A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner

Travel time estimation in congested urban networks using point detectors data

A model for estimating travel time on short arterial links of congested urban networks, using currently available technology, is introduced in this thesis. The objective is to estimate travel time, with an acceptable level of accuracy for real-life traffic problems, such as congestion management and emergency evacuation. To achieve this research objective, various travel time estimation methods, including highway trajectories, multiple linear regression (MLR), artificial neural networks (ANN) and K –nearest neighbor (K-NN) were applied and tested on the same dataset. The results demonstrate that ANN and K-NN methods outperform linear methods by a significant margin, also, show particularly good performance in detecting congested intervals. To ensure the quality of the analysis results, set of procedures and algorithms based on traffic flow theory and test field information, were introduced to validate and clean the data used to build, train and test the different models

Developing Sampling Strategies and Predicting Freeway Travel Time Using Bluetooth Data

Accurate, reliable, and timely travel time is critical to monitor transportation system performance and assist motorists with trip-making decisions. Travel time is estimated using the data from various sources like cellular technology, automatic vehicle identification (AVI) systems. Irrespective of sources, data have characteristics in terms of accuracy and reliability shaped by the sampling rate along with other factors. As a probe based AVI technology, Bluetooth data is not immune to the sampling issue that directly affects the accuracy and reliability of the information it provides. The sampling rate can be affected by the stochastic nature of traffic state varying by time of day. A single outlier may sharply affect the travel time. This study brings attention to several crucial issues - intervals with no sample, minimum sample size and stochastic property of travel time, that play pivotal role on the accuracy and reliability of information along with its time coverage. It also demonstrates noble approaches and thus, represents a guideline for researchers and practitioner to select an appropriate interval for sample accumulation flexibly by set up the threshold guided by the nature of individual researches’ problems and preferences. After selection of an appropriate interval for sample accumulation, the next step is to estimate travel time. Travel time can be estimated either based on arrival time or based on departure time of corresponding vehicle. Considering the estimation procedure, these two are defined as arrival time based travel time (ATT) and departure time based travel time (DTT) respectively. A simple data processing algorithm, which processed more than a hundred million records reliably and efficiently, was introduced to ensure accurate estimation of travel time. Since outlier filtering plays a pivotal role in estimation accuracy, a simplified technique has proposed to filter outliers after examining several well-established outlier-filtering algorithms. In general, time of arrival is utilized to estimate overall travel time; however, travel time based on departure time (DTT) is more accurate and thus, DTT should be treated as true travel time. Accurate prediction is an integral component of calculating DTT, as real-time DTT is not available. The performances of Kalman filter (KF) were compared to corresponding modeling techniques; both link and corridor based, and concluded that the KF method offers superior prediction accuracy in link-based model. This research also examined the effect of different noise assumptions and found that the steady noise computed from full-dataset leads to the most accurate prediction. Travel time prediction had a 4.53% mean absolute percentage of error due to the effective application of KF