A Real-Time Offset Transitioning Algorithm for Coordinating Traffic Signals

This report introduces an adaptive real-time offset transitioning algorithm that can be viewed as an integrated optimization approach designed to work with traditional coordinated-actuated systems. The Purdue Real-Time Offset Transitioning Algorithm for Coordinating Traffic Signals (PRO-TRACTS) adds to the controllers the ability to adaptively change their offsets in response to changes in traffic pattern, providing an intermediate solution between traditional coordinated-actuated control systems and adaptive control systems. To facilitate implementation, a new National Transportation Communication for ITS Protocol (NTCIP) object for capturing detector actuation at the controller’s level is defined in this report. The unique cycle-based tabulation of volume and occupancy profiles at upstream detectors is used by a newly defined metric to examine the existence of shockwaves generated due to a poor offset downstream. The procedure is modeled after the analysis of variance testing. This procedure is performed on cycle-by-cycle basis to evaluate the offset performance and adjust it accordingly. Simulations of two case studies revealed 0-16% savings in total travel time and up to 44% saving in total number of stops for the coordinated movement when applied to systems with poor offsets. The algorithm is best suited for arterials with primarily through traffic. Heavy movements from the side streets onto the arterial make it difficult for the algorithm to determine which movement should be favored. PRO-TRACTS mitigates problems such as early-return-to-green, waiting queues, and improperly designed offsets using current setups of traffic signals/detectors in the US. The algorithm capitalizes on the existing knowledge and familiarity of traffic engineers and personnel with the current actuated control system to provide a cost-effective solution to improving signal coordination. Future research is needed to improve the stability of the algorithm with highly dispersed platoons and oscillatory traffic patterns caused by situations such as controllers skipping phases due to light traffic volume. It is also recommended that the algorithm should be extended to improve two-way signal progression instead of one-way progression

Configuration Methodology for Traffic-Responsive Plan Selection: A Global Perspective

Although several studies have shown the potential great benefits of traffic-responsive plan selection (TRPS) control, time-of-day operation continues to be the primary method used to select patterns for signal control applications. This practice could be largely attributed to the minimal guidelines available on the setup of the TRPS mode. An innovative framework for TRPS system setup is provided, and guidelines for implementing TRPS in a simplified manner are shown. The guidelines, developed at Texas Transportation Institute (TTI), use a comprehensive approach that incorporates a multiobjective evolutionary algorithm and a supervised discriminant analysis. Engineers can directly implement the guidelines presented as an initial design. Hardware-in-the-loop simulation is used to illustrate the performance of TTI’s TRPS configuration methodology

Thoughts on the future of artificial intelligence and transportation

In this concluding chapter of the Circular, we have asked members of our committee to share with the readers their personal thoughts on the future of AI and transportation. We are pleased herein to present select quotes from the committee members, organized alphabetically, on that topic

Driver Behavior in Traffic

DTFH61-09-H-00007Existing traffic analysis and management tools do not model the ability of drivers to recognize their environment and respond to it with behaviors that vary according to the encountered driving situation. The small body of literature on characterizing drivers behavior is typically limited to specific locations (i.e., by collecting data on specific intersections or freeway sections) and is very narrow in scope. This report documented the research performed to model driver behavior in traffic under naturalistic driving data. The research resulted in the development of hybrid car-following model. In addition, a neuro-fuzzy reinforcement learning, an agent-based artificial intelligence machine-learning technique, was used to model driving behavior. The naturalistic driving database was used to train and validate driver agents. The proposed methodology simulated events from different drivers and proved behavior heterogeneities. Robust agent activation techniques were also developed using discriminant analysis. The developed agents were implemented in VISSIM simulation platform and were evaluated by comparing the behavior of vehicles with and without agent activation. The results showed very close resemblance of the behavior of agents and driver data. Prototype agents prototype (spreadsheets and codes) were developed. Future research recommendations include training agents using more data to cover a wider region in the Wiedemann regime space, and sensitivity analysis of agent training parameters

Air pollution perception in ten countries during the COVID-19 pandemic

NTNU Norwegian University of Science and TechnologypublishedVersionPaid Open AccessUNIT agreemen