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Feasibility of combined aerial photograph and tube counter for daily traffic counts.
Traffic congestion increases travel times, but also results in higher energy usage and vehicular emissions. To evaluate the impact of traffic emissions on environment and human health, the accurate estimation of their rates and location is required. Traffic emission models can be used for estimating emissions, providing emission factors in grams per vehicle and kilometre. Emission factors are defined for specific traffic situations, and traffic data is necessary in order to determine these traffic situations along a traffic network. The required traffic data, which consists of average speed and flow, can be obtained either from traffic models or sensor measurements. In large urban areas, the collection of cross-sectional data from stationary sensors is a costefficient method of deriving traffic data for emission modelling. However, the traditional approaches of extrapolating this data in time and space may not accurately capture the variations of the traffic variables when congestion is high, affecting the emission estimation. Static transportation planning models, commonly used for the evaluation of infrastructure investments and policy changes, constitute an alternative efficient method of estimating the traffic data. Nevertheless, their static nature may result in an inaccurate estimation of dynamic traffic variables, such as the location of congestion, having a direct impact on emission estimation. Congestion is strongly correlated with increased emission rates, and since emissions have location specific effects, the location of congestion becomes a crucial aspect. Therefore, the derivation of traffic data for emission modelling usually relies on the simplified, traditional approaches. The aim of this thesis is to identify, quantify and finally reduce the potential errors that these traditional approaches introduce in an emission estimation analysis. According to our main findings, traditional approaches may be sufficient for analysing pollutants with global effects such as CO2, or for large-scale emission modelling applications such as emission inventories. However, for more temporally and spatially sensitive applications, such as dispersion and exposure modelling, a more detailed approach is needed. In case of cross-sectional measurements, we suggest and evaluate the use of a more detailed, but computationally more expensive, data extrapolation approach. Additionally, considering the inabilities of static models, we propose and evaluate the post-processing of their results, by applying quasi-dynamic network loading.
The grouping of similar traffic patterns and cluster assignment process represent the most critical steps in AADT estimation from short-term traffic counts. Incorrect grouping and assignment often become a significant source of AADT estimation errors. For instance, grouping a commuter traffic trend pattern into a recreational traffic trend may produce an erroneous AADT value. The traditional knowledge-based methods, often aided with visual interpretation, introduce subjective bias while grouping traffic patterns. In addition, the grouping requires personnel resources to process large amounts of data and remains inefficient with unapparent traffic patterns. The functional class grouping, a traditional method, also produces larger errors. Under limited resources and constraints, better methods and techniques may group sites with similar characteristics. The study uses Gaussian Mixture Modeling (GMM) for clustering and an enhanced neural network model (OWO-Newton or ONN) for classification of continuous count data. The researchers compare this modified approach with volume factor grouping and a traditional approach. The study uses Automatic Traffic Recorder (ATR) data from the Oregon Department of Transportation (ODOT) as a comparative case study. Overall, the proposed two-step approach, GMM-ONN, exhibits improved performance. The study observes an error difference of 6% to 27%, which is statistically significant at 5 percent level, between the GMM-ONN and other methods. The GMM-ONN method produces less than five percent error for urban interstates and less than ten percent for urban arterials and freeways. The study method meets the FHWA recommended AADT forecasting error of less than ten percent for commuter patterns. The GMM-ONN also produces less error when compared to studies based on the national average and Minnesota and Florida DOT count data. The lower AADT estimation errors and its distribution show an effective and reliable approach for AADT estimation using short-term traffic counts. Moreover, the lower standard deviation of errors shows the satisfactory accuracy of the AADT estimates. The study recommends the improved two-step process due to its accuracy, economical approach by using daily patterns, and ability to meet the agency's need for a low-cost traffic counting program. The GMM-ONN method not only minimizes judgment errors but also supplements the FHWA guidelines on recommending clustering techniques for grouping the traffic patterns.