This research: (1) examines the impact of missing data from permanent counters on the accuracy of the AADT; and (2) analyses the effect of varying short term count durations on the accuracy of AADT estimates. Data gaps can occur at permanent counters due to equipment malfunction and lane closures and can result in no available useable data. For short term counts a balance between accuracy and cost efficiency drives a need for an ideal count duration. Using data from Manitoba’s permanent counters, controlled data gaps and simulated short term counts were created to estimate AADTs. 150,000 AADTs were obtained from the analysis and were then compared to the true AADT to determine the overall error. The findings of this research showed that larger data gaps and shorter duration counts carry more error. Additionally, factors including month of the year and traffic pattern group impact AADT estimates illustrating the need for context sensitivity when rejecting data from a permanent counter and selecting an appropriate count duration. / October 2015

Non-motorized (i.e., bicycle and pedestrian) traffic patterns are an understudied but important part of transportation systems. A key need for transportation planners is traffic monitoring programs similar to motorized traffic. Count campaigns can help estimate mode choice, measure infrastructure performance, track changes in volume, prioritize projects, analyze travel patterns (e.g., annual average daily traffic [AADT] and miles traveled [MT]), and conduct safety analysis (e.g., crash, injury and collision). However, unlike for motorized traffic, non-motorized traffic has not been comprehensively monitored in communities throughout the U.S. and is generally performed in an ad hoc fashion. My thesis explores how to (1) best count bicycles and pedestrians on the entire transportation network, rather than only focus on off-street trail systems or specific transportation corridors and (2) estimate AADT of bicycles and pedestrians in a small college town (i.e., Blacksburg, VA). I used a previously developed count campaign in Blacksburg, VA to collect bicycle and pedestrian counts using existing monitoring technologies (e.g., pneumatic tubes, passive infrared, and RadioBeam). I then summarized those counts to (1) identify seasonal, daily, and hourly patterns of non-motorized traffic and (2) develop scaling factors (analogous to those used in motor vehicle count programs) derived from the continuous reference sites to estimate long-term averages (i.e., AADT) for short-duration count sites. I collected ~40,000 hours of bicycle and pedestrian counts from early September 2014 to January 2016. The count campaign included 4 continuous reference sites (~ full year-2015 counts) and 97 short-duration sites (≥ 1-week counts) that covered different road and trail types (i.e., major road, local road, and off-street trails). I used 25 commercially available counters (i.e., 12 MetroCount MC 5600 Vehicle Classifier System [pneumatic tube counters], 10 Eco-counter 'Pyro' [passive infrared counters], and 3 Chambers RadioBeam Bicycle-People Counter [radiobeam counters]) to conduct the traffic count campaign. Three MetroCount, 4 Eco-counter, and 1 RadioBeam counter were installed at the 4 continuous reference sites; the remaining counters were rotated on a weekly basis at the short-duration count sites. I validated automated counts with field-based manual counts for all counters (210 total hours of validation counts). The validation counts were used to adjust automated counts due to systematic counter errors (e.g., occlusion) by developing correction equations for each type of counter. All automated counters were well correlated with the manual counts (MetroCount R2 [absolute error]: 0.90 [38%]; Eco-counter: 0.97 [24%]; RadioBeam bicycle: 0.92 [19%], RadioBeam pedestrian: 0.92 [22%]). I compared three bicycle-based classification schemes provided by MetroCount (i.e., ARX Cycle, BOCO and Bicycle 15). Based on the validation counts the BOCO (Boulder County, CO) classification scheme (hourly counts) had similar R2 using a polynomial correction equation (0.898) as compared to ARX Cycle (0.895) and Bicycle 15 (0.897). Using a linear fit, the slope was smallest for BOCO (1.26) as compared to ARX Cycle (1.29) and Bicycle 15 (1.31). Therefore, I used the BOCO classification scheme to adjust the automated hourly bicycle counts from MetroCount. To ensure a valid count dataset was used for further analysis, I conducted quality assurance and quality control (QA/QC) protocols to the raw dataset. Overall, the continuous reference sites demonstrated good temporal coverage during the period the counters were deployed (bicycles: 96%; pedestrians: 87%) and for the calendar year-2015 (bicycles: 75%; pedestrians: 87%). For short-duration sites, 98% and 94% of sites had at least 7 days of monitoring for bicycles and pedestrians, respectively; no sites experienced 5 days or less of counts. I analyzed the traffic patterns and estimated AADT for all monitoring sites. I calculated average daily traffic, mode share, weekend to weekday ratio and hourly traffic curves to assess monthly, daily, and hourly patterns of bicycle and pedestrian traffic at the continuous reference sites. I then classified short-duration count sites into factor groups (i.e., commute [28%], recreation [11%], and mixed [61%]). These factor groups are normally used for corresponding continuous reference sites with the same patterns to apply scaling factors. However, due to limitations of the number (n=4) of continuous reference sites, the factor groups were only used as supplemental information in this analysis. To impute missing days at the 4 continuous reference sites to build a full year-2015 (i.e., 365 days) dataset, I built 8 site-specific negative binomial regression models (4 for bicycles and 4 for pedestrians) using temporal and weather variables (i.e., daily max temperature, daily temperature variation compared to the normal 30-year averages [1980-2010], precipitation, wind speed, weekend, and university in session). In general, the goodness-of-fit for the models was better for the bicycle traffic models (validation R2 = ~0.70) as compared to the pedestrian traffic models (validation R2 = ~0.30). The selected variables were correlated with bicycle and pedestrian traffic and cyclists are more sensitive to weather conditions than pedestrians. Adding model-generated estimates of missing days into the existing observed reference site counts allowed for calculating AADT for each continuous reference site (bicycles volumes ranged from 21 to 179; pedestrian volumes ranged from 98 to 4,232). Since a full year-2015 dataset was not available at the short-duration sites, I developed day-of-year scaling factors from the 4 continuous reference sites to apply to the short-duration counts. The scaling factors were used to estimate site-specific AADT for each day of the short-duration count sites (~7 days of counts per location). I explored the spatial relationships among bicycle and pedestrian AADT, road and trail types, and bike facility (i.e., bike lane). The results indicated that bicycle AADT is significantly higher (p < 0.01) on roads with a bike lane (mean: 72) as compared to roads without (mean: 30); bicycle AADT is significantly higher (p < 0.01) on off-street trails (mean: 72) as compared to major roads (mean: 33). Pedestrian AADT is significantly higher (p < 0.01) on local roads (mean: 693) as compared to off-street trails (mean: 111); this finding is likely owing to the fact that most roads on the Virginia Tech campus are classified as local roads. In Chapter 5, I conclude with (1) recommendations for implementation (e.g., counter installation and data analysis), (2) key findings of bicycle and pedestrian traffic analysis in Blacksburg and (3) strengths, limitations, and directions for future research. This research has the potential to influence urban planning; for example, offering guidance on developing routine non-motorized traffic monitoring, estimating bicycle and pedestrian AADT, prioritizing projects and measuring performance. However, this work could be expanded in several ways; for example, deploying more continuous reference sites, exploring ways to monitor or estimate pedestrians where no sidewalks exist and incorporating other spatial variables (e.g., land use variables) to study pedestrian volumes in future research. The overarching goal of my research is to yield guidance for jurisdictions that seek to implement systematic bicycle and pedestrian monitoring campaigns and to help decision making to encourage healthy, safe, and harmonious communities. / Master of Urban and Regional Planning

Annual Average Daily Traffic (AADT) data in the transportation industry today is an important tool used in various fields such as highway planning, pavement design, traffic safety, transport operations, and policy-making/analyses. Systematic literature review was used to identify the current methods of estimating AADT and ranked. Ordinary linear kriging occurred most. Also, factors that influence the accuracy of AADT estimation methods as identified include geographical location and road type amongst others. In addition, further analysis was carried out to determine the most apposite kriging algorithm for AADT data. Three linear (universal, ordinary, and simple), three nonlinear (disjunctive, probability, and indicator) and bayesian (empirical bayesian) kriging methods were compared. Spherical and exponential models were employed as the experimental variograms to aid the spatial interpolation and cross-validation. Statistical measures of correctness (mean prediction and root-mean-square errors) were used to compare the kriging algorithms. Empirical bayesian with exponential model yielded the best result.

Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. By definition, AADT is the average 24-hour volume at a highway location over a full year. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. For example, AADTs are used by state Departments of Transportation (DOTs) to calculate the crash rates of all local roads in order to identify the top five percent of hazardous locations for annual reporting to the U.S. DOT. This dissertation develops a new method for estimating AADTs for local roads using travel demand modeling. A major component of the new method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The model uses the tax parcel data together with the trip generation rates and equations provided by the ITE Trip Generation Report. The generated trips are then distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The all-or-nothing assignment method is then used to assign the trips onto the roadway network to estimate the final AADTs. The entire process was implemented in the Cube demand modeling system with extensive spatial data processing using ArcGIS. To evaluate the performance of the new method, data from several study areas in Broward County in Florida were used. The estimated AADTs were compared with those from two existing methods using actual traffic counts as the ground truths. The results show that the new method performs better than both existing methods. One limitation with the new method is that it relies on Cube which limits the number of zones to 32,000. Accordingly, a study area exceeding this limit must be partitioned into smaller areas. Because AADT estimates for roads near the boundary areas were found to be less accurate, further research could examine the best way to partition a study area to minimize the impact.

No description available.

Annual average daily traffic (AADT) is used to estimate intersection performance across Kentucky. The Kentucky Transportation Cabinet (KYTC) currently collects AADTs for state maintained roads, but lacks this information on local roads. A method is needed to estimate local road AADTs in a cost-effective and reasonable manner. A literature review was conducted on AADT models and found no models suitable to Kentucky. Therefore an AADT model using non-linear regression was developed for local roads in Kentucky This model divided the state into three regions utilizing Kentucky’s highway districts. This partitioning accounted for geographic and socioeconomic variability across the state. Each regional model relied upon three independent variables: probe count, residential vehicle registration, and curve rating. HERE proprietary probe counts provide tracking visibility on a select portion of vehicles moving across Kentucky highways. Residential vehicle registrations were used to estimate trip generation information. Finally, the curve rating partially indicates accessibility. The models were adjusted to KYTC daily vehicle miles traveled (DVMT) county control totals for local roads. Sensitivity analysis was conducted to examine the impact of model errors for use in intersection safety analysis. Results indicate that the estimates generated can be effectively used for safety assessment and countermeasure prioritization.

Traffic volume data are input to many transportation analyses including planning, roadway design, pavement design, air quality, roadway maintenance, funding allocation, etc. Annual Average Daily Traffic (AADT) is one of the most often used measures of traffic volume. Acquiring the actual AADT data requires the collection of traffic counts continuously throughout a year, which is expensive, thus, can only be conducted at a very limited number of locations. Typically, AADTs are estimated by applying seasonal factors (SFs) to short-term counts collected at portable traffic monitoring sites (PTMSs). Statewide in Florida, the Florida Department of Transportation (FDOT) operates about 300 permanent traffic monitoring sites (TTMSs) to collect traffic counts at these sites continuously. TTMSs are first manually classified into different groups (known as seasonal factor categories) based on both engineering judgment and similarities in the traffic and roadway characteristics. A seasonal factor category is then assigned to each PTMS according to the site’s functional classification and geographical location. The SFs of the assigned category are then used to adjust traffic counts collected at PTMSs to estimate the final AADTs. This dissertation research aims to develop a more objective and data-driven method to improve the accuracy of SFs for adjusting PTMSs. A statewide investigation was first conducted to identify potential influential factors that contribute to seasonal fluctuations in traffic volumes in both urban and rural areas in Florida. The influential factors considered include roadway functional classification, demographic, socioeconomic, land use, etc. Based on these factors, a methodology was developed for assigning seasonal factors from one or more TTMSs to each PTMS. The assigned seasonal factors were validated with data from existing TTMSs. The results show that the average errors of the estimated seasonal factors are, on average, about 4 percent. Nearly 95 percent of the estimated monthly SFs contain errors of no more than 10 percent. It was concluded that the method could be applied to improve the accuracy in AADT estimation for both urban and rural areas in Florida.

In the first part, this thesis performed a study to compile and compare current procedures or methodologies for the estimation of traffic volumes on the roads where traffic counts are not easily available. In the second part, linear regression was practiced as an AADT estimation process, which was primarily based on known or accepted AADT values on the neighboring state and local roadways, population densities and other social/economic data. To develop AADT prediction models for estimating AADT values, two different types of database were created, including a social economic database and a roadway characteristics database. Ten years social economic data, from 1995 to 2005 were collected for each of the 67 counties in the state of Florida, and a social economic database was created by manually imputing data obtained from different resources into the social economic database. The roadway characteristics database was created by joining different GIS data layers to the Tele Atlas base map provided by Florida Department of Transportation (FDOT). Stepwise regression method was used to select variables that will be included into the final models. All selected independent variables in the models are statistically significant with a 90% level of confidence. In total, six linear regression models were built. The adjusted R2 values of the AADT prediction models vary from 0.166 to 0.418. Model validation results show that the MAPE values of the AADT prediction models vary from 31.99% to 159.49%. The model with the lowest MAPE value is found to be the minor state/county highway model for rural area. The model with the highest MAPE value is found to be the local street model for large metropolitan area. In general, minor state/county highway models provide more reasonable AADT estimates as compared to the local street model in terms of the lower MAPE values.

This research contributes to improved risk analysis of performance measure forecasts in road safety engineering by designing and applying a method to characterize uncertainty associated with forecast input data in cases where input uncertainty is not known. The research applies this method to quantify uncertainty in three categories of inputs used in risk analysis of performance measure forecasts in road safety engineering: (1) estimates of pedestrian exposure to collision risk; (2) estimates of vehicular exposure to collision risk; and (3) estimates of engineering economics parameters that assign valuations to mortality risk reductions based on individual willingness to pay. The common methods used in each of these categories are repeated comparisons of input ground truth to input estimations, the use of simulation approaches (e.g. the simulation of short-term counts by sampling permanent count data), and the use of non-parametric techniques to characterize input uncertainty. Some highlights of quantified input uncertainty levels include: (1) when obtaining pedestrian risk exposure estimates at a site in Winnipeg, MB by expanding two-hour short-term counts using the National Bicycle and Pedestrian Documentation Project method, 90% of errors are between 62% and 170%; (2) when obtaining estimates of vehicle exposure to collision risk by expanding two 48-hour counts using the individual permanent counter method for Manitoba highways, 92 % of errors are between 9.5% and 10.8%; and (3) when applying an income-disaggregated transfer function to estimate value of a statistical life for road safety in developing countries, 90% of errors are between 53% and 54%. The results provide further detail on the structure of these input uncertainties. Analytic and computational capabilities in forecasting and risk analysis have advanced beyond our understanding of corresponding input uncertainty levels; this research closes some of this gap and enables better risk analysis of performance measure forecasts in road safety engineering.

Bicycling as an alternate mode of transportation has been on the rise. It is environmentally friendly in nature and the associated health benefits have made it a popular choice for many types of trips. The purpose of this research is to increase understanding of the impacts of implementing bicycle corridors (as part of the Utah Department of Transportation's (UDOT) Inclusion of Active Transportation policy) on bicycle rate as a function of roadway characteristics. The results of this research will be used in determining when and where bicycle corridors will enhance the transportation system and an estimate of the overall impact of bicycle corridors on travel demand in Utah. Data collection was fundamental in this research project in determining the impacts of bicycle corridors on travel demands in the state of Utah. With limited amount of commuting bicycle data available throughout the state, it was necessary to gather bicycle volume data on corridors with and without bicycle infrastructure. In order to accomplish this data collection effort, two primary methods were used to collect bicycle volume data. The first method was to use automatic bicycle counters on roadways that had bicycle infrastructure. The second method was to gather bicycle volume data through manual counts on roads with and without bicycle infrastructure. After the bicycle volume data were collected the data were analyzed to identify trends. The first step in the analysis was to convert the bicycle volumes into rates to provide a more uniform comparison. Several analyses were run including an analysis of bicycle rate compared to Annual Average Daily Traffic (AADT), bicycle rate compared to posted speed limit, bicycle rate compared to number of vehicle lanes, and bicycle rate compared to roadway classification. A comparison of sites with bicycle infrastructure to sites without bicycle infrastructure (non-bicycle infrastructure) was also conducted to identify relationships. Comparison of bicycle rates to AADT resulted in no correlation or statistical relationship in the data but the data do suggest trends. Statistically significant results did occur when comparing bicycle rates to posted speed limits. No statistically significant relationships occurred when comparing bicycle rates to the number of lanes or roadway classification. It was determined that roadways with bicycle infrastructure tend to yield higher bicycle rates than roadways that do not have bicycle infrastructure. Lastly, using shared use path data it is determined that bicycle rates on shared use paths have increased between 1.7 to 7.5 percent from 2013 to 2014 and it is assumed that a similar trend would exist on bicycle infrastructure in the communities.