Signalized intersections play a vital role in urban transportation systems by ensuring the safe and efficient movement of both pedestrians and vehicles. Thus, the design and operations of signalized intersections are crucial for safety and mobility of all users. Traditionally, pedestrian signal operations have followed two methods: either automatically recalling pedestrian demand during each phase, assuming constant pedestrian presence, or requiring pedestrians to press a push button to activate a pedestrian phase. Recently, automated pedestrian video detection systems (APVDSs) have been introduced, which replaces the need for push buttons by automatically detecting pedestrians in designated zones and placing the necessary phase call.

APVDSs use strategically placed overhead cameras to capture images of pedestrians at intersections. These images are then analyzed using advanced vision processors and machine learning algorithms, which identify pedestrians within virtual detection zones. This automated system provides significant data about detected pedestrians, yet its accuracy can be influenced by factors such as the quality of the machine learning algorithms, lighting conditions, weather, and camera placement.

As is common with most signal technology, both pedestrian push buttons and APVDSs have their advantages and disadvantages. Push buttons may cause confusion among pedestrians, resulting in incorrect usage and jaywalking. APVDSs theoretically address these issues by determining the need for a pedestrian phase without requiring pedestrians to press the push button. However, challenges arise because pedestrians exhibit different behaviors compared to vehicles, and missed calls can lead to unsafe pedestrian behaviors. For instance, vehicles are always oriented in the direction of travel, whereas pedestrians do not always wait to cross oriented towards the crosswalk of interest.

It is worth mentioning that initially video detection systems were primarily developed for vehicle detection and have been the subject of extensive research so far. Despite technological advancements, these systems still face challenges in adverse weather and low-light conditions. While many studies have examined the performance of video detection systems for vehicles (Chitturi et al., 2010; Grenard et al., 2001; Martin et al., 2004; Medina et al., 2008; Medina et al., 2009; Rhodes et al., 2005), there has been less focus on the accuracy issues associated with APVDSs for pedestrians (Larson et al., 2020). Research has generally evaluated APVDS performance (Gavric et al., 2024; Gavric et al., 2024).

Wu et al. simulated vehicle-pedestrian conflicts in Vissim to examine whether SSAM and Vissim can effectively estimate field conflicts at signalized intersections Wu et al. (2018). While there is extensive research investigating pedestrian-vehicle conflicts in microsimulation, no study has thoroughly quantified the impact of false or missed pedestrian video detection calls on surrogate safety measures.

To address this gap, our study proposes a novel approach to analyzing the effects of missed and false pedestrian calls on surrogate safety in a microsimulation environment. Understanding these impacts is critical for enhancing the safety of traffic signal operations and urban planning process. This research aims to explore the consequences of missed pedestrian calls (where a pedestrian is present but not detected) and false pedestrian calls (where a phase call is placed without a pedestrian being present) on surrogate safety at signalized intersections.

Specifically, our objectives include comparing the effects of missed and false calls in automated pedestrian detection, pedestrian recall operations, and push button operations, with or without the pedestrian recycle feature of the controller, at various times of the day and under different pedestrian and vehicular demands on surrogate safety measures. A microsimulation software Vissim is coupled with Surrogate Safety Assessment Model (SSAM) to conduct experiments, allowing us to measure and analyze the impact of these events in a controlled environment that closely replicates real-world conditions.

The remainder of this paper is structured as follows: first, describe our methodology, including data collection, microsimulation setup, and experimental procedures. We then present and discuss our experimental results in detail. Finally, we conclude with remarks and suggestions for future research opportunities.

To achieve the objectives outlined in the introduction, a framework consisting of Vissim, video detection inaccuracies, and SSAM was created.

The evaluation was conducted using some of the surrogate safety measures (number of conflicts, TTC, PET) and some well established performance metrics such as average pedestrian delay for overall pedestrians (correct and missed) and missed pedestrians.

On the left side of Figure 8 , the average total number of vehicle-pedestrian conflicts is presented, along with average number of right and left turnig vehicle conflicts with pedestrians. It is evident that the lowest conflicts occur with the Recall treatment at Butler/40th, an intersection equipped with VDS that generates many false calls. Conversely, at intersections with overall lower pedestrian demand and more missed calls, the APVDS treatments perform better in terms of conflict mitigation. The average number of conflicts we reported specifically refers to vehicle-pedestrian conflicts.

Another reason pedestrian recall and PB_NPR yield the best results is because, in our simulation, the pedestrian green phase does not extend for the full duration of the maximum green for the concurrent vehicle phase. Consequently, some vehicles turn without being stopped due to pedestrian presence. In scenarios with pedestrian recycle, the flashing ‘don't walk’ interval can occur twice with shorter walk times. However, it is important to note that the absence of conflicts in simulation does not guarantee that pedestrians would not jaywalk in reality. Simulated environments, while useful for controlled analysis, cannot fully capture the complex behaviors and decision-making processes of pedestrians in real-world settings.

Additionally, pedestrian volumes at the studied locations should be considered: approximately 20 pedestrians per hour at Penn and 40 pedestrians per hour at Butler. Given these volumes, the occurrence of 3-7 conflicts per number of pedestrians crossing seems significant. The conflicts include both crossing conflicts involving right-turning vehicles and pedestrians, as well as permissive left turns where applicable (Figure 8).

On the right side of Figure 8 , the maximum speed during conflicts is shown. This represents the highest speed of either vehicle throughout the conflict (i.e. while the TTC is below the specified threshold). It can be observed that the highest maximum speed occurs at Butler/40th in the PB_PR scenario, closely followed by the PB_NPR scenario. At Penn/40th, the APVDS_NPR and APVDS_PR scenarios exhibit the highest maximum speeds, making them the less safe in terms of conflict speed.

For instance, when we examine pedestrian delay for missed and overall pedestrians at the Penn/40th intersection (see Figure 9), we observe delays exceeding 100 seconds. Such significant delays are concerning because, according to both the HCM 2000 and the NCHRP Guide to Pedestrian Analysis from 2022, delays of this magnitude suggest a high likelihood of pedestrians resorting to jaywalking (Reilly, 1997; Ryus et al., 2022) (Table 2). In real-world scenarios, pedestrians facing long wait times might become impatient and choose to cross the street unsafely, increasing the risk of accidents.

To fully understand the impacts of APVDS on surrogate safety, it is important to also consider the delays experienced due to missed pedestrian calls. These missed pedestrians occur only in APVDS scenarios, as it is assumed that all pedestrians press the push button in PB scenarios (Figure 9). The delay for missed pedestrians is high in the APVDS scenarios, exceeding 150 seconds at the Penn/40th intersection (as shown in Figure 9). Understandably, this delay is considerably lower at the Butler/40th intersection, where the system has a lower rate of missed pedestrian calls. Consequently, the overall pedestrian delay is comparable across all pedestrian signal treatments at the Butler/40th intersection.

Before delving into the PET and TTC results, it is important to note that the orange ‘X’ symbol in Figure 10 and Figure 11 represents the mode, while the blue circle represents the median for each scenario. It can be observed that the mode and median values are generally similar for both TTC and PET. When the mode (the most frequent value) and the median (the central value) are close or similar, this typically suggests that the dataset is not heavily skewed to one side (left or right). This may indicate that the data is normally distributed or, at the very least, roughly symmetric.

Figure 10 shows average TTC in seconds across different scenarios. At both intersections, TTC values generally tend to be lower during peak traffic demands compared to off-peak, indicating that collisions are more likely during busier times.

At Butler/40th, the APVDS_NPR and APVDS_PR treatments have higher TTC values during off-peak hours. The PB_NPR treatment shows the lowest TTC during off-peak hours, indicating higher collision risk compared to other treatments. During peak hours, the Recall treatment shows the highest TTC, while APVDS_PR has the lowest, indicating varied effectiveness based on traffic volume. The relatively low performance of PB_NPR during off-peak hours at Butler/40th is notable and warrants further investigation.

At Penn/40th, Recall treatment also exhibits high TTC values during off-peak hours, similar to Butler/40th, indicating consistent performance across different locations. The high TTC of Recall treatment across both intersections and traffic demands suggests it is particularly effective and reliable.

Figure 11 shows the average Post-Encroachment Time (PET) in seconds for different pedestrian timing treatments at two intersections: Butler/40th and Penn/40th. PET is a crucial safety measure representing the time interval between a vehicle and a pedestrian crossing the same point; higher PET values indicate safer conditions as they reflect greater separation between vehicles and pedestrians.

However, the differences in PET values between the intersections also highlight that intersection-specific factors can influence the effectiveness of pedestrian timing treatments. For instance, variations in traffic volume, pedestrian flow, signal timing configurations, and physical layout could all impact how well a particular treatment performs. Transportation agencies should consider the unique characteristics of each intersection, including traffic patterns, pedestrian behavior, and physical design, to optimize safety outcomes.

This study presents a thorough evaluation of inaccuracies in automated pedestrian detection within a high-fidelity microsimulation environment, focusing on their implications for safety measures at signalized intersections. It marks the first instance in literature where both missed and false pedestrian calls were adequately simulated and assessed, offering crucial insights into pedestrian safety at signalized intersections. Through extensive evaluation, the following key findings were established:

This research addresses a gap in the existing literature by introducing a novel methodology to evaluate various APVDS systems alongside alternative pedestrian signal treatments. Its practical significance lies in aiding transportation agencies in assessing APVDS safety through microsimulation. The primary limitation of this study is that the results cannot be generalized to all APVDSs, as they may have different missed and false call percentages compared to those considered in this study. Future research should explore the safety aspects of different automated pedestrian systems. Additionally, it should consider the percentage of pedestrians who do not press the push button and compare field-observed SSMs with the outputs from SSAM.

The authors report no competing interests.

Not applicable.

No external funding was used in this research.

The authors declare that no generative AI was used in this work.