Emergency response vehicles (ERVs) frequently navigate congested traffic conditions to reach their destinations as quickly as possible. In this report, several efforts performed by the research group are described, including micro-simulation, field-testing, and optimization, to determine mechanisms for facilitating safe and efficient ERV travel. Micro-simulation of a network based on the Northern Virginia Connected Vehicle Test Bed examined the effect of a variety of factors on ERV travel time, including the presence of vehicle-to-vehicle (V2V) communication, traffic volumes, cycle length, ERV speed distributions, non-ERV speed distributions, and traffic signal preemption. The results indicated that V2V communication could reduce travel time for an ERV in congested traffic conditions. The research group developed a V2V communication prototype to alert non-ERVs of an approaching ERV by triggering a flash of the infotainment system, followed by audible instructions to move to the left, move to the right, or stay put. Twelve drivers, aged 25 to 50, tested the V2V prototype on the Northern Virginia Connected Vehicle Test Bed during off-peak periods. Data from this field test and associated questionnaires were used to investigate reaction time to the instructions. The estimated reaction times using the developed model varied from 1.4 to 5.8 seconds. A mixed-integer nonlinear program (MINLP) optimization model was formulated to maximize the forward progress of ERVs by sending information to ERVs and non-ERVs within a given road segment. A single set of instructions was sent to each non-ERV, assigning them to a location out of the ERVs path. Numerical case analysis for a small, uniform section of roadway with a limited number of non-ERVs revealed the model is capable of optimizing the behavior of non-ERVs to maximize the speed of the ERV.