An adaptive corridor-wide signal timing optimization methodology for traffic networks with multiple highway-rail grade crossings

Highway-rail grade crossings (HRGCs) and the intersections in their proximity are areas where potential problems in terms of safety and efficiency often arise if only simple or outdated treatments, such as normal signal timing or passive railroad warning signs, are utilized. When it comes to a corridor or a network with multiple HRGCs and heavy train traffic, the problems will be more complicated due to randomness of train arrivals and frequent abruptions of normal signal timing operation of the whole corridor. This dissertation deals with this issue and develops a methodology of signal timing optimization that is specially designed for such a corridor/network. Due to high time and money costs associated with testing the methodology in the field, and safety issues related to field experiments, the proposed optimization program was instead developed, used, and evaluated in a micro-simulation environment using the VISSIM simulation software package. To replicate field conditions, real train data has been collected from the field test bed using advanced train detection technologies and input into the simulation models. Moreover, the stochastic nature of traffic has been considered in the simulation experiment by conducting multiple simulation runs with random seeds.;The developed optimization methodology consists of four parts: a train arrival prediction model, a GA-based optimization program, an advanced preemption strategy, and a calibrated VISSIM simulation model of the study network. The train arrival prediction model is developed using the train data collected from the test bed. Regression models and kinematic models are compared, and regression models outperformed kinematic models in prediction accuracy. A bootstrap method is used to obtain prediction error means and error bounds of the regression models. The GA-based optimization program is developed using Matlab and Visual Basic programming language. The advanced preemptions strategy is coded using VAP, an add-on programming module of VISSIM. The VISSIM simulation model of the study network is calibrated to local traffic conditions using a GA algorithm. A sensitivity analysis is conducted to test the proposed methodology. It can be concluded that the methodology can significantly improve both the safety and efficiency of the study corridor with HRGCs in both offline and online scenarios, however, at the cost of higher network delay. The effects of the prediction errors on the safety and operation of the study network are also analyzed.