Assessing the Effect of Compressed Work Week Strategy on Transportation Network Performance Measures

The focus of this paper is on evaluating and assessing the effect of a compressed work week strategy (say, not working a day each week) on transportation network performance measures such as link-level traffic speed, travel time, and volume-to-capacity ratio using data gathered for the Charlotte metropolitan area, North Carolina. The results obtained indicate that reducing 15% to 20% of work commute during the morning peak hours using compressed work week strategy would increase traffic speeds by up to 5 mph on at least 64% of center-lane miles (sum of the length of the center line of all lanes of traffic for each selected link). It would also decrease the travel time by up to two minutes on at least 61% of center-lane miles

Comparative Evaluation of Technologies and Data Sources to Capture Travel Time at Section-Level on Urban Streets

This paper focuses on capturing section-level (a signalized intersection to the next) travel times on urban street segments using Bluetooth detectors as well as from INRIX data source and comparing it with manual and Global Positioning System (GPS) floating test car methods (test car with a trained technician and GPS unit to capture travel time between selected points) for each travel time run. Results obtained indicate that section-level travel time data captured using Bluetooth detectors on urban street segments are less accurate and not dependable when compared with GPS unit and INRIX. The role of various on-network characteristics on the percentage difference in travel time from GPS unit, INRIX, and Bluetooth detectors was also examined

Artist's Statement: "Dragonfly Halo"

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Assessing the Effect of Compressed Work Week Strategy on Transportation Network Performance Measures

The focus of this paper is on evaluating and assessing the effect of a compressed work week strategy (say, not working a day each week) on transportation network performance measures such as linklevel traffic speed, travel time, and volume-to-capacity ratio using data gathered for the Charlotte metropolitan area, North Carolina. The results obtained indicate that reducing 15% to 20% of work commute during the morning peak hours using compressed work week strategy would increase traffic speeds by up to 5 mph on at least 64% of center-lane miles (sum of the length of the center line of all lanes of traffic for each selected link). It would also decrease the travel time by up to two minutes on at least 61% of center-lane miles

Factors associated with crashes due to overcorrection or oversteering of vehicles

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, Cras

Crash risk factors associated with injury severity of teen drivers

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 mode

Comparative Evaluation of Technologies and Data Sources to Capture Travel Time at Section-Level on Urban Streets

This paper focuses on capturing section-level (a signalized intersection to the next) travel times on urban street segments using Bluetooth detectors as well as from INRIX data source and comparing it with manual and Global Positioning System (GPS) floating test car methods (test car with a trained technician and GPS unit to capture travel time between selected points) for each travel time run. Results obtained indicate that section-level travel time data captured using Bluetooth detectors on urban street segments are less accurate and not dependable when compared with GPS unit and INRIX. The role of various on-network characteristics on the percentage difference in travel time from GPS unit, INRIX, and Bluetooth detectors was also examined