conflicting situations and crashes. This article focuses on evaluating the role of weaving section characteristics (configuration type, length and the number of required lane changes by weaving traffic) and traffic variables (entry volume, exit volume, and non-weaving volume) on crashes in weaving areas.

Assessing the role of pavement macrotexture in preventing crashes on highways.

Objective: The objective of this article is to assess the role of pavement macrotexture in preventing crashes on highways in the State of North Carolina.

Methods: Laser profilometer data obtained from the North Carolina Department of Transportation (NCDOT) for highways comprising four corridors are processed to calculate pavement macrotexture at 100-m (approximately 330-ft) sections according to the American Society for Testing and Materials (ASTM) standards. Crash data collected over the same lengths of the corridors were integrated with the calculated pavement macrotexture for each section.

New methods to identify and rank high pedestrian crash zones: an illustration.

Identifying and ranking high pedestrian crash zones plays a key role in developing efficient and effective strategies to enhance pedestrian safety. This paper presents (1) a Geographical Information Systems (GIS) methodology to study the spatial patterns of pedestrian crashes in order to identify high pedestrian crash zones, and (2) an evaluation of methods to rank these high pedestrian crash zones. The GIS based methodology to identify high pedestrian crash zones includes geocoding crash data, creating crash concentration maps, and then identifying high pedestrian crash zones.

Estimating windblown PM-10 emissions from vacant urban land using GIS.

This paper presents a Geographic Information Systems (GIS)-based methodology to estimate annual area-wide airborne particulate matter with an aerodynamic diameter of less than 10 microm (PM-10) emissions, and identify zones with high emissions in order to efficiently implement mitigation strategies. Application of the methodology is demonstrated using the land disposal boundary within Clark County, NV as the study area, which is currently classified as a non-attainment area by United States Environmental Protection Agency (US EPA). The estimated PM-10 emissions depend on the extent of disturbed vacant land area, undisturbed vacant land area, emission factors by soil group, and wind speeds.