Adaptive traffic control systems (ATCSs), have been in use since the late 1970s but have only received significant attention in the U.S. during the last 10 years. Since 2008, the number of these systems has increased by more than 600%. A brief literature review clearly shows a shortage of comprehensive studies that gather data from numerous field evaluations, categorize those evaluations based on several factors, summarize findings, and develop meaningful conclusions. This study addresses this gap by presenting a framework and a tool which enable comprehensive analysis of ATCSs deployed and evaluated in the U.S. Compared with some previous studies, where the focus was on brief and general experiences with ATCSs, the tool presented in this study allows detailed analysis of ATCS deployments and investigation of numerous criteria important for their evaluations. Relevant ATCS data from deploying agencies and literature are collected, discussed, and used to populate a database of the Assessment Tool for Adaptive Traffic Control ([AT]2C). The main purpose of (AT)2C is to help practitioners and researchers to identify, compare, and assess statistics of relevant ATCS technologies, mainly from the perspective of their field benefits.

Sustainability has become one of the most important goals when optimizing traffic signals. This goal is achieved through utilizing various objective functions to reduce sustainability metrics (e.g., fuel consumption and emissions). However, most available objective functions do not distinguish between the reduction mechanism of various types of emissions. Further, such functions do not consider the compound impact of multiple operational conditions (e.g., road gradient) influencing emissions on the optimized signal plans. This study derives a new Environmental Performance Index representing a surrogate measure for emission estimates that can be used as an objective function in signal timings optimization to reduce emissions under various operational conditions. The Environmental Performance Index is a linear combination of delays and stops. The key factor of the Environmental Performance Index is the emissions-based stop penalty, which represents an emission stop equivalency measured in seconds of delay. This study also uses traffic simulation and emission models to investigate the compound impact of several operational conditions on the stop penalty. Results show that the stop penalty varies significantly with all the investigated conditions and that the stop penalty is unique for different types of emissions. These findings may have significant implications on the current practice of sustainable signal timing optimization.

Pedestrian signal timings are one of the major issues to be addressed when developing signal timing plans. So far, two different treatments of pedestrian timings for coordinated systems exist. The first treatment accommodates pedestrian timing within the cycle. Therefore, it requires longer cycle lengths that usually increase the total delay on the network. In contrast, the second treatment uses shorter cycle lengths sufficient to serve vehicular demand only. The problem with the latter is that the concurrent phase is usually not long enough to serve pedestrians. Thus, with every pedestrian call, a follow-up transition process occurs, affecting cycle lengths and coordination quality. This study develops two novel pedestrian timing treatments to overcome the problems of the traditional ones. Novel pedestrian treatments utilize a cycle length optimized to cover necessary times for vehicular phases, although pedestrian timings may require longer time intervals. However, when pedestrian calls occur, their minimum safety timings are accommodated within such a shorter cycle, and thus the transition process is not required. Our study evaluates the proposed and traditional pedestrian timing treatments using a corridor of five signalized intersections in West Valley City, Utah. Various experiments were conducted in a microsimulation environment VISSIM and collected results were statistically compared. Results show the promising property of the proposed treatments, as they outperformed the traditional pedestrian timing treatments. Therefore, further investigation of these pedestrian timing treatments is warranted. Future research should investigate possibilities of implementing similar treatments to fully actuated control systems on various networks and traffic conditions.

Delay is one of the most important traffic signal performance measures. In coordinated networks, understanding the characteristics of vehicle arrivals is important for coordination purposes and to properly estimate delays. When observed on a cyclical basis in real-time, distinctive arrival patterns can lead to similar delays, which may go undetected by contemporary delay models. This study proposes a set of enhancements to the Incremental Queue Accumulation (IQA) delay model to overcome the limitations of current models. Additionally, this study proposes a hybrid signal performance measure that combines delay and arrival patterns to depict signal performance truthfully. The enhancements to IQA are realised through an algorithm for the identification of distinctive vehicle arrival groups based on high-resolution signal and detection data. The results demonstrate that the proposed model provides reliable delay estimates (MAPE score in range 4.3–11.2%) while reporting a number of traffic arrival characteristics that are not available from the benchmarked models.

Across the world, traffic congestion is increasing with alarming rapidity. Traffic signal control effectiveness, in coordinated networks, is often investigated in relation to the type of vehicle arrivals at the signalized intersections. Recently, several transportation agencies have switched from traditional loop detectors to video detection. When video cameras are accompanied by computer vision, one can extract more information about traffic “dynamics” than by using traditional inductive loop detectors. Collecting arrival times of multiple vehicles after the first arrival at the stop-bar detector might be challenging when using inductive loop detectors (since after the first arrival, detector status is always occupied). However, emerging video detection systems allow tracking of each vehicle’s entrance time in the detection zone, departure time from the detection zone, and the type of vehicle. This information can be used to estimate vehicular arrival and departure times, which then can be fed into machine learning algorithms to estimate arrivals on green (AOG). However, such research ideas have not been documented so far. Thus, this paper presents an estimation model for AOG, which was developed using multigene genetic programming. A robust experimental dataset was collected from a highly calibrated and validated microsimulation model of an 11-intersection corridor in Chattanooga, TN. The results of the model’s performance analysis showed the high accuracy of the training-, testing-, and validation datasets. The practical benefit of this model is that it can be applied to estimate arrival types at intersections where only stop-bar video detection exists.