Pedestrian-vehicle Interactions Research Articles

Safety evaluation of pedestrian-vehicle interaction at signalized intersections in Changsha, China

The purpose of this study is to investigate the unsafe conditions of pedestrian crossings at signalized intersections under the mixed traffic environment in Changsha, China. For this purpose, based really pedestrian-related crash data of Changsha city and visualized crash geographic information, six typical urban signalized intersections were selected. The peak and off-peak surveillance videos of six signalized intersections were collected via field surveys, 1,070 conflicts were extracted through using professional trajectory tracking software Traker to track pedestrian and vehicle trajectories. PSMT (Pedestrian Safety Margin Time) was used as an indicator to identify the severity of conflicts, and manually recorded the characteristics of pedestrian crossing behavior, vehicles and roads during the conflict. An Ordered Probit model was established to analyze the risk factors that led to different severities of the conflict between pedestrians and vehicles. The model outcome indicates a significant relationship between the severity level and characteristics of pedestrian behavior, vehicles and conflict. Moreover, it was identified that, in addition to the above characteristics, roadway characteristics significantly influenced likelihood of severe pedestrian-vehicle conflict. Finally, these factors are discussed, and suggestions for improving the pedestrian traffic environment are proposed from different perspectives.

A novel agent-based framework for evaluating pedestrian safety at unsignalized mid-block crosswalks

A critical safety problem in road networks is the conflicts on unsignalized crosswalks. Thus, a proactive approach to assess pedestrian safety performance is required. Simulating the microscopic road user behavior in a virtual platform is one of the typical approaches. However, current simulation tools are not flexible enough to properly reproduce various behaviors of pedestrians and drivers considering their interactions with the road environment and other road users. Therefore, the objective of this paper is to propose a novel agent-based framework for evaluating pedestrian safety at unsignalized crosswalks. Unsignalized mid-block crosswalks with refuge islands (UMCR) are considered as an example facility to implement the proposed framework, where relevant behavioral elements such as the reaction time, visual field with obstacles, and minimum safety margin time are addressed. Not only the pedestrian-vehicle interaction is modeled but also the vehicle-vehicle interaction is considered. Empirical validation on a UMCR shows that the proposed framework can reproduce reliable distributions of the post encroachment time compared to the observed distributions within an acceptable error range. The sensitivity analysis reveals that the high reaction time of drivers, small safety margin time, and visual obstacles near crosswalks increase the probability of serious conflicts. The results also show that the framework can reproduce traffic crashes under assumed extreme road conditions.

Validating a methodology for understanding pedestrian – vehicle interactions: A comparison of video and field observations

The successful deployment of automated vehicles (AVs) will depend on their capacity to travel within a mixed traffic environment, adopting appropriate interaction strategies across different scenarios. Thus, it is important to gain a detailed understanding of the specific types of interactions that are most likely to arise. The overall purpose of this paper was to present a methodology designed to facilitate the systematic observation of pedestrian-vehicle interactions, and to validate its use for both onsite and video based observations. A detailed observation protocol was developed to capture pedestrian and vehicle movement and communication patterns across four interaction phases. Onsite coders completed field observations of 50 pedestrian-vehicle interactions at a UK intersection, while video coders observed the same interactions recorded through a wireless camera mounted on a nearby rooftop. Results show that the observation protocol provides a reliable methodology for capturing patterns of pedestrian-vehicle interactions, with high levels of inter-coder consistency emerging across all categories of codes. A detailed examination of the specific descriptors selected suggests that onsite coding may be particularly beneficial in situations where the aim is to capture any explicit, and perhaps subtle, communication cues, whereas video based coding may be more appropriate in situations where exact sequences of behaviours or measurements of timings are desired. It is anticipated that this type of observation tool will be beneficial for AV developers to increase their understanding of how to interpret the movements of road users, along with increasing knowledge of when implicit and explicit communication techniques should be used.

CPX based synthesis for binaural auralization of vehicle rolling noise to an arbitrary positioned stander-by receiver

Virtual reality is becoming an important tool for studying the interaction between pedestrians and road vehicles, by allowing the analysis of potentially hazard situations without placing subjects in real risk. However, most of the current simulators are unable to accurately recreate traffic sounds that are congruent with the visual scene. This has been recognized as a fault in the virtual audio-visual scenarios used in such contexts. This study proposes a method for delivering a binaural auralization of the noise generated by a moving vehicle to an arbitrarily located moving listener (pedestrian). Building on previously developed methods, the proposal presented here integrates in a novel way a dynamic auralization engine, thus enabling real-time update of the acoustic cues in the binaural signal delivered via headphones. Furthermore, the proposed auralization routine uses Close ProXimity (CPX) tyre-road noise signal as sound source input, facilitating the quick interchangeability of source signals, and easing the noise collection procedure. Two validation experiments were carried out, one to quantitatively compare field signals with CPX-derived virtual signal recordings, and another to assess these same signals through psychoacoustic models. The latter aims to assure that the reproduction of the synthesized signal is perceptually similar to one occurring on pedestrian/vehicle interactions during situations of street crossing. Discrepancies were detected, and emphasized when the vehicle is within close distance from the receiver (pedestrian). However, the analysis indicated that these pose no hindrance to the study of vehicle–pedestrian interaction. Improvements to the method are identified and further developments are proposed.

Do detection-based warning strategies improve vehicle yielding behavior at uncontrolled midblock crosswalks?

Pedestrians being the most vulnerable road users account for a large proportion of injuries and fatalities from road traffic crashes. Pedestrians are involved in around one-third of the whole fatalities coming from the road traffic crashes in the state of Qatar. In areas with uncontrolled midblock crosswalks, it is very crucial to improve drivers’ alertness and yielding behavior. The objective of this driving simulator study is to investigate the impact of pedestrian detection strategies and pavement markings on driving behavior at high-speed uncontrolled crosswalks. To this end, an untreated condition (i.e. Control) was compared with three treatment conditions. The three treated conditions included two detection strategies, i.e., advance variable message sign (VMS) and LED lights, and road markings with pedestrian encircled. Each condition was tested with a yield/stop controlled marked crosswalk for two situations, i.e. with vs. without a pedestrian present. The experiment was conducted using the driving simulator at Qatar University. In total, 67 volunteers possessing a valid Qatari driving license participated in the study. Different analyses were conducted on vehicle-pedestrian interactions, driving speed, variations in acceleration/deceleration and lateral position. The results showed that both the LED and VMS conditions were helpful in increasing yielding rates up to 98.4 % and reducing the vehicle-pedestrian conflicts significantly. Furthermore, both treatments were effective in motivating drivers to reduce vehicle speed in advance. Considering the findings of this study, we recommend LED and VMS conditions as potentially effective solutions to improve safety at yield/stop controlled crosswalks.

VEHICLE–PEDESTRIAN INTERACTIONS INTO AND OUTSIDE OF CROSSWALKS: EFFECTS OF DRIVER ASSISTANCE SYSTEMS

This study aimed to analyse the driver’s behaviour during the interaction with a pedestrian crossing into and outside the zebra crossing, and evaluate the effectiveness of two kinds of Advanced Driver Assistance System (ADAS) that provided to the driver an auditory alert, and a visual alert to detect the pedestrian. 42 participants joined the experiment conducted using the fixed-base driving simulator of the Department of Engineering (Roma Tre University). They experienced different crossing conditions (legal and illegal) and ADAS conditions (no ADAS, visual warning and auditory warning) in an urban scenario. The parameters Time-To-Arrive (TTA) and Speed Reduction Time (SRT) were obtained from the drivers’ speed profiles in the last 150 m in advance of the conflict point with the pedestrian. Results clearly showed the criticality of illegal crossings. When the pedestrian crossed outside of the crosswalk, the highest number of collision occurred and the ANalysis Of VAriance (ANOVA) returned significant effects on both the dependent variables TTA and SRT, highlighting the higher criticality of the vehicle–pedestrian interaction and the more abrupt yielding manoeuvre. Positive effects (the vehicle–pedestrian interaction was less critical and the yielding manoeuvre was smoother) emerged for both the driver assistance systems, although not statistically significant. Besides, both the driver assistance systems positively affected the behaviour of the average cautious drivers. No significant effects of the warning systems were recorded on the aggressive drivers, which because of their behavioural characteristics ignored the warning alarm. In addition, no significant effects of the warning systems were recorded for the very cautious drivers, which adjusted their behaviour even before the alarm trigger. Finally, the outcomes of the questionnaire submitted to the participants highlighted the clear preference for the auditory warning, probably because of the different physical stimuli that are solicited by the warning signal. The results confirm that adequate pedestrian paths should be planned to avoid jaywalker conditions, which induce the driver to assume critical driving behaviour and provide useful findings of the effectiveness of driver assistance systems for pedestrian detection.

Literature review on the conflict analysis of vehicle-pedestrian interactions

An alternative to traffic safety analysis based on historical crash data the use of non-crash events is becoming more popular thanks to the rapid improvement in video-based vehicle trajectory processing. By means of Surrogate Measures of Safety (SMoS) in traffic conflict studies, the most critical elements on the road network can be identified and the probability of accidents can be proactively determined. This paper aims to summarize the state-of-the-art research regarding the analysis of pedestrian-vehicle interactions at unsignalized crossings, to synthetize the previous studies using Surrogate Measures of Safety (SMoS), and to identify the research gaps.

Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis

Introduction: Although stop signs are popular in North America, they have become controversial in cities like Montreal, Canada where they are often installed to reduce vehicular speeds and improve pedestrian safety despite limited evidence demonstrating their effectiveness. The purpose of this study is to evaluate the impact of stop-control configuration (and other features) on safety using statistical models and surrogate measures of safety (SMoS), namely vehicle speed, time-to-collision (TTC), and post-encroachment time (PET), while controlling for features of traffic, geometry, and built environment. Methods: This project leverages high-resolution user trajectories extracted from video data collected for 100 intersections, 336 approaches, and 130,000 road users in Montreal to develop linear mixed-effects regression models to account for within-site and within-approach correlations. This research proposes the Intersection Exposure Group (IEG) indicator, an original method for classifying microscopic exposure of pedestrians and vehicles. Results: Stop signs were associated with an average decrease in approach speed of 17.2 km/h and 20.1 km/h, at partially and fully stop-controlled respectively. Cyclist or pedestrian presence also significantly lower vehicle speeds. The proposed IEG measure was shown to successfully distinguish various types of pedestrian-vehicle interactions, allowing for the effect of each interaction type to vary in the model. Conclusions: The presence of stop signs significantly reduced approach speeds compared to uncontrolled approaches. Though several covariates were significantly related to TTC and PET for vehicle pairs, the models were unable to demonstrate a significant relationship between stop signs and vehicle–pedestrian interactions. Therefore, drawing conclusions regarding pedestrian safety is difficult. Practical Applications: As pedestrian safety is frequently used to justify new stop sign installations, this result has important policy implications. Policies implementing stop signs to reduce pedestrian crashes may be less effective than other interventions. Enforcement and education efforts, along with geometric design considerations, should accompany any changes in traffic control.

Optimizing Right-Turn Signals to Benefit Pedestrian–Vehicle Interactions

In most right-driving urban signalized intersections, right-turn vehicle signals do not usually control turns. To address the problem of signal control in a pedestrian–vehicle interaction, this paper establishes a right-turn signal optimization (RTSO) model that considers both efficiency and safety. First, the main factors influencing the behavior of vehicle and pedestrian during pedestrian–vehicle interaction are analyzed, and a pedestrian–vehicle interaction model (PVI model) at an urban road crosswalk is established. This model is used to determine the probabilities of four pedestrian–vehicle interaction situations. Then, based on the traffic conflict theory, the next step was to construct an objective function that minimizes the total delay of traffic participants considering pedestrian–vehicle interactions, and another objective function that minimizes the potential conflicts considering pedestrian–vehicle interactions. Then, an RTSO model is obtained by introducing a safety-efficiency coefficient to combine the previously described two constructed functions. Finally, the PVI model and delay model are verified through video observation data and the establishment of a cellular automata simulation platform of pedestrian–vehicle interaction. Using these models, a field signal plan, the delay minimization scheme, the conflict minimization scheme, and the proposed scheme are numerically analyzed under different yielding rates. This proposed scheme is further numerically analyzed under different safety-efficiency coefficients. The results show that this paper’s RTSO model has certain advantages in increasing safety and reducing delay. In addition, using these results, this paper gives a recommended value for the safety-efficiency coefficients in different application scenarios.

Safety and efficiency impact of pedestrian–vehicle conflicts at non signalized midblock crosswalks based on fuzzy cellular automata

Midblock crossing is very common in the high populated urban cities, especially in Southeastern Asia. To regulate unauthorized midblock crossing, Midblock Zebra Crosswalk (MZC) is applied to promote yielding behavior of drivers to pedestrians. It is necessary to set up a clear guidance for constructing MZC to help regulate the unlawful crossing, as well as analyzing the performance of existing MZCs. In this paper, a Fuzzy Cellular Automata (FCA) model is developed to analyze the safety and efficiency impact of pedestrian–vehicle interactions at non signalized MZCs, which explicitly explores the decision making process of individual drivers. In the proposed model, membership functions of conflict area gap and velocity of pedestrian in the fuzzy inference system are calibrated with field survey, in order to reason the output of each driver’s filtering decision in front of the crosswalk. Furthermore, by employing safety and efficiency indicators, including Time Difference to Collision (TDTC) and Deceleration Occurrences caused by Conflicts (DOC), the FCA model is capable of evaluating safety and efficiency impact of pedestrian–vehicle conflicts under different proportions of obedient drivers and traffic flows. Finally, we deduce the minimum pedestrian flow for constructing MZC in the midblock from the FCA model and national guideline of setting traffic lights in China.

Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction patterns of the ego vehicle with other nearby vehicles. Our model relaxes dependency on the number of surrounding vehicles by developing an acceleration-sensitive velocity field based on Gaussian processes. The experiment results demonstrate that the velocity field can represent the spatial interactions between the ego vehicle and its surroundings. Then, a discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering the sequential interaction data into interpretable granular patterns automatically. We then evaluate our approach in the highway lane-change scenarios using the highD dataset collected from real-world settings. Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian interactions. View the demos via https://youtu.be/z_vf9UHtdAM.

Micro-level Investigation of the Effects for Pedestrian-Pedestrian and Pedestrian-Vehicle Interactions and Other Factors on Crossing Speed in Pedestrian Crossings

Pedestrian crossings play an important role in the safety and mobility performance of signalized intersections. In improving pedestrian crossings, it is important to calculate the crossing speed. In practice, different average pedestrian crossing speeds can be effective in different pedestrian crossings. The differences in the design speeds also support the need to calculate local pedestrian crossing speeds in order to provide the most suitable designs. In the study, factors affecting pedestrian speeds at the selected signalized pedestrian crossing in Mersin city center were examined at a micro level. As a result of statistical analyses for the 1868 pedestrian crossing, the effects of gender, age, group behaviour, load condition in hand, telephone preoccupation, traffic and pedestrian volume on pedestrian crossing speeds were examined. The average crossing speed was calculated at 1.14 m/s. Individual situations affecting speed were found to be factors such as age, group behavior and having loads in hand. In addition, the actual subject studied was the signal effect, direction factor, and pedestrian-vehicle interaction factors studied by statistical methods and cumulative percentage pass rate graphs. Contrary to what is expected, pedestrians who exhibit angle crossing behavior with unregulated pedestrians have a lower crossing speed than canonical and normal passing pedestrians. In pedestrian crossing design, considering vehicle-pedestrian interaction while collecting data; It has been revealed by calculations that the cross and illegal crossings of pedestrians should be reduced and the width of the railings should be adjusted correctly. In addition, it is important to accurately determine the width of the crosswalk, which has an impact on the speed of the pedestrian.

Quantitative study of vehicle-pedestrian interactions: Towards pedestrian-adapted lighting communication functions for autonomous vehicles

This paper reports the main conclusions of a fielding observation of vehicle-pedestrian interactions at urban crosswalks, by describing the types, sequences, spatial distributions and probabilities of occurrence of the vehicle and pedestrian behaviors. This study was motivated by the fact that in a near future, with the introduction of autonomous vehicles (AVs), human drivers will become mere passengers, no longer being able to participate into the traffic interactions. With the purpose of recreating the necessary interactions, there is a strong need of new communication abilities for AVs to express their status and intentions, especially to pedestrians who constitute the most vulnerable road users. As pedestrians highly rely on the actual behavioral mechanism to interact with vehicles, it looks preferable to take into account this mechanism in the design of new communication functions. In this study, through more than one hundred of video-recorded vehicle-pedestrian interaction scenes at urban crosswalks, eight scenarios were classified with respect to the different behavioral sequences. Based on the measured position of pedestrians relative to the vehicle at the time of the significant behaviors, quantitative analysis shows that distinct patterns exist for the pedestrian gaze behavior and the vehicle slowing down behavior as a function of Vehicle-to-Pedestrian (V2P) distance and angle.

Conditions for Setting Exclusive Pedestrian Phases at Two-Phase Signalized Intersections considering Pedestrian-Vehicle Interaction

In order to analyze the effectiveness of setting exclusive pedestrian phase (EPP) under different vehicle yielding rates, the effect of EPPs on traffic efficiency is studied and a setting condition of EPP considering pedestrian-vehicle interaction is proposed in this paper. First, the main factors influencing the behavior of vehicles and pedestrians during pedestrian-vehicle interaction are analyzed, and a pedestrian-vehicle interaction (PVI) model at the crosswalk of urban road is established. Second, assuming that vehicle arrival obeys the Poisson distribution, the delay models of vehicle passengers and pedestrians crossing the street at the intersection are established, and taking the total delay of traffic participants as the main index, the setting condition of EPP are proposed. Third, based on the video of pedestrian-vehicle interaction at crosswalks, the parameters of the proposed model are calibrated. Through sensitivity analysis, the change of the total delay of traffic participants is analyzed under different conditions of pedestrian and vehicle arrival rates. Finally, by introducing pedestrian-vehicle interaction rules, a cellular automata (CA) simulation platform of pedestrian-vehicle interaction in crosswalk is established; based on the field data of Shanghai, a simulation model of intersection is established, and the total delay, queue length, and vehicle throughput under conventional signal control plan and EPP plan are compared. The results show that the pedestrian-vehicle interaction process has a great influence on the total delay of traffic participants at intersections, and pedestrian-vehicle interaction should be considered in the setting conditions of EPP. Under the same condition of vehicular flow, the more the pedestrian flow is, the smaller the delay increment will be. The higher the vehicle yielding rate is, the smaller the delay increment will be after setting EPP.

A reactive system for pedestrian mobility simulation

Abstract Handling the microscopic level of Vehicle-Pedestrian interactions is highly useful for analyzing patterns-based pedestrian accident exposure. Recently proposed systems are based on vehicle-to-pedestrian communication to analyze real-time situations and thus warning both sides of potential crashes. Two parts are composing those systems. The first concerns network technologies ensuring direct communication between vehicles and pedestrians and the second is related to information processing analyzing and predicting trajectories and dangers. One of the most challenges facing the last systems category is to provide them with precise and sufficient data to test and improve them. Precisely, to analyze vehicle-pedestrian interactions, we suggest detecting pedestrian behavior with respect to its surrounding vehicles as well as detecting vehicle behavior with respect to its surrounding pedestrians. In this work, we consider generating pedestrians and vehicles’ data using SUMO. Moreover, we design a handler for preparing Vehicle-Pedestrian interactions data for upper layers for deep analytic and patterns finding purposes. Mixed realistic traffic simulation is performed in Mohammedia city and the first results are shown.

Vehicle-Pedestrian Interaction: Distributed intelligence framework

Abstract In natural systems, many species are able to coordinate their individual intelligences to perform complex tasks. Swarm Intelligence also denotes the concept of distributed intelligence which relies on the coordination between a massive number of individual intelligences. In this paper, we develop a logical approach to consider distributed intelligence. Our proposal is based on a pedestrian safety issue. So, we extend the bases of a distributed non-monotonic theory to model the “vehicle-pedestrian” interaction system. This extension concerns the preliminary approach proposed in our previous work. Non-monotonic reasoning processes for distributed intelligent logic agents strongly contribute to the development of Pedestrian Collision Warning Systems. These systems benefit greatly from the efficiency of pedestrian detection which has reached a high level thanks to several technologies. We demonstrate the validity of our construction and we specify the knowledge representation of driver-pedestrian interaction in the context of the proposed theory.

Driver compliance and pedestrian safety at zebra crossings in the Cape Coast Metropolis, Ghana

ABSTRACT Vehicle-pedestrian interactions predispose pedestrians to road traffic crashes because of the importance of pedestrian trips for geographical mobility and spatial interaction which may be effectively reduced by zebra crossings. This study guided by the Safe System Approach assessed drivers’ compliance and pedestrian safety at zebra crossings in the Cape Coast Metropolis. The study investigation data were from observational checklists and pedestrian crash data from the Building and Road Research Institute. Findings showed that 336 pedestrian crashes were recorded between 2007 and 2016 with 35.4% and 15.2% of the victims being hospitalized or dead respectively. More than 70% of the pedestrian crashes occurred at places, not a junction. The findings further showed that there were 23 zebra crossings in the metropolis with low drivers’ compliance rate. The relevant stakeholders such as the Department of Urban Roads of the Ministry of Roads and Highways, National Road Safety Commission, officials of the Motor Transport and Traffic Department of the Ghana Police Service should identify short- and long-term measures to improve pedestrian safety and driver compliance at zebra crossings in the metropolis.

A detailed finite element model of a mid-sized male for the investigation of traffic pedestrian accidents.

The pedestrian is one of the most vulnerable road users and comprises approximately 23% of the road crash-related fatalities in the world. To protect pedestrians during Car-to-Pedestrian Collisions (CPC), subsystem impact tests are used in regulations. These tests provide insight but cannot characterize the complex vehicle-pedestrian interaction. The main purpose of this study was to develop and validate a detailed pedestrian Finite Element (FE) model corresponding to a 50th percentile male to predict CPC induced injuries. The model geometry was reconstructed using a multi-modality protocol from medical images and exterior scan data corresponding to a mid-sized male volunteer. To investigate injury response, this model included internal organs, muscles and vessels. The lower extremity, shoulder and upper body of the model were validated against Post Mortem Human Surrogate (PMHS) test data in valgus bending, and lateral/anterior-lateral blunt impacts, respectively. The whole-body pedestrian model was validated in CPC simulations using a mid-sized sedan and simplified generic vehicles bucks and previously unpublished PMHS coronal knee angle data. In the component validations, the responses of the FE model were mostly within PMHS test corridors and in whole body validations the kinematic and injury responses predicted by the model showed similar trends to PMHS test data. Overall, the detailed model showed higher biofidelity, especially in the upper body regions, compared to a previously reported simplified pedestrian model, which recommends using it in future pedestrian automotive safety research.

Active Collision Avoidance Strategy Considering Motion Uncertainty of the pedestrian

This work proposes an active collision avoidance between autonomous driving vehicle and pedestrian with motion uncertainty under urban road. A candidate trajectory planning method considering spatial and time sequences is proposed, which combines the polynomial path planning and the velocity planning with variable safety velocity. Then, a pedestrian-vehicle interaction model is constructed, which takes the pedestrian's uncertain motion as a superposition of the Markov process without interference and the motion caused by the vehicle, and predicts the pedestrian's motion probabilistically. On these bases, the optimal trajectory is evaluated from the candidate trajectories by safety, stability, and efficiency, as well as different driving styles. The proposed collision avoidance strategy is verified in conventional and emergency simulation scenarios. Simulation results show that it can effectively plan a safe, stable and efficient trajectory under normal and emergency conditions.

Modeling Vehicle–Pedestrian Interactions using a Nonprobabilistic Regression Approach

Understanding how vehicle drivers and pedestrians interact is key to identifying countermeasures that improve the safety of the interactions. As a result, techniques that can be used to evaluate the effectiveness of safety countermeasures and traffic control devices without the need to wait for the availability of crash data are needed. Using video, the interactions between right-turning vehicles and conflicting pedestrians were documented and quantified using vehicle and pedestrian position timestamps. Interactions documented were purposely narrow in scope to obtain a controlled dataset. Logged timestamps enabled the calculation of values such as time to complete a right turn and time for a pedestrian to reach a critical conflict point when a vehicle initiated a right turn. A nonprobabilistic regression model explaining the relationship between the calculated values was created. The model described the expected behavior of right-turning drivers: when drivers perceive the possibility of a pedestrian reaching a critical conflict point at the same time as them, they will modify their behavior, even if not coming to a complete stop. This behavior is not a surprise and has been previously documented in the literature. The primary contribution of this research is demonstrating that by analyzing a narrow set of interactions, clear and simple models that mostly explain the interactions between right-turning vehicles and pedestrians can be developed using nonprobabilistic linear regression techniques. An argument is made that the model parameters can be used to evaluate the effectiveness of traffic control devices.