How Effective are Toll Roads in Improving Operational Performance?

The main focus of this research is to develop a systematic analytical framework and evaluate the effect of a toll road on region’s traffic using travel time and travel time reliability measures. The travel time data for the Triangle Expressway in Raleigh, North Carolina, United States was employed for the assessment process. The spatial and temporal variations in the travel time distributions on the toll road, parallel alternate route, and near-vicinity cross-streets were analyzed using various travel time reliability measures. The results indicate that the Triangle Expressway showed a positive trend in reliability over the years of its operation. The parallel route reliability decreased significantly during the analysis period, whereas the travel time reliability of cross-streets showed a consistent trend. The stabilization of travel time distributions and the reliability measures over different years of toll road operation are good indicators, suggesting that further reduction in performance measures may not be seen on the near vicinity corridors. The findings from link-level and corridor-level analysis may help with transportation system management, assessing the influence of travel demand patterns, and evaluating the effect of planned implementation of similar projects

Effect of Weather Events on Travel Time Reliability and Crash Occurrence

The magnitude of the effect of adverse weather conditions on road operational performance varies with the type of weather condition and the road characteristics of the road links and adjacent links. Therefore, the relationship between weather and traffic is always a concern to traffic engineers and planners, and they have extensively explored ways to integrate weather information into transportation systems. Understanding the influence of weather on operational performance and safety helps traffic engineers and planners to proactively plan and manage transportation systems. The main objective of this research is to evaluate the effect of adverse weather conditions on travel time reliability and crash occurrence, by severity, using weather data, road data, travel time data, and crash data for North Carolina. The methodology and results from this research are useful for transportation system managers and planners to manage the traffic and improve safety under different weather conditions. They also help improve the functionality of weather-responsive management strategies like variable signs to indicate the change in reliability and safety under rainfall and low visibility conditions

Risk Factors Associated with Crash Injury Severity Involving Trucks

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, when, and why the likelihood of getting involved in a severe or moderate injury crash with a truck is higher. This research, therefore, focuses on capturing and exploring 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 proportionality odds model and identify risk factors influencing injury severity of crashes involving trucks. The findings from this research indicate that dark lighting condition, inclement weather condition, the presence of double yellow or no-passing zone, road sections with speed limit \u3e40 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 getting involved in a crash with a truck is also high in areas with high employment, government, light commercial, and light industrial land uses. The findings can be used to proactively plan and prioritize the allocation of resources to improve safety of transportation system users in these areas

Influence of Level 1 and Level 2 Automated Vehicles on Fatal Crashes and Fatal Crash Occurrence

Connected and automated vehicles (CAVs) 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% CAVs to 100% CAVs with different levels of vehicle autonomy. This research focuses on synthesizing literature and identifying risk factors influencing fatal crashes involving level 1 and level 2 CAVs in the United States. Fatal crashes involving level 0 vehicles—ones that are not connected and automated—were compared to minimize unobserved heterogeneity and randomness associated with the influencing risk factors. The research team used the fatal crash data for the years 2016 to 2019 for the analysis. A partial proportionality odds model is developed using crash, road, and vehicle characteristics as the independent variables and the fatal crash involving a vehicle with a specific level of automation as the dependent variable. The results of this research indicate that level 1 and level 2 CAVs are less likely to be involved in a fatal crash at four-way intersections, on two-way routes with wide medians, at nighttime, and in poor lighting conditions when compared to level 0 vehicles. However, they are more likely than level 0 vehicles to be involved in a fatal crash with pedestrians and bicyclists. Comparative analysis between vehicles with smart features and other vehicles indicated that pedestrian automatic emergency braking (PAEB) and lane-keeping assistance (LKA) improve the 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. This research adds to the growing body of literature that will identify potential areas for improvement in the safety of vehicular technologies and road geometry

Modeling and Predicting Geospatial Teen Crash Frequency

This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25 miles and 0.5 miles) at each selected road segment, with the number of crashes on each road segment used as the dependent variable. The generalized linear models with negative binomial distribution (GLM-based NB model) as well as the geographically weighted negative binomial regression (GWNBR) and geographically weighted negative binomial regression model with global dispersion (GWNBRg) were developed and compared. This research relied on data for 147 geographically distributed road segments for modeling and data for 49 segments for validation. The annual average daily traffic (AADT), light commercial land use, light industrial land use, number of household units, and number of pupils enrolled in public or private high schools are significant explanatory variables influencing the teen crash frequency. Both methods have good predictive capabilities and can be used to estimate the teen crash frequency. However, the GWNBR and GWNBRg better capture the spatial dependency and spatial heterogeneity among road teen crashes and the associated risk factors

Modeling Operational Performance of Urban Roads with Heterogeneous Traffic Conditions

The rapid growth in population and related demand for travel during the past few decades has had a catalytic effect on traffic congestion, air quality, and safety in many urban areas. Transportation managers and planners have planned for new facilities to cater to the needs of users of alternative modes of transportation (e.g., public transportation, walking, and bicycling) over the next decade. However, there are no widely accepted methods, nor there is enough evidence to justify whether such plans are instrumental in improving mobility of the transportation system. Therefore, this project researches the operational performance of urban roads with heterogeneous traffic conditions to improve the mobility and reliability of people and goods. A 4-mile stretch of the Blue Line light rail transit (LRT) extension, which connects Old Concord Rd and the University of North Carolina at Charlotte’s main campus on N Tryon St in Charlotte, North Carolina, was considered for travel time reliability analysis. The influence of crosswalks, sidewalks, trails, greenways, on-street bicycle lanes, bus/LRT routes and stops/stations, and street network characteristics on travel time reliability were comprehensively considered from a multimodal perspective. Likewise, a 2.5-mile-long section of the Blue Line LRT extension, which connects University City Blvd and Mallard Creek Church Rd on N Tryon St in Charlotte, North Carolina, was considered for simulation-based operational analysis. Vissim traffic simulation software was used to compute and compare delay, queue length, and maximum queue length at nine intersections to evaluate the influence of vehicles, LRT, pedestrians, and bicyclists, individually and/or combined. The statistical significance of variations in travel time reliability were particularly less in the case of links on N Tryon St with the Blue Line LRT extension. However, a decrease in travel time reliability on some links was observed on the parallel route (I-85) and cross-streets. While a decrease in vehicle delay on northbound and southbound approaches of N Tryon St was observed in most cases after the LRT is in operation, the cross-streets of N Tryon St incurred a relatively higher increase in delay after the LRT is in operation. The current pedestrian and bicycling activity levels seemed insignificant to have an influence on vehicle delay at intersections. The methodological approaches from this research can be used to assess the performance of a transportation facility and identify remedial solutions from a multimodal perspective

Twenty-three unsolved problems in hydrology (UPH) – a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come

Assessing the effect of a light rail transit system on road traffic travel time reliability

A travel time reliability-based approach is proposed to assess the effect of the light rail transit (LRT) system on the road network within its vicinity. A 4-mile stretch of the Blue Line LRT extension, which connects the Old Concord Road and the University of North Carolina at Charlotte (UNC Charlotte) main campus in Charlotte, North Carolina (NC), was considered as the study corridor. The raw travel time data was collected from the Regional Integrated Transportation Information System (RITIS) website at one-minute intervals. The average travel time (ATT), planning time (PT), buffer time (BT), buffer time index (BTI), and planning time index (PTI) were computed for each link, referred to as Traffic Message Channel (TMC) in this research, by day-of-the-week and time-of-the day. Further, the travel time reliability of the links on the LRT extension corridor and adjacent corridors (both the parallel route and the cross-streets) were computed for different scenarios: network without LRT, testing phase of LRT, first month of LRT operation, third month of LRT operation, sixth month of LRT operation, and ninth month of LRT operation. The travel time reliability of the alternate route and cross-streets was affected by the LRT system operation. Increased green times and better coordination on the LRT corridor and the benefits associated with the alternate mode/route choice for commuters may be the reason behind the steadiness in travel time performance measures due to the LRT. The methodology and findings help transportation planners and engineers in comparing the performance or efficiency of large-scale public transportation projects like LRT and bus rapid transit (BRT) on travel time reliability within its vicinity

Lipid Metabolism and Associated Molecular Signaling Events in Autoimmune Disease

Lipid metabolism, when dysregulated paves the way to many autoimmune disease conditions. One such recently explored mechanism was that of Liver X receptor (LXR) signaling which acts as a molecular link between lipid metabolism and inflammation. LXR plays a critical role in coupling immune cell lipid homeostasis with systemic immune responses. In this chapter, we will discuss how an altered lipid metabolite environment causes inflammation signaling via LXR-mediated molecular events which could lead to autoimmune disease. In a hyperlipidemic environment, Interferon regulatory factor 3(IRF3) mediated downregulation of LXR signaling in innate immune cells leading to an inflammatory auto-immune response. Meanwhile, dendritic cell-mediated cytokine generation amidst LXR downregulation leads to the differentiation of autoreactive T cells and B cells, conferring an autoimmune response. Recent advances in the therapeutic management of autoimmune diseases target specific metabolic events as a strategy to limit inflammation and the autoimmune outcome. Novel treatment regimes in autoimmune diseases featuring lipid metabolic pathways are also discussed

Examining associations with on-time performance and identifying relevant road network, demographic, socioeconomic and land use characteristics within the bus stop vicinity for proactive and reliable public transportation system planning

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