Mohammed Hadi

Understanding the safety and mobility impacts of connected vehicle (CV) applications is critical for ensuring effective implementations of these applications. This study provides an assessment of the safety and mobility impacts of the red-light violation warning (RLVW), a CV-based application at signalized intersections, under semi-actuated signal control utilizing a hardware-in-the-loop simulation environment. With actuated traffic signal operations, there is uncertainty in the end-of-green information provided to the vehicles using CV messages. The RLVW algorithm lacks the input information about when exactly the phase is going to be terminated since this termination occurs when a gap of a particular length is encountered at the detector. This study investigates a method recently proposed in a national effort to provide an assured green period (AGP) to grant definitive times for when the green interval will end. AGP mitigates the uncertainties related to termination of the green signal associated with actuated signal control and is expected to improve the performance of the CV RLVW application. Results showed that the safety benefits of RLVW without the use of AGP were limited. On the other hand, by introducing AGP with 100% RLVW, the number of red-light running events at signalized intersections was reduced by approximately 92%. However, the application of the AGP, as applied and assessed in this paper, can increase the number of stops and approach delay. This issue will need to be further investigated to determine the optimal setting of the AGP considering both mobility and safety impacts.

A promising traffic management strategy is the application of special signal timing plans on alternative routes during freeway incidents. The development of such plans requires the estimation of the route diversion during incident conditions. This study utilizes a data analytic approach to support the estimation of the diversion rate during incidents and to use this information as an input to the development of special signal timing plans during freeway incidents. First, a method is developed to predict the rate of traffic diversion caused by incidents based on the freeway mainline detector data combined with incident data. The diversion prediction method utilizes a combination of cumulative volume analysis, clustering analysis, and predictive data analytics. Three predictive data analytic methods: linear regression, multilayer perceptron, and support vector machine models, are investigated to predict diversion as a function of incident attributes. Next, a methodology is proposed to develop special signal plans to manage the demand surge on the diversion routes without deteriorating the intersection’s overall performance. The evaluation of the developed methodology indicates that it can significantly reduce the delays on the alternative routes.