Corridor mobility improvements require a new approach to corridor management planning and operations. Recent investigations are aimed at improving the safety and efficiency of existing transportation systems by integrating state-of-the-art operational analysis (such as microsimulation) into more traditional corridor planning. One of the important elements in developing corridor management improvements is better bottleneck analysis. Such analyses play a crucial role in corridor management planning for both performance assessment and simulation model calibration. New approaches are proposed for bottleneck identification and calibration in simulation. Identification is conducted with percentile speeds based on data from multiple days. It turns out that this method is more appropriate for urban congested freeways than use of single-day data. The algorithm for bottleneck calibration represents the first attempt to rigorously calibrate bottlenecks in microsimulation. It is a three-step process–including visual assessment, bottleneck area matching, and detailed speed calibration–aiming to calibrate bottlenecks in three levels of detail. With the I-880 corridor network in the San Francisco Bay Area of California, it has been shown that the identification method can adequately identify corridor bottlenecks; the calibration procedure complements and improves the current practice of simulation calibration.
In this study, a new vehicle classification algorithm was developed with inductive loop signature technology. There were two steps to the proposed algorithm. The first step was to use the Haar wavelet to transform and reconstruct inductive vehicle signatures, and the second step was to group vehicles into FHWA vehicle types through the use of the k nearest neighbor (KNN) approach with a Euclidean distance classifier. To determine the proper proportion of the wavelet to apply for reconstruction and feature extraction, transformed signatures were examined with percentages of large components of their corresponding wavelets. To implement the KNN approach, a library of vehicle signature templates for each FHWA vehicle class was composed. The proposed vehicle classification algorithm demonstrated promising classification results, with a 92.4% overall accuracy. The algorithm can be applied to the real world without the concerns about recalibration and transferability that arise with the use of signature data from single loops. Two additional vehicle classification schemes were applied for performance evaluation. For the inductive signature performance evaluation classification scheme, which aimed to facilitate emission analysis and easy interpretation, the overall accuracy was 94.1%. For the axle-based vehicle classification scheme proposed in this project, which aimed to group vehicles by use and the number of axles, the overall accuracy was 93.8%. Future research will focus on refinement of the signature template library for each FHWA vehicle type to further improve the performance of the proposed vehicle classification algorithm. The selection of the value of k for the KNN approach will be investigated also.
The authors propose a real-time adaptive signal control model that aims to maintain the adaptive functionality of actuated controllers while improving the performance of traffic-actuated control systems. In this model, a flow-prediction algorithm is formulated to estimate the future vehicle arrival flow for each signal phase at the target intersection on the basis of the available signal-timing data obtained from previous control cycles. Optimal timing parameters are determined on the basis of these estimations and are used as signal-timing data for further optimizations. To be consistent with the operation logic of existing signal-control devices, this model is developed to optimize the basic control parameters that can be found in modern actuated controllers. Microscopic simulation is used to test and evaluate the proposed control model in a calibrated network consisting of 38 actuated signals. Simulation results indicate that this model has the potential to improve the performance of the signalized network under different traffic conditions.
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