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Browsing by Author "Machiani, Sahar Ghanipoor"

Now showing 1 - 13 of 13
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    Behavior-based Predictive Safety Analytics – Pilot Study
    Engström, Johan; Miller, Andrew M.; Huang, Wenyan; Soccolich, Susan A.; Machiani, Sahar Ghanipoor; Jahangiri, Arash; Dreger, Felix; de Winter, Joost (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-04)
    This report gives an overview of the main findings from the Behavior-based Predictive Safety Analytics – Pilot Study project. The main objective of the project was to investigate the possibilities of developing statistical models predicting individual driver crash involvement based on individual driving style, demographic and behavioral history variables, using large sets of naturalistic driving data. The project was designed as a pilot project with the objective of providing the basis for a future more comprehensive research effort. Based on Second Strategic Highway Research Program (SHRP2) data, a subset of behavior and crash data including 2,458 drivers was created for analysis. The data were analyzed to investigate to what extent these drivers were differentially involved in crashes and near crashes, to what extent this was associated with individual characteristics, and if it is possible to predict individual drivers’ crash and near crash involvement based on variables representing individual characteristics. The results clearly demonstrated the presence of differential crash and near crash involvement and showed significant associations between enduring personal factors and crash involvement. Moreover, logistic regression and random forest classifiers were relatively successful in predicting crash and near crash involvement based on individual characteristics, but the ability to specifically predict involvement in crashes was more limited.
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    Big Data Visualization and Spatiotemporal Modeling of Risky Driving
    Jahangiri, Arash; Marks, Charles; Machiani, Sahar Ghanipoor; Nara, Atsushi; Hasani, Mahdie; Cordova, Eduardo; Tsou, Ming-Hsiang; Starner, Joshua (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-07)
    Statistical evidence shows the role of risky driving as a contributing factor in roadway collisions, highlighting the importance of identifying such driving behavior. With the advent of new technologies, vehicle kinematic data can be collected at high frequency to enable driver behavior monitoring. The current project aims at mining a huge amount of driving data to identify risky driving behavior. Relational and non-relational database management systems (DBMSs) were adopted to process this big data and compare query performances. Two relational DBMSs, PostgreSQL and PostGIS, performed better than a non-relational DBMS, MongoDB, on both nonspatial and spatial queries. Supervised and unsupervised learning methods were utilized to classify risky driving. Cluster analysis as an unsupervised learning approach was used to label risky driving during short monitoring periods. Labeled driving data, including kinematic information, were employed to develop random forest models for predicting risky driving. These models showed high prediction performance. Open source and enterprise visualization tools were also developed to illustrate risky driving moments in space and time. These tools can be used by researchers and practitioners to explore where and when risky driving events occur and prioritize countermeasures for locations in highest need of improvement.
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    Data Mining to Improve Planning for Pedestrian and Bicyclist Safety
    Jahangiri, Arash; Hasani, Mahdie; Sener, Ipek Nese; Munira, Sirajum; Owens, Justin M.; Appleyard, Bruce; Ryan, Sherry; Turner, Shawn M.; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-11)
    Between 2009 and 2016, the number of pedestrian and bicyclist fatalities saw a marked trend upward. Taken together, the overall percentage of pedestrian and bicycle crashes now accounts for 18% of total roadway fatalities, up from 13% only a decade ago. Technological advancements in transportation have created unique opportunities to explore and investigate new sources of data for the purpose of improving safety planning. This study investigated data from multiple sources, including automated pedestrian and bicycle counters, video cameras, crash databases, and GPS/mobile applications, to inform bicycle and pedestrian safety improvements. Data mining techniques, a new sampling strategy, and automated video processing methods were adopted to demonstrate a holistic approach that can be applied to identify facilities with highest need of improvement. To estimate pedestrian and bicyclist counts at intersections, exposure models were developed incorporating explanatory variables from a broad spectrum of data sources. Intersection-related crashes and estimated exposure were used to quantify risk, enabling identification of high-risk signalized intersections for walking and bicycling. The modeling framework and data sources used in this study will be beneficial in conducting future analyses for other facility types, such as roadway segments, and also at more aggregate levels, such as traffic analysis zones.
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    Developing a Framework for Prioritizing Bicycle Safety Improvement Projects using Crowdsourced and Image-Based Data
    Sadeghi, Amir Reza; Jahangiri, Arash; Machiani, Sahar Ghanipoor; Hankey, Steve; Abdollahpour, Seyed Sajjad (Safe-D University Transportation Center, 2023-08)
    Active transportation, including walking and cycling, has gained popularity due to the economic, environmental, and energy-efficient benefits. However, the rise of active transportation has also led to an increase in fatalities, particularly for bicyclists. A crash-risk scoring method was proposed to prioritize bicycle safety improvement projects for 50 bridges located in San Diego County. This study employs surrogate safety measures to estimate crash risk, addressing the limitations of traditional data collection methods, and incorporates transportation equity factors into the safety measure scoring method. To identify significant factors contributing to the likelihood of bicyclists exceeding 10 mph on bridges, binomial logistic regression models were employed, with three models focusing on different predictor variables. The results showed that factors such as race, home-to-work travel patterns, education levels, and crime rates influenced bicyclists' speeds on bridges. This study provides a foundation for understanding the factors associated with bicyclists' speeds on bridges and can inform future safety improvement projects in San Diego County and beyond. The findings highlight the importance of considering a range of factors to improve bicyclist safety and can ultimately lead to safer and more equitable transportation for all.
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    Developing an Intelligent Transportation Management Center (ITMC) with a Safety Evaluation Focus for Smart Cities
    Salehipour, Sina; Jahangiri, Arash; Paolini, Christopher P.; Machiani, Sahar Ghanipoor; Bergcollins, Django (Safe-D University Transportation Center, 2024-01)
    In the context of smart cities, ensuring transportation safety is a complex task that involves understanding the impact of new technologies, measuring the effectiveness of safety measures, and identifying high-risk locations. However, recent advances in communication and big data analytics have made it possible to address these challenges in a more efficient manner. Traditional transportation management centers (TMCs) are limited in their ability to analyze large amounts of data for safety evaluation. To overcome this limitation, this project aims to develop an intelligent transportation management center (ITMC) that utilizes automated video analysis to assess safety. By leveraging Intelligent Transportation Systems (ITS) technologies and big data analytics, the proposed ITMC can proactively evaluate safety at signalized intersections. Unlike conventional methods that rely on crash data, the ITMC uses safety surrogate measures (SSMs) to identify near-crash situations and calculate proactive risk. In this study, the results obtained from a machine vision model were used along with the Post Encroachment Time (PET) safety surrogate measure (SSM) to assess safety proactively at a selected signalized intersection. The study utilized the latest YOLO series model, YOLOX, for deep learning to detect and classify road users in video frames from four intersection traffic cameras.
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    Driver Training Research and Guidelines for Automated Vehicle Technology
    Manser, Michael P.; Noble, Alexandria M.; Machiani, Sahar Ghanipoor; Shortz, Ashley; Klauer, Charlie; Higgins, Laura L.; Ahmadi, Alidad (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-07)
    The advent of advanced driver-assistance systems presents the opportunity to significantly improve transportation safety. Complex sensor-based systems within vehicles can take responsibility for tasks typically performed by drivers, thus reducing driver-related error as a source of crashes. While there may be a reduction in driver errors, these systems fundamentally change the driving task from manual control to supervisory control. A significant challenge, given this fundamental change in the driving task, is that there are no established methods to train drivers on the use of these systems, which may be counterproductive to safety improvements. The aim of the project was to develop training protocol guidelines that could be used by advanced driver-assistance system trainers to optimize driving safety. The guidelines were developed based on the results of three activities that included the development of a taxonomy of the knowledge and skills necessary to operate advanced driver-assistance systems, a driving simulator study that examined the effectiveness of traditional training protocols, and a test track study that examined the efficacy of a vehicle-based training protocol. Results of both studies suggest that differing training protocols are most beneficial in terms of driver cognitive load and visual scanning as opposed to short-term changes in performance.
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    Evaluating the Safe Routes to School (SRTS) Transportation Program in Socially Vulnerable Communities in San Diego County, California
    Fernandez, Gabriela; Etaati, Bita; Mercado, Andrick; Jahangiri, Arash; Machiani, Sahar Ghanipoor; Tsou, Ming-Hsiang; Mejia, Christian (Safe-D University Transportation Center, 2023-05)
    Child safety concerns are among the strongest impediments to children walking or biking to school, but some students must walk or bike due to financial or other circumstances. These travel modes are more than twice as common among students from low-income households than students from higher income households. The Safe Routes to School (SRTS) program fosters opportunities for students to walk and bike to school safely and routinely. This study provides insights into the SRTS program’s effectiveness and potential to improve walking and biking safety in socially vulnerable communities by evaluating the program’s impact on schools in the Chula Vista Elementary School District, a vulnerable area in San Diego County. (i) A linear regression model was used to assess the program’s impact on each school, and a logistic regression model was employed to identify factors influencing students’ walking behavior. (ii) An SRTS web-based interactive tool (ArcGIS Experience) was developed to identify traffic incident hot spots and facilitate future routing improvements. (iii) A virtual reality (VR) road safety training tool for children was developed, and a case study at Feaster Charter Elementary School was conducted to assess its effectiveness. Twenty-six students played the VR game before and after watching traffic safety educational videos, and observations from the VR session were recorded. (iv) The outreach and deliverables from this study strengthened community collaboration across San Diego County.
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    Evaluation of Innovative Approaches to Curve Delineation for Two-Lane Rural Roads
    Gibbons, Ronald B.; Flintsch, Alejandra Medina; Williams, Brian M.; Li, Yingfeng; Machiani, Sahar Ghanipoor; Bhagavathula, Rajaram (Virginia Transportation Research Council, 2018-06)
    Run-off-road crashes are a major problem for rural roads. These roads tend to be unlit, and drivers may have difficulty seeing or correctly predicting the curvature of horizontal curves. This leads to vehicles entering horizontal curves at speeds that are too high, which can often lead to vehicles running off the roadway. This study was designed to examine the effectiveness of a variety of active and passive curve warning and curve delineation systems on two two-lane rural roads to determine which is the most effective at reducing vehicle speeds and assisting lane-keeping. The study consisted of a human-factors study, as well as an observational study. There were nine curves examined in the study on two road sections in Southwest Virginia. The human-factors study included participants whose speed and lane position were tracked as they drove through eight curves, both before and after new treatments were installed in each of the eight curves. The observational study examined the speed and lane position of traffic on all the curves before and after the installation of the new treatments. The observational study included a curve on a road near the primary study section. The results of the study were mixed, with every tested system leading to some reductions in speed or encroachments at some parts of the curve while also leading to increases in the same values at other parts of the curve. No clear difference was discovered between passive and active systems or between delineation and warning systems. The study recommends that in addition to a safety assessment, specific curve characteristics and budget should be the main considerations in the selection of a treatment for a curve.
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    Evaluation of Transportation Safety Against Flooding in Disadvantaged Communities
    Tavakol-Davani, Hassan; O'Hara-Rhi, Vincent T.; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-05)
    Flooding in urban areas, especially in low-income or disadvantaged communities, poses a serious problem to drivers. While techniques exist to map and predict flooding events, a knowledge gap exists in accurate mapping and prediction of urban flooding. It is important to have an understanding of how much flooding a region may experience given a certain weather event so that drivers may preemptively avoid flooded areas. This paper synthesizes several approaches to build an understanding of the spatial extent of urban flooding in the frequently flooded parts of San Diego, California. First, flooding reported during major storms was used as validation data for a Generalized Linear Regression model to create a map of flood risk. Then, a Support Vector Machine model was used to extract areas of possible flooding from a satellite image. Finally, model performance was compared. Each model provided robust and meaningful results, with the Generalized Linear Model indicating which areas of the city are most at risk for flooding and the image classification Support Vector Machine model successfully identifying water bodies during both dry and wet conditions.
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    Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego
    Hasani, Mahdie; Jahangiri, Arash; Sener, Ipek Nese; Munira, Sirajum; Owens, Justin M.; Appleyard, Bruce; Ryan, Sherry; Turner, Shawn M.; Machiani, Sahar Ghanipoor (Hindawi, 2019-06-16)
    Over the last decade, demand for active transportation modes such as walking and bicycling has increased. While it is desirable to provide high levels of safety for these eco-friendly modes of travel, unfortunately, the overall percentage of pedestrian and bicycle fatalities increased from 13% to 18% of total road-related fatalities in the last decade. In San Diego County, although the total number of pedestrian and bicyclist fatalities decreased over the same period of time, a similar trend with a more drastic change is observed; the overall percentage of pedestrian and bicycle fatalities increased from 19.5% to 31.8%. This study aims to estimate pedestrian and bicyclist exposure and identify signalized intersections with highest risk for walking and bicycling within the city of San Diego, California, USA. Multiple data sources such as automated pedestrian and bicycle counters, video cameras, and crash data were utilized. Data mining techniques, a new sampling strategy, and automated video processing methods were adopted to demonstrate a holistic approach that can be applied to identify facilities with highest need of improvement. Cluster analysis coupled with stratification was employed to select a representative sample of intersections for data collection. Automated pedestrian and bicycle counting models utilized in this study reached a high accuracy, provided certain conditions exist in video data. Results from exposure modeling showed that pedestrian and bicyclist volume was characterized by transportation network, population, traffic generators, and land use variables. There were both similarities and differences between pedestrian and bicycle models, including different spatial scales of influence by mode. Additionally, the study quantified risk incorporating injury severity levels, frequency of victims, distance crossed, and exposure into a single equation. It was found that not all intersections with the highest number of pedestrian and bicyclist victims were identified as high-risk after exposure and other factors such as crash severity were taken into account.
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    Predicting Vehicle Trajectories at Intersections using Advanced Machine Learning Techniques
    Jazayeri, Mohammad Sadegh; Jahangiri, Arash; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
    The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety systems that aim to identify critical events such as near-crash situations and traffic violations. In a connected environment, important information about these critical events can be communicated to road users or the infrastructure to avoid or mitigate potential crashes. Intersections require special attention in this context because they are hotspots for crashes and involve numerous and complex interactions between road users. In this project, we developed an advanced machine learning method for trajectory prediction using B-spline curve representations of vehicle trajectories and Inverse Reinforcement Learning (IRL). B-spline curves were used to represent vehicle trajectories, and a neural network model was trained to predict the coefficients of these curves. Small perturbations of these predicted coefficients were used to create candidate trajectories. These candidate trajectories were then ranked according to a reward function that was obtained by training an IRL model on the (spline smoothed) vehicle trajectories and the surroundings of the vehicles. In our experiments we found that the neural network model outperforms a Kalman filter baseline and the addition of the IRL ranking module further improves the performance of the overall model.
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    Preventing Crashes in Mixed Traffic with Automated and Human-Driven Vehicles
    Talebpour, Alireza; Lord, Dominique; Manser, Michael P.; Machiani, Sahar Ghanipoor (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-11)
    Reducing crash counts on saturated road networks is one of the most significant benefits of autonomous vehicle (AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but less attention has been paid to car-following scenarios between AVs and human drivers. Braking and accelerating decision mismatches in this car-following scenario can lead to rear-end near-crashes and therefore warrant further study. This project aims to investigate the behavior of human drivers following an AV leader vehicle in a car-following situation and compare the results with a scenario in which the leader is a vehicle with human-modeled braking behavior. In this study, speed trajectory data was collected from 48 participants using a driving simulator.The results indicated a significant difference between the overall deceleration rates and braking speeds of the participants and the designated AV lead vehicle; however, no such difference was found between the participants and the human-modeled lead vehicle.
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    Safety Impact Evaluation of a Narrow-Automated Vehicle-Exclusive Reversible Lane on an Existing Smart Freeway
    Machiani, Sahar Ghanipoor; Jahangiri, Arash; Melendez, Benjamin; Katthe, Anagha; Hasani, Mahdie; Ahmadi, Alidad; Musial, Walter B. (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    This study fills the gap in the limited research on the effect of emerging Automated Vehicle (AV) technology on infrastructure standards. The main objective of this research is to evaluate implications of an innovative infrastructure solution, exclusive AV lanes, for safe and efficient integration of AVs into an existing transportation system. Examining a real-world case study, this project investigates implications of adding a narrow reversible AV exclusive lane to the existing configuration of the I-15 expressway in San Diego, resulting in a 9-foot AV reversible lane, and in both directions of travel, two 12-feet lanes for HOV and HOT vehicles. Given the difference between the operation of AVs and human-driven vehicles and reliance of AVs on sensors as opposed to human capabilities, the question is whether we can provide exclusive and narrower roadways for AVs while maintaining proper safety and mobility? To accomplish the project’s goal, the research team conducted a series of research approaches including a literature review, an AV manufacturers product review, expert interviews, a consumer questionnaire review, a crash data analysis, and a traffic simulation analysis. Recommendations and guidelines from the results of the study may be used for practitioners and professional organizations involved or interested in AV development.