Conference Papers

2019

Application of Region Specific Depreciation Formulas in Highway Capital Stocks: Evidence from New York and New Jersey
  Author: Onur Kalan

 Conference: Transportation Research Board 98th Annual Meeting, Washington DC, United States, January 13-17, 2019

 Abstract: Highways are significant instruments for transportation in United States, and the common opinion is that increasing highway networks leads to major changes in economic development. In general, the quantification of the highways is performed by capital stock approach in monetary terms. In capital theory, the increase of the stock value can be calculated by gross investment values. On the contrary, deduction is performed with the assumption of different depreciation patterns which their representations of the real deduction in the assets have been questioned. This paper provides an empirical evaluation that focuses on the representability of new depreciation patterns which are derived from the deterioration functions of New Jersey State bridge decks. This study measures the Gross County Product (GCP) change between 1999 and 2013 in 18 counties of NY/NJ regional area in an econometric model with geometric depreciation pattern and proposed 22 new depreciation patterns. The results show that an increase in highway capital stock has a significant and positive impact on economic growth in the region. The models are used in the forecast of year 2016 GCP values of each county. Although some of the counties in the models do not reflect significant improvement in the new depreciation models, some of the counties show an observable decrease in the estimation errors. It is indicated that in the counties where the models give positive improvements, the new depreciation patterns can be used as a tool to estimate more precise GCP values for policy makers.

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Blockchain: A Safe, Efficient Solution for Driver Privacy and Connected Vehicle Transportation Data Sharing
  Author: Yingxi Cao, Abdullah Kurkcu, Kaan Ozbay

 Conference: Transportation Research Board 98th Annual Meeting, Washington DC, United States, January 13-17

 Abstract: Connected and automated vehicles (CAVs) are becoming increasingly prevalent, bringing with them potential for better safety and mobility. However, these vehicles can create many thousands of transactions in a flash, creating a challenge for current technologies that are not capable of transmitting such big data privately and securely. Distributed ledger technologies such as Blockchain have the potential to address this challenge by using decentralized system. Blockchain-based system allows users to enter into direct relationships with each other following commonly agreed terms with a high degree of trust, eliminating the need for a central authority while retaining security and privacy. This study investigates the potential for Blockchain to support safer delivery of CAV data. By using an actual simulation based implementation of the proposed architecture, it demonstrates how Blockchain can improve the level of security and privacy of data sharing and attempts to answer two fundamental questions: 1) how to securely and privately get and store data from CAVs and 2) how to find the best method to connect them using Blockchain technology. The proposed eight-layer framework uses Hyperledger Fabric as an underlying Blockchain technology and uses machine learning models for analyzing data collected in chain. The traffic data in the physical layer are simulated using microscopic traffic simulation tool SUMO and then incorporated into the Blockchain platform. The experiments highlight that the CAV system can be effectively combined with Blockchain technologies while enhancing security in a significant manner.

2018

Analytical Modeling of Information Dissipation in Urban Arterials with Connected Vehicles
  Author: Abdullah Kurkcu, Kaan Ozbay

 Conference: Transportation Research Board 97th Annual Meeting, Washington DC, United States, January 7-11, 2018

 Abstract: In this study the authors developed a macroscopic analytical model for modeling the vehicle-to-vehicle (V2V) communication process. The proposed information propagation methodology is based on the Susceptible-Infected-Removed (SIR) model that is used to model the spread of epidemics in a fixed region. The enhanced version of this epidemic model with the addition of exposed class is used to represent the information dissemination in connected vehicle (CV) environments. The proposed analytical model predicts the time it takes to inform all vehicles present on the given roadway. The model is developed in a way that it can adopt to a variety of connected vehicle market penetration levels. Finally, it is validated using simulation results obtained from a calibrated model coded using PARAMICS, traffic micro-simulation software. The results showed that the analytical model can accurately predict the contact rate of infected nodes which explains how fast the information will dissipate in dense urban conditions.

A Life-Cycle Cost-Analysis Approach for Emerging Intelligent Transportation Systems with Connected and Autonomous Vehicles
  Author: Jingqin Gao, Kaan Ozbay, Fan Zuo, Abdullah Kurkcu

 Conference: Transportation Research Board 97th Annual Meeting, Washington DC, United States, January 7-11, 2018

 Abstract: The objective of this paper is to describe five fundamental differences of Life Cycle Cost Analysis (LCCA) between a conventional transportation system and a technology-oriented Intelligent Transportation System (ITS). These five differences are related to the temporal behavior inflation, consideration of uncertainty, out-of-pocket costs, risks in terms technical obsolescence, and inventory management. A novel conceptual ITS LCCA framework which is introduced to capture these differences has the potential to be more effective in a connected and autonomous vehicle (CAV) environment. The findings from an in-depth discussion in the inflation rate indicate that the trend of the inflation rate for ITS components does not need to follow the general trend of consumer and producer price index. In addition, a viable alternative to quantify user cost is introduced by utilizing outputs from traffic simulations combined with traffic delay, vehicle operation, and crash risk cost models. Hypothetical failure rate scenarios were developed through the use of an open-source micro-simulation software namely, SUMO, in a connected vehicle environment. This approach is shown to be useful in quantifying user costs. Moreover, it can be readily implemented within the ITS LCCA framework when actual failure rate information becomes available.

2017

Crowdsourcing Incident Information for Disaster Response Using Twitter
  Author: Abdullah Kurkcu, Fan Zuo, Jingqin Gao, Ender Faruk Morgul, Kaan Ozbay

 Conference: 96th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Social media data have the potential to be used as a source of valuable information for real-time traffic operations to supplement existing systems such as 511. This paper analyzed incident data from two different data sources: 1) A traditional data provider that collects incident reports from multiple agencies, and 2) User posts from Twitter during Hurricane Sandy that flooded many areas in New York metropolitan area in 2012. A text classifier, built by utilizing extracted keywords from actual incident reports, is trained to find incident related Twitter data. The keywords are identified by Term FrequencyInverse Document Frequency (TF-IDF) and nave Bayesian method. The filtered Twitter data are cleaned and classified into various incident types to be compared geographically with that from the traditional data provider. The result showed that Twitter could provide detailed location information of a specific incident along with its intensity, duration and. Furthermore, it also provides information about incidents such as gas shortage that may not be easy to be obtained by traditional detection systems. It is not recommended to use Twitter as the only data source since it is biased and can be misleading depending on the type of analysis, yet it can be very powerful as a complementary data source. It is not only a real-time and inexpensive data provider but it also has a wide geographical coverage. It is worth to mention that Twitter data also contains incidents that are not available in TRANSCOM data set such as long lines at gas stations, crowdsourced traffic and closure conditions, but more accidents were reported by TRANSCOM. Therefore, merging these two sources will be useful especially for building models predicting incidents and generating resiliency maps.

Evaluating Resilience and Recovery of Public Transit System Using Big Data: Case Study from New Jersey
  Author: Sandeep Mudigonda, Kaan Ozbay, Bekir Bartin

 Conference: 96th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Analyzing the resilience and vulnerability of public transit networks is extremely important in the context of natural disasters as they serve as important evacuation means. In this study, we analyzed the public transit systems in New Jersey based on their vulnerability, resilience and efficiency in recovery following Hurricane Sandy. We apply diverse traffic, infrastructure, events and web-based sources of Big Data. Due to the sparsity of public transit measures for vulnerability, recovery and resilience we adapt many measures from existing literature to public transit. Following Hurricane Sandy, the NJTRANSIT bus transit network recovered much faster than rail network, as road infrastructure recovered much faster and most critical link for NJTRANSIT buses remained intact and loss of power for driving and signaling rail and subway systems. We also estimate the reliability of specific bus routes on the NJTRANSIT bus network.

Modeling Salt Usage During Snow Storms: Application of Hierarchical Linear Models with Varying Dispersion
  Author: Kun Xie, Kaan Ozbay, Yuan Zhu, Sami Demiroluk, Hong Yang, Hani Nassif

 Conference: 96th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Snow can cause dangerous driving conditions by reducing the pavement friction and covering the road surface markings. Salt is widely used by highway maintenance managers in the U.S. for reducing the impact of snow or ice on traffic. To develop long-term plans especially for the next winter season, it is essential to know what are the factors affecting salt usage and to determine sufficient amount of salt needed in each depot location. This can be done by estimating statistically robust models for salt usage prediction. In this study, historical data regarding storm characteristics and salt usage of New Jersey Turnpike (NJT) and Golden State Parkway (GSP) are used to estimate those models. The linear models, the hierarchical linear (HL) models and the hierarchical linear models with varying dispersion (HLVD) are developed to predict the salt usage of these highways. Results show that districts with higher average snow depth, longer storm duration and lower average temperature are associated with greater salt usage. The HLVD models are found to have the best predictive performance by including random parameters to account for unobserved spatial heterogeneity and by including fixed effects in the dispersion term. In addition, by estimating case-specific dispersion based on storm characteristics, the HLVD models could be used appropriately to estimate the upper bounds of salt usage, which are not extremely large and could satisfy the salt demand in most cases. The findings of this paper can provide highway authorities with valuable insights into the use of statistical models for more efficient inventory management of salt and other maintenance materials.

Exploring Taxi and Uber Demand in New York City: Empirical Analysis and Spatial Modeling
  Author: Diego E. Correa Barahona, Kun Xie, Kaan Ozbay

 Conference: 96th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: This study aims to investigate the impact of the emerging app-based for-hire vehicles on taxi industry through quantitative analyses of Uber and taxi demands for neighborhoods of New York City (NYC). Demand forecasting models, which can account for the spatial dependence of Uber and taxi trips are developed. In the empirical analysis, we explore the spatio-temporal patterns of Uber and taxi pick-up data. A high correlation between taxi and Uber pick-ups can be observed, especially in the central areas of the City. From 2014 to 2015, Uber trips increased dramatically by 10 million (223.3%), while taxi trips (include both yellow and green taxis) decreased slightly by 0.8 million (1.0%). The rate of growth of Uber is the lowest in Manhattan (201.2%), and the highest in the outer boroughs like Bronx (597.0 %) and Staten Island (573.0%). Results of the Morans I tests confirm the spatial dependence of both taxi and Uber demands. Linear models, spatial error models, and spatial lag models are developed to estimate the taxi and Uber demands of each neighborhood using socio-economical and transportation-related characteristics. The spatial error models are found to outperform the other two by capturing the spatial dependence via a spatially lagged dependent variable. Neighborhoods with lower transit access time (TAT), higher length of roadways, lower vehicle ownership, higher income and more job opportunities are associated with higher taxi/Uber demands.

  Keywords: Uber, taxi, demand, spatial dependence, spatial lag model, New York City

Data-Driven Approach to Estimate Double Parking Events Using Machine Learning Techniques
  Author: Jingqin Gao, Kaan Ozbay

 Conference: 96th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Double parking is a common occurrence in dense urban areas. It routinely causes danger for cyclists, pedestrians and short-term traffic disruptions that impede traffic flow. Using New York City as a case study, this paper introduces a novel data-driven framework for understanding the influential factors and estimating the actual frequency of double parking through utilizing parking violation tickets, 311 service requests, and social media information with surrounding street characteristics. The number of hotel rooms, traffic volume, commercial usage, block length and curbside parking spaces are ranked as the top five important factors contributing to double parking. Three feature selection methods, LASSO, stability selection and Random Forests techniques are applied to identify those contributing factors. Random Forests, as one of the most effective machine learning techniques is also applied to predict double parking performance of 50 locations in Midtown Manhattan, New York, where ground truth data is available. The Random Forests model achieves 85% prediction accuracy. The study demonstrates that the violation tickets and 311 service requests supplemented with additional street characteristics are able to offer a higher level of prediction accuracy for double parking events. This predictive power can be further applied to a macroscopic or microscopic traffic simulation model to evaluate double parking impacts on traffic delay and safety. In addition, this study can provide transportation agencies insights into effective data collection strategies to identify potential double parking hotspots for better policy-making, enforcement, and management.

WeatherEVANT: Real-Time Weather-Related Event Visualization and Analytics Tool
  Author: Sami Demiroluk, Kaan Ozbay, Bekir Bartin, Matthew D. Maggio, Hani Nasif, Daniel L. Hesslein

 Conference: 96th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Weather related events cause major disruptions to our overall transportation system. During the recent winter seasons, the east coast of the United States has struggled with heavy snowstorms, which has drawn attention to the efficiency of storm operations. In this paper, we describe a web-based tool called WeatherEVANT that was developed as a result of past research efforts conducted by the authors of this paper for the New Jersey Turnpike Authority (NJTA). The tool extracts the information residing in the snow operations database, which is updated frequently by NJTA maintenance operators, and provides various visualizations of this real-time data on its web-based interface integrated into GoogleMaps. WeatherEVANT also takes advantage of real-time data available from other sources including traffic cameras, weather reports, traffic data, and incident data among others. In that sense, it is a real-time data integration tool with extensive visualization and reporting capabilities. It can also automatically generate a variety of performance reports for the use by decision makers, such as salt usage, storm information, equipment usage, etc. One important salient feature of this tool is that it is being actively used by NJTA for the last two years, and improvements are implemented as a result of active feedback from its everyday users during the winter season.

Evaluation of Mobile Ticketing Technologies for Public Transit
  Author: Bekir Bartin, Kaan Ozbay, Hong Yang

 Conference: 96th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Both in the U.S. and abroad, various transit agencies have started to implement mobile ticketing applications, which save commuters time during ticket purchase and allows them to avoid surcharges typically incurred when purchasing tickets on-board. Mobile ticketing applications also have a high potential to help transit agencies reduce costs, give them a better understanding of commuter behavior, and allow them to make their services more efficient. However, it is not a straightforward task to predict the customers reaction and gain their trust and to increase the adoption rate of this new technology. A comprehensive evaluation plan is required to detect and fix critical usability problems before a system wide implementation. In this study, a 4-stage evaluation methodology is proposed for the development and implementation of mobile ticketing applications in public transit. The proposed methodology was employed when NJ TRANSITs mobile ticketing application, MyTix, introduced in 2013, was being developed.

Work Zone Coordination Tool
  Author: Bekir Bartin, Kaan Ozbay, Matthew D. Maggio, Hao Wang

 Conference: 96th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Faced with growing number of work zones, the challenge for transportation agencies is to effectively manage the impacts of work zones to alleviate congestion and maintain the safety of motorists and workers without disrupting project schedules.

Data Visualization Tool for Monitoring Transit Operation and Performance
  Author: Abdullah Kurkcu, Fabio Miranda, Kaan Ozbay, Claudio T. Silva

 Conference: 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

 Abstract: Using the automated vehicle location data combined with other technologies such as automated incident reporting, transit decision makers can now execute a variety of real-time strategies and performance evaluations. In this study, we show that it is possible to develop an easy to use but powerful web-based tool which acquires, stores, processes, and visualizes bus trajectory data. The developed web-based tool makes it easy for the end users to access stored data and to query it without any delay or external help. Moreover, the tool allows the users to conduct a series of data visualization and analysis operations demonstrating the potential of a such web-based tool for future applications.

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Investigating Transit Passenger Arrivals using Wi-Fi and Bluetooth Sensors
  Author: Abdullah Kurkcu, Kaan Ozbay, Kuo Ren

 Conference: 12th ITS European Congress ITS Beyond Borders, Strasbourg, France 19-22 June 2017

 Abstract: This paper developed a methodology to relate passenger data collected by Wi-Fi and Bluetooth sensors to scheduled bus departures for the purpose of studying passenger arrival behavior. One major advantage of using these sensors is their simplicity and cost. For stations at which AFC systems are not available, wireless sensors can easily be deployed. The developed Wi-Fi and Bluetooth sensors require are simple to operate, easily transported, and require minimal maintenance. The results pointed out that passenger arrival times at a transit stop are sensitive to the service frequency as it proposed in the literature. Furthermore, Wi-Fi and Bluetooth sensorscan be a cost-effective alternative to understanding furtherand analyzetheprobabilisticdistribution of passenger arrivals and wait times for stops and services where automated passenger data are not available to decision makers and researchers.

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A Hierarchical Clustering Based Travel Time Estimation Model in a Connected Vehicle Environment
  Author: Abdullah Kurkcu, Kaan Ozbay

 Conference: International Conference on Intelligent Traffic and Transportation (ICITT), Zurich, Switzerland 1-3 September 2017

 Abstract: The Connected Vehicle (CV) technology has the potential to transform driver behavior and will become a promising real-time data source that provides information required to accurately estimate traffic conditions. The information generated by CVs -including speed, position, and acceleration- can be used to analyze, evaluate, and improve the efficiency of the existing transportation infrastructure. In this study, the hierarchical clustering approach based on Wasserstein distances is used to estimate travel times using simulated CV data in an urban setting. The proposed methodology combines segments within a roadway section that have similar speed profiles into clusters and uses these grouped sections to compute the travel time on an individual section. The Basic Safety Messages (BSM) data are simulated from a calibrated traffic model using the Trajectory Conversion Tool (TCA). The generated messages with 5 and 10% market penetration levels are used as input for the clustering based travel time estimation algorithm. The results show that it is possible to accurately estimate travel time using CV data even with lower market penetration levels.

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Simulation Based Quantificaiton of the Potential Impacts of Incidents on Connected Vehicle Applications
  Author: Abdullah Kurkcu, Fan Zuo, Jingqin Gao, Kaan Ozbay

 Conference: INFORMS Transportation and Logistics Society First Triennial Conference Loyola University Chicago, USA 26-29 July 2017

2016

A Data-Driven Method for Predicting Future Evacuation Zones in the Context of Climate Change
  Author: Kun Xie, Kaan Ozbay, Yuan Zhu, Hong Yang

 Conference: 95th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: The determination of evacuation zones where inhabitants are prone to hurricane-related risk is helpful in estimating the demand of evacuees. The evacuation zones defined currently cannot remain the same in the future, since the long-term climate change such as the rise of sea level would have major impacts on hurricane-related risks. Traditional methods for the prediction of future evacuation zones rely heavily on the storm surge models and could be time-consuming and costly. This study aims to develop a novel data-driven method which can promptly predict future evacuation zones in the context of climate change. The map of Manhattan, which is the central area of NYC, was uniformly split into 150150 feet2 grid cells as the basic geographical units of analysis. A decision tree and a random forest were used to capture the relationship between grid cell-specific features such as geographical features, historical hurricane information, evacuation mobility, and demographic features and current zone categories which could reflect the risk levels during hurricanes. Ten-fold cross-validation was used to evaluate model performance and it was found that the random forest outperformed the decision tree in term of the accuracy and Kappa statistic. The random forest was used to predict the delineation of evacuation zones in the 2050s and 2090s, in the context of sea level rises. Compared with the current zoning, the areas with need of evacuation are expected to expand in the future. The proposed algorithm could be used to estimate evacuation demand in the future and thus support decision-making in the evacuation planning and the management of emergency resources.

  Keywords: Evacuation Zone, Emergency Management, Random Forest, Hurricane, Climate Change

Joint Analysis of Secondary Collisions and Injury Severity Levels Using Structural Equation Models
  Author: Kun Xie, Kaan Ozbay, Hong Yang

 Conference: 95th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: This study aims to investigate the contributing factors to secondary collisions and the effects of secondary collisions on injury severity levels. Manhattan, which is the most densely populated urban area of New York City, is used as a case study. In Manhattan, about 7.5% of crash events get involved with secondary collisions and as high as 9.3% of those secondary collisions lead to incapacitating and fatal injuries. Structural equation models (SEMs) are proposed to jointly model the presence of secondary collisions and injury severity levels. This study contributes to the literature by fully exploring the determinants of secondary collisions such as speeding, alcohol, fatigue, brake defective, limited view and rain. To assess the temporal effects, we use time as a moderator in the proposed SEM framework and results indicate that it is more likely to sustain secondary collisions and severe injuries at night. The parameter estimates of the proposed SEM are further compared with those of the standard probit models which estimate the presence of secondary collisions and injury severity independently. Results show that standard probit models overestimate the safety effects of confounding variables (i.e. variables that can affect both secondary collision occurrence and injury severity) by mixing the direct and indirect effects. In addition, it is found that the standard probit models significantly overestimate the effects of secondary collisions on injury severity propensity by 127.6% for daytime crashes and by 121.2% for nighttime crashes, since the endogeneity of the presence of secondary collisions is ignored in the estimation. Understanding the causes and impacts of secondary collisions can help the transportation agencies and automobile manufacturers develop effective injury prevention countermeasures.

  Keywords: Safety Analysis, Secondary Collisions, Injury Severity, Endogeneity, Structural Equation Model, Urban Area

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Analysis of Pedestrian Safety Using Big Data
  Author: Kun Xie, Kaan Ozbay, Hong Yang

 Conference: 95th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: This study aims to explore the potential of using big data technologies in advancing the pedestrian safety analysis including the investigation of contributing factors and the hotspot identification. Manhattan, which is the most densely populated urban area of New York City, is used as a case study. A massive amount of data from a variety of sources were collected, integrated and processed, including taxi trip, subway turnstile, traffic volume, road network, land use, demo-economic data. A parallel computation program was designed to process the massive amount of taxi data in Hadoop-based platform. We attempt to investigate the overall safety patterns of pedestrian rather than selected samples, so the whole study area was uniformly split into grid cells as the basic geographical units of 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 grid cell-specific contributing factors to crash costs which are left-censored at zero. Effects of contributing factors on pedestrian safety were fully explored using the grid cell-based tobit model. The potential for safety improvement (PSI) which 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 hotspot identification method proposed takes into account two important factors that are generally ignored, i.e., injury severity and effects of exposure indicators. In addition, a pedestrian hotspot map of the whole study area was obtained with higher resolution than conventional methods based on census tracts or traffic analysis zones. The hotspots ranking obtained in this study can support government agencies in making better decisions on allocation of resources for countermeasure development.

  Keywords: Safety Analysis, Big Data, Pedestrian Crashes, Hotspot Identification, Tobit Model, Kernel Density Function

Evaluating Usability of Geo-located Twitter as a Tool for Human Activity and Mobility Patterns: Case Study for New York City
  Author: Abdullah Kurkcu, Kaan Ozbay, Ender Faruk Morgul

 Conference: 95th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: The increasing use of new mobile devices and location sharing services such as Twitter has provided novel approaches for studying human mobility patterns with satisfactory quantities. The role of location in digital world has changed as expanding numbers of internet users including location information to their posts. These digital footprints allowed researchers to study the spatial and temporal characteristics of human activity and mobility patterns. This paper introduces an approach to collecting and utilizing geo-located Twitter status updates to report a quantitative assessment of human mobility. The results show that Twitter users follow the Lvy Flight mobility patterns. Moreover, the estimated mobility flows are found to be similar to the ground-truth data obtained from Regional Household Travel Survey.

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Development of an Automated Approach for Quantifying Spatiotemporal Impact of Traffic Incidents
  Author: Hong Yang, Kaan Ozbay, Kun Xie, Yifang Ma

 Conference: 95th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Traffic congestion on roadways seriously affect travel experience and cause economic and environmental problems. Part of the recurrent congestion is due to roadway bottlenecks such as lane drops or exit/entry ramps. Another major type of congestion is induced by traffic incidents such as traffic crashes. The former can be remedied by removing physical bottlenecks through the improvement of roadway capacity, geometry etc. However, the latter usually randomly occurs due to the high level of stochasticity of incident events. Therefore, it is a challenge to capture these non-recurrent congestion hot spots due to incidents. Nevertheless, the availability of real-time traffic sensor data provides the opportunity to address this issue through the use of data-driven solutions. Thus, the main objective of this study is to develop an automated approach to quantify incident induced congestion using sensor data. A practice-ready data-driven non-recurrent congestion quantification algorithm is developed and its implementation is demonstrated through real-world case study. It has been shown that the proposed automated approach can be used to efficiently identify incident-induced congestion.

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Network Modeling of Hurricane Evacuation Using Data-Driven Demand and Incident-Induced Capacity Loss Models
  Author: Yuan Zhu, Kaan Ozbay, Kun Xie, Hong Yang, Ender Faruk Morgul

 Conference: 95th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Modeling and simulation of hurricane evacuation is an important task in emergency planning and management. There are two major issues that affect development of a reliable evacuation model. The first one is how to estimate evacuation demand based on socio-economic characteristics, and the second one is how to deal with the uncertainty due to the roadway capacity loss as a result of highway incidents. Either of these factors can affect the planning of optimal evacuation routes due to their spatial-temporal impact on evacuation demand and the roadway network capacity.

  Keywords: Hurricane evacuation, modeling and simulation, evacuation demand, traffic incidents, capacity loss

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Modeling Double-Parking Impact on Urban Streets
  Author: Jingqin Gao, Kaan Ozbay

 Conference: 95th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Double parking (DP) is one of the key contributors to traffic congestion on urban streets. Double parking violations of commercial vehicles while they load and unload at delivery locations with insufficient curbside space can have significant negative impact on traffic. Motivated by the need to study such impact in urban cities, this paper utilizes parking violation records for New York City along with field data collected using video recording, and adopts a comprehensive modeling approach that combines available data with two types of models. The first is an M/M/? queueing model used to estimate double parking effect on the average travel time. The second is a micro-simulation model developed and calibrated to study individual and combined effects of various explanatory variables. Both models account for different effects of general vehicles and commercial trucks. Via case studies in Midtown Manhattan and Downtown Brooklyn (New York, US), double parking activities and driver behaviors are investigated and used for comparative analysis. The M/M/? queueing model has been empirically validated using field data collected as part of this study. Comparison results show a good fit for uncongested traffic conditions. Micro-simulation results indicate different impact levels for 21 scenarios in four categories namely, travel demand, double parking locations, frequency, and durations. This study can provide traffic agencies a potential approach to quantify the impact of double parking in a large-scale network and insights into the management and alleviation of on-street parking problems including incentives for encouraging off-hour deliveries and more effective enforcement during peak hours.

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Review and Evaluation of New Jersey Rail Grant Program
  Author: Bekir Bartin, Kaan Ozbay, W. Allen,Shrisan, IyerMartin Robins, Marc Weiner, Hani Nassif

 Conference: 95th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2016.

 Abstract: Short line railroads are crucial to economic activity and the transport of goods, and their operations are critical for maintaining a low volume of goods transported by trucks on highways. In order to preserve the railroad systems operations and network, many state departments of transportation (DOT)s have programs, which provide loans or grants for rehabilitation and improvement projects. The purpose of the rail investments is to promote and sustain economic development and maintain a balanced transportation system where rail is more economically viable than other transportation systems.

Exploring Pedestrian Bluetooth and WiFi Detection at Public Transportation Terminals
  Author: Neveen Shlayan, Abdullah Kurkcu, Kaan Ozbay

 Conference: 19th IEEE Intelligent Transportation Systems Conference, Brazil 2016

 Abstract: This is an on-going study that explores the potential benefits of using pedestrian data for evaluation and enhancement of public transportation. The research team proposes the utilization of Bluetooth (BT) andWiFi technologies to estimate time-dependent origin-destination (OD) demands and station wait-times of transit bus and subway users. The

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2015

Modeling Crash Risk of Highway Work Zones with Relatively Short Durations
  Author: Hong Yang, Kaan Ozbay, Kun Xie, Bekir Bartin

 Conference: 94th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2015.

 Abstract: Highway work zones greatly affect operational and safety performance of traffic. Existing studies have primarily focused on exploring safety issues of long-term work zones whereas safety issues associated with a large number of relatively shorter duration work zones are seldom examined. Thus, this paper aims to present an investigation of traffic safety in these work zones with relatively short duration. Considering low frequency of crashes due to this type of short duration work zones with respect to its crash condition and non-crash condition, a rare event logistic regression model is developed to explore the causal relationship between a set of contributing factors and the crash risk. The proposed model accounts for the imbalance issues of events (crashes) vs. non-event (non-crash) conditions in modeling low frequency crash occurrences. In addition, the model uses actual traffic data to reduce the bias of using aggregated exposure data (i.e, averaged traffic volume over a day, month, year, etc.). The modeling results based on a case study show that the work zone length, traffic volume and lane closure are positively associated with the crash risk in those work zones with short duration. The proposed model with specific model corrections and actual input data is found to improve the depiction of the relationship between these factors and the crash risk based on the comparison of the area under the ROC (Receiver Operating Characteristics) curves of different models.

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Impact of Truck-Auto Separation on Crash Severity
  Author: Hong Yang, Kaan Ozbay, Kun Xie

 Conference: 94th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2015.

 Abstract: Truck traffic has significant impact on traffic operations and safety. Especially, safety concerns due to truck traffic continue to draw increasing attention of transportation engineers and policy makers who are proposing a number of practical strategies such as lane restrictions and exclusive facilities. The key aspect of all these strategies lies in separating trucks from cars to create more homogeneous traffic conditions. Earlier research demonstrated merits of these strategies whereas very limited findings on their safety impacts have been reported so far. The objective of this study is to examine the impact of truck-auto separation on highway crash severity. Specifically, the safety impact of separation through dual-dual roadways, a system that simultaneously provide separated car-only lanes and mixed traffic flow lanes, is of great interest since this kind of system was barely studied in the past. In order to achieve this goal, a detailed crash data set from a major highway section with dual-dual roadways for several years was examined. Comparative analyses were conducted and a statistical model have been developed. The results of this study show that the deployment of the dual-dual roadways with car-only lanes has statistically significant impact on crash severity. The model results show higher risk of having injury crashes in dual-dual lanes (both inner and outer lanes) compared with regular mix traffic lanes. This finding suggests that other than considering crash frequency as a measure of safety, crash severity should also be considered to fully assess the performance of the truck-auto separation strategies similar to the one studied in this paper. However, other than the impact of the truck-auto separation, these findings can be (partially) changed due to other factors that are not accounted for by the model, such as the actual operational speed when crash occurred, trucks being loaded or empty, etc. In short, due to the unavailability of detailed data, current results need to be considered with great care and should be considered preliminary at best. More research with better and more detailed data is needed to be able to make any final conclusion and recommendation.

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Revisiting Labor Supply of New York City Taxi Drivers: Empirical Evidence from Large-Scale Taxi Data
  Author: Ender Faruk Morgul, Kaan Ozbay

 Conference: 94th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2015.

 Abstract: Taxicab activity patterns in urbanized regions have been subject to major changes over the past few years, especially after the introduction of online taxi-hailing applications. Demand-and-supply characteristics of taxicab services remain as a significant question to be analyzed when deciding on transportation policy improvements in big cities. On the other hand, labor supply theories that seek to explain possible income-targeting behavior in transitory wage changes have also been tested with taxicab driver data. However, empirical work on investigating the relation between income and work hours have resulted in conflicting findings mainly due to methodological differences and, to some extent, limited number of observations. One of the main limitations of taxi demand-supply questions have been lack of sufficient and reliable data. This paper presents an empirical assessment of taxicab drivers labor supply using a novel large-scale data source from New York City. Electronically collected data provides detailed information about work hours of drivers and collected fares. The methodological framework employed in this paper was mainly borrowed from the previous literature and the findings were compared with results from earlier studies. Some of the results using the large-scale data were found to deviate from these earlier findings. Moreover, seasonal variations in labor supply response to transitory wage changes were empirically ident1ied. The results of this study provide empirical support for the income-targeting hypothesis.

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Bayesian Spatial Modeling and Risk Mapping of Downed Trees along the Roadways Using Data from Hurricanes Irene and Sandy
  Author: Sami Demiroluk, Kaan Ozbay

 Conference: 94th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2015.

 Abstract: Two recent hurricanes namely, Irene and Sandy, crippled the roadway network due to storm flooding and downed trees. Roadways are critical for the operations of the agencies responding to hurricanes as well as the evacuation of the affected people. Therefore, there is a need to improve roadway safety and accessibility in the aftermath of the hurricanes. By better handling hurricane related incidents on the roadways such as downed trees, roadway safety and accessibility can be improved. This paper presents a municipality level zero inflated hierarchical Bayesian model for predicting the downed trees during these two recent hurricanes in New Jersey. First, the most influential factors on downed trees are identified and then a spatial Poisson model is estimated. In order to better visualize the model estimations, several maps are developed based on the modeling results. These maps enable a fast and intuitive understanding of model predictions especially for non-statisticians. This model can be applied to future hurricanes by using known roadway characteristics and weather forecast data. Since the model is developed using hierarchical Bayesian modeling framework, it is also possible to make best/worst case scenario analysis based on the lower/upper bound values of the predicted parameters to facilitate resource allocation during hurricanes. The model is also portable to the other regions since the data used in the estimation is available in other regions of the US as well.

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A Doubly Stochastic Point Process Model for Modeling Crashes along a Corridor
  Author: Sami Demiroluk, Kaan Ozbay

 Conference: 94th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2015.

 Abstract: In this paper, a doubly stochastic model for point level modeling of the crashes is proposed. Unlike traditional approaches, the roadway is represented as a continuous entity in this model. Such model enables us to include covariates at different levels of resolution in the model. Many spatial covariates are incorporated into the model and their effects are analyzed. It is found that pavement characteristics, which are at point level, such as surface distress and rut depth significantly affect the crash risk. Moreover, it is observed that speed limit, number of lanes, lane drop and off ramp covariates are found to be significant and they might be associated with the drivers difficulty to adjusting to changing traffic and geometric conditions. Then, a Poisson MCMC model is developed at the link level using link level covariates. Poisson MCMC model was unable to capture the significant effects captured in the log-Gaussian Cox process (LGCP) model. Finally, the modeling results and Bayesian inference are used to develop risk maps for visually identifying the crash risks along the roadway. These maps showed that it is easier to pinpoint the locations with higher crash risk using the LGCP model. We believe that the maps generated from the modeling results gives better insights then the maps based on simple link level crash counts since the point level maps allows zooming into the problematic locations with greater accuracy compared with the common approach of averaging out crashes over a predefined link segment.

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Using Big Data of Automated Fare Collection System for Analysis and Improvement of Bus Rapid Transit Line in Istanbul, Turkey
  Author: Kevser Simsek, Ilgin Gokasar, Kaan Ozbay

 Conference: 94th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2015.

 Abstract: Istanbuls smart card fare collection system generates large amounts of operational data from the BRT-Bus Rapid Transit line. In addition to ridership, it captures system-wide transaction data and provides comprehensive data records on usage. Processing and analysis of these data open new opportunities in transportation and travel behavior research. This paper presents a qualitative analysis of smart card (Istanbulkart) activity for the BRT-Bus Rapid Transit and investigates its potential for understanding complexities of the system and characterizing travel behavior. In this paper, an assessment of spatial and temporal travel behavior of commuters including mode choice, travel, and waiting times, is performed. As a result of this qualitative analysis an evaluation of the automated fare collection system with some recommendations for improving the planning and management of the BRT-Bus Rapid Transit line are also provided.

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Investigating the Impact of Work Zones on Crash Severity by Comparative Analysis
  Author: Ozgur Ozturk, Kaan Ozbay, Hong Yang

 Conference: 94th TRB Annual Conference (CD-ROM), Washington, D.C., January, 2015.

 Abstract: Work zone safety has received much attention in recent years due to numerous highway construction projects that have resulted in many work zone crashes. To minimize the impact of work zones on roadway safety, the contributing potential factors that influence these risks need to be investigated. This can be done by identifying the possible causal factors in terms of crash severity and implementing countermeasures to ensure the motorists safety.

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Bogazici Universitesi'nde Bisiklet Kullaniminin Yayginlastirilmasi
  Author: Ilgin Gokasar, Murat Bayrak, Onur Kalan

 Conference: 11. Ulastirma Kongresi

 Abstract: All over the world, the studies are significantly increased in dissemination of bicycle use area to decrease the emission of exhaust gases, as well as the decrease of traffic density and therefore saving in fuel. In Turkey, especially in Kocaeli, Izmir and Eskisehir provinces, the systems are in operation that users can rent bicycles from the bicycle stations and smart phone applications where the users also reach the bicycle information easily. In the scope of the recent developments of green campus projects, in-campus bicycle rent and in-campus bicycle lane systems are established in a number of universities. However, integration of bicycle with the campus and city urban transportation, especially in the city of Istanbul (metropolitan), is not an easy task in a dense traffic conditions and inconvenient in terms of topographic characteristics. Bogazici University has four campuses in Sariyer county of Istanbul: Kuzey, Guney, Hisar, Ucaksavar. These campuses are close to each other, but seperated by urban traffic. The transportation is being provided by shuttle services in every 3 minutes and 20 seconds. In the peak hours, urban traffic congestion leads to long queues in the shuttle line, therefore a new type of tranpsortation need is arised. With the ongoing research projects in Bogazici University Traffic Control Center about bicycle lane, bicycle sled and bicycle sharing systems, an alternative solution is suggested to shuttle services and decrease in demand of shuttle services, transportation costs and emission of exhaust gases are aimed. In the scope of this paper, survey studies are performed and with the results, in the case of setup of bicycle sled, the future demands of bicycle sled and sharing system are estimated. Bicycle sled will have a?

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2014

Estimating the Impact of Work Zones on Highway Safety
  Author: Ozgur Ozturk, Kaan Ozbay, Hong Yang

 Conference: Transportation Research Board 93rd Annual Meeting, Washington D. C., January 12-16, 2014

 Abstract: Investigating the operational and safety impacts of work zones on traffic are of great interest to transportation agencies. Despite the increasing attention in modeling work zone crash frequency, only a limited number of studies directly examined the change in crash rates under work zone conditions versus the corresponding non-work zone conditions. The main research question is thus whether or not a given roadway experiences increased number of crashes in the presence of a work zone. Another important goal of this study is to address the main research question using a large number of relatively long-term work zones. This is a challenging task because a major effort for building a comprehensive data set of a large number of work zones is needed. To answer this important question, an integrated data set is created by aggregating information from multiple data sources for sixty long-term work zones. Then descriptive analysis is performed to examine the characteristics of work zone crashes. Preliminary analysis results show that the crash rate increased by 24.4 percent under the work zone condition. To further statistically quantify the impact of work zones on traffic safety, negative binomial regression models are developed to identify relationship between a set of covariates and the crash counts aggregated by time and crash severity. An indicative variable is incorporated into the model to represent the work zone and non-work zone conditions for the studied sites. Overall, the descriptive analysis and modeling results suggest that the presence of work zones significantly increases the risk of crashes on roads.

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Work Zone Safety Analysis and Modeling: A State-of-the-Art Review
  Author: Hong Yang, Ozgur Ozturk, Kaan Ozbay, Kun Xie

 Conference: Transportation Research Board 93rd Annual Meeting, Washington D. C., January 12-16, 2014

 Abstract: Prevention of work zone crashes is one of the top priorities for transportation agencies. To make more informed decisions on initiating appropriate programs and countermeasures, more precise information on the underlying mechanisms of work zone crashes is needed. Considerable research effort has been directed to examine work zone crash characteristics and possible causal factors in the last five decades. This study is designed to provide a thorough review of existing research focused on work zone crash related analysis and modeling. Literature from multiple sources is carefully combed to determine about the state-of-knowledge related to the work zone safety problem. Experience and lessons learned from these studies are presented to highlight critical gaps in the knowledge of the safety of work zones. Future challenges are also discussed to address some missing information needs related to work zone safety analysis and modeling.

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Before-After Safety Evaluations Using Parametric Bayesian Survival Analysis
  Author: Kun Xie, Kaan Ozbay, Hong Yang

 Conference: Transportation Research Board 93rd Annual Meeting, Washington D. C., January 12-16, 2014

 Abstract: Before-after safety study is a key procedure to justify the performance of remedial treatments. The empirical Bayes (EB) and full Bayes (FB) methods have been widely used to assist such safety assessment. However, the practical use of EB and FB methods are often limited because of the extensive data needs from additional reference sites to develop safety performance functions (SPF). To overcome this issue, this study proposes a new safety evaluation approach by expanding the theory of survival analysis. A parametric Bayesian survival analysis model with a random effect term to address the heterogeneity across sites is developed. As a case study, the proposed model is used to evaluate the safety effectiveness of the recent red light running photo enforcement program in the state of New Jersey. As demonstrated in the case study, the proposed approach requires no data from reference sites. Using individual crashes as the analysis units, the survival analysis explicitly accounts for time-dependent covariates and makes it feasible to include up-to-date crash data into models. In addition, the survival analysis, takes an appropriate consideration of censoring effects, has the ability to address the regression-to-the-mean issue, and can be used to capture the probability of crash occurrence beyond a specified time for study sites.

Effects of Hurricanes Irene and Sandy in New Jersey: Evacuation Traffic Patterns
  Author: Jian Li, Kaan Ozbay, Bekir Bartin

 Conference: Transportation Research Board 93rd Annual Meeting, Washington D. C., January 12-16, 2014

 Abstract: This study analyzes spatio-temporal evacuation traffic patterns based on traffic data collected during Hurricanes Irene and Sandy in New Jersey. The temporal analysis shows that the total number of evacuees in Sandy was less than 30 percent of those in Irene. This is possibly due to the late-summer season, differences in background traffic, as well as so called crying wolf effect. However, similar to the evacuation departure behavior during Hurricane Irene, people who evacuated during Hurricane Sandy responded very quickly to the mandatory evacuation order. Traffic volumes increased significantly when the official mandatory evacuation order was issued. The spatial analysis shows that the most significant evacuation movements were located in the southern portion of the State, closest to the shore areas. The vast majority of this evacuation traffic moved westward, instead of traveling northbound along the shore areas made vulnerable by the storm. These evacuation traffic patterns are similar to the typical outbound traffic patterns seen in the shore areas at the end of a summer weekend but with departure times that were three to four hours earlier than usual. Moreover, in examining the evacuation traffic patterns and collected travel time data, this study was also able to identify traffic bottlenecks that arose during Hurricane Irene and Sandy evacuations. The results show that the bottlenecks were generally located at merging areas in the vicinity of on/off ramps or interchanges. These empirical observations can be used to improve real-world emergency response operations and evacuation models.

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Time Allocation Theory-Based Methodology for Valuation of Travel Time Reliability
  Author: Ender Faruk Morgul, Kaan Ozbay

 Conference: Transportation Research Board 93rd Annual Meeting, Washington D. C., January 12-16, 2014

 Abstract: Over the last few decades, the value of reliability has been recognized as a significant factor in drivers choice behavior. To measure reliability valuation, numerous studies have utilized the empirical scheduling-delay formulation developed by Small (1), which was originally based on classical microeconomic time allocation theory. In this paper we revisit the general time allocation model which was first introduced by Becker (2) and then formalized using time and goods constraints by De Serpa (3). We add schedule delay parameters in the constraints and provide an analytical derivation of a non-linear utility function. Next, we relax the constant marginal utility assumption following Blayac and Causse (4), which is considered an economical restriction that forces a single value of reliability estimation. Finally we give formulations for value of reliability calculation and present an empirical analysis for departure time choice using revealed preference data from New Jersey Turnpike (NJTPK) traveler survey. The results of the analysis show that travelers departing right before the peak congestion periods (i.e. during pre-peak) have a higher average value of reliability compared to the people departing at post-peak periods.

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Spatial Analysis of County-Level Crash Risk in New Jersey Using Severity-Based Hierarchical Bayesian Models
  Author: Sami Demiroluk, Kaan Ozbay

 Conference: Transportation Research Board 93rd Annual Meeting, Washington D. C., January 12-16, 2014

 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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Dunyada ve Turkiye'de AUS icin Yapilan Yatirimlar
  Author: Ilgin Gokasar, Onur Kalan

 Conference: Karayolu 3. Ulasim Kongresi

 Abstract: In the developing world, the cities that are getting crowded began to face with significant traffic problems. Traffic density, accidents and air pollution are making this problem more complicated. Traditional methods are insufficient to solve these problems. For these reasons, for a long time period, Intelligent Transportation Systems are being used to solve metropolitan cities problems. In this paper, the use of ITSs in developed and developing countries as well as the allocated resources and investments for them are scrutinized. In addition, the existing investments and financial studies for ITS in Turkey is scrutinized and and some information is provided. At the conclusion part, comments are provided in terms of the results obtained.

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2013

Modeling Driver Injury Severity of Single-Vehicle Crashes in Freeway Construction Work Zones
  Author: Hong Yang, Ozgur Ozturk, Kaan Ozbay, and Bekir Bartin

 Conference: Selected Proceeding of 13th World Conference on Transport Research Society, Rio de Janeiro, Brazil, July 15-18, 2013

Simulation-Based Evaluation of a Feedback-Based Dynamic Congestion Pricing Strategy for Alternate Facilities-Tracking Impacts of Value of Reliability
  Author: Ender Faruk Morgul, Kaan Ozbay, Hong Yang

 Conference: Selected Proceeding of 13th World Conference on Transport Research Society, Rio de Janeiro, Brazil, July 15-18, 2013

Effect of Removing Freeway Mainline Barrier Toll Plazas on Safety
  Author: Hong Yang, Kaan Ozbay, Bekir Bartin, and Ozgur Ozturk

 Conference: In Transportation Research Board 92nd Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 13-17

 Abstract: Toll plaza safety is a critical issue. Toll plazas induce motor vehicle crashes and also put workers such as toll collectors at risk. Therefore, enhancing safety at a toll plaza is crucial to improving safety on tolled roadways. This study aims to evaluate the safety effect of removing mainline barrier toll plazas on highways using Empirical Bayesian (EB) methodology. Recent removals of barrier toll plaza on the Garden State Parkway in New Jersey were used as a case study. Multiple-year traffic and crash data before and after the removals of the barrier toll plazas were used for analysis. Toll plaza crash frequency models as a function of traffic flow and other factors were developed, with the modeling results suggesting that there is a nonlinear relationship between toll plaza crash occurrences and both traffic flow as well as toll booth configurations. The EB approach is also used to predict crash frequency assuming that the barrier toll booths were not removed. These EB-based estimates were compared with the observed number of crashes after the removals of the toll plazas. Individual comparisons show reductions in crash frequency at almost all of the toll plazas and an estimated reduction of 47.2 percent overall at all toll plazas due to the removal of the barrier toll booths. The estimated crash cost was reduced by 43.2 percent. These estimated reductions demonstrate that the removal of barrier toll plazas is a very beneficial step towards improving safety of toll roads.

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Evaluation of Supplementary Traffic Control Devices for Surveyor Safety Enhancement
  Author: Hong Yang, Kaan Ozbay, Bekir Bartin, and S. Chien

 Conference: In Transportation Research Board 92nd Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 13-17

 Abstract: Many DOT employees who conduct geodetic/land surveying work alongside a roadway are constantly exposed to fast-moving traffic. Existing studies on traffic control and safety devices are predominately focused on traditional work zones such as construction and maintenance zones. There are limited studies on using traffic control and safety devices to address the safety issues encountered by surveyors at their unique short-term work sites. Information on traffic control and safety devices that can be used to enhance surveyor safety is greatly needed. Therefore, this study aims to evaluate the effectiveness of two traffic control and safety devices for surveyors and other employees whose work cover a large area within predetermined limits. The selected devices are portable rumble strips and warning lights. These devices were tested extensively at actual surveyor work sites in New Jersey. All tests sites were on four-lane two-way urban roadways. The effectiveness of the selected devices was assessed by a set of surrogate safety measures including reduction in mean speed, 85th percentile speed, changes in speed distributions, speed limit compliance, and braking rate. The results show that portable rumble strips overall outperformed the warning lights while both devices positively affected the surrogate safety measures at surveyor work sites. In addition, the combination of the two devices performed more effectively than using them separately. The potential safety benefits and relative ease of use of these devices make them potential traffic control and safety devices for surveyors as well as other employees who work in similar work sites.

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Investigating the Characteristics of Secondary Crashes on Freeways
  Author: Hong Yang, Bekir Bartin, and Kaan Ozbay

 Conference: In Transportation Research Board 92nd Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 13-17

 Abstract: Prevention of secondary crashes is one of the priorities in traffic incident management. However, limited information on secondary crashes has largely impeded the selection of appropriate countermeasures. The primary goal of this paper is to improve the understanding of secondary crashes, which is achieved by two major steps. First, an analysis framework is developed to accurately identify secondary crashes by integrating rich traffic sensor data with the statewide crash data sets. Second, the characteristics of the identified secondary crashes are investigated in detail. Secondary crashes that occurred on a 27-mile section of a major highway in New Jersey were mined using the proposed analysis framework. A thorough examination of their characteristics was then performed. Empirical findings on the frequency of secondary crashes, their spatio-temporal distributions, clearance time, crash type, severity, and major contributing factors were highlighted. These preliminary results can 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 based on more high-resolution sensor data and high-quality incident records are suggested.

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Crash Frequency Modeling for Highway Construction Zones
  Author: Ozgur Ozturk, Kaan Ozbay, Hong Yang, and Bekir Bartin

 Conference: In Transportation Research Board 92nd Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 13-17

 Abstract: Throughout the country, work zone safety issues have received considerable attention in recent years due to increasing work zone crashes along numerous highway renovation and reconstruction projects. In general, previous studies were able to consider limited number of contributing factors mainly due to the limitations in data availability. The main goal of this paper is to remedy this major problem related to data availability and estimate improved models using data from multiple sources. Work zone project drawings, crash database and straight line diagrams are used to create an integrated work zone safety database. Work zone crash data is plotted in time and space to validate, locate and adjust work zone related information. The negative binomial regression approach is used as the appropriate model to predict crash frequency within these work zones. Traffic volume is adjusted for daytime and night time conditions in terms of hourly distribution of daily traffic. The duration-based and period-based models are also developed to address relationship between potential factors and to predict crash frequency on work zones in terms of property damage only (PDO) and injury crashes. Compared with previous frequency models, additional parameters such as number of lanes closed and speed reduction are used. These additional factors identified as significant can help traffic engineers to further improve safety of work zone projects.

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Empirical Evacuation Response Curve during Hurricane Irene in Cape May County, New Jersey
  Author: Jian Li, Kaan Ozbay, Bekir Bartin, Shrisan Iyer and Jon A.Carnegie

 Conference: In Transportation Research Board 92nd Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 13-17

 Abstract: Understanding evacuation response behavior is critical for public officials in deciding when to issue emergency evacuation orders during an impending hurricane. Such behavior is typically measured by an evacuation response curve that represents the proportion of total evacuation demand over time during evacuation. This study analyzes evacuation behavior and constructs an evacuation response curve based on traffic data collected during Hurricane Irene (2011) in Cape May County, New Jersey. The evacuation response curve follows a general S-shape with sharp upward changes in slope following the issuance of mandatory evacuation notices. The sharp upward changes in slope represent quick response behavior, which may be in part caused by an easily mobilized tourist population, lack of hurricane evacuation experience, and/or the nature of the location, which in this case is a rural area with limited evacuation routes. Moreover, the widely used S-curves with different mathematical functions and the state-of-art behavior models are calibrated and compared with empirical data. The results show that the calibrated S-curves with Logit and Rayleigh functions fit empirical data better. The evacuation behavior analysis and calibrated evacuation response models based on this recent Hurricane evacuation event may benefit evacuation planning in similar areas. In addition, traffic data used in this study may also be valuable for the comparative analysis of traffic patterns between the evacuation periods and regular weekdays/weekends.

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Evaluation of a Methodology for Scalable Dynamic Vehicular Ad Hoc Networks in a Well-Calibrated Test Bed for Vehicular Mobility
  Author: Sandeep Mudigonda, Junichiro Fukuyama, Kaan Ozbay

 Conference: In Transportation Research Board 92nd Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 13-17

 Abstract: Connected vehicles are becoming ubiquitous with each passing year. Increase in mobile computing is proliferating the possible applications of connected vehicles. Many of these applications involve a continuous need for vehicles to connect to the communication infrastructure. This could result in congestion of the communication network. In this study we evaluate a novel dynamic grouping methodology that combines vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication schemes to make the optimal use of the communication infrastructure. The methodology for dynamic grouping of instrumented vehicles is implemented in a realistic and well-calibrated microscopic traffic simulation test bed of the New Jersey Turnpike for the application of sensor data collection. A reduction in communication infrastructure load of 66-91% can be achieved using the dynamic grouping for systematic aggregation of vehicular information. The maximum bandwidth usage is used as a measure to show that the name-address mapping is scalable. We show that the dynamic grouping methodology is very scalable with negligible loss in data quality as compared to the scenario where each vehicle connects to the communication infrastructure independently. The scalability is shown by generating response surfaces for the load on communication channels for different market penetration and communication ranges. These response surfaces can also be useful in predicting the channel load under future scenarios with increasing market penetration and power of communication radios. The data quality is validated using reported speed and estimated travel times over the network. It is shown that on an average the error in speed is 5.5-8% albeit using far lesser bandwidth using the dynamic grouping approach. Similarly, travel time along different paths is shown to be within 5% during regular conditions and within 10% during non-recurrent congestion.

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A Simplified Emissions Estimation Methodology Based on MOVES to Estimate Vehicle Emissions from Transportation Assignment and Simulation Models
  Author: Eren Erman Ozguven, Kaan Ozbay, Shrisan Iyer

 Conference: In Transportation Research Board 92nd Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 13-17

 Abstract: The use of complex emissions models such as EPAs MOVES to estimate air pollution impacts transportation models is constrained by significant runtimes when multiple scenarios and large networks are considered. The ultimate goal of this work is to approximate the emissions estimations generated by the most advanced emissions estimations tool, MOVES, with a significantly lower run time and without compromising accuracy. Accuracy is important because a very small error in the emissions estimation can cause large overall errors when large network sizes and vehicular volumes are analyzed. Approximated emissions functions are developed for pollutant emissions for different vehicle types by running several scenarios of a sample network in MOVES, and integrated into a GIS-based tool, known as Assist-Me, that post-processes transportation model output. This allows for quick estimation of pollutant emissions from multiple scenarios or networks without encountering the computation time challenges of MOVES. Additionally, vehicle idling and its effect on air pollution are included in the estimation and analysis, which are found to be a significant component of total vehicular emissions. In this paper, the development of the pollutant estimation approximation functions is described, along with an example applied to two test networks. The results are generated both directly from MOVES runs and the distribution fit analysis tool developed in this study. Results are compared based on the estimated fit functions and are also compared with the previously used MOBILE estimation model. Finally, a discussion is presented on runtime and applicability to more complex networks.

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ASSIST-ME: Postprocessing Tool for Transportation Planning Model Output
  Author: Kaan Ozbay, Bekir Bartin, Sandeep Mudigonda, Shrisan Iyer

 Conference: In Transportation Research Board 92nd Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 13-17

 Abstract: In this paper, we present ASSIST-ME (Advanced Software for State-wide Integrated Sustainable Transportation System Monitoring and Evaluation), a software application developed on a customized version of the ArcGIS 9.2 Developer Engine in Microsoft .NET Framework, as a tool to visualize and analyze the output of transportation planning models in a geographic information system (GIS) environment. The tool is built on a flexible framework that allows for adoption of any traditional transportation planning model, as demonstrated in this paper using the output of two major transportation planning models from different software platforms used by separate agencies: New York Metropolitan Transportation Councils (NYMTC) New York Best Practice Model (NYBPM) running in TransCAD North Jersey Transportation Planning Authoritys (NJTPA) North Jersey Regional Transportation Model Enhanced (NJRTM-E) running in CUBE. ASSIST-ME was conceived as a tool to allow agencies and planners to easily work with transportation planning model output, analysis of which is often time-consuming and requires extensive training. It offers four key functionalities: Data Visualization, Demand Analysis, Path Analysis, and Benefit / Cost Analysis. While data visualization and demand analysis enable the user to easily work with direct model output, custom path and cost analysis tools were developed to conduct analyses beyond what other software packages and tools allow. In particular, the benefit/cost analysis functions utilize the latest quantification/monetization approaches employed in research and by government agencies, without the need to run external applications or procedures (such as emission functions generated from EPAs MOVES). This process can be used for any planning scenario, but ASSIST-ME also allows for customization to alter/modify the input data or analysis procedures as per the users needs. The most important aspect of ASSIST-ME is that it incorporates data visualization, data analysis, and output reporting functionalities in a single user-friendly setting, which requires minimal training or knowledge of the models themselves.

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Commercial Vehicle Travel Time Estimation in Urban Networks using GPS Data from Multiple Sources
  Author: Ender Faruk Morgul, Kaan Ozbay, Shrisan Iyer, Jose Holguin-Veras

 Conference: In Transportation Research Board 92nd Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 13-17

 Abstract: Realistic travel time estimation for urban commercial vehicle movements is challenging due to limited observed data, large number of Origin-Destination (OD) pairs, and high variability of travel times due to congestion. Moreover most traditional data collection methods can only provide information in an aggregated form which is not sufficient for micro-level analysis. On the other hand, the usage of Global Positioning Systems (GPS) data for traffic monitoring and planning has been continuously growing with significant technological advances in the last two decades. In this paper we provide a comprehensive review of the current usage of GPS data in transportation planning applications and present a practical integrated methodology for using a robust source of GPS data, for commercial vehicle travel time prediction. A comparison with observed truck travel times collected from a limited source of truck-GPS data reveals that travel times obtained from taxi-GPS data approximate those of trucks, and can be used to supplement truck-GPS travel time data on a wider scale. While the amount of truck-GPS data is limited to a small number of trucks serving very few OD pairs, taxi-GPS provide citywide penetration and can estimate travel times between most OD pairs in a city. The provided methodology leads to simple and effective travel time estimations using taxi-GPS data without a need for an extra data collection effort.

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2012

A Spare Part Inventory Management Model for Better Maintenance of IntelligentTransportation Systems
  Author: Kaan Ozbay, Eren Erman Ozguven, Sami Demiroluk

 Conference: Transportation Research Board 91th Annual Meeting

 Abstract: New Jerseys highway transportation system is highly dependent on the performance of its roadways in order to maximize operational capability and to minimize travel time and congestion. Long-term sustainability of the highways performance is directly related to the efficiency of the Intelligent Transportation Systems (ITS) that need to be maintained and operated properly. One of the most important concerns within the inspection and maintenance procedures of ITS equipment is timely availability of the spare parts of essential components of ITS. Long-term down time of ITS equipment due to the unavailability of spare parts will not only increase personnel and repair time requirements, costs of replacement parts but also might lead to increased delays, poor air quality and fuel consumption. In this paper, we propose an efficient spare parts inventory control model that can determine the optimum levels of the safety stocks under probabilistic failure and availability assumptions for components of various ITS equipment. When this inventory control model is fully integrated into Rutgers Intelligent Transportation Systems Inspection and Maintenance Software (RITSIMS) (1), it will allow its users to efficiently manage DOTs ITS spare parts inventory using historic maintenance and inspection data that is being collected by respective databases of RITSIMS. This inventory control model will also be able to account for the worst case scenarios that DOT can experience in terms of unexpectedly high level of equipment failures due to natural or other disasters such as hurricanes.

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Comparison of Two Novel Travel Time Estimation Techniques Based on Probe Vehicle Data: Kriging versus Non-linear Programming Based Approaches
  Author: Mehmet Yildirimoglu, Kaan Ozbay

 Conference: Transportation Research Board 91th Annual Meeting

 Abstract: This study proposes and compares two novel approaches for estimating link travel times using data collected by an electronic toll collection system deployed on a closed roadway system instead of sensors and AVI readers specifically deployed for traffic monitoring. This dual use of toll readers for travel time estimation can be an attractive approach since it eliminates additional costs of deploying and maintaining sensors. However, readers are not located on the mainline, but instead on the ramps. Aside from the fact that the special configuration of the readers can present an important challenge in terms of accuracy of the estimates, the demand level associated with particular OD pairs is not always enough to obtain accurate average travel times. This study proposes two distinct statistical and mathematical programming based approaches to estimate link travel times, and studies pros and cons of each approach in terms of the accuracy of travel time estimates given the existing the infrastructure of the road system.

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Link Criticality Evaluation for Day-to-Day Degradable Transportation Networks
  Author: Jian Li, Kaan Ozbay

 Conference: Transportation Research Board 91th Annual Meeting

 Abstract: Link criticality evaluation is an important problem for public officials to make hazard mitigation planning. However, this type of analysis presents numerous challenges in terms of accurately capturing the impacts of highly stochastic hazard events. This study proposes an analytical framework and an efficient solution procedure for link criticality evaluation, which considers the impact of day-to-day degradable transportation network conditions. Link capacity is considered as a multi-status variable, and a sampling technique is used to generate realizations of transportation network capacity values. With different capacity realizations, traffic demand is repeatedly assigned on the regional planning model network, and the assignment results are measured with multiple criteria and analyzed using several statistical indices. A case study based on a portion of the New Jersey roadway network is presented to verify the proposed approach.

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A GIS-Based Interactive Lane Closure and Traffic Information Tool
  Author: Bekir Bartin, Kaan Ozbay, Sandeep Mudigonda

 Conference: Transportation Research Board 91th Annual Meeting

 Abstract: This paper describes the development of an interactive computer tool, namely RILCA, for planning lane closures for work zones. This user-friendly tool is aimed at providing traffic engineers with a computerized and easy to use lane closure application along with various other useful features. RILCA was developed using ArcView GIS software package as the main development environment. The interactive GIS map of New Jersey Turnpike (NJTPK), Garden State Parkway (GSP), and other major freeways in NJ and its surrounding network is displayed using ArcView. Various analysis and visualization options are provided for interactive planning of lane closures, traffic volume information and accident queries. RILCA was successfully tested and is currently being used by NJDOT and New Jersey Turnpike Authority (NJTA) engineers with all its features presented in this paper. It has also received the annual implementation award from the NJDOT in 2009 in recognition of its usefulness and potential future contributions to improve efficiency of traffic operations as a practical decision support tool for lane closure decisions.

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2011

Calibration of Micro-Simulation Models to Account for Safety and Operation Factors for Traffic Conflict Risk Analysis
  Author: Hong Yang and Kaan Ozbay

 Conference: In Proceeding of the 3rd International Conference on Road Safety & Simulation (RSS2011), Indianapolis, Indiana, USA, Sept. 14-16

 Abstract: Road safety is a critical issue for transportation systems. The use of crash data-based methodologies to analyze traffic safety problems has been problematic due to shortcomings such as unavailability and low quality of historical crash data. Other than crash data-based analysis, development of micro-simulation models in conjunction with surrogate safety measures has been shown to complement traditional safety analysis. However, for the adopted simulation model to achieve high fidelity, it is important to calibrate and validate it before use. This paper proposes a numerical optimization approach to calibrate a traffic simulation model for rear-end traffic conflict risk analysis on highways. The proposed calibration approach is developed based on the stochastic gradient approximation algorithm to find optimal parameters of stochastic traffic simulation models. The calibration methodology accounts for multiple calibration criteria and is implemented on a selected traffic simulation platform to test its performance. Simulated operational measurements and traffic conflict risk in terms of surrogate safety measures are quantified and compared with observations derived from real-world vehicle trajectory data from the Next Generation Simulation (NGSIM) program. The calibrated traffic model has been validated by using independent vehicle trajectory data saved as a hold-out sample. The results show that the fine-tuning of parameters using the proposed calibration approach can significantly improve the performance of the simulation model to describe actual traffic conflict risk and operational performances.

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2010

Application of Simulation-Based Traffic Conflict Analysis for Highway Safety Evaluation
  Author: Hong Yang, Kaan Ozbay, and Bekir Bartin

 Conference: In Selected Proceedings of the 12th World Conference on Transport Research Society, Lisbon, Portugal, July 11-15

 Abstract: 25

Investigating the Performance of Automatic Counting Sensors for Pedestrian Traffic Data Collection
  Author: Hong Yang, Kaan Ozbay, and Bekir Bartin

 Conference: In Selected Proceedings of the 12th World Conference on Transport Research Society, ISBN 978-989-96986-1-1 (Editors: J. Viegas and R. Macrio), Lisbon, Portugal, July 11-15

 Abstract: 26

Calibration of Automatic Pedestrian Counter Data Using a Nonparametric Statistical Method
  Author: Hong Yang, Kaan Ozbay, and Bekir Bartin

 Conference: In Proceedings of Seventh Triennial Symposium on Transportation Analysis (TRISTAN VII), Troms, Norway, June 20-25

 Abstract: 27

Calibration of Infrared-Based Automatic Counting System for Pedestrian Traffic Flow Data Collection
  Author: Kaan Ozbay, Hong Yang, and Bekir Bartin

 Conference: In Transportation Research Board 89th Annual Meeting Compendium of Papers CD-ROM, Washington D. C., January 10-14

 Abstract: 28

Comprehensive Analysis of Important Questions Related to Incident Durations Based on Past Studies and Recent Empirical Data
  Author: Anil Yazici, Kaan Ozbay, Sl Chien

 Conference: Transportation Research Board 89th Annual Meeting

 Abstract: 41

A Microscopic Simulation Study of Automated Headway Control of Buses Traveling on the Exclusive Bus Lane on the Lincoln Tunnel Corridor
  Author: Kaan Ozbay, Teja Indrakanti, Ozlem Yanmaz-Tuzel, Shrisan Iyer

 Conference: Transportation Research Board 89th Annual Meeting, Washington DC, United States, January 7-11, 2018

 Abstract:

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2007

Importance of Information Collection and Dissemination for Evacuation Modeling and Management
  Author: Anil Yazici, Kaan Ozbay

 Conference: Intelligence and Security Informatics

 Abstract: 45

2006

Analysis of network-wide impacts of behavioral response curves for evacuation conditions
  Author: Kaan Ozbay, Anil Yazici

 Conference: Intelligent Transportation Systems Conference, 2006. ITSC'06

 Abstract: 46

Effect on Transit Ridership of Time-of-Day Pricing Initiative at Port Authority of New York and New Jersey Facilities
  Author: Kaan Ozbay, Anil Yazici, Jose Holguin-Veras

 Conference: Transportation Research Board 85th Annual Meeting

 Abstract: 47