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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.

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. Data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA were considered for the evaluation. Data related to teen crashes were obtained from the Highway Safety Information System (HSIS). Demographic and land use characteristics were extracted around each selected road segment using two different buffer widths (0.25 miles and 0.5 miles). Teen crash frequency of each road segment was used as the dependent variable. The generalized linear models with the negative binomial distribution (GLM-based NB model) and geographically weighted negative binomial regression models (GWNBR and GWNBRg) were developed and compared. The annual average daily traffic (AADT), light commercial land use, number of household units, and number of pupils enrolled in public or private high schools are significant explanatory variables influencing teen crash frequency. Both methods have good predictive capabilities and can be used to compute teen crash frequency. However, the GWNBR and GWNBRg better capture the spatial dependency and spatial heterogeneity of associated risk factors influencing teen crash frequency.

Collecting traffic data and/or estimating and reporting annual average daily traffic (AADT) is important for planning, designing, building, and maintaining the road infrastructure. However,...

This research is carried out to evaluate the efficacy of toll roads in reducing travel time and improving travel time reliability on links within its vicinity, using data for the Triangle Expresswa...

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.

The highway capacity manual (HCM-2010) is widely referred document around the globe for planning and design of roads. However, the transferability of HCM recommendations for heterogenous traffic condition represents a significant research issue as they are often not suitable to adequately explain the traffic complexities of a mixed-traffic state. This research investigates the suitability of HCM equations for determining the entry capacity of a four-legged roundabout under mixed traffic condition and proposes a methodology for validating and calibrating HCM equations for performance evaluation. Data corresponding to traffic composition, traffic volume, critical gap and follow-up time have been extracted from the video records collected at two roundabouts. The relationship between entry flow and circulatory flow has been plotted from the observed data when there is a complete saturation in the approach leg and depicted that it follows a negative exponential behavior. It implies that the entry capacity reduces exponentially with the increase in circulating flow. The critical gap has been estimated by various methods such as Maximum Likelihood Method (MLM), Root Mean Square (RMS) method and Probability Equilibrium Method (PEM). Further, a stream equivalent critical gap of 1.60 seconds and stream equivalent follow-up time of 1.24 seconds have been derived due to the mixed traffic scenario at the study location. A multiplicative adjustment factor of 1.1 is suggested for the use of HCM 2010 equation directly to estimate entry capacity under heterogeneous traffic condition. The study results may use for planning and designing of roundabout under mixed-traffic flow condition.

The focus of this research is to model the influence of road, socioeconomic, and land-use characteristics on local road annual average daily traffic (AADT) and assess the model's predictability in non-covered location AADT estimation. Traditional ordinary least square (OLS) regression and geographically weighted regression (GWR) methods were explored to estimate AADT on local roads. Ten spatially distributed counties were considered for county-level analysis and modeling. The results indicate that road density, AADT at the nearest nonlocal road, and land use variables have a significant influence on local road AADT. The GWR model is found to be better at estimating the AADT than the OLS regression model. The developed county-level models were used for estimating AADT at non-covered locations in each county. The methodology, findings, and the AADT estimates at non-covered locations can be used to plan, design, build, and maintain the local roads in addition to meeting reporting requirements. The prediction error is found to be higher at urban areas and in counties with a smaller number of local road traffic count stations. Recommendations are made to account for influencing factors and enhance the local road count-based AADT sampling methodology.

The study is intended to investigate the effect of traffic composition and the use of a paved emergency lane on capacity. Ahmedabad–Vadodara four-lane divided intercity expressway (Mahatma Gandhi e...