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.

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.

As urban traffic complexity increases, realistic multimodal simulation environments are essential for evaluating transportation safety and human behavior. This study introduces a novel multimodal, multi-participant co-simulation framework designed to comprehensively model interactions between drivers, bicyclists, and pedestrians. The framework integrates CARLA, a high-fidelity driving simulator, with PTV Vissim, a widely used microscopic traffic simulation tool. This integration was achieved through the development of custom scripts in Python and C++ that enable real-time data exchange and synchronization between the platforms. Additionally, physiological sensors, including heart rate monitors, electrodermal activity sensors, and EEG devices, were integrated using Lab Streaming Layer to capture physiological responses under different traffic conditions. Three experimental case studies validate the system’s capabilities. In the first, cyclists showed a significant rightward lane shift (from 0.94 m to 1.14 m, $p<0.00001$) and elevated heart rates (69.45 to 72.75 bpm, $p<0.00001$) in response to overtaking vehicles. In the second, pedestrians exhibited more conservative gap acceptance behavior at 50 mph vs. 30 mph (gap acceptance time: 3.70 vs. 3.18 s, $p<0.00001$), with corresponding increases in HR (3.54 bpm vs. 1.91 bpm post-event). In the third case study, mean vehicle speeds recorded during simulated driving were compared with real-world field data along urban corridors, demonstrating strong alignment and validating the system’s ability to reproduce realistic traffic conditions. These findings demonstrate the system’s effectiveness in capturing dynamic, real-time human responses and provide a foundation for advancing human-centered, multimodal traffic research.

Improperly scheduled signal timing plans are one of the main reasons for reduced efficiency of traffic signals at coordinated urban arterials. Recently, most urban arterial roads are equipped with intelligent transportation systems devices capable of reporting the collected data on high temporal and spatial resolution, which gives us the opportunity to overcome traditional signal timing planning flaws. Previous studies have proposed methods for scheduling signal timing plans based on small quantities of data combined with various optimization approaches that ultimately require domain expert intervention to fine-tune proposed solutions. Consequently, the signal timing plans scheduling problem is still being addressed without a comprehensive approach. In this study, we propose a novel data-driven procedure based on visual analytics principles to identify the dominant traffic profiles and appropriate scheduling of signal timing plans. The medium-resolution volume data collected over a one-year period on a real-world corridor consisting of 12 signalized intersections were used to validate the proposed methodology. Applied principles from the visual analytics domain allow for a better understanding of traffic characteristics and ultimately alleviate the development of appropriate signal timing schedules. The results show that the proposed method more reliably schedules signal timing plans when compared to current practice.

Existing research has primarily focused on the accuracy of automated pedestrian detection systems, overlooking the consequential environmental impacts arising from false or missed pedestrian detections. To fill these research gaps, this study investigates the emissions and fuel consumption resulting from incorrect pedestrian detection at signalized intersections in microsimulation. To carry out experiments, the authors employ Vissim microsimulation software and the Comprehensive Modal Emission Model (CMEM). For the first time in the literature, missed and false calls are modeled in microsimulation and their environmental impacts are accurately measured. The research highlights the limitations of current automated pedestrian (video) detection systems (APVDSs) technologies in reducing emissions and fuel consumption effectively. While APVDSs offer potential benefits for traffic management, their inability to accurately detect pedestrians undermines their environmental efficacy. This study emphasizes the importance of considering environmental impacts of APVDSs, and challenges the belief that pedestrian recall treatment is the least eco-friendly. Also, the study showed that coupling APVDS or push-button treatments with pedestrian recycle features increases fuel consumption and CO₂ by 10% at the intersections with higher pedestrian demand. By understanding the emissions and fuel consumption associated with incorrect detections, transportation agencies can make more informed decisions regarding the implementation and improvement of APVDS technologies.

To mitigate the negative impacts of traffic congestion, researchers developed adaptive traffic control systems (ATCSs) that adjust signal timings based on real-time traffic conditions. Despite significant attention over the past 15 years, it is not clear how beneficial ATCSs are. The major focus has been on ATCS logics. We shift the focus to examines three ATCS implementation methods by using National Transportation Communications for Intelligent Transportation System Protocols (NTCIPs) in fieldlike operations. The first method represents ATCSs that utilize common controller functionalities to extend or shorten phases; the second involves manipulation of detection calls (to control which phase operates and for how long); the third applies an “adaptive” pattern from the controller’s time-of-day database. We aimed to determine whether any of these methods offers distinct advantages over the others. We developed a “simplistic” adaptive traffic control (ATC) logic for calculating adaptive traffic signal parameters. Note that ATC logic serves as the brain that computes these parameters, whereas ATCSs encompass the ATC logic, NTCIPs, and physical connections required to make the system functional. To conduct experiments involving various demand levels, implementation methods, and frequency of applied changes, a microsimulation model of a network in Chattanooga, TN was coupled with SEPAC m60 controllers in a software-in-the-loop simulation. Regardless of method, frequent corrections of offset and cycle length resulted in limited ATCS benefits, as the controller spent most of the time in “transition mode.” This study aims to inform practitioners and decision makers about the most effective methods for implementing ATCSs in the field.