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Mountainous areas pose unique challenges to transportation safety, with their complex terrain and harsh weather conditions. The presence of steep terrain can lead to winding roads with a high degree of azimuth change per mile, thereby increasing the potential for hazardous driving conditions. Additionally, steep terrain is often associated with fog formation, which can obscure roads by clouds. Thus, there is a need to study weather-related crashes and identify associated risk factors in mountainous areas. To address this issue, the Tobit, latent class Tobit (LCT), and two-part truncated log normal (TPTLN) models were employed to investigate the potential interactions between topography and weather-related crash rates, while accounting for the censored nature of crash data. Data from the western region of North Carolina, which is home to numerous mountains experiencing a wide range of weather conditions was used for this purpose. This region comprises a 3570-mile road network across seven cities, with elevations ranging from 1000 ft. to >6158 ft. above mean sea level. Crash data from 2015 to 2017 was obtained from the Highway Safety Information System (HSIS). The results revealed that topography significantly affect weather-related crashes in mountainous areas. Higher elevations and steeper slopes are associated with lower crash rates. Also, the study highlights the importance of considering topography when assessing transportation safety in mountainous areas. The findings help develop transportation safety policies and interventions aimed at improving safety in these areas.

The focus of this study is to identify potential road links suitable for implementing a variable speed limit (VSL) sign by analyzing real-world traffic speed data collected over one year in Charlotte, North Carolina, United States. Exploratory and bivariate analyses were conducted to examine variations in traffic speed patterns during weekdays and weekends across eight specific timespans. The results revealed that road links with lower posted speed limits consistently experienced traffic speeds exceeding the posted speed limits. The mean traffic speeds are generally close to the posted speed limits for road links with higher posted speed limits while the 85th percentile speeds exceeded the posted speed limits, indicating a potential need for VSL sign implementation. The road links with 40 mph or 50 mph posted speed limits have a unique pattern compared to road links of other posted speed limit clusters. The mean traffic speed on these road links decreased as the standard deviation increased. The findings contribute to an improved understanding of traffic speed patterns and provide valuable insights for implementing a VSL sign.

This paper presents the findings from an autonomous shuttle pilot program conducted at the University of North Carolina at Charlotte between June and December 2023 as part of the North Carolina Department of Transportation’s Connected Autonomous Shuttle Supporting Innovation (CASSI) initiative. The shuttle completed 825 trips, transporting 565 passengers along a 2.2-mile mixed-traffic campus route. The study evaluates the shuttle’s operational performance, reliability, and challenges using data from onboard sensors, system logs, and operator reports. Key analyses are divided into four areas: service reliability, which assesses autonomy disengagements caused by signal loss, technical issues, and environmental factors; service robustness, focusing on the shuttle’s ability to maintain operations under adverse conditions; performance metrics, including average speed, autonomy percentage, and battery usage; and service usage, which examines the number of trips and passengers to gauge efficiency. Signal loss and battery-related issues were the primary causes of service interruptions, while environmental factors like weather and vegetation also affected shuttle performance. Recommendations include enhancing vehicle-to-infrastructure communication and optimizing battery management.

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. Further, it also requires research to quantify the benefits of vehicles with one or multiple DWS in terms of safety during multivehicle crashes. This study presents a methodological framework to compare factors affecting fatal crashes involving vehicles with no, one and two DWSs. A three-step method is proposed to incorporate unobserved heterogeneity while modeling. Fixed parameter and correlated random parameter order logit models are employed. The results shows that correlated random parameters ordered logit model outperforms traditional fixed parameter ordered logit model. Vehicles equipped with DWSs are safer than vehicles without DWSs during wet or snowy road conditions, when the vehicle skids laterally or longitudinally, and at intersections. Vehicles with one or both DWSs can reduce drink-and-drive and speeding-related crash involvement than vehicles without DWSs. Female and elderly drivers are at a higher risk while driving a vehicle with one or both DWSs compared to driving a vehicle without DWSs, demanding vehicular modifications.

Growing population, rapid urbanization, and increasing travel demand emphasize the need for reliable public transportation systems and sustainable transportation planning. A reliable bus service fosters a more significant, satisfied, and committed base of users. This research focuses on examining the association between on-time performance (OTP) and road network, demographic, socioeconomic, and land use characteristics to identify relevant external factors for proactive and reliable public transportation system planning. The analysis was conducted at a bus stop level. Bus arrival/departure data from the Charlotte Area Transit System (CATS) was obtained. The road network, demographic, socioeconomic, and land use characteristics were captured within 0.25-mile and 0.50-mile buffers. Pearson correlation analysis was conducted to understand the association between OTP and road network, demographic, socioeconomic, and land use characteristics by day of the week and time of the day. The results show that OTP is associated with external factors such as the number of signalized or cul-de-sac/dead-end intersections, number of lanes, network density, population, income (median and total), and land use types related to residential and commercial/employment purposes within the bus stop vicinity. The findings provide vital insights for transit agencies to enhance scheduling, service and maximize benefits, effectively utilize available resources, plan, and provide equitable services to all riders.

The focus of this research is to develop a robust model for accurately estimating link-level annual average daily traffic (AADT) of all the local functionally classified roads. The capabilities of one-dimensional convolutional neural network (1D-CNN), a deep learning architecture, and the domain knowledge pertaining to local road travel characteristics were combined to estimate local road AADT. The AADT based on traffic counts collected at 12,769 traffic count stations on local roads in North Carolina during 2014, 2015, and 2016 were considered for model training, validation, and testing. A total of eight existing state-of-the-art statistical, geospatial, and selected other machine learning models were compared with the 1D-CNN model to estimate local road AADT. These include ordinary least square (OLS) regression, geographically weighted regression (GWR), ordinary kriging, natural neighbor (NN) interpolation, inverse distance weighting (IDW), backpropagation artificial neural network (BP-ANN), random forest (RF), and support vector machine (SVM). The model development and test results showed that the 1D-CNN model performed better than the other considered models. The architecture of the 1D-CNN model can learn the intricate patterns in the local road AADT. The outputs from the methodological framework proposed in this research help practitioners perform safety evaluation, planning and implementing infrastructure improvements, fund allocation and prioritization, air quality estimates, and meeting Highway Safety Improvement Program (HSIP) reporting requirements.

Nearly 499,000 motor vehicle crashes involving trucks were reported across the United States in 2018, out of which 22% resulted in fatalities and injuries. Given the growing economy and demand for trucking in the future, it is crucial to identify the risk factors to understand where and why the likelihood of getting involved in a severe or moderate injury crash with a truck is higher. The focus of this research, therefore, is on developing a methodology, capturing and integrating data, exploring, and identifying risk factors associated with surrounding land use and demographic characteristics in addition to crash, driver, and on-network characteristics by modeling injury severity of crashes involving trucks. Crash data for Mecklenburg County in North Carolina from 2013 to 2017 was used to develop partial proportional odds model and identify risk factors influencing injury severity of crashes involving trucks. The findings indicate that dark lighting condition, inclement weather condition, the presence of double yellow or no-passing zone, road sections with speed limit >40 mph and curves, and driver fatigue, impairment, and inattention have a significant influence on injury severity of crashes involving trucks. These outcomes indicate the need for effective geometric design and improved visibility to reduce the injury severity of crashes involving trucks. The likelihood of a severe or moderate injury crash involving a truck is also high in areas with high employment, government, light commercial, and light industrial land uses. The findings can be used to identify potential risk areas, proactively plan and prioritize the allocation of resources to improve safety of transportation system users in these areas.

The differences in travel times of passenger cars, traffic stream, and trucks depend on the area type, temporal factors, reference speed, and traffic condition. These explanatory variables account for the effect of geometric conditions and variations in the traffic flow. The focus of this research is to examine the correlations and estimate truck travel time to passenger car or traffic stream travel time ratio of a road link (dependent variable) as a function of these explanatory variables. Travel time data for Mecklenburg County and Iredell County in North Carolina, USA were gathered for the year 2017 to examine correlations, develop generalized estimating equations (GEE) models, and identify explanatory variables influencing the ratios. Gamma log-link distribution-based models are the best-fitted models to estimate the average travel time (ATT) of trucks to the ATT of passenger cars or traffic stream ratios. Notable differences in the coefficients were observed when the ATT of trucks was compared with the ATT of passenger cars or traffic stream. The area type (urban or rural) was observed to influence the ratios differently. The influence of traffic condition, reference speed (or free-flow speed), day-of-the-week (DOW) and time-of-the-day (TOD) on the ratios also varied with the area type.

The focus of this research is to assess the impact of COVID-19 outbreak in 2020 on the Capital Bikeshare (CaBi) ridership in Washington, D.C. area compared to 2019. Correlation analysis was conducted and mixed-effects negative binomial (MENB) models were developed to assess CaBi ridership by the type of CaBi user, as the available bike-sharing trip data followed a time-series cross-section panel data structure and the variables (season, stay-at-home order and lift, and weekday) have time-specific effects. The correlation coefficients between CaBi variables, COVID-19 variables, and characteristics of D.C.’s population profile and their statistical significance are sensitive to the year (2019 or 2020), the season of the year (Winter, Spring, Summer, or Fall), and the type of CaBi user (member or casual user). The number of stations between pickups and drop-offs, and the dummy variables representing the before and after stay-at-home order and lift were found significant in the MENB models. There was no significant difference in travel time of CaBi casual users and members during the outbreak. Riding for leisure or commuting has been potentially limited with the stay-at-home order. The findings have practical implications and help bike-sharing operators to increase their resilience during unexpected situations such as the pandemic.

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. A fixed parameter ordered logit model was explored to explain non-motorists’ safety perception for respondents who changed their opinion after a crash occurrence and those who did not. A random parameter ordered logit model was then explored to address the difference in residual variance. The finding from the random parameter ordered logit model indicates that two factors (i.e., future impact of AVs on traffic injuries and willingness to adopt Pittsburgh's streets as a proving ground) were significant for the respondents with negative change in opinion after the crash. The result also suggests that negative information such as the crash in Arizona may influence non-motorists’ safety perception of AVs, but this effect can be mitigated by providing positive information about the AVs. The results also demonstrate that non-motorists who perceive the operational competence of the AV manufacturers to be high have higher safety perception of the AVs, while those who perceive the operational competence of the AV manufacturers to be low have lower safety perception of the AVs. This study offers recommendations for policies and interventions to enhance non-motorists' safety based on the examination of factors associated with safety perception among respondents who changed their opinion following a crash and those who maintained their initial stance.

The focus of this paper is on evaluating the safety effectiveness of restricted crossing U-turn (RCUT) intersections in rural and suburban areas based on prior control type. Both, unsignalized and signalized RCUT intersections were evaluated using the Empirical Bayes (EB) before-after evaluation method. The 42 RCUT intersections selected for this research were converted from a two-way stop-controlled (TWSC) intersection or signalized intersection in the rural and suburban areas. The results show a 70.63% reduction in the total number of crashes and a 76.10% reduction in the number of fatal and injury crashes at unsignalized stop-controlled RCUT intersections in the rural area. Also, an 89.25% reduction in the total number of crashes and a 94.42% reduction in the number of fatal and injury crashes was observed at offset three-legged unsignalized stop-controlled RCUT intersections converted from four-legged TWSC intersections in rural areas. In the suburban areas, a 64.86% reduction in the total number of crashes and a 73.39% reduction in the number of fatal and injury crashes was observed at unsignalized stop-controlled RCUT intersections. Further, a 10.15% and a 31.08% reduction in the total number of crashes, and an 84.26% and 41.31% reduction in the number of fatal and injury crashes was observed at a signalized RCUT intersection in the rural and suburban areas, respectively. The safety effectiveness of unsignalized RCUT intersections in the rural areas with a larger sample size was found to be higher than was observed by researchers in the past. While unsignalized RCUT intersections in the suburban areas are effective, there is not enough evidence to support the installation of signalized RCUT intersections. These findings help researchers and practitioners in making informed decisions and installing RCUT intersections from a safety perspective.

Though automobile manufacturers are investing efforts to make newer vehicles safer to drive, an element of uncertainty with the new vehicle seems to persist with the drivers during the early years of ownership. This could be due to a lack of familiarity of the vehicle's power, dimensions or available technologies/features. While the uncertainty in itself is a potential cause of a crash, it is important for the policy-makers, practitioners, and automobile manufacturers to understand the factors that could further aggravate the problem. This research focuses on identifying the factors influencing the likelihood of getting involved in a crash and its severity when driving a new vehicle. Crash data for North Carolina for the years 2013 to 2018 (six years) was used develop partial proportionality odds models, compute the odds ratios, analyze the effects of explanatory variables, and identify factors influencing crashes by the age of the vehicle. The likelihood of getting involved in a severe or moderate injury crash when driving a new vehicle is less for drivers in the age group ≤19 years. Erratic driving behavior (like making wide turns, weaving and swerving in traffic, driving with headlights off, driving on center-line or lane-line, etc.) and speeding increase the risk of getting involved in a moderate injury crash when driving a new vehicle. Likewise, the odds of getting involved in a crash are high on weekends and in adverse weather conditions when driving a new vehicle. They are higher when driving a new motorcycle, heavy vehicle or farm machinery. The findings help policy-makers and practitioners formulate strategies to educate drivers on factors influencing crash risk when driving a new vehicle. Further, automobile manufacturers can establish guidance programs and documentation that explain what to expect when buying and driving a new vehicle.

Automated vehicles (AVs) are expected to improve safety by gradually reducing human decisions while driving. However, there are still questions on their effectiveness as we transition from almost 0% AVs to 100% AVs with different levels of vehicle autonomy. The focus of this study, therefore, is on synthesizing and identifying risk factors influencing fatal crashes involving partially automated vehicles (PAVs) i.e., level 1 AVs and level 2 AVs, in the United States. Fatal crashes involving non-AVs (level 0 vehicles) within their vicinity were used for comparison, to minimize unobserved heterogeneity and randomness associated with the influencing risk factors. The fatal crash data for the years 2016 to 2019 is used for the analysis. A partial proportional odds model is developed using crash, road, and vehicle characteristics as independent variables while the fatal crash involving a vehicle with a specific level of automation (0, 1, or 2) is used as the dependent variable. The level of automation was captured by developing tools and identifying advanced features in each vehicle based on the vehicle identification number (VIN). The odds ratios varied for PAVs compared to non-AVs. PAVs are safer but are more likely to be involved in a fatal crash with non-motorists. Pedestrian automatic emergency braking (PAEB) and lane-keeping assistance (LKA) were observed to improve safety by reducing possible collision with a pedestrian and roadside departure, respectively. Contrarily, vehicles with other smart features are still highly likely to be involved in fatal crashes, demanding further research and attention.

ABSTRACT: Multimodal facilities are being built in a majority of urban areas to accommodate both motorized and non-motorized traffic. The resulting transportation system is susceptible to complex interactions between different modes and users. These complex interactions are hard to quantify and capture on a regular basis. There is a need to rely on a microsimulation platform to replicate such real-world scenarios and forecast future conditions. This study focuses on assessing the effect of heterogeneous traffic conditions involving multiple modes like light rail transit (LRT), walking, bicycling, and motorized traffic on vehicle delay at intersections using Vissim traffic simulation software. A 2.5-mile urban arterial corridor comprising of seven at-grade and two grade-separated signalized intersections along US-29 route in the city of Charlotte, NC, USA was chosen for analysis and modeling. Base simulation model was developed by replicating the traffic scenario for the year 2018 (without LRT and non-motorized traffic). External controllers like VisVAP and Viswalk were used to assign signal priority and model heterogenous traffic conditions. The vehicle operational performance of the corridor improved along the major street after the addition of the LRT. An increase in vehicle delay on the major street was observed with an increase in the non-motorized traffic. Contrarily, the operational performance of the cross-streets at at-grade intersections did not see any significant change due to the addition of non-motorized traffic. The proposed framework assists planners to assess the impact of adding a new transit system like LRT and associated non-motorized traffic on the urban corridor.

This research explores a city-oriented and inclusive bicycle-vehicle crash frequency model through the integration of bicycle-sharing system and other surrogates pertaining to the built environment like road network, land use developments, and socio-demographic characteristics. Bicycle-vehicle crash data from 2016 to 2019, along with the road network, land use, and socio-demographic characteristics within a 1-mile radius of 62 randomly selected Capital Bikeshare (CaBi) stations (referred to as stations), was used to estimate and validate bicycle-vehicle crash frequency models (negative binomial regression models with log-link distribution) and assess the role of explanatory variables. The selected stations are geographically and fairly distributed in low-risk, moderate-risk, high-risk, and very high-risk areas while covering 99.8% of the total number of recorded bicycle-vehicle crashes. From such an inclusive approach, it was observed that bicyclists are more likely to be involved in crashes in commercial, mixed-use, and densely populated areas. In addition, they are more often involved in crashes in areas with more six-lane roads. Also, many bicycle-vehicle crashes have occurred at intersections. The nonexistence of bicycle crossings could explain this finding. The findings are substantial, and characteristics related to the built environment like road network (including vehicle and bicycle infrastructure), land use developments, and socio-demographics are good surrogates for traffic volume and bicycle count.

The Highway Capacity Manual (HCM) recommends the use of passenger car equivalent (PCE) to account for the influence of trucks on the operational performance of roads. Researchers have derived PCE based on capacity, headway, queue discharge, and delay in the past. However, it is not clear if the influence of trucks on the operational performance of roads would be the same in terms of travel time. The focus of this research is to assess the influence of trucks on the transportation system performance from a travel time perspective. The travel time data for Mecklenburg County (urban area) and Iredell County (rural area) were gathered for the year 2017 and processed to generate sixty-nine datasets accounting for the area type, temporal variation, reference speed, and traffic condition. Ordinary Least Square (OLS) regression models were then developed to examine the relationship between the travel time of trucks (dependent variable) and the travel time of passenger cars (explanatory variable). The coefficients indicate that the average travel time (ATT) of trucks is greater than the ATT of passenger cars irrespective of the dataset used for analysis, except in two cases of Iredell County. The ATT of trucks could be up to 1.18 times the ATT of passenger cars in some cases. The area type, day-of-the-week (DOW), time-of-the-day (TOD), reference speed of the link, and traffic condition at the time of data collection all seem to influence the ATT of trucks at different scales compared to the ATT of passenger cars.

Drivers experience variations in travel time due to congestion and delays associated with road construction projects on existing transportation facilities. The effect on the travel time of a link decreases as its distance from the road construction project increases. In addition, the effect could extend to the connecting arterial street links within the vicinity of the road construction project. Therefore, the focus of this research is to model the effect of a road construction project on the freeway and connecting arterial street link-level travel times. Data pertaining to a resurfacing construction project, which lasted for six months, was gathered from the traffic incident management systems (TIMS). The travel time data was collected and processed, for each selected freeway and connecting arterial street link, for six months before the start of the resurfacing construction project and six months during the construction of the resurfacing project. Generalized linear models (GLM) were developed and validated for before and during the construction project periods with the average travel time on a link as the dependent variable. Data such as time-of-the-day, day-of-the-week, the distance of a link from the work zone, subject link characteristics, and upstream and downstream link characteristics were collected and used as the predictor variables. The results indicate that the subject link, upstream link and downstream link characteristics have a significant effect on the freeway and connecting arterial street link-level travel times due to the resurfacing construction project.

This paper evaluates the effectiveness of toll roads in reducing congestion and improving travel time reliability on streets within its vicinity, over time. The data, from the year 2013 to 2017, for the Triangle Expressway in the city of Raleigh, North Carolina was used in the systemic evaluation process. The spatial and temporal variations in the travel time distributions on the toll road, parallel route, and near-vicinity cross-streets (in a 2-mile buffer) were evaluated. The average travel time (ATT), the 95th percentile travel time (or planning time, PT), the buffer time (BT), and the buffer time index (BTI) were computed for each link by the day-of-the-week and the time-of-the-day. Though there was an improvement in travel time after the speed limit change in the year 2014, the travel time reliability got reduced on the Triangle Expressway. Over time, there was a gradual improvement in BT and BTI. The parallel route reliability decreased significantly over time, whereas the travel time reliability of cross-streets showed a consistent trend. The stabilization of travel time distributions and the reliability measures in the year 2017 indicate that further reduction in performance measures may not be expected in the next few years on the near-vicinity links.

A road construction project has a significant effect on traffic congestion and operational performance of the project corridor and connecting street links. However, not many researchers focused on travel time performance-based measures to assess the effect of a road construction project on the project corridor and connecting street links. Therefore, travel time data for a freeway road resurfacing project on 100 links within its vicinity were gathered and processed to assess the effect on freeway and connecting arterial street links. A paired t-test was used to examine the differences in means between the before and during road resurfacing project period travel time performance-based measures. There is a significant difference in travel time performance-based measures before and during the road resurfacing project period on freeway links and connecting arterial links. Their significance varied by the time-of-the-day and day-of-the-week on the freeway and connecting arterial links. The average travel time, planning time, and travel time index can consistently explain the effect of a road resurfacing (construction) project on transportation system performance when compared to buffer time and buffer time index. The findings can be used to enhance temporary traffic control practices in the vicinity of road construction projects.

The objective of this paper is to evaluate drivers’ risk perception toward crash related traffic rule violations and identify violations that are perceived as low risk to better educate drivers. Risk perceptions on crash related traffic rule violations was gathered from 3,593 participants as a part of Naturalistic Driving Study. The variations in risk perceptions by driver characteristics such as age, gender, education, and household income were studied. The risk perception of violating traffic rules was observed to increase with an increase in drivers’ age, except for driving under the influence of alcohol and drugs. Drivers older than 25 years perceive disregarding traffic signals as the riskiest traffic rule violation. Exceeding speed limit by 10 to 20 mph is perceived as the least risky among the considered traffic rule violations, irrespective of age, gender, education, and income level of the driver. The risk perception of disregarding traffic signals and following vehicle closely are statistically the same for both male drivers and female drivers. For all other traffic rule violations, female drivers’ risk perception is greater than male drivers’ risk perception. Participants with lower education level perceive violating traffic rules as not risky, except for DUI. Graduates or professionals with no advanced degree perceive risk of violating traffic rules greater than the average risk for the entire sample population. Dissemination of risk perception information as well as enhanced educational programs are necessary to increase awareness about the risk associated with violating traffic rules that are perceived as low risk by drivers.

The focus of this paper is to examine the influence of network, land use, and demographic characteristics on the number of bicycle-vehicle crashes, and to develop area-level bicycle-vehicle crash estimation models (safety performance functions) for urban roads. Mecklenburg County in the State of North Carolina was considered as the study area. The reported bicycle-vehicle crash data, from 2010 to 2015, along with the network, land use, and demographic characteristics data were obtained from the local agencies. Data within a one-mile buffer of 119 selected locations was then captured. Data for 99 selected locations were used for the modeling purpose, while data for the remaining 20 selected locations were used for validating the models. Six alternate models were developed, considering various combinations of explanatory variables that are not correlated with each other. As the bicycle-vehicle crash dataset used in this research was observed to be over-dispersed (variance greater than the mean), Negative Binomial log-link distribution-based models were developed. The validation dataset was used to compare the estimated number of bicycle-vehicle crashes from each model with the actual number of bicycle-vehicle crashes. The results obtained from the analysis and modeling suggest that bicyclists are more often involved in crashes while traveling on segments with no bicycle lane, the traffic light, 45 mph as the speed limit, and in commercial activity, research activity, institutional, multi-family residential (densely populated), and heavy industrial areas. The computed Moran's Index values indicate weak to no spatial correlation between the residuals of each model. However, the residuals seem to depend on the area type and the number of bicycle-vehicle crashes. Keywords: Bicycle, Crash, Network, Land use, Demographics, SPF

Work zone area types include advance warning area, transition area, and activity area. The geometric conditions, traffic control aspects, traffic operations, and driver's maneuverability differ within each work zone area type. Therefore, the odds of getting involved in a crash and factors associated with injury severity vary by work zone area type. The focus of this research is to examine the odds of getting involved in a crash in work zone advance warning, transition, and activity areas by injury severity. Five years (2010–2014) of crash data for the state of North Carolina was obtained from the Highway Safety Information Systems (HSIS) and used in this research. Three partial proportional odds models and one proportional odds model were developed using Statistical Analysis Software (SAS) in this research. The results indicate that the odds of getting involved in a work zone crash in the transition area when compared to the advance warning area is higher during cloudy weather condition, on wet roads and interstates, and on roads equipped with double yellow / no passing zone, with rigid post barrier, grass, and flexible post barrier median. Further, the odds of getting involved in a work zone crash in the activity area when compared to the advance warning area is higher during cloudy weather condition, on interstate and US routes, and on roads with stop and go signal, double yellow / no passing zone and flexible post barrier median. Overall, the findings indicate that the odds and factors associated with crash occurrence depend on the work zone area type. The odds of getting involved in a severe or moderate injury crash is higher on curved roads in all the three work zone area types compared to straight roads. It is higher 1) in the advance warning area on roads with semi-flexible post barrier medians, 2) in the transition area on US routes, and 3) in the activity area on dark lighted roads, US routes, and State routes. Overall, the odds of getting involved in a severe or moderate injury crash and associated factors vary by work zone area type. The findings from this research assist the practitioners to take precautionary measures and reduce the odds of getting involved in a crash by implementing work zone area-specific safety countermeasures. Keywords: Work zone, Injury severity, Partial proportional odds model, Road safety

Automotive companies have been developing vehicles with advanced features that aid in various driving tasks. These features are aimed at enhancing safety by either warning the drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane or a constant headway. They are part of vehicles with driver assistance technology and are vital for the successful deployment of connected and automated vehicles in the future. However, drivers’ behavioral response when driving vehicles with such advanced driver assistance systems (ADAS) compared to vehicles without ADAS may vary and is meagerly explored. This research evaluates drivers’ behavioral response to scenarios when driving vehicles with ADAS like lane departure warning (LDW), blind spot warning (BSW), and over speed warning (OSW) compared to vehicles without ADAS. Rural, urban and freeway driving scenarios were developed in a driver simulator and tested on 43 participants aged sixteen years to sixty-five years. The results show that ADAS influence driving behavior by making drivers less aggressive and harmonizing the driving environment. The influence of ADAS on the driver behavior was different in rural, urban, and freeway driving scenarios. The drivers’ behavioral response to scenarios varied with the lighting and weather condition as well as with the age, gender, and ethnicity of the participant. While ADAS help by reducing lane departures or speeding and enhance safety, an indirect influence on braking, turning, and car-following were also observed. The findings help assess driver behavior when driving vehicles with advanced features and build better systems.

Trucks transport a significant amount of freight and are susceptible to traffic congestion related delays. Practitioners are interested in identifying suitable truck travel time performance measures and examining their association with on-network (road) and surrounding off-network (land use and demographic) characteristics for planning, designing, and building better transportation facilities. The focus of this research is on the investigation of truck travel time performance measures and their association with on-/off-network characteristics using Pearson correlation coefficient analysis. Truck travel time data for the year 2019 and on-network and off-network characteristics captured using geospatial methods were considered for 501 links in Mecklenburg County, Buncombe County, and Wake County in North Carolina, USA for this research. The average travel time (ATT), planning time index (PTI), buffer time index (BTI), and travel time index (TTI) were selected based on the correlations within the truck travel time performance measures. Road functional class, number of through lanes, area type, annual average daily traffic (AADT), and speed limit are significantly but lowly associated with the truck travel time performance measures. The agriculture, light commercial, and residential land uses in the near vicinity have a moderate to strong association with the truck travel time performance measures. The associations varied with the truck travel time performance measure, day of the week, time of the day, and buffer width to capture off-network characteristics. The methodology and findings assist in identifying potential corridors/areas for the implementation of truck exclusive lanes, truck signal priority at intersections, decentralized delivery locations, or other unconventional strategies.

This paper focuses on identifying crash risk factors associated with injury severity of teen drivers. Crash data obtained from the Highway Safety and Information System (HSIS) for the entire state of North Carolina, for years 2011 to 2013, was used for analysis and modeling. Among all the crashes during the study period, a total of 62,990 crashes involving teen drivers (15 to 19 years) were analyzed. A partial proportionality odds model was developed to identify factors contributing to injury severity of teen drivers. The results obtained indicate that teen drivers driving sports utility vehicles and pickup trucks are more likely to be severely injured when compared to teen drivers driving passenger cars. Teen drivers are more likely to be severely injured on weekdays, particularly during peak hours. The chances of teen drivers getting involved in severe injury crashes on Tuesdays and Fridays is higher when compared to Sundays. Age, gender, road configuration, terrain, adverse weather condition, and access control are observed to have a significant effect on teen driver's injury severity. Keywords: Teen drivers, Crash, Injury severity, Contributing factor, Partial proportionality odds model

This paper provides insights pertaining to the validity of a regional travel demand model in mimicking real-world travel times. The estimated travel times from the regional travel demand model, for the base year 2015, for Mecklenburg County in the city of Charlotte, North Carolina (NC) were compared with travel time statistics from a private data source, for the same year. The results indicate that the estimated travel times from the regional travel demand model are typically lower than the 85th percentile travel times, irrespective of the link speed limit. The estimated travel times for the Central Business District (CBD) area type are moderately correlated with the travel time statistics from the private data source, irrespective of the time of the day. For all the other area types, stronger correlations were observed when the estimated travel times from the regional travel demand model are compared with 10th to 50th percentile travel times. The calculated Pearson correlation coefficients are low for morning and evening peak periods compared to mid-day and night-time period, indicating the inability of the regional travel demand model in mimicking congested traffic conditions accurately.

The objective of this research is to model the effect of increasing the freeway posted speed limit on entry ramp speed-change lane crash frequency. The entry ramps along I-85, I-77, I-277, and I-485 in Charlotte, North Carolina, United States were considered to collect data, identify critical predictor variables, develop safety performance functions (SPFs) by severity, and validate the SPFs. Data for 76 and 20 entry ramp speed-change lanes (reference and comparison groups) were used for modeling and validation/calibration, respectively. The Negative Binomial log-link distribution-based models were the best-fit as the crash data are over-dispersed. The traffic volume, the speed difference between the freeway and the ramp, and the ramp configuration are significant predictor variables to estimate total, property damage only (PDO), and non-incapacitating and possible injury crash frequency at entry ramp speed-change lanes. The Empirical Bayes (EB) method was then applied to examine the effect of increasing the freeway posted speed limit on 48 entry ramp speed-change lanes (treatment/test group) on I-485. The findings from the EB method-based analysis indicate that there was a decrease in the total, non-incapacitating and possible injury, and PDO crash frequencies at 18, 37, and 18 (38%, 77%, and 38%) of the selected 48 entry ramp speed-change lanes after the increase in the freeway posted speed limit on I-485. An increase or no change in the total, non-incapacitating and possible injury, and PDO crash frequencies was also observed at some entry ramp speed-change lanes after the increase in the freeway posted speed limit on I-485.

The objective of this research is to model and examine the influence of predictor variables on merging speed change lane crash risk by interchange type in urban areas. Data for selected merging speed change lanes in Charlotte, North Carolina, USA, for five-years, were collected and used in this research. Multinomial logistic regression models were developed to examine the risk of getting involved in a fatal or injury crash and property damage only (PDO) crash by interchange type. The findings indicate that merging speed change lanes at multi-lane ramps are safer than single-lane ramps (odds ratio > 50). The ramp average daily traffic (ADT) and the speed difference between the freeway and the ramp influence merging speed change lane crash risk at a cloverleaf interchange while the freeway annual average daily traffic (AADT) and the speed change lane length influence merging speed change lane crash risk at a diamond interchange (odds ratio > 1.4). The risk of getting involved in a fatal-injury crash in the merging speed change lane increases (odds ratio > 1.6) if an upstream or downstream ramp is too close to the subject cloverleaf interchange ramp or if a downstream ramp is too close to the subject diamond interchange ramp. Contrarily, the upstream ramp distance does not influence the merging speed change lane PDO crash risk at a cloverleaf or diamond interchange. It can be concluded that predictor variables influencing the merging speed change lane crash severity risk are different for cloverleaf, diamond, and other interchange types.

The focus of this research is to simulate and assess the effect of a protected intersection design (PID) for bicyclists on traffic operational performance and safety. The PID was modelled and evaluated at the intersection of Tyvola Rd and South Blvd in Charlotte, North Carolina, USA. Traffic was modelled considering the existing intersection design and proposed PID using Vissim microscopic traffic simulation software under conditions of 0% bicycles to 15% bicycles. Safety was then analysed using Surrogate Safety Assessment Model (SSAM), and conflicts were defined as a 1.5-second intersection of two or more trajectories. There is no conclusive evidence that PID negatively effects operational performance (vehicle delay and level of service) at the selected study intersection. The type and total number of bicycle-related conflicts per bicycle varied with the percentage of bicycles. As much as an 80% reduction in the total number of bicycle-related conflicts could be observed if the percentage of bicycles exceed 10%. Benefits due to the implementation of PID could be greater at intersections with high bicycling activities. The findings support the hypothesis that the PID reduces the number of conflicts, and therefore improves safety.

Single-vehicle run-off-road (ROR) crashes account for a significant number of all the single-vehicle crashes and all the fatalities in the United States. The variable categories influencing single-vehicle ROR crashes and their severity could be different from other single-vehicle crashes. This paper, therefore, focuses on investigating single-vehicle ROR crashes to identify and compare variable categories influencing their occurrence and severity. A logistic regression model was developed to evaluate and identify variable categories associated with single-vehicle ROR crashes compared to other single-vehicle crashes. The results indicate that variables such as road characteristics (number of lanes, road surface condition, road terrain, functional classification, and geometric condition), weather condition, road lighting condition, vehicle type, and, driver characteristics (driver physical condition, age, and gender) are observed to have a statistically significant influence on the occurrence of single-vehicle ROR crashes. A partial proportionality odds model was developed to evaluate and identify variable categories associated with injury severity of single-vehicle ROR crashes. The results indicate that variables and variable categories such as weather condition (cloudy and rain), speed limit ( 88.5 kmph [55 mph]), vehicle type (primarily buses), drivers’ age (

Fog and rain influence the ability of the drivers to see other vehicles, pedestrians, bicyclists, and fixed objects, and, hence, increase the likelihood of getting involved in a crash on a road. Resource constraints limit installing weather monitoring sensors at regular intervals (for example, every 1, 2, 3, or 4 km) on a road. Therefore, the objective of this paper is to develop a feed-forward back-propagation neural network (BPNN) model to predict fog / low visibility condition, and, apply at a road link-level. Five years of meteorological data were collected from 238 weather monitoring stations in and around North Carolina to develop and validate the BPNN visibility model. The predicted visibility using meteorological data was compared with visibility from the weather monitoring sensor installed at five geographically distributed locations in North Carolina. The BPNN visibility model has better visibility prediction capability during a normal foggy condition when compared to visibility during thick/dense fog condition.

The effect of a planned special event on travel time performance measures varies spatially and temporally based on the type of the planned special event. Evaluating and assessing the effect helps practitioners proactively plan and improve traffic operations within its vicinity during the future planned special event. The objective of this paper, therefore, is to adopt a systemic evaluation method and examine the effect of a planned special event on travel time performance measures, over time and space, from the location of the planned special event. Raw travel time data obtained from a private data source was used to assess the spatial and temporal effects of two planned special events on travel time performance measures. Data were captured for each major road link within 3-miles of each planned special event location. Statistical analyses such as the t-test and Cohen's D were conducted to assess the difference in travel time performance measures during the day of the planned special event compared to the normal day. A statistically significant increase in travel times and travel time variations was observed on the day of the planned special event when compared to the normal day. However, the effect of the increase in travel times was observed to be small, whereas, the effect of the increase in the travel time variation was observed to be higher. Buffer time index (BTI) and planning time index (PTI) were observed to be significantly higher during the NASCAR race and for longer periods compared to the NFL game.

The objective of this research is to identify factors associated with crashes due to overcorrection or oversteering of vehicles. Crash data was collected from 2011 to 2013 for the State of North Carolina in the United States. Logistic regression modeling was used to analyze crash data because of the dichotomous nature of the dependent variable (overcorrection or oversteering). The crash involvement due to overcorrection or oversteering of a vehicle decreased as the age of the driver increased. Drivers are 2.22 times more likely to overcorrect or oversteer when ill, 3.44 times more likely to overcorrect or oversteer when under fatigue, and 1.61 times more likely to overcorrect or oversteer when fallen asleep compared to normal physical conditions. Overall, driver characteristics and speed limit tend to play a major role in overcorrection or oversteering of vehicles. Programs to reduce impaired driving might help in the reduction of overcorrection or oversteering related crash fatalities or injuries. Additionally, training and driver education programs focusing on identified factors associated with crashes due to overcorrection or oversteering of vehicles will benefit drivers on how to respond during emergency or panic situations. Keywords: Overcorrection, Oversteering, Vehicle, Logistic regression, Crash

Macroscopic parameters, such as density, volume-to-capacity ratio, and speed, are traditionally used to assess the level of service (LOS) of freeway/expressway or arterial road links. Capturing real-world data pertaining to these macroscopic parameters for all the links in the transportation network is expensive, tedious, and challenging. Additionally, density and volume-to-capacity ratio are not easily perceived by the commuters. Recent trends indicate that large-scale travel time data can be collected anonymously and without the use of field technicians, through global positioning systems (GPS), sensors, smartphones with apps, and other technological devices. The focus of this research is to develop link-level travel time measures-based LOS thresholds for urban areas, using large-scale travel time data from a private data source. The posted speed limit for each selected link in the city of Charlotte, North Carolina was integrated with the raw travel time data for the corresponding link. Travel time measures such as the average travel time, the 95th percentile travel time (planning time, PT), the planning time index (PTI), and the buffer time index (BTI) were computed and categorized by the posted speed limit of each selected link. The relationships between estimated speeds from the regional network model and computed travel time measures were then examined to develop LOS thresholds at link-level by the posted speed limit.

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). Four years of meteorological data, from January 2011 to December 2014, were collected within NC. Ordinary least squares (OLS) and weighted least squares (WLS) regression models were developed for different visibility and elevation ranges. The results indicate that elevation and cloud cover are negatively associated with low visibility. The chances of low visibility are higher between six to twelve hours after rainfall when compared to the first six hours after rainfall. A visibility sensor was installed at four different locations in NC to compare hourly visibility from the selected regression model, High-Resolution Rapid Refresh (HRRR) data, and the nearest weather station. The results indicate that the number of samples with zero error range was higher for the selected regression model compared with the HRRR and weather station observations.