Traffic congestion has become a more and more severe problem for metropolitan areas all over the world. Although lots of effort has been devoted to improve the operation on major corridors, how to efficiently and effectively manage traffic based on the existing infrastructure is still a challenging task, and the task seems even more challenging for signalized arterials due to lack of traffic monitoring and data collection system. This research aims to improve the traffic signal performance based on the collected high-resolution traffic signal data and the derived performance measures by the SMART-Signal system developed at the University of Minnesota. In particular, this research focuses on the following three areas: 1) Optimize offsets to reduce congestion Traditionally, offset optimization for coordinated traffic signals fails to consider the stochastic nature of field traffic. Using the archived high-resolution traffic signal data, in this research, we develop an arterial offset optimization model which will take two well-known problems with vehicle-actuated signal coordination into consideration: the early return to green problem and the uncertain intersection queue length problem. To account for the early return to green problem, we introduce the concept of conditional distribution of the green start times for the coordinated phase. To handle the uncertainty of intersection queue length, we adopt a scenario-based approach that generates optimal offsets using a series of traffic demand scenarios as the input to the optimization model. Both the conditional distributions of the green start times and traffic demand scenarios can be obtained from the archived high-resolution traffic signal data. Under different traffic conditions, queues formed by side-street and main-street traffic are explicitly considered in the derivation of intersection delay. The objective of this model is to minimize total delay for the main coordinated direction and at the same time it considers the performance of the opposite direction. The results from the field implementation show that the proposed model can reduce travel delay of coordinated direction significantly without compromising the performance of the opposite approach. 2) Manage oversaturated signal arterials.Under oversaturated traffic conditions, signal timings need to be adjusted accordingly in order to alleviate the detrimental impacts caused by oversaturation. In this research, our focus is to mitigate two types of detrimental effects, signal phase failure with residual queue and downstream queue spillover. Building upon the previous work on the oversaturation severity indices, a maximum-flow based approach to manage oversaturated intersections is developed. The proposed model maximizes the discharging capacity along oversaturated routes, while satisfying the constraints on available green times. We show that a simple forward-backward procedure (FBP) can be used to obtain the optimal solution to the maximum flow model. The forward process aims to increase green time to mitigate oversaturation, therefore improve the throughput for the oversaturated approach; and the backward process aims to gate the traffic at some intersections to prevent residual queues and downstream queue spill-back when the available green time is insufficient. The algorithm is tested using a microscopic traffic simulation model for an arterial network in the City of Pasadena, CA. The results indicate the model can effectively and efficiently reduce oversaturation and improve system performance.3) Mange integrated corridors An integrated control model is proposed to manage traffic congestion along a freeway and a parallel signalized arterial. This model focuses on freeway diversion, which aims to utilize available capacities along parallel arterial routes to reduce network congestion. The potential impact of the diverting traffic to the performance of the arterial route is considered in this research and the maximum flow based signal control model is utilized to manage congestion on the arterial route. The integrated control model does not need the time-dependent traffic demand information as most of previous approaches do and it is suitable for online applications because of its low computation burden. The model is tested using the microscopic traffic simulation in the I-394 and TH 55 corridor in Minneapolis, MN. The results indicate that the model can effectively and efficiently reduce network congestion.