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Understanding Crisis Communication and Mobility Resilience during Disasters from Social Media
Title
Understanding Crisis Communication and Mobility Resilience during Disasters from Social Media.
Creator
Roy, Kamol, Hasan, Samiul, Eluru, Naveen, Wu, Yina, University of Central Florida
Abstract / Description
Rapid communication during extreme events is one of the critical aspects of successful disaster management strategies. Due to their ubiquitous nature, social media platforms offer a unique opportunity for crisis communication. Moreover, social media usage on GPS enabled devices such as smartphones allow us to collect human movement data which can help understanding mobility during a disaster. This study leverages social media (Twitter) data to understand the effectiveness of social media...
Show moreRapid communication during extreme events is one of the critical aspects of successful disaster management strategies. Due to their ubiquitous nature, social media platforms offer a unique opportunity for crisis communication. Moreover, social media usage on GPS enabled devices such as smartphones allow us to collect human movement data which can help understanding mobility during a disaster. This study leverages social media (Twitter) data to understand the effectiveness of social media-based communication and the resilience of human mobility during a disaster. This thesis has two major contributions. First, about 52.5 million tweets related to hurricane Sandy are analyzed to assess the effectiveness of social media communication during disasters and identify the contributing factors leading to effective crisis communication strategies. Effectiveness of a social media user is defined as the ratio of attention gained over the number of tweets posted. A model is developed to explain more effective users based on several relevant features. Results indicate that during a disaster event, only few social media users become highly effective in gaining attention. In addition, effectiveness does not depend on the frequency of tweeting activity only; instead it depends on the number of followers and friends, user category, bot score (controlled by a human or a machine), and activity patterns (predictability of activity frequency). Second, to quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility infrastructure system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socio-economic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation's overall disaster resilience.
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Date Issued
2018
Identifier
CFE0007362, ucf:52090
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007362
Investigation of factors contributing to fog-related single vehicle crashes
Title
Investigation of factors contributing to fog-related single vehicle crashes.
Creator
Zhu, Jiazheng, Abdel-Aty, Mohamed, Hasan, Samiul, Wu, Yina, University of Central Florida
Abstract / Description
Fog-related crashes continue to be one of the most serious traffic safety problems in Florida. Based on the historical crash data, we found that single-vehicle crashes have the highest severity among all types of crashes under fog conditions. This study first analyzed the contributing factors of the fog-related single-vehicle crashes' (i.e., off road/rollover/other) severity in Florida from 2011 to 2014 using association rules mining. The results show that lane departure distracted driving,...
Show moreFog-related crashes continue to be one of the most serious traffic safety problems in Florida. Based on the historical crash data, we found that single-vehicle crashes have the highest severity among all types of crashes under fog conditions. This study first analyzed the contributing factors of the fog-related single-vehicle crashes' (i.e., off road/rollover/other) severity in Florida from 2011 to 2014 using association rules mining. The results show that lane departure distracted driving, wet road surface, and dark without road light are the main contributing factors to severe fog-related single vehicle crashes. Some suggested countermeasures were also provided to reduce the risk of fog-related single vehicle crashes. Since lane departure is one of the most important contributing factors to the single-vehicle crashes, an advanced warning system for lane departure under connected vehicle system was tested in driving simulation experiments. The system was designed based on the Vehicle-to-Infrastructure (V2I) with the concept of Augmented Reality (AR) using Head-Up Display (HUD). The results show that the warning with sound would reduce the lane departure and speed at curves, which would enhance the safety under fog conditions. In addition, the warning system was more effective for female drivers.
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Date Issued
2018
Identifier
CFE0007118, ucf:51935
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007118
INVESTIGATING AND MODELING THE IMPACTS OF ILLEGAL U-TURN VIOLATIONS AT MEDIANS LOCATED ON FLORIDA'S LIMITED ACCESS HIGHWAYS
Title
INVESTIGATING AND MODELING THE IMPACTS OF ILLEGAL U-TURN VIOLATIONS AT MEDIANS LOCATED ON FLORIDA'S LIMITED ACCESS HIGHWAYS.
Creator
Al-Sahili, Omar, Al-Deek, Haitham, Hasan, Samiul, Mantzaris, Alexander, University of Central Florida
Abstract / Description
Illegal U-turn violations are considered part of the Wrong-Way Driving (WWD) maneuvers that could result in head-on crashes and severe injuries, which are often severe because of the high speed of the approaching traffic and limited time to avoid such crash. Therefore, reviewing this type of violation and understanding the contributing factors that may lead drivers to commit such illegal maneuver would help officials foresee and consequently minimize the potential risks that could lead to WWD...
Show moreIllegal U-turn violations are considered part of the Wrong-Way Driving (WWD) maneuvers that could result in head-on crashes and severe injuries, which are often severe because of the high speed of the approaching traffic and limited time to avoid such crash. Therefore, reviewing this type of violation and understanding the contributing factors that may lead drivers to commit such illegal maneuver would help officials foresee and consequently minimize the potential risks that could lead to WWD crashes. The purpose of this thesis is to investigate the illegal U-turn maneuvers on limited access facilities and find the significant contributing factors that encourage or discourage drivers to commit this type of violation. The study area included the Central Florida area (CF), and the South Florida (SF) area. About 6 crossover crashes and 620 citations were found at the median facilities in the study areas from year 2011 to 2016.The modeling methodology for this thesis had three goals: predicting the number of illegal U-turn violations across the traversable grass median sections per year using a Poisson regression model, selecting the most effective variables in predicting the illegal U-turn violations using the least absolute shrinkage and selection operator (LASSO) variable selection method, and estimating the probability of an illegal U-turn violation occurrence at a paved median opening for official use only per year, using a logistic regression model. To determine the variables that influence the illegal U-turn violations, 9 geometric design and 2 traffic conditions exploratory variables were analyzed in the models mentioned earlier. Several variables were found significant from the Poisson model such as the distance to the nearest interchange, the length of the median segment, the number of access points in the segment, the median design, and the speed limit. Afterwards, the LASSO method concluded that the most effective variables found were the median design and the distance of to the nearest interchange. The logistic regression model in the CF area indicated that the speed limit and the AADT as the significant contributing factors. However, in the SF area the significant variables were the distance to the nearest access point and the spacing between the median openings. The variation in results indicates a considerable difference between the two study areas that should be accounted for during the planning phases for allocating the median countermeasures. The significant variables found in the mentioned modeling approach provide a first attempt to understand the illegal U-turn violations on limited access highways, and interpret the variables which influence drivers' behavior in performing such illegal maneuver. Along with required design guidelines, the models found could be used as effective planning tools to select the appreciate locations for installing new median openings and reevaluating the existing median openings to identify locations with the lowest potential risk.Other modeling techniques that include additional factors could be tested in future research so that appropriate countermeasures can be installed to reduce or eliminate these illegal U-turns. Furthermore, the methodology could be extended to arterials (or roads with partially controlled access).
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Date Issued
2017
Identifier
CFE0006708, ucf:51905
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006708
Analyzing Destination Choices of Tourists and Residents from Location Based Social Media Data
Title
Analyzing Destination Choices of Tourists and Residents from Location Based Social Media Data.
Creator
Hasnat, Md Mehedi, Hasan, Samiul, Abdel-Aty, Mohamed, Eluru, Naveen, University of Central Florida
Abstract / Description
Ubiquitous uses of social media platforms in smartphones have created an opportunity to gather digital traces of individual activities at a large scale. Traditional travel surveys fall short in collecting longitudinal travel behavior data for a large number of people in a cost effective way, especially for the transient population such as tourists. This study presents an innovating methodological framework, using machine learning and econometric approaches, to gather and analyze location...
Show moreUbiquitous uses of social media platforms in smartphones have created an opportunity to gather digital traces of individual activities at a large scale. Traditional travel surveys fall short in collecting longitudinal travel behavior data for a large number of people in a cost effective way, especially for the transient population such as tourists. This study presents an innovating methodological framework, using machine learning and econometric approaches, to gather and analyze location-based social media (LBSM) data to understand individual destination choices. First, using Twitter's search interface, we have collected Twitter posts of nearly 156,000 users for the state of Florida. We have adopted several filtering techniques to create a reliable sample from noisy Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The performance of the proposed classifier has been validated using manually labeled data and compared against the state-of-the-art classification methods. Second, using different clustering methods, we have analyzed the spatial distributions of destination choices of tourists and residents. The clusters from tourist destinations revealed most popular tourist spots including emerging tourist attractions in Florida. Third, to predict a tourist's next destination type, we have estimated a Conditional Random Field (CRF) model with reasonable accuracy. Fourth, to analyze resident destination choice behavior, this study proposes an extensive data merging operation among the collected Twitter data and different geographic database from state level data libraries. We have estimated a Panel Latent Segmentation Multinomial Logit (PLSMNL) model to find the characteristics affecting individual destination choices. The proposed PLSMNL model is found to better explain the effects of variables on destination choices compared to trip-specific Multinomial Logit Models. The findings of this study show the potential of LBSM data in future transportation and planning studies where collecting individual activity data is expensive.
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Date Issued
2018
Identifier
CFE0007012, ucf:52028
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007012
Assessing Pedestrian Safety Conditions on Campus
Title
Assessing Pedestrian Safety Conditions on Campus.
Creator
Morris, Morgan, Abdel-Aty, Mohamed, Hasan, Samiul, Wu, Yina, University of Central Florida
Abstract / Description
Pedestrian-related crashes are a significant safety issue in the United States and cause considerable amounts of deaths and economic cost. Pedestrian safety is an issue that must be uniquely evaluated in a college campus, where pedestrian volumes are dense. The objective of this research is to identify issues at specific locations around UCF and suggest solutions for improvement. To address this problem, a survey that identifies pedestrian safety issues and locations is distributed to UCF...
Show morePedestrian-related crashes are a significant safety issue in the United States and cause considerable amounts of deaths and economic cost. Pedestrian safety is an issue that must be uniquely evaluated in a college campus, where pedestrian volumes are dense. The objective of this research is to identify issues at specific locations around UCF and suggest solutions for improvement. To address this problem, a survey that identifies pedestrian safety issues and locations is distributed to UCF students and staff, and an evaluation of drivers reactions to pedestrian to vehicle (P2V) warning systems is studied through the use of a NADS MiniSim driving simulator. The survey asks participants to identify problem intersections around campus and other issues as pedestrians or bicyclists in the UCF area. Univariate probit models were created from the survey data to identify which factors contribute to pedestrian safety issues, based off the pedestrian's POV and the driver's POV. The models indicated that the more one is exposed to traffic via walking, biking, and driving to campus contributes to less safe experiences. The models also show that higher concerns with drivers not yielding, unsafety of crossing the intersections, and the number of locations to cross, indicate less safe pedestrian experiences from the point of view of pedestrians and drivers. A promising solution for pedestrian safety is Pedestrian to Vehicle (P2V) communication. This study simulates P2V connectivity using a NADS MiniSim Driving Simulator to study the effectiveness of the warning system on drivers. According to the results, the P2V warning system significantly reduced the number of crashes in the tested pre-crash scenarios by 88%. Particularly, the P2V warning system can help decrease the driver's reaction time as well as impact velocity if the crash were to occur.
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Date Issued
2019
Identifier
CFE0007839, ucf:52818
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007839
Applications of Deep Learning Models for Traffic Prediction Problems
Title
Applications of Deep Learning Models for Traffic Prediction Problems.
Creator
Rahman, Rezaur, Hasan, Samiul, Abdel-Aty, Mohamed, Zaki Hussein, Mohamed, University of Central Florida
Abstract / Description
Deep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first...
Show moreDeep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first problem, we present a modeling framework using deep LSTM NN model to predict traffic speeds in freeways during regular traffic condition as well as under extreme traffic demand, such as a hurricane evacuation. The approach is tested using real-world traffic data collected during hurricane Irma's evacuation for the interstate 75 (I-75), a major evacuation route in Florida. We perform several experiments for predicting speeds for 5 min, 10 min, and 15 min ahead of current time. The results are compared against other traditional prediction models such as K-Nearest Neighbor, Analytic Neural Network (ANN), Auto-Regressive Integrated Moving Average (ARIMA). We find that LSTM-NN performs better than these parametric and non-parametric models. Apart from the improvement in traffic operation, the proposed method can be integrated with evacuation traffic management systems for a better evacuation operation. In our second problem, we develop a data-driven real-time queue length prediction technique using deep LSTM NN model. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync Adaptive Traffic Control System (ATCS) data to train a Long Short-Term Memory Neural Network model capturing time-dependent patterns of a queue of a signal. To select the best combination of hyperparameters, we use sequential model-based optimization (SMBO) technique. Our experiment results show that the proposed modeling framework performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well. Queue length prediction is a crucial part of an ATCS to optimize control parameters and this method can improve the existing signal optimization technique for ATCS.
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Date Issued
2019
Identifier
CFE0007516, ucf:52654
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007516
Decision-making for Vehicle Path Planning
Title
Decision-making for Vehicle Path Planning.
Creator
Xu, Jun, Turgut, Damla, Zhang, Shaojie, Zhang, Wei, Hasan, Samiul, University of Central Florida
Abstract / Description
This dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate...
Show moreThis dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate world models and path planning behaviors.There are many different practical applications that map to this model. In this dissertation we propose algorithms for two applications that are very different in domain but share important formal similarities: the scheduling of taxi services in a large city and tracking wild animals with an unmanned aerial vehicle.The first application models a centralized taxi dispatch center in a big city. It is a multivariate optimization problem for taxi time scheduling and path planning. The first goal here is to balance the taxi service demand and supply ratio in the city. The second goal is to minimize passenger waiting time and taxi idle driving distance. We design different learning models that capture taxi demand and destination distribution patterns from historical taxi data. The predictions are evaluated with real-world taxi trip records. The predicted taxi demand and destination is used to build a taxi dispatch model. The taxi assignment and re-balance is optimized by solving a Mixed Integer Programming (MIP) problem.The second application concerns animal monitoring using an unmanned aerial vehicle (UAV) to search and track wild animals in a large geographic area. We propose two different path planing approaches for the UAV. The first one is based on the UAV controller solving Markov decision process (MDP). The second algorithms relies on the past recorded animal appearances. We designed a learning model that captures animal appearance patterns and predicts the distribution of future animal appearances. We compare the proposed path planning approaches with traditional methods and evaluated them in terms of collected value of information (VoI), message delay and percentage of events collected.
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Date Issued
2019
Identifier
CFE0007557, ucf:52606
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007557
Evaluation and Augmentation of Traffic Data from Private Sector and Bluetooth Detection System on Arterials
Title
Evaluation and Augmentation of Traffic Data from Private Sector and Bluetooth Detection System on Arterials.
Creator
Gong, Yaobang, Abdel-Aty, Mohamed, Hasan, Samiul, Cai, Qing, University of Central Florida
Abstract / Description
Traffic data are essential for public agencies to monitor the traffic condition of the roadway network in real-time. Recently, public agencies have implemented Bluetooth Detection Systems (BDS) on arterials to collect traffic data and purchased data directly from private sector vendors. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation. The thesis presents a study utilizing high-resolution GPS trajectories to evaluate data from HERE...
Show moreTraffic data are essential for public agencies to monitor the traffic condition of the roadway network in real-time. Recently, public agencies have implemented Bluetooth Detection Systems (BDS) on arterials to collect traffic data and purchased data directly from private sector vendors. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation. The thesis presents a study utilizing high-resolution GPS trajectories to evaluate data from HERE, one of the private sector data vendors, and BDS of arterial corridors in Orlando, Florida. The results showed that the accuracy and reliability of BDS data are better than private sector data, which might be credited to a better presentation of the bimodal traffic flow pattern on signalized arterials. In addition, another preliminary study aiming at improving the quality of private sector data was also demonstrated. Information about bimodal traffic flow extracted by a finite mixture model from historical BDS is employed to augment real-time private sector data by a Bayesian inference framework. The evaluation of the augmented data showed that the augmentation framework is effective for the most part of the studied corridor except for segments highly influenced by traffic from or to the expressway ramps.
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Date Issued
2018
Identifier
CFE0007330, ucf:52120
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007330
Wrong-Way Driving: A Regional Approach To A Regional Problem
Title
Wrong-Way Driving: A Regional Approach To A Regional Problem.
Creator
Faruk, Md. Omar, Al-Deek, Haitham, Uddin, Nizam, Hasan, Samiul, University of Central Florida
Abstract / Description
Wrong-way driving (WWD) has been problematic on United States highways for decades despite its rare occurrence. Since WWD crashes are rare, recent researchers have studied WWD non-crash events such as WWD 911 calls and WWD citations to understand the overall nature and trend of WWD. This paper demonstrates the regional nature of the WWD problem and proposes regional transportation systems management and operations (Regional TSM(&)O) solutions to combat this problem. Specifically, it was found...
Show moreWrong-way driving (WWD) has been problematic on United States highways for decades despite its rare occurrence. Since WWD crashes are rare, recent researchers have studied WWD non-crash events such as WWD 911 calls and WWD citations to understand the overall nature and trend of WWD. This paper demonstrates the regional nature of the WWD problem and proposes regional transportation systems management and operations (Regional TSM(&)O) solutions to combat this problem. Specifically, it was found that 11% of all WWD multi-data events (e.g., multiple 911 calls for the same WWD event) traveled from one county to another. Additionally, 30% of all WWD single-data and multi-data events occurred at or near interchanges between two limited access highways in counties with multiple operating agencies. This indicates that a significant proportion of WWD events could potentially travel from one limited access facility to another. Moreover, 28% of WWD events occurred on limited access facilities shared by multiple agencies. To emphasize the regional nature of WWD, this paper determined the vulnerable demographic groups in different regions of Florida by developing WWD crash and citation prediction models. The models' findings indicate that certain demographic groups (such as elderly or Hispanic) increase WWD risk. The models' results can be used to improve driver education and increase law enforcement presence in high risk WWD locations. Regional TSM(&)O solutions, such as coordination and communication among agencies and regional traffic management centers (RTMCs), law enforcement co-location with RTMCs, and strengthening statewide TSM(&)O programs to manage WWD events are also proposed.
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Date Issued
2017
Identifier
CFE0006874, ucf:51736
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006874
Development and Application of an Optimization Approach for Cost-Effective Deployment of Advanced Wrong-Way Driving Countermeasures
Title
Development and Application of an Optimization Approach for Cost-Effective Deployment of Advanced Wrong-Way Driving Countermeasures.
Creator
Sandt, Adrian, Al-Deek, Haitham, Eluru, Naveen, Hasan, Samiul, Zheng, Qipeng, University of Central Florida
Abstract / Description
Wrong-way driving (WWD) is a dangerous behavior, especially on high-speed divided highways. The nature of WWD crashes makes it difficult for agencies to combat them effectively. Advanced WWD countermeasures equipped with flashing lights, detection devices, and cameras can significantly reduce WWD. However, these countermeasures' high costs mean that agencies often cannot deploy them at all exit ramps. To help agencies identify the most cost-effective deployment locations for advanced WWD...
Show moreWrong-way driving (WWD) is a dangerous behavior, especially on high-speed divided highways. The nature of WWD crashes makes it difficult for agencies to combat them effectively. Advanced WWD countermeasures equipped with flashing lights, detection devices, and cameras can significantly reduce WWD. However, these countermeasures' high costs mean that agencies often cannot deploy them at all exit ramps. To help agencies identify the most cost-effective deployment locations for advanced WWD countermeasures, an innovative WWD countermeasure optimization approach was developed. This approach consists of a WWD hotspots model and a WWD countermeasures optimization algorithm. The WWD hotspots model uses non-crash WWD events, interchange designs, and traffic volumes to predict the number of WWD crashes on multi-exit roadway segments and identify hotspot segments with high WWD crash risk (WWCR). Then, the optimization algorithm uses these WWCR values to identify the optimal exits for advanced WWD countermeasure deployment based on available resources and other applicable constraints. This approach was applied to the Central Florida Expressway Authority (CFX) and Florida's Turnpike Enterprise (FTE) toll road networks. In both applications, the optimization algorithm provided significant WWCR reduction while meeting investment and other constraints and better allocated the agencies' resources compared to only deploying advanced WWD countermeasures in WWD hotspots. The optimization algorithm was also used to identify mainline sections on the CFX network with high WWCR. Additionally, the optimization algorithm was used to evaluate existing Rectangular Flashing Beacon (RFB) and Light-Emitting Diode (LED) advanced WWD countermeasures on the CFX (RFBs) and FTE (RFBs and LEDs) networks. These evaluations showed that the crash reduction and injury reduction benefits of these advanced WWD countermeasures have exceeded their costs since these countermeasures have been deployed. By using this WWD countermeasures optimization approach, agencies throughout the United States could proactively and cost-effectively deploy advanced WWD countermeasures to reduce WWD.
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Date Issued
2018
Identifier
CFE0007364, ucf:52093
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007364
Modeling of Wrong Way Driving Entries and Developing Innovative Approaches for Evaluating the Effectiveness of Advanced Wrong Way Driving Countermeasures
Title
Modeling of Wrong Way Driving Entries and Developing Innovative Approaches for Evaluating the Effectiveness of Advanced Wrong Way Driving Countermeasures.
Creator
Kayes, Md Imrul, Al-Deek, Haitham, Eluru, Naveen, Hasan, Samiul, Uddin, Nizam, University of Central Florida
Abstract / Description
Wrong-way driving (WWD) is a hazardous behavior on interstates, toll roads, and other high-speed limited access facilities. Since WWD crashes are rare, recent researchers have studied WWD events such as WWD 911 calls and WWD citations to understand the overall nature and trend of WWD. It is very difficult to build credible statistical models based solely on crashes due to the small sample size since these are only 3% of all crashes. Modeling of WWD non-crash events can result in more accurate...
Show moreWrong-way driving (WWD) is a hazardous behavior on interstates, toll roads, and other high-speed limited access facilities. Since WWD crashes are rare, recent researchers have studied WWD events such as WWD 911 calls and WWD citations to understand the overall nature and trend of WWD. It is very difficult to build credible statistical models based solely on crashes due to the small sample size since these are only 3% of all crashes. Modeling of WWD non-crash events can result in more accurate models. A model was developed for Florida limited access facilities to identify roadway factors and traffic characteristics of exit ramp terminals that influence WWD entries. This model indicated that interchange type, intersection angle of exit ramp terminals, presence of tolling at the entrance ramp, presence of channelizing island between the exit ramp lanes, number of lanes on the exit ramp, area (rural or urban), and traffic volumes significantly affect the likelihood of WWD entries at exit ramps. Conventional (")Wrong Way(") signs can reduce WWD incidents but can be insufficient in some cases. In areas with many WWD crash and non-crash events, transportation agencies can be proactive by considering the use of countermeasures with advanced technologies to actively warn motorists of WWD violations. To help agencies select the most effective countermeasure, two innovative evaluation of performance approaches were developed so they can be used to evaluate and compare among different advanced WWD countermeasures. These approaches consist of before-after analysis of WWD non-crash events (WWD 911 calls and citations) and turn around rates of wrong way vehicles to self-correct their WWD acts. With this research, transportation agencies can better predict WWD entries at exit ramps; identify suitable locations for possible countermeasures deployment; and improve their current design, signing, and pavement marking practices while still following national and state standards.
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Date Issued
2019
Identifier
CFE0007474, ucf:52672
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007474
Rethinking Routing and Peering in the era of Vertical Integration of Network Functions
Title
Rethinking Routing and Peering in the era of Vertical Integration of Network Functions.
Creator
Dey, Prasun, Yuksel, Murat, Wang, Jun, Ewetz, Rickard, Zhang, Wei, Hasan, Samiul, University of Central Florida
Abstract / Description
Content providers typically control the digital content consumption services and are getting the most revenue by implementing an (")all-you-can-eat(") model via subscription or hyper-targeted advertisements. Revamping the existing Internet architecture and design, a vertical integration where a content provider and access ISP will act as unibody in a sugarcane form seems to be the recent trend. As this vertical integration trend is emerging in the ISP market, it is questionable if existing...
Show moreContent providers typically control the digital content consumption services and are getting the most revenue by implementing an (")all-you-can-eat(") model via subscription or hyper-targeted advertisements. Revamping the existing Internet architecture and design, a vertical integration where a content provider and access ISP will act as unibody in a sugarcane form seems to be the recent trend. As this vertical integration trend is emerging in the ISP market, it is questionable if existing routing architecture will suffice in terms of sustainable economics, peering, and scalability. It is expected that the current routing will need careful modifications and smart innovations to ensure effective and reliable end-to-end packet delivery. This involves new feature developments for handling traffic with reduced latency to tackle routing scalability issues in a more secure way and to offer new services at cheaper costs. Considering the fact that prices of DRAM or TCAM in legacy routers are not necessarily decreasing at the desired pace, cloud computing can be a great solution to manage the increasing computation and memory complexity of routing functions in a centralized manner with optimized expenses. Focusing on the attributes associated with existing routing cost models and by exploring a hybrid approach to SDN, we also compare recent trends in cloud pricing (for both storage and service) to evaluate whether it would be economically beneficial to integrate cloud services with legacy routing for improved cost-efficiency. In terms of peering, using the US as a case study, we show the overlaps between access ISPs and content providers to explore the viability of a future in terms of peering between the new emerging content-dominated sugarcane ISPs and the healthiness of Internet economics. To this end, we introduce meta-peering, a term that encompasses automation efforts related to peering (-) from identifying a list of ISPs likely to peer, to injecting control-plane rules, to continuous monitoring and notifying any violation (-) one of the many outcroppings of vertical integration procedure which could be offered to the ISPs as a standalone service.
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Date Issued
2019
Identifier
CFE0007797, ucf:52351
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007797
Arterial-level real-time safety evaluation in the context of proactive traffic management
Title
Arterial-level real-time safety evaluation in the context of proactive traffic management.
Creator
Yuan, Jinghui, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Cai, Qing, Wang, Liqiang, University of Central Florida
Abstract / Description
In the context of pro-active traffic management, real-time safety evaluation is one of the most important components. Previous studies on real-time safety analysis mainly focused on freeways, seldom on arterials. With the advancement of sensing technologies and smart city initiative, more and more real-time traffic data sources are available on arterials, which enables us to evaluate the real-time crash risk on arterials. However, there exist substantial differences between arterials and...
Show moreIn the context of pro-active traffic management, real-time safety evaluation is one of the most important components. Previous studies on real-time safety analysis mainly focused on freeways, seldom on arterials. With the advancement of sensing technologies and smart city initiative, more and more real-time traffic data sources are available on arterials, which enables us to evaluate the real-time crash risk on arterials. However, there exist substantial differences between arterials and freeways in terms of traffic flow characteristics, data availability, and even crash mechanism. Therefore, this study aims to deeply evaluate the real-time crash risk on arterials from multiple aspects by integrating all kinds of available data sources. First, Bayesian conditional logistic models (BCL) were developed to examine the relationship between crash occurrence on arterial segments and real-time traffic and signal timing characteristics by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted from four urban arterials in Central Florida. Second, real-time intersection-approach-level crash risk was investigated by considering the effects of real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Orange County. Third, a deep learning algorithm for real-time crash risk prediction at signalized intersections was proposed based on Long Short-Term Memory (LSTM) and Synthetic Minority Over-Sampling Technique (SMOTE). Moreover, in-depth cycle-level real-time crash risk at signalized intersections was explored based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). All the possible real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather conditions. Above all, comprehensive real-time safety evaluation algorithms were developed for arterials, which would be key components for future real-time safety applications (e.g., real-time crash risk prediction and visualization system) in the context of pro-active traffic management.
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Date Issued
2019
Identifier
CFE0007743, ucf:52398
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007743
Assessing the Safety and Operational Benefits of Connected and Automated Vehicles: Application on Different Roadways, Weather, and Traffic Conditions
Title
Assessing the Safety and Operational Benefits of Connected and Automated Vehicles: Application on Different Roadways, Weather, and Traffic Conditions.
Creator
Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
Abstract / Description
Connected and automated vehicle (CAV) technologies have recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. Connected vehicle (CV) technologies provide real-time information about the surrounding traffic condition (i.e., position, speed, acceleration) and the traffic management center's decisions. The CV technologies improve the safety by increasing driver situational awareness and reducing crashes through vehicle-to-vehicle (V2V) and vehicle-to...
Show moreConnected and automated vehicle (CAV) technologies have recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. Connected vehicle (CV) technologies provide real-time information about the surrounding traffic condition (i.e., position, speed, acceleration) and the traffic management center's decisions. The CV technologies improve the safety by increasing driver situational awareness and reducing crashes through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Vehicle platooning with CV technologies is another key element of the future transportation systems which helps to simultaneously enhance traffic operations and safety. CV technologies can also further increase the efficiency and reliability of automated vehicles (AV) by collecting real-time traffic information through V2V and V2I. However, the market penetration rate (MPR) of CAVs and the higher level of automation might not be fully available in the foreseeable future. Hence, it is worthwhile to study the safety benefits of CAV technologies under different MPRs and lower level of automation. None of the studies focused on both traffic safety and operational benefits for these technologies including different roadway, traffic, and weather conditions. In this study, the effectiveness of CAV technologies (i.e., CV /AV/CAV/CV platooning) were evaluated in different roadway, traffic, and weather conditions. To be more specific, the impact of CVs in reduced visibility condition, longitudinal safety evaluation of CV platooning in the managed lane, lower level of AVs in arterial roadway, and the optimal MPRs of CAVs for both peak and off-peak period are analyzed using simulation techniques. Currently, CAV fleet data are not easily obtainable which is one of the primary reasons to deploy the simulation techniques in this study to evaluate the impacts of CAVs in the roadway. The car following, lane changing, and the platooning behavior of the CAV technologies were modeled in the C++ programming language by considering realistic car following and lane changing models in PTV VISSIM. Surrogate safety assessment techniques were considered to evaluate the safety effectiveness of these CAV technologies, while the average travel time, average speed, and average delay were evaluated as traffic operational measures. Several statistical tests (i.e., Two sample t-test, ANOVA) and the modelling techniques (Tobit, Negative binomial, and Logistic regression) were conducted to evaluate the CAV effectiveness with different MPRs over the baseline scenario. The statistical tests and modeling results suggested that the higher the MPR of CAVs implemented, the higher were the safety and mobility benefits achieved for different roadways (i.e., freeway, expressway, arterials, managed lane), weather (i.e., clear, foggy), and traffic conditions (i.e., peak and off-peak period). Interestingly, from the safety and operation perspective, at least 30% and 20% MPR were needed to achieve both the safety and operational benefits of peak and off-peak period, respectively. This dissertation has major implications for improving transportation infrastructure by recommending optimal MPR of CAVs to achieve balanced mobility and safety benefits considering varying roadway, traffic, and weather condition.
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Date Issued
2019
Identifier
CFE0007709, ucf:52442
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007709
Safety, Operational, and Design Analyses of Managed Toll and Connected Vehicles' Lanes
Title
Safety, Operational, and Design Analyses of Managed Toll and Connected Vehicles' Lanes.
Creator
Saad, Moatz, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Oloufa, Amr, Yan, Xin, University of Central Florida
Abstract / Description
Managed lanes (MLs) have been implemented as a vital strategy for traffic management and traffic safety improvement. The majority of previous studies involving MLs have explored a limited scope of the impact of the MLs segments as a whole, without considering the safety and operational effects of the access design. Also, there are limited studies that investigated the effect of connected vehicles (CVs) on managed lanes. Hence, this study has two main objectives: (1) the first objective is...
Show moreManaged lanes (MLs) have been implemented as a vital strategy for traffic management and traffic safety improvement. The majority of previous studies involving MLs have explored a limited scope of the impact of the MLs segments as a whole, without considering the safety and operational effects of the access design. Also, there are limited studies that investigated the effect of connected vehicles (CVs) on managed lanes. Hence, this study has two main objectives: (1) the first objective is achieved by determining the optimal managed lanes access design, including accessibility level and weaving distance for an at-grade access design. (2) the second objective is to study the effects of applying CVs and CV lanes on the MLs network. Several scenarios were tested using microscopic traffic simulation to determine the optimal access design while taking into consideration accessibility levels and weaving lengths. Both safety (e.g., standard deviation of speed, time-to-collision, and conflict rate) and operational (e.g., level of service, average speed, average delay) performance measures were included in the analyses. For the first objective, the results suggested that one accessibility level is the optimal option for the 9-mile network. A weaving length between 1,000 feet to 1,400 feet per lane change was suggested based on the safety analysis. From the operational perspective, a weaving length between 1,000 feet and 2,000 feet per lane change was recommended. The findings also suggested that MPR% between 10% and 30% was recommended when the CVs are only allowed in MLs. When increasing the number of MLs, the MPR% could be improved to reach 70%. Lastly, the findings proposed that MPR% of 100% could be achieved by allowing the CVs to use all the lanes in the network.
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Date Issued
2019
Identifier
CFE0007719, ucf:52428
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007719
Scalable Map Information Dissemination for Connected and Automated Vehicle Systems
Title
Scalable Map Information Dissemination for Connected and Automated Vehicle Systems.
Creator
Gani, S M Osman, Pourmohammadi Fallah, Yaser, Vosoughi, Azadeh, Yuksel, Murat, Chatterjee, Mainak, Hasan, Samiul, University of Central Florida
Abstract / Description
Situational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle...
Show moreSituational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle this challenge, the inter-vehicle communication system is envisioned that allows vehicles to exchange self-status updates aiming to extend their effective field of view and thus compensate for the limitations of the vehicle tracking subsystem that relies substantially on onboard sensing devices. Tracking capability in such systems can be further improved through the cooperative sharing of locally created map data instead of transmitting only self-update messages containing core basic safety message (BSM) data. In the cooperative sharing of safety messages, it is imperative to have a scalable communication protocol to ensure optimal use of the communication channel. This dissertation contributes to the analysis of the scalability issue in vehicle-to-everything (V2X) communication and then addresses the range issue of situational awareness in CAV systems by proposing a content-adaptive V2X communication architecture. To that end, we first analyze the BSM scheduling protocol standardized in the SAE J2945/1 and present large-scale scalability results obtained from a high-fidelity simulation platform to demonstrate the protocol's efficacy to address the scalability issues in V2X communication. By employing a distributed opportunistic approach, the SAE J2945/1 congestion control algorithm keeps the overall offered channel load within an optimal operating range, while meeting the minimum tracking requirements set forth by upper-layer applications. This scheduling protocol allows event-triggered and vehicle-dynamics driven message transmits that further the situational awareness in a cooperative V2X context. Presented validation results of the congestion control algorithm include position tracking errors as the performance measure, with the age of communicated information as the evaluation measure. In addition, we examine the optimality of the default settings of the congestion control parameters. Comprehensive analysis and trade-off study of the control parameters reveal some areas of improvement to further the algorithm's efficacy. Motivated by the effectiveness of channel congestion control mechanism, we further investigate message content and length adaptations, together with transmit rate control. Reasonably, the content of the exchanged information has a significant impact on the map accuracy in cooperative driving systems. We investigate different content control schemes for a communication architecture aimed at map sharing and evaluate their performance in terms of position tracking error. This dissertation determines that message content should be concentrated to mapped objects that are located farther away from the sender to the edge of the local sensor range. This dissertation also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system.
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Date Issued
2019
Identifier
CFE0007634, ucf:52470
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007634
Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level
Title
Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level.
Creator
Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
Abstract / Description
This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based...
Show moreThis thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model's estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model.
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Date Issued
2018
Identifier
CFE0007358, ucf:52103
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007358
Development of Decision Support System for Active Traffic Management Systems Considering Travel Time Reliability
Title
Development of Decision Support System for Active Traffic Management Systems Considering Travel Time Reliability.
Creator
Chung, Whoibin, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Cai, Qing, Huang, Hsin-Hsiung, University of Central Florida
Abstract / Description
As traffic problems on roadways have been increasing, active traffic management systems (ATM) using proactive traffic management concept have been deployed on freeways and arterials. The ATM aims to integrate and automate various traffic control strategies such as variable speed limits, queue warning, and ramp metering through a decision support system (DSS). Over the past decade, there have been many efforts to integrate freeways and arterials for the efficient operation of roadway networks....
Show moreAs traffic problems on roadways have been increasing, active traffic management systems (ATM) using proactive traffic management concept have been deployed on freeways and arterials. The ATM aims to integrate and automate various traffic control strategies such as variable speed limits, queue warning, and ramp metering through a decision support system (DSS). Over the past decade, there have been many efforts to integrate freeways and arterials for the efficient operation of roadway networks. It has been required that these systems should prove their effectiveness in terms of travel time reliability. Therefore, this study aims to develop a new concept of a decision support system integrating variable speed limits, queue warning, and ramp metering on the basis of travel time reliability of freeways and arterials.Regarding the data preparation, in addition to collecting multiple data sources such as traffic data, crash data and so on, the types of traffic data sources that can be applied for the analysis of travel time reliability were investigated. Although there are many kinds of real-time traffic data from third-party traffic data providers, it was confirmed that these data cannot represent true travel time reliability through the comparative analysis of measures of travel time reliability. Related to weather data, it was proven that nationwide land-based weather stations could be applicable.Since travel time reliability can be measured by using long-term periods for more than six months, it is necessary to develop models to estimate travel time reliability through real-time traffic data and event-related data. Among various matrix to measure travel time reliability, the standard deviation of travel time rate [minute/mile] representing travel time variability was chosen because it can represent travel time variability of both link and network level. Several models were developed to estimate the standard deviation of travel time rate through average travel time rate, the number of lanes, speed limits, and the amount of rainfall.Finally, a DSS using a model predictive control method to integrate multiple traffic control measures was developed and evaluated. As a representative model predictive control, METANET model was chosen, which can include variable speed limit, queue warning, and ramp metering, separately or combined. The developed DSS identified a proper response plan by comparing travel time reliability among multiple combinations of current and new response values of strategies. In the end, it was found that the DSS provided the reduction of travel time and improvement of its reliability for travelers through the recommended response plans.
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Date Issued
2019
Identifier
CFE0007615, ucf:52542
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007615
Integrating the macroscopic and microscopic traffic safety analysis using hierarchical models
Title
Integrating the macroscopic and microscopic traffic safety analysis using hierarchical models.
Creator
Cai, Qing, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Lee, JaeYoung, Yan, Xin, University of Central Florida
Abstract / Description
Crash frequency analysis is a crucial tool to investigate traffic safety problems. With the objective of revealing hazardous factors which would affect crash occurrence, crash frequency analysis has been undertaken at the macroscopic and microscopic levels. At the macroscopic level, crashes from a spatial aggregation (such as traffic analysis zone or county) are considered to quantify the impacts of socioeconomic and demographic characteristics, transportation demand and network attributes so...
Show moreCrash frequency analysis is a crucial tool to investigate traffic safety problems. With the objective of revealing hazardous factors which would affect crash occurrence, crash frequency analysis has been undertaken at the macroscopic and microscopic levels. At the macroscopic level, crashes from a spatial aggregation (such as traffic analysis zone or county) are considered to quantify the impacts of socioeconomic and demographic characteristics, transportation demand and network attributes so as to provide countermeasures from a planning perspective. On the other hand, the microscopic crashes on a segment or intersection are analyzed to identify the influence of geometric design, lighting and traffic flow characteristics with the objective of offering engineering solutions (such as installing sidewalk and bike lane, adding lighting). Although numerous traffic safety studies have been conducted, still there are critical limitations at both levels. In this dissertation, several methodologies have been proposed to alleviate several limitations in the macro- and micro-level safety research. Then, an innovative method has been suggested to analyze crashes at the two levels, simultaneously. At the macro-level, the viability of dual-state models (i.e., zero-inflated and hurdle models) were explored for traffic analysis zone based pedestrian and bicycle crash analysis. Additionally, spatial spillover effects were explored in the models by employing exogenous variables from neighboring zones. Both conventional single-state model (i.e., negative binomial) and dual-state models such as zero-inflated negative binomial and hurdle negative binomial models with and without spatial effects were developed. The model comparison results for pedestrian and bicycle crashes revealed that the models that considered observed spatial effects perform better than the models that did not consider the observed spatial effects. Across the models with spatial spillover effects, the dual-state models especially zero-inflated negative binomial model offered better performance compared to single-state models. Moreover, the model results clearly highlighted the importance of various traffic, roadway, and sociodemographic characteristics of the TAZ as well as neighboring TAZs on pedestrian and bicycle crash frequency. Then, the modifiable areal unit problem for macro-level crash analysis was discussed. Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), traffic analysis zones (TAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs). Poisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) were developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposed a method to compare the modeling performance of the three types of geographic units at different spatial configuration through a grid based framework. Specifically, the study region was partitioned to grids of various sizes and the model prediction accuracy of the various macro models was considered within these grids of various sizes. These model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperformed the ones that do not consider it. Finally, based on the modeling results, it is recommended to adopt TADs for transportation safety planning.After determining the optimal traffic safety analysis zonal system, further analysis was conducted for non-motorist crashes (pedestrian and bicycle crashes). This study contributed to the literature on pedestrian and bicyclist safety by building on the conventional count regression models to explore exogenous factors affecting pedestrian and bicyclist crashes at the macroscopic level. In the traditional count models, effects of exogenous factors on non-motorist crashes were investigated directly. However, the vulnerable road users' crashes are collisions between vehicles and non-motorists. Thus, the exogenous factors can affect the non-motorist crashes through the non-motorists and vehicle drivers. To accommodate for the potentially different impact of exogenous factors we converted the non-motorist crash counts as the product of total crash counts and proportion of non-motorist crashes and formulated a joint model of the negative binomial (NB) model and the logit model to deal with the two parts, respectively. The formulated joint model was estimated using non-motorist crash data based on the Traffic Analysis Districts (TADs) in Florida. Meanwhile, the traditional NB model was also estimated and compared with the joint model. The results indicated that the joint model provides better data fit and could identify more significant variables. Subsequently, a novel joint screening method was suggested based on the proposed model to identify hot zones for non-motorist crashes. The hot zones of non-motorist crashes were identified and divided into three types: hot zones with more dangerous driving environment only, hot zones with more hazardous walking and cycling conditions only, and hot zones with both. At the microscopic level, crash modeling analysis was conducted for road facilities. This study, first, explored the potential macro-level effects which are always excluded or omitted in the previous studies. A Bayesian hierarchical model was proposed to analyze crashes on segments and intersection incorporating the macro-level data, which included both explanatory variables and total crashes of all segments and intersections. Besides, a joint modeling structure was adopted to consider the potentially spatial autocorrelation between segments and their connected intersections. The proposed model was compared with three other models: a model considering micro-level factors only, one hierarchical model considering macro-level effects with random terms only, and one hierarchical model considering macro-level effects with explanatory variables. The results indicated that models considering macro-level effects outperformed the model having micro-level factors only, which supports the idea to consider macro-level effects for micro-level crash analysis. Besides, the micro-level models were even further enhanced by the proposed model. Finally, significant spatial correlation could be found between segments and their adjacent intersections, supporting the employment of the joint modeling structure to analyze crashes at various types of road facilities. In addition to the separated analysis at either the macro- or micro-level, an integrated approach has been proposed to examine traffic safety problems at the two levels, simultaneously. If conducted in the same study area, the macro- and micro-level crash analyses should investigate the same crashes but aggregating the crashes at different levels. Hence, the crash counts at the two levels should be correlated and integrating macro- and micro-level crash frequency analyses in one modeling structure might have the ability to better explain crash occurrence by realizing the effects of both macro- and micro-level factors. This study proposed a Bayesian integrated spatial crash frequency model, which linked the crash counts of macro- and micro-levels based on the spatial interaction. In addition, the proposed model considered the spatial autocorrelation of different types of road facilities (i.e., segments and intersections) at the micro-level with a joint modeling structure. Two independent non-integrated models for macro- and micro-levels were also estimated separately and compared with the integrated model. The results indicated that the integrated model can provide better model performance for estimating macro- and micro-level crash counts, which validates the concept of integrating the models for the two levels. Also, the integrated model provides more valuable insights about the crash occurrence at the two levels by revealing both macro- and micro-level factors. Subsequently, a novel hotspot identification method was suggested, which enables us to detect hotspots for both macro- and micro-levels with comprehensive information from the two levels. It is expected that the proposed integrated model and hotspot identification method can help practitioners implement more reasonable transportation safety plans and more effective engineering treatments to proactively enhance safety.
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Date Issued
2017
Identifier
CFE0006724, ucf:51891
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006724