This dissertation presents a functional unit that will be used within the Intermodal Simulator for the Analysis of Pedestrian Traffic (ISAPT). Such simulation systems can be used by infrastructure designers and public transport designers for evaluating and optimizing building designs and layouts. Activity-based travel demand analysis is one of the most important methods for studying pedestrian behavior in that it reflects how people travel inside intermodal facilities driven by a clear purpose based on their prior knowledge of the facility layout and its resources. In this dissertation, the proposed framework used for pedestrian travel simulation inside an intermodal facility is divided into three aspects, activity scheduling, destination/route choice analysis and rescheduling modeling. Discrete choice models were developed to describe in sufficient detail the various activity scheduling and destination choice behavior of pedestrians in the planning stage, as well as to describe their rescheduling behavior at the execution stage while they are traveling inside airports. In order to demonstrate the applicability of the models, a numerical example was provided for the activity choice model and a case study was also included of destination choice. Both revealed preference (RP) survey data and stated preference (SP) survey data have been used to calibrate and validate the discrete choice models. The main conclusion of this dissertation is that modeling pedestrian behavior inside an intermodal facility is feasible. Use of this framework will provide simulation system with the capability to take into account natural pedestrian behavior, not only of what they will do, where they will perform it, and which path they will use to get there, but also enhancing the travel efficiency by providing rescheduling behavior under various traffic conditions. Due to the limited knowledge concerning pedestrian behavior in public transportation facilities, laboratory experiments were conducted in order to fill the blank. Both the setup of the laboratory experiments and the data collection methods employed in this research are original. Numerical examples have shown the validity and the applicability of each developed model for each of three types of behavior.

Fully Autonomous Vehicles (FAVs) have the potential to provide safer vehicle operation and to enhance the overall transportation system. However, drivers and vehicles are not the only components that need to be considered. Research has shown that pedestrians are among the most unpredictable and vulnerable road users. To achieve full and successful implementation of FAVs, it is essential to understand pedestrian acceptance and intended behavior regarding FAVs. Three studies were developed to address this need: (1) development of a standardized framework to investigate pedestrians’ behaviors for the U.S. population; (2) development of a framework to evaluate their receptivity of FAVs; and (3) investigation of the influence of the external interacting interfaces of FAVs on pedestrian receptivity toward them. The pedestrian behavior questionnaire (PBQ) categorized pedestrian general behaviors into five factors: violations, errors, lapses, aggressive behaviors, and positive behaviors. The first four factors were found to be both valid and reliable; the positive behavior scale was not found to be reliable nor valid. A long (36-item) and a short (20-items) versions of the PBQ were validated by regressing scenario-based survey responses to the fiveactor PBQ subscale scores. The pedestrian receptivity questionnaire for FAVs (PRQF) consisted of three subscales: safety, interaction, and compatibility. This factor structure was verified by a confirmatory factor analysis and the reliability of each subscale was confirmed. Regression analyses showed that pedestrians’ intention to cross the road in front of a FAV was significantly predicted by both safety and interaction scores, but not by the compatibility score. On the other hand, acceptance of FAVs in the existing traffic system was predicted by all three subscale scores. Finally, an experimental study was performed to expose pedestrians to a simulated environment where they could experience a FAV. The FAV in the simulated environment was either equipped with external features (audible and/or visual) or had no external (warning) feature. The least preferred options were the FAVs with no features and those with a smiley face but no audible cue. The most preferred interface option, which instilled confidence for crossing in front of the FAV, was the walking silhouette.

A multi-scale agent-based simulation framework is firstly proposed to analyze pedestrian delays at signalized crosswalks in large urban areas under different conditions. The aggregated-level model runs under normal conditions, where each crosswalk is represented as an agent. Pedestrian counts collected near crosswalks are utilized to derive the binary choice probability from a utility maximization model. The derived probability function is utilized based on the extended Adam's model to estimate an average pedestrian delay with corresponding traffic flow rate and traffic light control at each crosswalk. When abnormality is detected, the detailed-level model with each pedestrian as an agent is running in the affected subareas. Pedestrian decision-making under abnormal conditions, physical movement, and crowd congestion are considered in the detailed-level model. The detailed-level model contains two sub-level models: the tactical sub-level model for pedestrian route choice and the operational sub-level model for pedestrian physical interactions. The tactical sub-level model is based on Extended Decision Field Theory (EDFT) to represent the psychological preferences of pedestrians with respect to different route choice options during their deliberation process after evaluating current surroundings. At the operational sub-level model, physical interactions among pedestrians and consequent congestions are represented using a Cellular Automata model, in which pedestrians are allowed biased random-walking without back step towards their destination that has been given by the tactical sub-level model. In addition, Dynamic-Data-Driven Application Systems (DDDAS) architecture has been integrated with the proposed multi-scale simulation framework for an abnormality detection and appropriate fidelity selection (between the aggregate level and the detailed level models) during the simulation execution process. Various experiments have been conducted under varying conditions with the scenario of a Chicago Loop area to demonstrate the advantage of the proposed framework, balancing between computational efficiency and model accuracy. In addition to the signalized intersections, pedestrian crossing behavior under unsignalized conditions which has been recognized as a main reason for pedestrian-vehicle crashes has also been analyzed in this dissertation. To this end, an agent-based model is proposed to mimic pedestrian crossing behavior together with drivers' yielding behavior in the midblock crossing scenario. In particular, pedestrian-vehicle interaction is first modeled as a Two-player Pareto game which develops evaluation of strategies from two aspects, delay and risk, for each agent (i.e. pedestrian and driver). The evaluations are then used by Extended Decision Field Theory to mimic decision making of each agent based on his/her aggressiveness and physical capabilities. A base car-following algorithm from NGSIM is employed to represent vehicles' physical movement and execution of drivers' decisions. A midblock segment of a typical arterial in the Tucson area is adopted to illustrate the proposed model, and the model for the considered scenario has been implemented in AnyLogic® simulation software. Using the constructed simulation, experiments have been conducted to analyze different behaviors of pedestrians and drivers and the mutual impact upon each other, i.e. average pedestrian delay resulted from different crossing behaviors (aggressive vs. conservative), and average braking distance which is affected by driving aggressiveness and drivers' awareness of pedestrians. The results look interesting and are believed to be useful for improvement of pedestrians' safety during their midblock crossing. To the best of our knowledge, the proposed multi-scale modeling framework for pedestrians and drivers is one of the first efforts to estimate pedestrian delays in an urban area with adaptive resolution based on demand and accuracy requirement, as well as to address pedestrian-vehicle interactions under unsignalized conditions.

The aim of this research was to analyze if current generation PC-driven virtual reality simulations can be used to accurately mimic and therefore, observe behavior at a crosswalk. Toward that goal, the following research tasks were carried out: a) Designing a 3D virtual crosswalk and recruiting volunteers to wear the HTC Vive headset and to walk across the street, b) Setting up cameras near the midblock crosswalk on University Drive at California Polytechnic State University, San Luis Obispo to observe pedestrians, and c) Comparing pedestrian behavior data from both the virtual and real midblock crosswalk. The comparison was based on the following criteria: a) Pedestrian walking speed, b) Observation patterns prior to crossing the road, characterized by glancing left and right to detect cars, and c) Pedestrians’ decisions as to where to cross, defined by if they chose to walk directly on or outside of the midblock crosswalk. Walking speed and the number of pedestrians who looked left and right before crossing were significantly different in both the virtual and real environments. On the other hand, the proportion of people who chose to walk on the crosswalk was similar in both environments. This result indicates that there is a future potential in using virtual reality to analyze pedestrian behavior at roundabouts. Although this study showed that PC-driven virtual reality is not effective in replicating pedestrian walking speeds or pedestrian observation patterns at a midblock crosswalk, researchers may expect PC-driven virtual reality to have greater applications within the transportation discipline once the technology improves over the years. Potential improvements in technology that would help include being wireless, allowing users to walk in a non-confining space, and making the equipment more affordable, allowing the technology to become more mainstream.

Traffic cameras are those cameras operated with the purpose of observing traffic, often streaming video in real-time to traffic management centers. These camera video streams allow transportation authorities to respond to traffic events and maintain situational awareness. However, traffic cameras also have the potential to directly capture crashes and conflicts, providing enough information to perform reconstruction and gain insights regarding causation and remediation. Beyond crash events, traffic camera video also offers an opportunity to study normal driving. Normal driver behavior is important for traffic planners, vehicle designers, and in the form of numerical driver models is vital information for the development of automated vehicles. Traffic cameras installed by state departments of transportation have already been placed in locations relevant to their interests. A wide range of driver behavior can be studied from these locations by observing vehicles at all times and under all weather conditions. Current systems to analyze traffic camera video focus on detecting when traffic events occur, with very little information about the specifics of those events. Prior studies into traffic event detection or reconstruction used 1-7 cameras placed by the researchers and collected dozens of hours of video. Crashes and other interesting events are rare and cannot be sufficiently characterized by camera installations of that size. The objective of this dissertation was to explore the utility of traffic camera data for transportation research by modeling and characterizing crash and non-crash behavior in pedestrians and drivers using a captured dataset of traffic camera video from the Commonwealth of Virginia, named the VT-CAST (Virginia Traffic Cameras for Advanced Safety Technologies) 2020 dataset. A total of 6,779,726 hours of traffic camera video was captured from live internet streams from December 17, 2019 at 4:00PM to 11:59PM on December 31, 2020. Video was analyzed by a custom R-CNN convolutional neural network keypoint detector to identify the locations of vehicles on the ground. The OpenPifPaf model was used to identify the locations of pedestrians on the ground. The location, pan, tilt, zoom, and altitude of each traffic camera was reconstructed to develop a mapping between the locations of vehicles and pedestrians on-screen and their physical location on the surface of the Earth. These physical detections were tracked across time to determine the trajectories on the surface of the Earth for each visible vehicle and pedestrian in a random sample of the captured video. Traffic camera video offers a unique opportunity to study crashes in-depth which are not police reported. Crashes in the traffic camera video were identified, analyzed, and compared to nationally representative datasets. Potential crashes were identified during the study interval by inspecting Virginia 511 traffic alerts for events which occurred near traffic cameras and impacted the flow of traffic. The video from these cameras was manually reviewed to determine whether a crash was visible. Pedestrian crashes, which did not significantly impact traffic, were identified from police accident reports (PARs) as a separate analysis. A total of 292 crashes were identified from traffic alerts, and six pedestrian crashes were identified from PARs. Road departure and rear-end crashes occurred in similar proportions to national databases, but intersection crashes were underrepresented and severe and rollover cases were overrepresented. Among these crashes, 32% of single-vehicle crashes and 50% of multi-vehicle crashes did not appear in the Virginia crash database. This finding shows promise for traffic cameras as a future data source for crash reconstruction, indicating traffic cameras are a capable tool to study unreported crashes. The safe operation of autonomous vehicles requires perception systems which make accurate short-term predictions of driver and pedestrian behavior. While road user behavior can be observed by the autonomous vehicles themselves, traffic camera video offers another potential information source for algorithm development. As a fixed roadside data source, these cameras capture a very large number of traffic interactions at a single location. This allows for detailed analyses of important roadway configurations across a wide range of drivers. To evaluate the efficacy of this approach, a total of 58 intersections in the VT-CAST 2020 dataset were sampled for driver trajectories at intersection entry, yielding 58,180 intersection entry trajectories. K-means clustering was used to group these trajectories into a family of 45 trajectory clusters. Likely as a function of signal phase, distinct groups of accelerating, constant speed, and decelerating trajectories were present. Accelerating and decelerating trajectories each occurred more frequently than constant speed trajectories. The results indicate that roadside data may be useful for understanding broad trends in typical intersection approaches for application to automated vehicle systems or other investigations; however, data utility would be enhanced with detailed signal phase information. A similar analysis was conducted of the interactions between drivers and pedestrians. A total of 35 crosswalks were identified in the VT-CAST 2020 dataset with sufficient trajectory information, yielding 1,488 trajectories of drivers interacting with pedestrians. K-means clustering was used to group these trajectories into a family of 16 trajectory clusters. Distinct groups of accelerating, constant speed, and decelerating trajectories were present, including trajectory clusters which described vehicles slowing down around pedestrians. Constant speed trajectories occurred the most often, followed by accelerating trajectories and decelerating trajectories. As with the prior investigation, this finding suggests that roadside data may be used in the development of driver-pedestrian interaction models for automated vehicles and other use cases involving a combination of pedestrians and vehicles. Overall, this dissertation demonstrates the utility of standard traffic camera data for use in traffic safety research. As evidence, there are already three current studies (beyond this dissertation) using the video data and trajectories from the VT-CAST 2020 dataset. Potential future studies include analyzing the mobile phone use of pedestrians, analyzing mid-block pedestrian crossings, automatically performing roadway safety assessments, considering the behavior of drivers following congested driving, evaluating the effectiveness of work zone hazard countermeasures, and understanding roadway encroachments. / Doctor of Philosophy / Traffic cameras are those cameras operated with the purpose of observing traffic, often streaming video in real-time to traffic management centers. These video streams allow transportation authorities to maintain situational awareness and respond to traffic events. However, traffic cameras also have the potential to directly capture crashes, providing enough information to perform reconstruction and gain insights regarding causation and remediation. Beyond crash events, traffic camera video also offers an opportunity to study normal driving, which is vital information for the operation of automated vehicles. Traffic cameras installed by state departments of transportation have already been placed in thousands of locations around the country capturing traffic scenes relevant to their interests. A wide range of driver and pedestrian behavior can be studied from these locations by observing vehicles at all times and under all weather conditions. Current systems to analyze traffic camera video focus on detecting when traffic events occur, with very little information about the specifics of those events. Previous studies into traffic event detection or reconstruction used 1-7 cameras placed by the researchers and collected dozens of hours of video. Crashes and other interesting events are rare and cannot be sufficiently characterized by camera installations of that size. The objective of this dissertation was to explore the utility of traffic camera data for transportation research by modeling and characterizing crash and non-crash behavior in pedestrians and drivers using a dataset of statewide traffic camera video captured from the Commonwealth of Virginia. A total of 6,779,726 hours of traffic camera video from live internet streams was captured from December 17, 2019 at 4:00PM to 11:59PM on December 31, 2020. This captured video was processed by a trajectory analysis system which determined the path on the ground for each visible vehicle and pedestrian in a random sample of the captured video. Additionally, 298 crashes visible in the traffic camera video were analyzed, comparing them to nationally representative crash datasets. With anticipated uses in traffic modeling and automated vehicle development, two additional potential use cases of the dataset were explored: cases where a driver enters an intersection, and cases where a driver interacts with a pedestrian.

Approximately 47,700 pedestrians were killed between the years of 2000 - 2009. School buses are one of the safest modes of transportation (National Highway Traffic Safety Administration, 2004). However, the Central Florida school district eliminated bus transportation within the 2-mile radius from schools just last year. Children must prepare for an alternative mode of transportation; walking and biking. The purpose of this research was two-fold. First to develop an online safety training program for elementary school children; and second, a self-report questionnaire was constructed and piloted to measure how safety training and school infrastructure affects students' pedestrian risk-taking attitudes and risk perceptions to avoid the dangers of walking and biking to and from school. A 2x2 Factorial Multivariate Analysis of Variance (MANOVA) was used to test two categorical independent variables (safety awareness training, school infrastructure) for each of the two continuous dependent variables (pedestrian risk-taking attitudes and risk perceptions of pedestrian behavior). Using data from the pilot study, the researcher developed, self-reported questionnaires demonstrated that there was a significant difference between schools. Those receiving the training had lower mean scores in risk-taking attitudes than those who did not receive the training. Regardless of intervention, School 2 (complete infrastructure) takes fewer risks than School 1(incomplete infrastructure). The mean difference between groups was not statistically significant.

Walking is the oldest and foremost mode of transportation through history and the prevalence of walking has increased. Effective pedestrian model is crucial to evaluate pedestrian facility service level and to enhance pedestrian safety, performance, and satisfaction. The objectives of this study were to: (1) validate the efficacy of utilizing queueing network model, which predicts cognitive information processing time and task performance; (2) develop a generalized queueing network based cognitive information processing model that can be utilized and applied to construct pedestrian cognitive structure and estimate the reaction time with the first moment of service time distribution; (3) investigate pedestrian behavior through naturalistic and experimental observations to analyze the effects of environment settings and psychological factors in pedestrians; and (4) develop pedestrian level of service (LOS) metrics that are quick and practical to identify improvement points in pedestrian facility design. Two empirical and two analytical studies were conducted to address the research objectives. The first study investigated the efficacy of utilizing queueing network in modeling and predicting the cognitive information processing time. Motion capture system was utilized to collect detailed pedestrian movement. The predicted reaction time using queueing network was compared with the results from the empirical study to validate the performance of the model. No significant difference between model and empirical results was found with respect to mean reaction time. The second study endeavored to develop a generalized queueing network system so the task can be modeled with the approximated queueing network and its first moment of any service time distribution. There was no significant difference between empirical study results and the proposed model with respect to mean reaction time. Third study investigated methods to quantify pedestrian traffic behavior, and analyze physical and cognitive behavior from the real-world observation and field experiment. Footage from indoor and outdoor corridor was used to quantify pedestrian behavior. Effects of environmental setting and/or psychological factor on travel performance were tested. Finally, adhoc and tailor-made LOS metrics were presented for simple realistic service level assessments. The proposed methodologies were composed of space revision LOS, delay-based LOS, preferred walking speed-based LOS, and ‘blocking probability’.

<div> <div> <div> <p>For pedestrians and autonomous vehicles (AVs) to co-exist harmoniously and safely in the real-world, AVs will need to not only react to pedestrian actions, but also anticipate their intentions. In this thesis, we propose to use rich visual and pedestrian-environment interaction features to improve pedestrian crossing intention prediction from the ego-view. We do so by combining visual feature extraction, graph modeling of scene objects and their relationships, and feature encoding as comprehensive inputs for an LSTM encoder-decoder network. </p> <p>Pedestrians react and make decisions based on their surrounding environment, and the behaviors of other road users around them. The human-human social relationship has already been explored for pedestrian trajectory prediction from the bird’s eye view in stationary cameras. However, context and pedestrian-environment relationships are often missing in current research into pedestrian trajectory, and intention prediction from the ego-view. To map the pedestrian’s relationship to its surrounding objects we use a star graph with the pedestrian in the center connected to all other road objects/agents in the scene. The pedestrian and road objects/agents are represented in the graph through visual features extracted using state of the art deep learning algorithms. We use graph convolutional networks, and graph autoencoders to encode the star graphs in a lower dimension. Using the graph en- codings, pedestrian bounding boxes, and human pose estimation, we propose a novel model that predicts pedestrian crossing intention using not only the pedestrian’s action behaviors (bounding box and pose estimation), but also their relationship to their environment. </p> <p>Through tuning hyperparameters, and experimenting with different graph convolutions for our graph autoencoder, we are able to improve on the state of the art results. Our context- driven method is able to outperform current state of the art results on benchmark dataset Pedestrian Intention Estimation (PIE). The state of the art is able to predict pedestrian crossing intention with a balanced accuracy (to account for dataset imbalance) score of 0.61, while our best performing model has a balanced accuracy score of 0.79. Our model especially outperforms in no crossing intention scenarios with an F1 score of 0.56 compared to the state of the art’s score of 0.36. Additionally, we also experiment with training the state of the art model and our model to predict pedestrian crossing action, and intention jointly. While jointly predicting crossing action does not help improve crossing intention prediction, it is an important distinction to make between predicting crossing action versus intention.</p> </div> </div> </div>

Public health is an increasingly important issue addressed from both environmental and public health sectors for the future development of urban environments. From a planning perspective, one possible solution is to increase walkability throughout the cities. Many assessment methods are being developed and administered to evaluate the quality of existing urban environments to promote walkable cities/communities. The results from using these methods provide policymakers and stakeholders with valuable information regarding the existing physical conditions of the environment. Although several US cities started to develop and refocus plans toward pedestrian-oriented policies approaches, results from this particular study determined that the quality of pedestrian environments cannot solely be determined by using available assessment tools and recommend additional analytical methods used in conjunction with source data to provide a complete perspective to successfully increase the quality of life. The condition of the physical environment – high, average, and low quality – was important contributing factors to increase walkability, yet, it is equally important to understand and consider the needs, preferences and perceptions of end users when public officials are charged with the task of developing plan proposals for pedestrian neighborhoods. This study addresses these issues through a case study examining the quality of pedestrian environment and how people perceive those surroundings of downtown San Luis Obispo.

En zonas con alto flujo peatonal y vehicular se presentan mayores congestiones e incidentes que afectan principalmente a los peatones y esto ocurre debido al reducido tiempo de cruce peatonal, consideraciones de diseño deficientes y el inadecuado comportamiento de los usuarios viales. Por ello, es necesario realizar el estudio y la aplicación de medidas que contribuyan a salvaguardar la seguridad de los peatones en estas áreas. Actualmente, en la intersección de las avenidas Abancay con Nicolas de Piérola, se prioriza el paso vehicular, a pesar de la alta demanda peatonal y la alta tasa de accidentes. La presente investigación tiene como objetivo reducir la cantidad de conflictos peatón-vehículo por medio de la aplicación de un crucero peatonal diagonal en esta intersección. Para ello, se construye un modelo de microsimulación en el programa VISSIM que represente el comportamiento de los usuarios y permita el análisis de la propuesta. La metodología se desarrolla en tres partes. Primero, se describe y caracteriza la intersección por medio de las visitas de campo y videos recopilados por dron. Posteriormente, los datos obtenidos se introducen al programa, el modelo resultante es calibrado y validado empleando 5 parámetros de fuerza social. Luego, se realizan modificaciones para incluir el crucero peatonal diagonal con la fase semafórica exclusiva generada en VISTRO. Finalmente, se plantea el diseño de la intersección con crucero peatonal diagonal. Como resultado, se comprueba la reducción de conflictos peatón-vehículo en un 74% y el incremento de la seguridad vial con un enfoque en los peatones. / In areas with high vehicular and pedestrian flow there are greater congestion and incidents that mainly affect pedestrians, this occurs due to many factors such as reduced crossing time, insufficient design considerations and inadequate user behavior. That is why it is necessary to study countermeasures that help safeguard pedestrians in these areas. Currently, at the intersection between Abancay and Nicolas de Pierola Avenues, the vehicular crossing is prioritized despite of the high-density pedestrian traffic and the high rates of accidents. This investigation aims to reduce the number of pedestrian-vehicle conflicts by implementing a scramble crossing at the intersection. Therefore, a microsimulation model is generate using VISSIM Software that replicate the road user behavior and allows analysis of the applied proposal. The methodology used includes three main parts. First, the intersection is described and characterized by site visits and drone videos. Second, the collected data is entered into a model that represents the current situation, for which it is calibrated and validated using 5 parameters of the social force model. Then, on current situation model, a design is made with the crosswalk proposed. An exclusive pedestrian phase is included adapting the traffic signal optimization generated by VISTRO Software. Finally, an intersection design with an appropriate pedestrian interval is presented to help to increase the road safety and it is verified with a checklist. As a result, this verifies the reduction of road incidents caused mainly by the pedestrian-vehicle conflict by 74% and focuses on improvement of road safety for pedestrians. / Tesis