Journal Papers


Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies
  Author: Jingqin Gao, Kaan Ozbay, Hani N Nassif, Onur Kalan

  Journal: Transportation Research Record: Journal of the Transportation Research Board

  Abstract: The sustainability of transportation infrastructure depends on the adoption of new construction materials and technologies with great promise for improved performance and productivity. However, most agencies would like to evaluate these new materials and technologies at both project level and network level before replacing the traditional ones. It also remains a challenge to reliably estimate the costs and lifetime performance of new construction materials and technologies due to limited implementation data. To address these issues, this paper presents a comprehensive bottom-up methodology based on Life Cycle Cost Analysis (LCCA) to integrate project- and network-level analysis that can fast-track the acceptance of new materials or technologies. Hypothesized improvement rates are applied to the deterioration functions of existing materials to represent the expected improved performance of a new material compared with a conventional material with relatively similar characteristics. This new approach with stochastic treatment allows us to probabilistically evaluate new materials with limited data for their future performance. Feasible maintenance and rehabilitation schedules are found for each facility at the project level and near optimal investment strategies are identified at the network level by using a metaheuristic evolutionary algorithm while satisfying network-wide constraints. This provides an effective solution to many issues that have not been completely addressed in the past, including the trade-off between multiple objectives, effect of time, uncertainty and outcome interpretation. A hypothetical bridge decks system from New Jerseys bridge inventory database is used to demonstrate the applicability of the proposed methodology in construction planning and management decision support procedure.

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Modeling of time-dependent safety performance using anonymized and aggregated smartphone-based dangerous driving event data
  Author: Di Yang, Kun Xie, Kaan Ozbay, Hong Yang, Budnick, N.

  Journal: Accident Analysis & Prevention 132, 105286


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Is Additive Utility Function Always a Sufficient Method in the Project Prioritization Process? A Bridge Management Perspective
  Author: Onur Kalan, Abdullah Kurkcu, Kaan Ozbay

  Journal: Transportation Research Record: Journal of the Transportation Research Board

  Abstract: The prioritization of maintenance activities in bridges has great importance in Bridge Asset Management Systems as they are mentioned in MAP-21. One of the most common used prioritization methodologies in Bridge Management Systems is Multi-Attribute Utility Theory process. In this study, the problem is defined as using the additive functional form in this process without testing Additive Independence (AI) assumption which is one of the properties of Multi-Attribute Utility Theory. This study aims to emphasize the strength of the use of multiplicative functional forms when the multiplicative form is proven to be more appropriate by AI assumption test. To demonstrate this vital point, mathematical expressions are derived for the feasible regions of Indifference Curves. Then, the optimum region for both additive and multiplicative approaches are calculated using these analytical expressions to demonstrate the difference between the two in terms of maximizing utility. This comparison is aimed at preventing the suboptimal decisions due to the use additive approach when the multiplicative approach is more representative of the actual decision-making process. The relevance of this claim is also demonstrated using a simple hypothetical scenario. Findings of the paper provide valuable insights to decision-makers and policymakers about the importance of choosing the most appropriate functional form for utility functions employed in a prioritization. We hope that policy-makers at State DOTs will use the comparative analysis of the effect of utility functions on the final project selection process presented in this paper as part of their routine decision-making process.

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Estimating Uncertainty of Work Zone Capacity using Neural Network Models
  Author: Zilin Bian, Kaan Ozbay

  Journal: Transportation Research Record: Journal of the Transportation Research Board

  Abstract: This study aims to develop a neural network model to predict work zone capacity including various uncertainties stemming from traffic and operational conditions. The neural network model is formulated in terms of the number of total lanes, number of open lanes, heavy vehicle percentage, work intensity, and work duration. The data used in this paper are obtained from previous studies published in open literature. To capture the uncertainty of work zone capacity, this paper provides two recent methods that enable neural network models to generate prediction intervals which are determined by mean work zone capacity and prediction standard error. The research first builds a Bayesian neural network model with the application of black-box variational inference (BBVI) technique. The second model is based on a regular artificial neural network with an application of the recently proposed Monte-Carlo dropout technique. Both of the neural network models construct prediction intervals under various confidence levels and provide the coverage rates of the actual work zone capacities. The statistical accuracy (MAPE, MAE, MSE, and RMSE) of the models is then compared with traditional estimation methods in predicted mean work zone capacity. BBVI produces better statistical results than the other three models. Both of the models provide predicted work zone capacity distribution and prediction intervals, whereas traditional models only provide a single estimate.

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Use of real-world connected vehicle data in identifying high-risk locations based on a new surrogate safety measure
  Author: Kun Xie, Di Yang, Kaan Ozbay, Hong Yang

  Journal: Accident Analysis & Prevention 125, 311- 319


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Design, Implementation and Testing of a New Mobile Multi-Function Sensing Device for Identifying High-Risk Areas for Bicyclists in Highly Congested Urban Streets
  Author: Suzana Duran Bernardes, Abdullah Kurkcu, Kaan Ozbay

  Journal: Procedia Computer Science

  Abstract: The number of bicycle riders in New York City has been increasing steadily in the past few years. These numbers include private and shared bicycles. NYC bicycle network has been expanded to accommodate this new volume. Although this new infrastructure has reduced the number of cyclists killed or seriously injured (KSI) in some areas, in other areas similar improvements were not observed. This inconsistency of how the number of bicycle crashes varies from one region to another in the city is the primary motivation of this paper. A highly portable and inexpensive sensing device for measuring the distance between a bicycle and lateral objects is designed from scratch and developed. The developed mobile sensing device can also map bicycle trajectories to highlight critical segments where the safe distance from passing vehicles is not respected. This device which is powered by a portable power source is comprised of two ultrasonic sensors namely, a Global Positioning System (GPS) receiver, and a real-time clock (RTC). The sensor is secured inside a custom design 3D printed case. The case can be easily attached to any bicycle including shared Citi Bike bicycles for testing. The final prototype is entirely functional and used to collect sample data to demonstrate its effectiveness to address safety-related problems mentioned above. Finally, a dashboard is created to display collected key information. This key information can be used by researches and agencies for a better understanding of the factors contributing to the safety of bicycle routes.

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Calibration and validation of large-scale traffic simulation networks: a case study
  Author: Bekir Bartin, Kaan Ozbay, Jingqin Gao, Abdullah Kurkcu

  Journal: Procedia Computer Science, Volume 130 Pages 844-849

  Abstract: The availability, accuracy and relevance of real world input data are essential for developing a reliable traffic simulation model. Large-scale traffic simulation models, in particular, require data from many sources and in great detail. Though it is now possible to obtain detailed field data with the advent of new technologies such as GPS, cellular phones, RFIDs, it is still a challenge to gather all available data, especially traffic flow data, in the required spatial and temporal accuracy. The central theme of this paper is the calibration and validation (C&V) development of a large-scale traffic simulation model using data from multiple sources.

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Crowdsourcing Incident Information for Emergency Response Using Open Data Sources in Smart Cities
  Author: Fan Zuo, Abdullah Kurkcu, Kaan Ozbay, Jingqin Gao

  Journal: Transportation Research Record: Journal of the Transportation Research Board

  Abstract: Emergency events affect the human security and safety as well as the integrity of the local infrastructure. Emergency response officials are required to make decisions using limited information and time. During emergency events, people post updates to social media networks such as Twitter containing information about their status, help requests, incident reports and other useful information. In this research, the Latent Dirichlet Allocation (LDA) model is used to automatically classify incident related tweets and incident types using Twitter data. Unlike the previous social media information models proposed in the related literature, the LDA is an unsupervised learning model which can be utilized directly without prior knowledge and preparation for data in order to save time during emergencies. Twitter data including messages and geolocation information during the recent Chelsea explosion and Hurricane Sandy both in New York City are used as two case studies to test the accuracy of the LDA model for extracting incident-related tweets and labeling them by incident type. Results showed that the model could extract emergency events and classify them for both small and large-scale events, and the models hyper-parameters can be shared in a similar language environment to save model training time. Furthermore, the list of keywords generated by the model can be used as prior knowledge for emergency event classification and training of supervised classification models such as Support Vector Machine (SVM) and Recurrent Neural Network.

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Exploring the Spatial Dependence and Selection Bias of Double Parking Citations Data
  Author: Jingqin Gao, Kun Xie, Kaan Ozbay

  Journal: Journal of Transportation Research Record, (The Standing Committee on Urban Transportation Data and Information Systems (ABJ30)), DOI:10.1177/0361198118792323

  Abstract: Parking violation citations, often used to identify contributing factors to parking violation behaviors, is one of the most valuable datasets for traffic operation research. However, little has been done to examine its spatial dependence caused by location-specific differences in features such as traffic, land use, etc., and potential selection biases resulting from different levels and coverage of traffic enforcement. This study leveraged extensive data on double parking citations in Manhattan, New York in 2015, along with other datasets including land use, transportation and socio-demographic features. Moran's I statistics confirmed that double parking tickets were spatially correlated so that spatial lag and spatial error models were proposed to account for the spatial dependence of parking tickets to avoid biased estimates. To investigate whether selection bias exists in issuing tickets, we estimated the effects of parking ticket density and police precinct distance, when controlling for variables such as commercial area, truck activity, taxi demand, population, hotel and restaurant. Parking ticket density and police precinct distance were used as indicators of the enforcement levels and coverage and were found to be statistically significant. This indicated the existence of selection bias due to the heterogeneity in enforcement levels or coverage across different regions. Moreover, traffic enforcement units patrolling patterns revealed that the majority of the units have less than three daily patterns. These findings can assist proper usage of the citation data by suggesting researchers and agencies to consider spatial dependence as well as selection bias, and provide insights for parking violation management strategies.

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A new methodology for before-after safety assessment using survival analysis and longitudinal data
  Author: Kun Xie, Kaan Ozbay, Hong Yang, Di Yang

  Journal: Risk analysis


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Estimating Pedestrian Densities, Wait Times, and Flows Using Wi-Fi and Bluetooth Sensors
  Author: Abdullah Kurkcu, Kaan Ozbay

  Journal: Journal of Transportation Research Record, (Standing Committee on Highway Traffic Monitoring (ABJ35))

  Abstract: Monitoring non-motorized traffic is gaining more attention in the context of transportation studies. Most of the traditional pedestrian monitoring technologies focus on counting pedestrians passing through a fixed location in the network. It is thus not possible to anonymously track the movement of individuals or groups as they move outside of each particular sensors range. Moreover, most agencies do not have continuous pedestrian counts mainly because of technological limitations. However, wireless data collection technologies can capture crowd dynamics by scanning mobile devices. Data collection that takes advantage of mobile devices has gained much interest in the transportation literature due to its low cost, ease of implementation and richness of captured data (1). In this paper, algorithms to filter and aggregate data collected by wireless sensors and how to fuse additional data sources to improve the estimation of various pedestrian based performance measures are investigated. The developed methodologies are applied to a 2-month long public transportation terminal data collected by six sensors and the results are reported.

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Data-driven Spatial Modeling for Quantifying Network-wide Resilience in the aftermath of Hurricanes Irene and Sandy
  Author: Yuan Zhu, Kun Xie, Kaan Ozbay, Fan Zuo, Hong Yang

  Journal: Journal of Transportation Research Record, (Standing Committee on Critical Transportation Infrastructure Protection (ABR10))

  Abstract: In recent years, New York City metropolitan area was hit by two major hurricanes, Irene and Sandy. These extreme weather events had major impacts on the transportation infrastructures, including road and subway networks. As an extension of our recent research on this topic, this study explores the spatial patterns of infrastructure resilience in New York City using taxi and subway ridership data. Neighborhood Tabulation Areas (NTAs) are used as units of analysis. The recovery curve of each NTA is modeled using the logistic function to quantify the resilience of road and subway systems. Morans I tests confirm the spatial correlation of recovery patterns for taxi and subway ridership. To account for this spatial correlation, citywide spatial models are estimated, and found to outperform linear models. Factors such as the percentage of area influenced by storm surges, the distance to the coast and the average elevation are found to affect the infrastructure resilience. The findings in this study provide insights into vulnerability of transportation networks and can be utilized for more efficient emergency planning and management.

  Keywords: Hurricane, recovery curve, resilience, spatial analysis, taxi and subway data

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Analysis of Traffic Crashes Involving Pedestrians using Big Data: Investigation of Contributing Factors and Identification of Hotspots
  Author: Kun Xie, Kaan Ozbay, Abdullah Kurkcu, Hong Yang

  Journal: Risk Analysis , Volume 37 Issue 8 Pages 1459-1476 ISSN 1539-6924 DOI 10.1111/risa.12785

  Abstract: This study aims to explore the potential of using big data in advancing the pedestrian risk analysis including the investigation of contributing factors and the hotspot identification. Massive amounts of data of Manhattan from a variety of sources were collected, integrated, and processed, including taxi trips, subway turnstile counts, traffic volumes, road network, land use, sociodemographic, and social media data. The whole study area was uniformly split into grid cells as the basic geographical units of analysis. The cell-structured framework makes it easy to incorporate rich and diversified data into risk analysis. The cost of each crash, weighted by injury severity, was assigned to the cells based on the relative distance to the crash site using a kernel density function. A tobit model was developed to relate gridcell-specific contributing factors to crash costs that are left-censored at zero. The potential for safety improvement (PSI) that could be obtained by using the actual crash cost minus the cost of similar sites estimated by the tobit model was used as a measure to identify and rank pedestrian crash hotspots. The proposed hotspot identification method takes into account two important factors that are generally ignored, i.e., injury severity and effects of exposure indicators. Big data, on the one hand, enable more precise estimation of the effects of risk factors by providing richer data for modeling, and on the other hand, enable large-scale hotspot identification with higher resolution than conventional methods based on census tracts or traffic analysis zones.

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Application of Bayesian Stochastic Learning Automata for Modeling Lane Choice Behavior in SR-167 HOT Lanes
  Author: Ender Faruk Morgul, Kaan Ozbay, Abdullah Kurkcu

  Journal: Journal of Transportation Research Record, (Traffic Flow Theory and Characteristics (AHB45))

  Abstract: This paper investigates learning behavior in SR-167 HOT lanes using a six-month toll tag reading data. Bayesian-Stochastic Learning Algorithm (SLA) theory is employed to model drivers sequential lane choice occasions. Reward and penalty parameters are used to update users lane choice probabilities. The results show that the effect of reward parameters which increases selecting probability of an alternative after a satisfactory experience is more obvious than penalty parameters that decrease the probability of selecting an unfavorable choice. Low magnitudes of learning parameters might indicate strong habit formation among the users. Moreover, posterior distribution of learning parameters indicates there exist user perception heterogeneity when evaluating the outcomes of choices. Finally, user familiarity is investigated with a less experienced subsample and it is shown that learning rates of more familiar users are lower than less familiar users.

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Modeling Evacuation Behavior under Hurricane Conditions
  Author: Hong Yang, Ender Faruk Morgul, Kaan Ozbay, Kun Xie

  Journal: Journal of Transportation Research Record, ( Emergency Evacuations (ABR30))

  Abstract: The understanding of evacuation behavior is critical to establish policies, procedures and organizational structure for effective response to emergencies. This study specifically investigated the evacuation behavioral responses under hurricane conditions. It aimed to explore the association between different contributing factors and the evacuation decision choices as well as evacuation destination choices. Unlike previous studies that model each response behavior separately, this paper proposed to use the structural equation modeling approach to examine the interrelationship between different response behaviors. A case study using the data set from a survey conducted in New Jersey was performed. With the Bayesian estimation approaches, the proposed structural equation models have been estimated and the effect of each predictive variable has been captured. An important finding is that the individuals preference to evacuate did not significantly affect their choices of evacuation destinations. In addition, other socio-economic and demographic characteristics that affected evacuation behavior have been identified.

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Using Big Data to Study Resilience of Taxi and Subway Trips for Hurricanes Sandy and Irene
  Author: Yuan Zhu, Kaan Ozbay, Kun Xie, Hong Yang

  Journal: Journal of Transportation Research Record, (Logistics of Disaster Response and Business Continuity (ABR20))

  Abstract: Hurricanes Irene and Sandy had significant impact on New York City, resulting in devastating damage to its transportation systems which took days, even months to recover. This study explores post-hurricane recovery process by analyzing travel patterns of the roadway and subway systems of New York City based on taxi trip data from Taxi and Limousine Commission (TLC) and subway turnstile ridership data from Metropolitan Transportation Authority (MTA). Both these datasets are examples of big data with millions of individual ridership records per month. The study investigates spatio-temporal variations of transportation system recovery behavior using Neighborhood Tabulation Areas (NTAs) as units of analysis. Recovery curves are estimated for each evacuation zone category to model time-dependent recovery patterns of the roadway and transit systems. The recovery rate for Hurricane Sandy is found to be lower than that of Hurricane Irene. Moreover, the results indicate higher resilience of the road network compared to the subway network. The methodology proposed in this study can be used to evaluate the resilience of transportation systems to natural disasters and the findings can provide government agencies with useful insights into emergency management.

  Keywords: Emergency management, hurricane, recovery curve, evacuation zone, taxi and subway data

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Feature Selection for Ranking of Most Influential Variables for Evacuation Behavior Modeling across Disasters
  Author: Sami Demiroluk, Anil Yazici, Kaan Ozbay,Jon Carnegie

  Journal: Journal of Transportation Research Record, (Emergency Evacuations (ABR30))

  Abstract: The extensive list of factors which affect evacuee decision process makes it difficult to design effective surveys and develop decision models with high predictive power. Regression models and significance levels can help identify relevant variables and overcome this problem to an extent. However, such approaches fall short of ranking them or recognizing the redundant ones. In this paper, the use of a feature selection method is proposed to ensure that the selected features are relevant and not redundant at the same time. This method, called Conditional Mutual Information Maximization, consists of picking features, at each step, which minimizes the uncertainty in the decision, conditionally to the response of any feature already picked. As a case study, the variables influencing evacuation behavior in Northern New Jersey Evacuation Survey were ranked and compared for different for different disaster scenarios. To validate the method and to demonstrate how it compares with the traditional methods, logistic regression models were also estimated using the same dataset. It was found that the top-ranked variables may be available through existing database such as census and some can be calculated based on the threat type and government action. This fact can be useful for emergency planners, when an evacuation survey for a study area is not readily available. Overall, feature selection algorithm succeeds to identify the most influential factors for all threat types. The suggested approach can help both pre-processing (e.g. defining set of input variables) and post-processing (e.g. identification of variables that should be kept) for behavioral modeling.

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Case Studies for Data-Oriented Emergency Management/Planning in Complex Urban Systems
  Author: Kun Xie, Kaan Ozbay, Yuan Zhu, Hong Yang

  Journal: LNCS Transactions on Large-Scale Data- and Knowledge-Centered Systems

  Abstract: To reduce the losses caused by natural disasters such as hurricanes, it is necessary to build effective and efficient emergency management/planning systems for cities. With increases in volume, variety and acquisition rate of urban data, major opportunities exist to implement data-oriented emergency management/planning. New York/New Jersey metropolitan area is selected as the study area. Large datasets related to emergency management/planning including, traffic operations, incidents, geographical and socio-economic characteristics, and evacuee behavior are collected from various sources. Five related case studies conducted using these unique datasets are summarized to present a comprehensive overview on how to use big urban data to obtain innovative solutions for emergency management and planning, in the context of complex urban systems. Useful insights are obtained from data for essential tasks of emergency management and planning such as evacuation demand estimation, determination of evacuation zones, evacuation planning and resilience assessment.

  Keywords: Emergency management, planning Complex urban systems, Big data, Evacuation modeling, Hurricane

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Improved Travel Time Estimation for Reliable Performance Measure Development for Closed Highways
  Author: Hong Yang, Kaan Ozbay, Kun Xie

  Journal: Journal of Transportation Research Record, (Travel Time, Speed and Reliability Joint Subcommittee (ABJ30(3))

  Abstract: Accurate travel time information is not only valuable for travelers, but also critical for transportation agencies quantifying the performance of their systems. Thus, increasing interest has been devoted to develop reliable approaches for estimating travel time from various sensor data. Unlike the extensively studied estimation approaches based on point sensor measurements, experience on using probe data from closed highway systems is relatively limited. To complement existing understanding, this study aims to develop an approach that estimates travel time based on probe data from an electronic toll collection (ETC) system on closed freeways. This is different from the studies relying on automatic vehicle identification systems deployed on mainlines as well as those estimated based on point detectors. The proposed approach breaks down individual journal time into section travel time and fuses the probe data from vehicles that have used the links. The results based on real-world case studies illustrate the potential of mining the ETC data for travel time estimation under both incident-free and incident conditions. In addition, the estimated results are shown to outperform the instantaneous travel time estimates based on point sensor data in terms of capturing traffic dynamics. This in turn provides more accurate information to derive reliable performance measures to depict travel time reliability.

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Multimodal Logical Architecture for Emergency Transportation Toward Better Decision Making in Humanitarian Logistics
  Author: Eren Erman Ozguven, Kaan Ozbay, Shrisan Iyer, Ryan A. G. Whytlaw, Jon A. Carnegie

  Journal: Journal of Transportation Research Record, (TRB's Logistics of Disaster Response and Business Continuity Committee (AT065))

  Abstract: Humanitarian logistics has emerged as a vital tool to reduce and alleviate the harmful impacts and suffering caused by extreme events. A significant task of planners and decision makers involved in humanitarian logistics is planning for and satisfying the vital needs of the people during highly stochastic disaster/catastrophe conditions. To accomplish this, there is a clear research need to describe and evaluate a multi-modal logical architecture of efficient emergency transportation operations that will support decision makers to generate and evaluate decision alternatives for solving the problems related to transporting vital supplies during highly stochastic disaster conditions. To be more specific, the humanitarian multi-modal logical architecture described in this paper is created as a comprehensive needs assessment effort and knowledge base that can be used in the creation of software tools for the movement of emergency supplies. This paper carefully describes the steps needed to create such a logical architecture for multi-modal humanitarian logistics with an emphasis on the sustainability and resiliency of the emergency relief system in the event of a disaster/catastrophe. During the extensive process of evaluation of the proposed logical architecture, a thorough study and general assessment of the transportation network and infrastructure based on system profiles, availability, allocation, and optimal assignment of critical resources and database requirements are also conducted, followed by a case study applied to the NY-NJ-CT-PA CSA region. Finally, future directions on the usage of this comprehensive logical architecture are discussed.

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Modeling the Safety Impacts of Off-Hour Delivery Programs in Urban Areas
  Author: Kun Xie, Kaan Ozbay, Hong Yang, Jose Holguin-Veras, Ender Faruk Morgul

  Journal: Journal of Transportation Research Record, (Urban Freight Transportation Committee (AT025)), (Best Paper Award)

  Abstract: Trucks traveling in urban road networks during daytime can be one of the major contributors to traffic congestion. A possible approach to relieve traffic congestion in urban areas can be to shift a portion of trucks from the regular daytime hours to the nighttime off-hours. The congestion relief benefits of this off-hour delivery strategy can be noticeable, however its safety impacts need to be investigated too. Manhattan, which is the most densely populated borough of New York City with a large demand for truck deliveries, was used as the study area. Truck crashes, traffic volumes and geometric design features of 256 road segments in Manhattan were collected to develop safety evaluation models. To accurately quantify the safety impacts of off-hour deliveries, we proposed an improved modeling approach that involved the use of the multivariate Poisson-lognormal model integrated with measurement errors in truck volumes. The proposed model could address the inherent correlation of specific truck crash types and correct the estimation bias for the safety effects of daytime and nighttime truck volumes. Bayesian approach was employed to estimate the parameters of the proposed model. According to the Bayesian posterior distributions, it was found that daytime and nighttime truck volumes didnt have significantly different effects on either minor or serious crashes. Additionally, the truck crash counts were estimated using the proposed model under scenarios with different proportions of truck traffic shifted to nighttime. The results showed that off-hour delivery programs were not expected to increase the overall risk of truck-involved crashes significantly. The findings of this study can provide transportation planners and policy makers with insight into decision making on the deployment of off-hour delivery programs.

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Spatial Analysis of Highway Incident Durations in the Context of Hurricane Sandy
  Author: Kun Xie, Kaan Ozbay, Hong Yang

  Journal: Accident Analysis & Prevention, 74 (2015): 77-86, (Freeway Operations Committee (AHB20))

  Abstract: The objectives of this study are 1) to develop an incident duration model which can account for spatial dependence of duration observations, and 2) to investigate the impacts of hurricane on incident duration. Highway incident data from New York City and its surrounding regions before and after hurricane Sandy was used for the study. Morans I statistics confirmed that durations of neighboring incidents were spatially correlated. Moreover, Lagrange Multiplier tests suggested that the spatial dependence should be captured in a spatial lag specification. A spatial error model, a spatial lag model and a standard model without consideration of spatial effects were developed. The spatial lag model is found to outperform the others by capturing the spatial dependence of incident durations via a spatially lagged dependent variable. It was further used to assess the effects of hurricane-related variables on incident duration. The results show that the incidents during and post the hurricane are expected to have 116.3% and 79.8% longer durations than those occurred in the regular time. However, no significant increase in incident duration is observed in the evacuation period before Sandys landfall. Those findings can provide insights to aid in the development of hurricane evacuation plans and emergency management strategies.

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Value of Reliability by Time of Day: Evidence from Actual Usage Data from SR-167 HOT Lanes
  Author: Ender Faruk Morgul, Kaan Ozbay

  Journal: Journal of Transportation Research Record, (Traveler Behavior and Values Committee (ADB10))

  Abstract: The emphasis of this study is on the estimation of the valuation of travel time reliability (VOR) along SR 167 High Occupancy Toll (HOT) lanes in the State of Washington using revealed preference data. More than 5 months of tolling records that include over one million lane choices were used in the analysis. A Mixed logit model was estimated to account for individual level heterogeneity. Drivers were assumed to evaluate VOR based on the schedule delays relative to their desired or expected arrival times (i.e. reference points). Three empirically determined reference point specifications were employed to calculate schedule delays and the sensitivity of the results with respect to reference points were reported accordingly. The model estimations showed that there were significant variations in the estimated VOR values for different times of day and also in different travel directions. The differences in VOR estimations can be as high as $17 per hour in a single time period depending on the reference point assumption. Reference point assumption was also shown to have significant effect on the VOR when scheduling delays were used for measuring travel time reliability. Empirical findings of this study will provide useful insights in terms of better understanding the value of travel time variability of users traveling in different time periods. In particular, these results can help developing policies for more effective allocation of traffic capacity to HOT lanes especially during peak congestion periods.

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Extended Implementation Methodology for Virtual Sensors: Web-based Real Time Transportation Data Collection and Analysis for Incident Management
  Author: Abdullah Kurkcu, Ender Faruk Morgul, Kaan Ozbay

  Journal: Journal of Transportation Research Record, (Information Systems and Technology Committee (ABJ50))

  Abstract: Open data sources and the use of social media data are increasingly gaining attention as important information providers in transportation and incident management. In this paper, we present practical evidence for the emerging potential of on-line and open data sources. We combine and extend our prior research on virtual sensors (1) by integrating real-time incident information and social media network engagement. The fundamental contribution of this paper is to develop an extended virtual sensor (EVS) framework to provide an automated travel time data collection methodology as incidents occur. In addition, it has also been shown that social media data can be potentially useful for more effective real-time incident response. The proposed framework can be easily modified and used to evaluate travel time impacts of incidents on roadways, clearance times, and make use of social media data in terms of obtaining time critical incident related information.

  Keywords: Twitter, accident, tweets, virtual sensors, GPS, maps, Google, social media, microblogs, incidents, travel time, clearance, big data, analytics, open source, event detection, sentiment

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Assessing the impact of urban off-hour delivery program using city scale simulation models
  Author: Satish V. Ukkusuri, Kaan Ozbay, Wilfredo F. Yushimito, Shri Iyer, Ender Faruk Morgul, Jose Holguin-Veras

  Journal: EURO Journal on Transportation and Logistics, 1-26

  Abstract: This paper describes two different types of models to assess the traffic impacts of an off-hour delivery program for the New York City (NYC) borough of Manhattan. Traffic impacts are measured in New York City metropolitan region using both a regional travel demand model and a mesoscopic simulation model. Analysis is conducted to determine the effectiveness and impacts of the scenarios modeled; focusing on the changes predicted by the traffic models. The results from both models are compared and analyzed, and a discussion on the usage of these models is presented. While macroscopic models can be used to measure traffic effects in a large urban region, mesoscopic models similar to the one used in this paper have their advantages in terms of better quantifying traffic impacts of system wide benefits. However, simulation time makes impractical to use mesoscopic simulation for large urban regions. In the work, both the macroscopic regional travel demand model and a mesoscopic sub-simulation network show a measurable impact to congestion and network conditions. However, even when the results show an increasing benefit in terms of travel time savings and increasing speeds, cost-benefit analysis show that when compared with the costs (in this case implementation costs by providing incentives), only small receiver participation justifies the costs of the Odd-Hour Deliveries (OHD) program. As incentive amounts increase, receiver participation increases greatly; though the monetized traffic benefits do not necessarily increase at the same rate. Additional analysis was also performed with a targeted program where large traffic generators and large businesses were the recipients of the incentive. The benefits of the targeted program are estimated to be roughly equivalent to the cheapest scenario run for the broad-based program ($5,000 tax incentive assumption) at a fraction of the cost.

  Keywords: Freight Modeling, Off-hour Deliveries, Planning Model, Traffic Simulation, Urban Freight

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Development of an On-line Scalable Approach for Identifying Secondary Crashes
  Author: Hong Yang, Kaan Ozbay, Ender Faruk Morgul, Bekir Bartin, Kun Xie

  Journal: Journal of Transportation Research Record, (Freeway Operations Committee (AHB20))

  Abstract: Secondary crashes are one of the most critical incidents occurring on highways. They can induce extra traffic delays and affect highway safety performance. Transportation agencies are interested in understanding the mechanism of the secondary crash occurrence and implementing appropriate countermeasures. However, there is no well-established procedure to identify secondary crashes, which in turn impedes the possibility of investigating the underlying mechanism of their occurrence. This study intend to develop an on-line scalable approach to help identify secondary crashes for a large number of highways that have insufficient traffic surveillance units collecting continuous traffic data required to classify secondary crash accurately. The developed approach consists of two major components: (a) acquisition of open source traffic data and (b) identification of secondary crashes through the use of these data. Unlike existing approaches based on static thresholds, queuing models or infrastructure-based sensor data, the developed approach takes advantage of various open sources data to identify traffic conditions in the presence of incidents. In this study, we propose to develop virtual sensors collecting traffic data from private traffic information providers such as Bing Maps, Google Maps and MapQuest. The availability of such data greatly expands our ability to cover more highways without installing infrastructure sensors. The virtual sensor output provides the basic input to run the developed automatic identification algorithm for identifying secondary crashes. The algorithm is described in a step-by-step manner to provide a readily deployable approach for transportation agencies interested in identifying secondary crashes on their highway networks.

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Virtual Sensors: A Web-based Real-Time Data Collection Methodology for Transportation Operation Performance Analysis
  Author: Ender Faruk Morgul, Hong Yang, Abdullah Kurkcu, Kaan Ozbay, Bekir Bartin, Camille Kamga, R. Salloum

  Journal: Journal of Transportation Research Record, (Travel Time, Speed and Reliability Joint Subcommittee (ABJ30(3))

  Abstract: Recent advances in mobile networks and increase in the number of GPS-equipped vehicles have led to an exponential growth in real-time data generation. Over the last decade, a number of online mapping or vehicle tracking services haves made their data available for third-party users. This paper explores opportunities in utilizing the real-time traffic data provided by online services and introduces a virtual sensor methodology for collecting, storing and processing large volumes of network-level data. In order to assess the validity of the collected data using the proposed methodology, we compare these with data from physical loop detectors and electronic toll tag readers. Statistical analyses show that there is a strong correlation between the travel time measurements from infrastructure based sensors and virtual sensors. We then conduct a travel time reliability analysis using the virtual sensor data methodology and conclude that the results are promising for future research and implementation.

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Highway Versus Urban Roads: Analysis of Travel Time and Variability Patterns Based on Facility Type
  Author: Anil.Yazici, Camille Kamga, Kaan Ozbay

  Journal: , (Speed and Reliability Joint Subcommittee (ABJ30(3))

  Abstract: In this paper, the differences in travel time variability patterns between urban roads and highways are analyzed. For urban roads, a GPS dataset which includes all taxi trips in New York City is used. For highways, New Jersey Turnpike (NJTP) Automatic Vehicle Location data is employed. Moreover, NJTP is divided into two sections as urban and suburban highway based on urbanization level, time of day demands, and physical roadway features. Hence, the analysis does not only compare the travel time patterns between highways and urban roads, but also investigates the travel time characteristics along the same highway facility. First, the temporal variation of travel times at both facility types are calculated and compared. Second, the travel time distributions are extracted for different time periods and compared visually to determine the distributional patterns. Last, the relationship between the average travel time and variability is investigated. Travel time patterns not only differ between urban roads and highways, but major differences in travel time characteristics can also be observed along the same highway. Higher travel times correspond to lower reliability at the highways, yet correspond to higher reliability at the urban roads. Overall, the findings suggest that attributing travel time variability pattern differences to facility type may actually be an oversimplification of the phenomenon.

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Comparison of Mode Cost by Time of Day for Nondriving Airport Trips to and from New York City's Pennsylvania Station
  Author: Ci Yang, Ender Faruk Morgul, Eric.J.Gonzales, Kaan Ozbay

  Journal: Journal of Transportation Research Record, (Airport Terminals and Ground Access Committee (AV050))

  Abstract: In this study we develop a novel methodology using taxi GPS data and high-resolution transit schedule information to compare travel times and travel fares of the two main non-driving travel modes for airport ground access: taxi and transit. Five origin-destination (OD) pairs between Penn Station in New York City (NYC) and three airports in Greater NYC area are used as an example to demonstrate our methods. The total cost analysis considers both travel time and fare spent. A binary logit model is used to model modal choice of travelers. The results indicate that the transit mode is the more likely choice most of the time except the midnight period when transit service has longer headways. A sensitivity analysis shows the relationship between the value of time and total cost for different numbers of passengers traveling together and different times of day. The higher the value of time and the number of passengers, the more likely that taxi will be chosen for airport trips. The attractiveness of one mode relative to the other varies spatially and temporally according to the travel time and price. This paper focuses on understanding temporal variation of total cost of each mode and the effect that this is likely to have on mode share.

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Quantifying Transportation Benefits of Transit-Oriented Development in New Jersey
  Author: Sandeep Mudigonda, Kaan Ozbay, Ozgur Ozturk, Shrisan Iyer, Robert B.Noland

  Journal: Journal of Transportation Research Record, (Public Transportation Planning and Development Committee (AP025))

  Abstract: The cost of transportation plays an important role in residential location choice. Reducing transportation costs not only benefits the user but also improves the performance of the system as a whole. A direct impact of transit-oriented development (TOD) is the change in out-of-pocket costs for users, as well as the changes in costs of externalities and agency benefits. The prime mover for these changes is the shift in population when TOD is built near train stations, and the induced mode shift from driving to transit. In this study, several sites throughout New Jersey are evaluated to determine the cost of driving versus the cost of using rail transit to major employment destinations in New Jersey and New York City. Driving costs are composed of vehicle operating costs (including fuel, wear and tear, and depreciation), value of time is based on the highway travel time from origin to destination, parking cost, and cost of externalities such as air and noise pollution, road maintenance, and accidents. Transit costs are composed of fares, parking costs, value of travel time, waiting time, and transfer time. The likely changes in population resulting from the TOD are used to estimate changes in highway and transit trips. The costs are compared to derive the net benefit for transportation system users, as a result of TOD development. We observe that generally, TOD results in financial benefits to the user and for the transportation system.

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Adaptive Learning in Bayesian Networks for Incident Duration Prediction
  Author: Sami Demiroluk, Kaan Ozbay

  Journal: Journal of Transportation Research Record, (Freeway Operations Committee (AHB20))

  Abstract: This study presents an innovative hierarchical Bayesian model for spatial modeling of county level crashes in New Jersey. First, the model is estimated using raw crash counts. Then, weights are applied to crashes with different severities to obtain a weighted crash count. The goal in incorporating severities in the spatial model is to demonstrate the importance of representing spatial variation of crashes as well as their severity. As a contribution to existing literature, crash rates are also analyzed by road type. Finally, crash rate maps are developed based on modeling results to visualize the effects of spatial covariates. The results of the study indicate that the most influential covariate for the crashes is the road curvature, followed by roadway mileage and roadway defects. It is also found that it is possible to represent the crash risk better by applying severity weights to the individual crashes. The developed crash rate maps can help transportation professionals on identifying and ranking the locations at an aggregate level, which requires closer attention.

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Mining the Characteristics of Secondary Crashes on Highways
  Author: Hong Yang, Bekir Bartin, Kaan Ozbay

  Journal: Journal of Transportation Engineering, DOI: 10.1061/(ASCE)TE.1943-5436.0000646

  Abstract: The prevention of secondary crashes is a high priority task in traffic incident management. However, the limited knowledge regarding the nature of secondary crashes largely impeded the development of established countermeasures. The primary goal of this paper is to improve the literature's understanding of secondary crashes. This goal is achieved in two steps: first, with an analysis framework that accurately identifies secondary crashes by integrating rich traffic-sensor data with statewide-crash data and, second, by carefully investigating the characteristics of these identified secondary crashes. To that end, secondary crashes within a 27-mile section of a major highway in New Jersey were mined using the developed analysis framework, and a thorough examination of their characteristics has been performed. Empirical findings on the frequency of secondary crashes, their spatio-temporal distributions, clearance time, crash type, severity, and major contributing factors have been highlighted. Taken together, these preliminary results could potentially help transportation agencies make more informed decisions on mitigating secondary crashes and improve their incident management operations. To complement the results, further in-depth investigations using more high-resolution sensor data and high-quality incident records are suggested.

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Assessing the Risk of Secondary Crashes on Freeways
  Author: Hong Yang, Kaan Ozbay, Kun Xie

  Journal: Journal of Safety Research,

  Abstract: Introduction: The occurrence of secondary crashes is one of the critical yet understudied highway safety issues. Induced by the primary crashes, the occurrence of secondary crashes does not only increase traffic delays but also the risk of inducing additional incidents. Many highway agencies are highly interested in the implementation of safety countermeasures to reduce this type of crashes. However, due to the limited understanding of the key contributing factors, they face a great challenge for determining the most appropriate countermeasures.Method: To bridge this gap, this study makes important contributions to the existing literature of secondary incidents by developing a novel methodology to assess the risk of having secondary crashes on highways. The proposed methodology consists of two major components, namely: (a) accurate identification of secondary crashes and (b) statistically robust assessment of causal effects of contributing factors. The first component is concerned with the development of an improved identification approach for secondary accidents that relies on the rich traffic information obtained from traffic sensors. The second component of the proposed methodology is aimed at understanding the key mechanisms that are hypothesized to cause secondary crashes through the use of a modified logistic regression model that can efficiently deal with relatively rare events such as secondary incidents. The feasibility and improved performance of using the proposed methodology are tested using real-world crash and traffic flow data. Results: The risk of inducing secondary crashes after the occurrence of individual primary crashes under different circumstances is studied by employing the estimated regression model. Marginal effect of each factor on the risk of secondary crashes is also quantified and important contributing factors are highlighted and discussed. Practical applications: Massive sensor data can be used to support the identification of secondary crashes. The occurrence mechanism of these secondary crashes can be investigate by the proposed model. Understanding the mechanism helps deploy appropriate countermeasures to mitigate or prevent the secondary crashes.

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Crash Frequency Modeling for Signalized Intersections in a High-Density Urban Road Network
  Author: Kun Xie, X. Wang, Kaan Ozbay, and Hong Yang

  Journal: Analytic Methods in Accident Research,

  Abstract: Conventional crash frequency models rely on an assumption of independence among observed crashes. However, this assumption is frequently proved false by spatially related crash observations, particularly for intersection crashes observed in high-density road networks. Crash frequency models that ignore the hierarchy and spatial correlation of closely spaced intersections can lead to biased estimations. As a follow-up to our previous paper (Xie et al., 2013), this study aims to address this issue by introducing an improved crash frequency model. Data for 195 signalized intersections along 22 corridors in the urban areas of Shanghai was collected. Moran?s I statistic of the crash data confirmed the spatial dependence of crash occurrence among the neighboring intersections. Moreover, Lagrange Multiplier test was performed and it suggested that the spatial dependence should be captured in the model error term. A hierarchical model incorporating a conditional autoregressive (CAR) effect term for the spatial correlation was developed in the Bayesian framework. A deviance information criterion (DIC) and cross-validation test were used for model selection and comparison. The results showed that the proposed model outperformed traditional models in terms of the overall goodness of fit and predictive performance. In addition, the significance of the corridor-specific random effect and CAR effect revealed strong evidence for the presence of heterogeneity across corridors and spatial correlation among intersections.

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Investigating Motorists' Behaviors in Response to Supplementary Traffic Control Devices at Land Surveying Work Sites
  Author: Hong Yang, Bekir Bartin, Kaan Ozbay, and S. Chien

  Journal: Traffic Injury Prevention, DOI:

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Modeling Work Zone Crash Frequency by Quantifying Measurement Errors in Work Zone Length
  Author: Hong Yang, Kaan Ozbay, Ozgur Ozturk, and M. Yildirimoglu

  Journal: Accident Analysis & Prevention, Vol. 55, June 2013, pp. 192-201

  Abstract: Work zones are temporary traffic control zones that can potentially cause safety problems. Maintaining safety, while implementing necessary changes on roadways, is an important challenge traffic engineers and researchers have to confront. In this study, the risk factors in work zone safety evaluation were identified through the estimation of a crash frequency (CF) model. Measurement errors in explanatory variables of a CF model can lead to unreliable estimates of certain parameters. Among these, work zone length raises a major concern in this analysis because it may change as the construction schedule progresses generally without being properly documented. This paper proposes an improved modeling and estimation approach that involves the use of a measurement error (ME) model integrated with the traditional negative binomial (NB) model. The proposed approach was compared with the traditional NB approach. Both models were estimated using a large dataset that consists of 60 work zones in New Jersey. Results showed that the proposed improved approach outperformed the traditional approach in terms of goodness-of-fit statistics. Moreover it is shown that the use of the traditional NB approach in this context can lead to the overestimation of the effect of work zone length on the crash occurrence.

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Identifying Secondary Crashes on Freeways Using Sensor Data
  Author: Hong Yang, Bekir Bartin, and Kaan Ozbay

  Journal: Journal of Transportation Research Record, (In Press, 2013)

  Abstract: Non-recurring traffic incidents such as motor vehicle crashes increase not only travel delays but also the risk of secondary crashes. Secondary crashes can cause additional traffic delays, and reduce safety. In order to implement effective countermeasures to prevent and/or reduce secondary crashes, first their characteristics should be investigated. However, the related research has been limited largely due to the lack of detailed incident and traffic data necessary to first identify the secondary crashes. Existing approaches such as static methods employed to identify secondary crashes cannot fully capture potential secondary crashes due to fixed spatio-temporal identification criteria. Improved approaches are needed to accurately categorize secondary crashes for further analysis. Therefore, this paper attempts to develop an enhanced approach for identifying secondary crashes using the existing crash database and archived traffic data from highway sensors. The proposed method is threefold: (a) defining secondary crashes; (b) examining the impact range of primary crashes that possibly relate to secondary crashes; and (c) identifying secondary crashes. The proposed methodology establishes a practical framework for mining secondary crashes from existing sensor data and crash records. A case study is performed on a 27-mile segment of a major highway in New Jersey to illustrate the performance of the proposed approach. The results show that the proposed method provides a more reliable and efficient categorization of secondary crashes than commonly used approaches.

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A Secure and Efficient Inventory Management System for Disasters
  Author: Eren Erman Ozguven, Kaan Ozbay

  Journal: Transportation Research Part C, Volume 29, pp. 171-196 DOI:

  Abstract: An efficient humanitarian inventory control model and emergency logistics system plays a crucial role in maintaining reliable flow of vital supplies to the victims located in the shelters and minimizing the impacts of the unforeseen disruptions that can occur. This system should not only allow the efficient usage and distribution of emergency supplies but should also offer the ability to be integrated with emerging ITS technologies such as Radio Frequency Identification Devices (RFIDs) for commodity tracking and logistics. This paper proposes a comprehensive methodology for the development of a humanitarian emergency management framework based on the real-time tracking of emergency supplies and demands through the use of RFID technology integrated with a multi-commodity stochastic humanitarian inventory management model (MC-SHIC). First, logistics and management aspects of RFID technologies in the context of the emergency disaster relief framework are discussed. Then, MC-SHIC model proposed to determine the optimal emergency inventory levels to prevent possible disruptions at the minimal cost is presented. The solution of the model with several sensitivity analyses obtained using the pLEPs algorithm is presented and discussed. Realizing that actual emergency inventory levels can deviate from optimal values during the actual disaster relief period due to the possible stochastic disruptions such as fluctuating demand for vital supplies in the shelters, a comprehensive on-line inventory control framework is proposed to minimize impacts of these unforeseen disruptions, or at least to address the problem at hand as fast as possible. Within this methodology, we obtain an approximation of the MC-SHIC model using a simultaneous perturbation stochastic approximation (SPSA) based functional approximator, and compare the performance of these algorithms for solving the new unconstrained optimization problem. Finally, proposed model-free on-line control methodology is discussed using examples to understand the efficiency and practicality of both algorithms in terms of computational times and accuracy of results.

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Modeling of Bus Transit Driver Availability for Effective Emergency Evacuation in Disaster Relief
  Author: Ender Faruk Morgul, Ozlem Cavus, Kaan Ozbay, Cem Iyigun

  Journal: Transportation Research Record: Journal of Transportation Research Board)

  Abstract: Potential evacuees without access to personal automobiles are expected to utilize transit, especially buses, to reach safer regions. For a transit agency, operation problems to be considered include establishing bus launch areas, positioning the minimum number of required buses and, coordination of transit operators, especially addressing the question of whether the number of drivers will be sufficient to cover the number of vehicles (i.e. buses) planned to be used during the evacuation. It is also highly probable that during an emergency, absenteeism rates for the bus drivers might increase. In this study, we develop two stochastic models to determine extra driver needs during an emergency evacuation operation and to provide optimal solutions using well-established concepts in mathematical programming. First we reviewed the existing literature needed to develop an effective methodology for the development of optimal extraboard management strategies and found out that although several recent reports clearly mention the problem of not having sufficient number of bus drivers during emergency evacuation operations, there is no analytical study that incorporates the optimal extraboard size problem into emergency evacuation operations. Second, two mathematical models are presented in this report. The aim of the developed models is to fill the gap in the literature for determining optimal extraboard size for transit operations during emergency evacuations. Our models are specifically designed to capture risk-averse behavior of decision makers. Finally, these models are tested using hypothetical examples based on real-world data extracted from New Jersey. Results show that both models give reasonable extraboard size estimates and under different conditions these models are responsive to the changes in cost and quality of service preferences. The results are encouraging in terms of the models usefulness for real-world applications.

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Effects of the Open Road Tolling on Safety Performance of Freeway Mainline Toll Plazas
  Author: Hong Yang, Kaan Ozbay, and Bekir Bartin

  Journal: Journal of Transportation Research Record, No. 2324, pp. 101-109

  Abstract: Advances of Intelligent Transportation Systems (ITS) technologies promoted the implementation of open road tolling (ORT) on tolled freeways worldwide. This new tolling solution converts existing barrier tollbooths to express lanes capable of collecting tolls at high-speeds. ORT has demonstrated numerous benefits in reducing traffic congestion and air pollution. However, effects of ORT on safety are still not clear, as most of ORT systems have only been operated for a relatively short period of time. Therefore, this study aims to evaluate the safety impacts of ORT by studying locations where such tolling solution was recently deployed on the Garden State Parkway in New Jersey. Multiple-year crash data at the toll plazas before-after the implementation of the ORT systems were used for analysis. Full Bayes methodology is employed to estimate crash frequency models as a function of traffic and toll plaza configurations. These models were used to estimate the crash frequency assuming that the ORT systems were not installed. Then, these estimations were compared with the observed number of crashes occurred after the deployment of the ORT systems. Individual comparisons show that crash reductions are observed at most of the toll plazas. The overall comparison shows that crashes at locations where ORT systems were deployed are decreased by about 24 percent after deployment of these systems. It can thus be concluded that the use of ORT is a beneficial solution towards improving toll road safety. From an implementation point of view, the analyses results indicate that special attention should be paid to operational elements such as signage, diversion and merge designs of the ORT systems.

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Use of a Regional Transportation Planning Tool for Modeling of Emergency Evacuation: A Case Study of Northern New Jersey
  Author: Kaan Ozbay, Anil Yazici, s. Iyer, Jian Li, Eren Erman Ozguven, J. Carnegie

  Journal: Journal of Transportation Research Record, Volume 2312, pp. 89-97

  Abstract: 33

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Case Study Based Evaluation of Stochastic Multi-Commodity Emergency Inventory Management Model
  Author: Eren Erman Ozguven, Kaan Ozbay

  Journal: Journal of Transportation Research Record, Volume 2283, pp. 12-24

  Abstract: Over the last three decades, disasters worldwide claimed more than 3 million lives and adversely affected lives of at least 1 billion people (1). Emergency disaster management has emerged as a vital tool to reduce the harm and alleviate the suffering these disasters cause to its victims. A significant task of planners involved in the emergency disaster management is the ability to plan for and satisfy the vital needs of the people located in the emergency shelters, such as the Superdome shelter at New Orleans. This task requires to find a way to reduce the uncertainties associated with the emergency operations, and to estimate the possible expected costs of delivery and consumption of vital supplies throughout these operations. This paper attempts to address these issues by applying a case study based approach to demonstrate the usefulness of using a multi-commodity stochastic humanitarian inventory control model while estimating the minimum safety stock levels of the emergency inventories. First, emergency inventory management problem is discussed and previous emergency inventory studies are reviewed to identify the need for a stochastic multi-commodity emergency inventory management model. After introducing the mathematical formulation for the model, it is applied to a number of realistic case studies built based on the experiences in recent major disasters, such as Katrina. The paper is concluded by a summary of lessons learned for the model when applied on a wide range of scenarios drawn from real-life experiences, and used to create emergency inventory management strategies for different types of disasters.

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Estimating Traffic Conflict Risk Associated with Merging Vehicles on a Highway Merge Section
  Author: Hong Yang and Kaan Ozbay

  Journal: Journal of Transportation Research Record, No. 2236, pp. 58-65

  Abstract: This study proposes a methodology for estimating rear-end conflict risk of merging vehicles on freeway merge sections as a probabilistic measure. The methodology consists of two major components. The first part estimates the merging probability of a vehicle given its position on a merge lane. Detailed vehicle trajectory data from Next Generation Simulation (NGSIM) program are used to find the underlying probability density function of merging decision. The second part derives the probabilistic risk of a merging vehicle conflicting with vehicles around it as a function of a surrogate safety measure, namely modified time-to-collision (MTTC). Combining these two parts together, an index is proposed to describe the conflict risk of each merging vehicle at each time step. By aggregating the conflict risk over time and space, a risk map for describing the level of conflict risk can be created. A case study demonstrates the implementation of the proposed method for traffic conflict analysis in detail. The result of this study can be used to evaluate the safety level of merge sections and develop real-time traffic control strategies to reduce conflicts associated with merging traffic.

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Enhancing the Quality of Infrared-Based Automatic Pedestrian Sensor Data by Nonparametric Statistical Method
  Author: Hong Yang, Kaan Ozbay, and Bekir Bartin

  Journal: Journal of Transportation Research Record, No. 2264, pp. 11-17

  Abstract: With the advent of Intelligent Transportation Systems in the last several decades, sensors are being extensively used to detect and count vehicle movements. On the other hand, the use of similar sensing technologies to detect pedestrian movements is relatively new. Pedestrian counts are essential for decision-making for pedestrian facility planning, signal timing, and pedestrian safety modeling. Conventional methods such as manual counting and videotaping can hardly satisfy the requirements of long-term pedestrian data collection programs. Fortunately, advances in sensing technologies have increased the ability of automating pedestrian data collection using infrared sensors. However, data quality of the infrared sensors is still a problem because several field studies showed that this type of sensors do not always perform perfectly. Field tests conducted by this study and by other research teams show that infrared sensors usually count significantly less than the actual number of pedestrians. Thus there are needs to enhance the data quality of infrared sensors. This paper proposes a nonparametric statistical method to calibrate raw sensor data to achieve this goal. Instead of using regression-based approaches that are traditionally preferred by traffic engineers, a bivariate bootstrap sampling procedure is used to obtain correction factors for new counts. Two case studies are used to test the validation of the proposed calibration procedure. Test results show that the proposed procedure can improve the sensor data quality in terms of reducing the discrepancy between sensor counts and ground truth data. The transferability of the calibration procedure is also verified through the case studies.

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Evacuation network modeling via dynamic traffic assignment with probabilistic demand and capacity constraints
  Author: Anil Yazici, Kaan Ozbay

  Journal: Journal of Transportation Research Record, Volume 2196, pp. 11-20

  Abstract: 39

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Manual of Guidelines for Inspection and Maintenance of Intelligent Transportation Systems
  Author: Kaan Ozbay, Eren Erman Ozguven, T Sertel, N Aboobaker, B Littleton and K Caglar

  Journal: Journal of Transportation Research Record, Volume 2129, pp. 90-100

  Abstract: 35

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Derivation and Validation of New Simulation-Based Surrogate Safety Measure
  Author: Kaan Ozbay, Hong Yang, Bekir Bartin, and S. Mudigonda

  Journal: Journal of Transportation Research Record, No. 2083, pp. 105-113

  Abstract: 13

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Simultaneous Perturbation Stochastic Approximation Algorithm for Solving Stochastic Transportation Network Analysis Problems: Performance Evaluation
  Author: Eren Erman Ozguven, Kaan Ozbay

  Journal: Journal of Transportation Research Record, Volume 2085, pp. 12-20

  Abstract: 36

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Nonparametric Bayesian Estimation of Freeway Capacity Distribution from Censored Observations
  Author: Eren Erman Ozguven, Kaan Ozbay

  Journal: Journal of Transportation Research Record, Volume 2061, pp. 20-29

  Abstract: 37

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Evacuation Modelling in the United States: Does the Demand Model Choice Matter?
  Author: Anil Yazici, Kaan Ozbay

  Journal: Transport Reviews, 28 (6), 757-779

  Abstract: 43

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Stochastic Humanitarian Inventory Control Model for Disaster Planning
  Author: Kaan Ozbay, Eren Erman Ozguven

  Journal: Journal of Transportation Research Record, Volume 2022, pp. 63-75

  Abstract: 38

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Impact of probabilistic road capacity constraints on the spatial distribution of hurricane evacuation shelter capacities
  Author: Anil Yazici, Kaan Ozbay

  Journal: Journal of Transportation Research Record, Volume 2022, pp. 55-62

  Abstract: 44

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