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.

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.

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.

Numerous studies have assessed video detection systems, but automated pedestrian video detection systems (APVDS) have not received as much attention. Although some researchers have evaluated the accuracy of APVDS, none quantified the consequential effects that false or missed pedestrian calls have on critical vehicular and pedestrian performance metrics. To address this gap, this study introduces a microsimulation-based approach to analyze the impact of missed and false pedestrian calls on signalized intersection operation. This method employs simulation inputs that mimic various detection scenarios from the field, allowing for greater flexibility in analysis. Additionally, the study compares the impacts of incorrect calls under different operational strategies, including APVDS with and without the pedestrian recycle feature of the controller, pedestrian recall operations, and common push-button operations with or without pedestrian recycle. Results show that the proposed microsimulation approach is a valuable tool for future studies, especially considering the idiosyncratic nature of specific sites where the percentage of false and missed calls depends on local conditions. The study reveals that neither of the two analyzed APVDS systems is mature enough to work as a standalone option. Microsimulation results show that even though the single-camera video detection system (VDS) appears to be a safer option than the double-camera VDS, it fails to provide any benefits in pedestrian and vehicular delay over the pedestrian recall operations. This research may be of practical significance as transportation agencies could utilize the proposed microsimulation approach to evaluate APVDSs effectively. Knowing the strengths and limits of APVDS can help agencies make informed implementations.

The design and operations of signalized intersections are critical for the safety and mobility of all road users. Traditionally, video detection systems have surpassed conventional inductive loops in vehicle detection, offering advantages such as user-friendly interfaces, easy installation, versatile applications, and cost-effective maintenance. Recent technological advancements have expanded the capabilities of video detection systems to include automated pedestrian detection. Pedestrian timing treatment at signalized intersections typically involves two approaches: assuming pedestrian demand during each phase (pedestrian recall operations) or activating pedestrian demand when a button is pressed. Automated Pedestrian Video Detection Systems (APVDS) represent a newer approach where pedestrian calls are automatically initiated upon detecting pedestrians within the detection zone. Both pedestrian push buttons and APVDSs have their pros and cons. While push buttons can cause confusion and incorrect usage among pedestrians, APVDSs theoretically address these issues by autonomously determining pedestrian phase needs. However, challenges arise due to differing pedestrian behaviors compared to vehicles, potentially leading to missed or false calls and unsafe pedestrian actions. This study examines the impact of two APVDS issues: missed pedestrian calls and false pedestrian calls. It also compares the effects of incorrect calls under various operational strategies, including APVDS with and without controller pedestrian recycle, pedestrian recall operations, and push-button operations, with and without pedestrian recycle, across different times of the day to replicate varying vehicular and pedestrian demands. Using Vissim microsimulation software and Surrogate Safety Assessment Model (SSAM), experiments were conducted to replicate field conditions closely. This microsimulation approach enables the measurement and analysis of missed and false calls' impact on surrogate safety measures, providing valuable insights challenging to obtain in real-world conditions. The results contribute to pedestrian traffic signal strategies and enhancing pedestrian safety at signalized intersections in urban environments.