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Bluetooth technology has been widely used in transportation studies to collect traffic data. Bluetooth media access control (MAC) readers can be installed along roadways to collect Bluetooth-based data. This data is commonly used to measure traffic performance. One of the advantages of using Bluetooth technology to measure traffic performance is that travel time can be measured directly with a certain level of error instead of by estimation. However, travel time outliers can commonly be observed due to different travel mode on arterials. Since travel mode information cannot be directly obtained from the raw Bluetooth-based data, a mathematical methodology is in need to identify travel mode. In this study, a genetic algorithm and neural network (GANN)-based model was developed to identify travel mode. GPS-enabled devices were used to collect ground truth travel time. In order to additionally compare the model performance, K nearest neighbor (KNN) and support vector machine (SVM) were also implemented. N-fold cross validation was applied to statistically assess the models’ results. Since the model performances depend on the model inputs, seven collections of model inputs were tested in order to achieve the best travel mode identification performance. An arterial segment with four consecutive links and three intersections was selected to be the study segment. The results suggested that correctly identifying the three travel modes successfully every time was not possible, although the GANN based model had low misidentification rates. In our study, 6.12% of autos were misidentified as bikes and 10.53% of bikes were misidentified as autos using three links. [ABSTRACT FROM AUTHOR]

Travel times on the urban roadways are intrinsically uncertain. For known traffic conditions, a wide travel time distribution can be observed. Among all the components of travel times, delays incurred when approaching intersections constitute a large part of travel times that vehicles experience in urban trips. In this article, a model is presented for the delay distribution function for an urban trip with two fixed-time controlled intersections. Most parameters of the model are related to traffic control and flows, which can be directly calibrated from observations. The overflow queue distribution provides important parameters in the delay distribution function that have to be calibrated indirectly from traffic measurements, for example, from the measured delays and flows. Based on the directly observed and estimated model parameters, the delay distribution can be reconstructed. This article discusses the calibration procedure for delays. Two parameter estimation methods, namely, maximum likelihood (ML) and least squares (LS), are applied to estimate the overflow queue distribution from sample data simulated by VISSIM. Results show that estimation accuracy is not so susceptible to the choice of the estimation methods, sampling techniques, and sample size. The proposed delay model is able to successfully capture the delay process. [ABSTRACT FROM PUBLISHER]