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

Modeling the Influence of Land Use Developments on Transportation System Performance

The growth in the urban population has influenced urban sprawl, congestion, and subsequently, delays on the existing road infrastructure. New land use developments occur in every part of the city due to rapid economic development and to meet the demand for better living standards. The induced traffic volume generated from such land use developments often results in increased congestion and vehicular delay on the existing roads. With recent advancements in the technology, it is possible to capture continuous, and comprehensive travel time data for every major corridor in a city. Therefore, the goal of this research is to model the influence of land use developments on travel time variations to improve the mobility of people and goods. Data for 259 road links were selected within the city of Charlotte, North Carolina (NC). Three years of travel time data, from the year 2013 to the year 2015, were collected from the private agency. Thirty-five different types of land use developments were considered in this research. The spatial dependency was incorporated by considering the land use developments within 0.5 miles, 1 mile, 2 miles, and 3 miles of the selected road link. Forty-eight statistical models were developed. The results obtained indicate that land use developments have a significant influence on travel times. Different land use categories contribute to the average travel time based on the buffer width, area type, and the link speed limit. Developing the models by classifying the links based on the speed limit (\u3c 45 mph, 45 to 50 mph, and \u3e 50 mph) was observed to be the best approach to examine the relationship between land use developments and the average travel time. Also, typically travel time on a selected road link is higher during the evening peak period compared to the morning peak and the afternoon off-peak period. Further, the results obtained indicate that the number of lanes and the posted speed limit are negatively associated with the travel time of the selected link

Modeling the Effect of a Road Construction Project on Transportation System Performance

Road construction projects create physical changes on roads that result in capacity reduction and travel time escalation during the construction project period. The reduction in the posted speed limit, the number of lanes, lane width and shoulder width at the construction zone makes it difficult for the road to accommodate high traffic volume. Therefore, the goal of this research is to model the effect of a road construction project on travel time at road link-level and help improve the mobility of people and goods through dissemination or implementation of proactive solutions. Data for a resurfacing construction project on I-485 in the city of Charlotte, North Carolina (NC) was used evaluation, analysis, and modeling. A statistical t-test was conducted to examine the relationship between the change in travel time before and during the construction project period. Further, travel time models were developed for the freeway links and the connecting arterial street links, both before and during the construction project period. The road network characteristics of each link, such as the volume/ capacity (V/C), the number of lanes, the speed limit, the shoulder width, the lane width, whether the link is divided or undivided, characteristics of neighboring links, the time-of-the-day, the day-of-the-week, and the distance of the link from the road construction project were considered as predictor variables for modeling. The results obtained indicate that a decrease in travel time was observed during the construction project period on the freeway links when compared to the before construction project period. Contrarily, an increase in travel time was observed during the construction project period on the connecting arterial street links when compared to the before construction project period. Also, the average travel time, the planning time, and the travel time index can better explain the effect of a road construction project on transportation system performance when compared to the planning time index and the buffer time index. The influence of predictor variables seem to vary before and during the construction project period on the freeway links and connecting arterial street links. Practitioners should take the research findings into consideration, in addition to the construction zone characteristics, when planning a road construction project and developing temporary traffic control and detour plans

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

Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics

Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations

Drivers’ Response to Scenarios when Driving Connected and Automated Vehicles Compared to Vehicles with and without Driver Assist Technology

Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver assistance systems (ADAS) that aid in various driving tasks. These features are aimed at enhancing safety by either warning drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane. These features are already part of vehicles with Driver Assistance Technology, and they are vital for successful deployment of connected and automated vehicles in the near future. However, drivers\u27 responses to driving vehicles with advanced features have been meagerly explored. This research evaluates driver participants\u27 response to scenarios when driving connected and automated vehicles compared to vehicles with and without Driver Assistance Technology. The research developed rural, urban, and freeway driving scenarios in a driver simulator and tested on participants sixteen years to sixty-five years old. The research team explored two types of advanced features by categorizing them into warnings and automated features. The results show that the advanced features affected driving behavior by making driver participants less aggressive and harmonizing the driving environment. This research also discovered that the type of driving scenario influences the effect of advanced features on driver behavior. Additionally, aggressive driving behavior was observed most in male participants and during nighttime conditions. Rainy conditions and female participants were associated with less aggressive driving behavior. The findings from this research help to assess driver behavior when driving vehicles with advanced features. They can be inputted into microsimulation software to model the effect of vehicles with advanced features on the performance of transportation systems, advancing technology that could eventually save millions of dollars and thousands of lives

Traffic Impact Analysis (TIA) and Forecasting Future Traffic Needs: Lessons from Selected North Carolina Case Studies

The focus of this paper is to conduct an evaluation of selected traffic impact analysis (TIA) case studies, review current practice, and recommend procedures that could be adapted to better forecast and plan future traffic needs. Lessons from the evaluations indicate that considering regional traffic growth rate, peak hour factor (PHF), heavy vehicle percentage, and other off-site developments would yield relatively better TIA forecasts. Incomplete development with vacant parcels was observed at several case sites, possibly due to the state of the economy. Therefore, conducting analysis assuming multiple “build out” years (say, three and five years based on the magnitude of the development) as complete build out years would help state and local transportation agencies plan and better allocate resources based on the need

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

Weights from a Safety Perspective for Interchange Lighting Prioritization

The focus of this paper is to research and update weights (values indicating the effect) to multiply ratings of selected factors used in the Total Design Process (TDP) for interchange lighting prioritization from a safety perspective. Results based on analysis using data collected at 80 interchanges along nine segments in North Carolina showed differences in weights for currently used factors such as freeway median width, freeway number of lanes and night-time traffic volume per lane. Results also showed that considering the number of night-time crashes by severity instead of night-to-day crash rate ratio, for prioritization of interchange lighting system installation or maintenance, would reduce the bias towards interchanges with fewer numbers of crashes and lead to better utilization of limited available transportation funds

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