The primary focus of this paper is to develop models to estimate link-level crash frequency using land use data extracted and integrated through the use of a distance gradient method. The on-network characteristics were added to the integrated land use characteristics database and were also used in the development and validation of link-level crash frequency estimation models. Both statistical and back-propagation neural network (BPNN)-based approaches were tested and evaluated for modeling. Mean absolute deviation (MAD), median error, 85th percentile error, and root-mean squared error (RMSE) were computed to validate the developed link-level crash frequency estimation models and compare the two approaches. The results obtained from validation of the link-level crash frequency estimation models indicate that the computed errors are low for models based on both statistical and neural network approaches. Both the approaches have reasonably good predictive capability and can be used to estimate crash frequency. The role of predictor (includes integrated land use) variables on crash frequency along links can be easily understood using outputs from the statistical modeling approach. Also, findings indicate that models based on integrated land use and on-network characteristics (excluding traffic volume) have good predictive capability and can be used as surrogate data to estimate crash frequency if traffic volume data are not available.

Floating car method, technologies such as global positioning system (GPS) units or data from private sources such as INRIX are used for travel time data collection on urban streets. Time, man-power, and cost deter (limit) collection and purchase of such data for the entire transportation network. An alternate source of the travel time data is using buses as probe vehicles. This is practically feasible and inexpensive as most buses operating in urban areas are equipped with automatic vehicle location (AVL) units. However, the travel time of buses is generally greater than the travel time of a typical vehicle (car). The use or applicability could only be justified if there is strong correlation between car and bus travel times. This paper, therefore, examines the relationship between car and bus travel time. The role of key influential factors (such as the number of signalized intersections, the number of unsignalized intersections, the number of driveways, the number of bus-stops, traffic volume, the number of lanes and the number of turns made by the bus) on the ratio between the two travel times is also evaluated and examined to assess the use of buses as probe vehicles. Results indicate a moderately strong relationship between car and bus travel time. Variables other than the number of signalized intersections per unit distance, the number of lanes, and traffic volume do not seem to play a statistically significant role on the ratio of car to bus travel time irrespective of time-of-the-day. Models by time-of-the-day, developed to estimate car travel time, could be used if bus travel time, the number of signalized intersections per unit distance, the number of lanes, and traffic volume are known.

The focus of this paper is (1) to explore features available in commercial Geographic Information Systems (GIS) software and estimate accessibility index as a function of potential captive riders for each transit stop, and, (2) identify spatial gaps based on accessibility index to provide improved public transportation systems that maximizes market potential. Potential captive riders are identified within a pre-defined accessible walking distance or walking time (0.25 miles or 5 minutes) from each transit stop in the City of Charlotte, North Carolina. These riders comprise unemployed, household population with 0 or 1 automobiles, population by age group, low income population (< $25,000), and ethnicity. Results from statistical analysis conducted considering transit ridership and estimated accessibility index show that market potential can be maximized by selecting transit stop locations with high accessibility index or in locations with high accessibility index. Therefore, the methodology and estimated accessibility index to identify spatial gaps helps expand transit area coverage, identify new routes and transit stops.

There has been a paradigm shift in focus from intersection-level to corridor-and area-level analysis and performance measures in recent years. The possibility of capturing dynamic and continuous travel time and/or speed data from private sources such as INRIX opens many pragmatic avenues to predict reliability of transportation systems. Travel time reliability (or index or variability) is considered the most viable performance measure for corridor-level analysis though it is not being widely used for transportation planning, project prioritization, and allocation of resources. The definition of reliability as a performance measure and associated thresholds to understand or assess transportation system performance could vary for a link, corridor or an area. These definitions and thresholds to assess reliability need to be clearly established prior to its large scale application. This research aims to fill this gap by computing, comparing, and assessing link-, corridor- and area-level reliability measures. Data for the city of Charlotte, North Carolina was used to compute reliability measures, examine temporal and spatial variations, and illustrate how reliability can be used for transportation planning, prioritization and allocation of resources.