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Conventional Transit Signal Priority (TSP) controls often reach the limitation for arterials accommodating heavy bus flows since the priority function can significantly increase delay at minor streets. Under such conditions, a proper signal progression plan that accounts for the benefits of buses may offer the potential to improve the reliability of bus operations and increase the bus ridership. This study proposes a bus-based progression model to reduce the delay of buses on local arterials. Given the cycle length and green splits at each intersection, the bus-based progression model, grounded on the same notion as conventional signal progression methods, considers the operational characteristics of transit vehicles, such as the impact of bus dwell time and the capacity constraints at bus stops. Also, to deal with the stochastic nature of dwell time, this study introduces additional constraints to maximize the percentage of buses which can stay within the green band after leaving bus stops. Taking an arterial with five intersections and three two-way bus stops as an example, this study applies VISSIM as an unbiased tool for model evaluation. The simulation results demonstrate that the proposed model can significantly reduce bus passenger delays and the average person delays for the entire arterial, compared with the conventional progression models. [ABSTRACT FROM AUTHOR]

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]

Mainstream traffic flow control (MTFC) on freeways, enabled via variable speed limits (VSL), is a traffic management tool capable of avoiding or, at least, postponing congestion formation on freeway bottlenecks, and may complement other existing measures. Two simple feedback controllers for local MTFC via VSL are proposed. Both controllers are evaluated and compared to a previously developed local MTFC feedback cascade controller and to a sophisticated optimal control approach. All feedback controllers rely on readily available real-time measurements, are simple and robust, and take into account a number of practical and safety restrictions; therefore, they are suitable for implementation in the field as a new or as part of an existing intelligent transportation systems infrastructure. The evaluations are conducted for a hypothetical freeway network by use of a validated second-order macroscopic traffic flow model. The results show that the feedback controllers exhibit a satisfactory control behavior and, indeed, approach the optimal control results. Recommendations for the operation of the feedback controllers are given. [ABSTRACT FROM AUTHOR]