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2017 Transportation Research Board Annual Meeting

Welcome UB TRB Reception

Reception

University Transportation Center Award

Maria Torres
University Transportation Center Student of the Year Award

Congratulations to the University of Puerto Rico at Mayagüez's Maria Torres, TransInfo's UTC Outstanding Student of the Year!

Read Maria's Bio

Maria Gertrudis Torres Rodriguez is currently pursuing a Master’s Degree in the Transportation Engineering Program of the Civil Engineering and Surveying Department of the University of Puerto Rico at Mayaguez (UPRM), where she obtained her Bachelor’s Degree in Civil Engineering. Her graduate project consists on performing a safety analysis and evaluation of a highway in the western region of Puerto Rico and identifying countermeasures to improve the safety of its users. Miss Rodriguez joined the research team of TransInfo-UPRM in January 2016 where she has been working on the development of mobile applications for the police officers of Puerto Rico. Miss Rodriguez is the current president of the UPR-Mayaguez student chapter of the Institute of Transportation Engineers, and she is also an Eisenhower Fellow. 

TransInfo Partners: Posters, Papers and Awards

Check out all of the sessions faculty and students from TransInfo partner institutions are participating in below. 

Sunday, January 8th, 2017

9:00 AM- 12:00 PM 
Workshop: The Nexus of Transportation and Environmental Health Governance: Intersections and Collisions Across and Within Borders

O. A. Elrahman, Rensselaer Polytechnic Institute (RPI), presiding
George Giannopoulos, Hellenic Institute of Transport (HIT), presiding
Heidi Guenin, presiding

 

9:00 AM- 5:00 PM 
Workshop: Walking at Night: Pedestrian's Perspective, or “The Dangers of the Night-Walkers” (HF-C Ticket required)

New Approaches to Lighting for Pedestrian Safety and Sense of Personal Security 

John Bullough, Rensselaer Polytechnic Institute (RPI)

 

9:00 AM- 1:00 PM 
Workshop: Unmanned Aircraft Systems

Making It Happen: Applications and Research 

Lance Sherry, George Mason University
Parimal Kopardekar, National Aeronautics and Space Administration
Dallas Brooks, Mississippi State University
Theresia Schatz, Transportation Research Board
Colin Brooks, Michigan Technological University

Topics: Discuss integration considerations for various UAS operations, UAS synergy with and impact on surface transportation, UAS traffic management approaches, potential benefits of UAS for transportation agencies, UAS Integration into and around airport operations, business considerations/viability for airports.

7:00 PM- 8:30 PM 
International Participants Welcome Reception

George Giannopoulos, Hellenic Institute of Transport (HIT), presiding
William Anderson, Transportation Research Board, presiding
O. A. Elrahman, Rensselaer Polytechnic Institute (RPI), presiding

Monday, January 9th, 2017

8:00 AM- 9:45 AM
Poster Program: Urban Freight Innovations

Crowdsourcing Last-Mile Delivery of Online Orders by Exploiting Social Networks of Retail Store Customers

Aashwinikumar Devari, University at Buffalo
Alexander Nikolaev, University at Buffalo
Qing He, University at Buffalo

Abstract: Most major retailers and organizations strive to provide speedy and efficient delivery of products and explore the opportunities for saving on their last mile delivery costs. Crowd logistics is a subject of high interest in such endeavors. However, at its current state of development and adoption, further research is required to control and improve upon crowdsourced delivery times, risks and costs. This paper demonstrates the potential benefits of crowdsourcing delivery operations exploiting a social network of the customers of a retail store in assisting with the last mile delivery. In this paper, we conceive of a social network that connects the customers who are co-workers and/or neighbors of each other. The presented models and analyzes are informed by the results of a survey conducted with 101 participants to gauge people’s attitudes towards package delivery to and by friends or acquaintances. Relying on the survey responses, a logistic regression model is built to predict the probability of a package being delivered from a store to a customer by the customer’s friends. In order to study a potential large-scale impact of such delivery mechanism, we set up a simulation environment in TRANSIMS, an activity-based transportation modeling tool with the data collected from a real-world city. The results of the simulated experiments indicate that, by exploiting crowdsourcing, a retailer in a small city can reduce truck mileage by 57%, which is equivalent to reducing delivery costs by 8600USD per day. On average, each delivery adds extra 10 minutes to the regular trip of the party providing the delivery assistance. As a result of this assistance, the expected achieved reduction in pollutants, i.e., NOx, PM and PM, emitted by delivery trucks amounts to nearly 55%.

Analysis of Commercial Vehicle Parking Duration in New York City 

Joshua Schmid, Rensselaer Polytechnic Institute (RPI)
Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)
Alison Conway, City College of New York

Abstract: Parking has long been an issue in urban areas, especially when freight vehicles are concerned. Literature is limited and little attention has been paid to parking duration. This paper analyzed field-collected freight parking observation data in New York City. A parametric survival model was estimated to predict parking durations based on several characteristics of a parked freight vehicle. It was noted that vehicles delivering different types of items parked for differing durations, while vehicles that parked illegally were likely to park for a shorter period of time. We used the model to make predictions regarding how parking durations would differ based on vehicle and delivery characteristics. An elasticity analysis was performed on each significant variable and planning recommendations were made in order to maximize the amount of vehicles parking legally. Recommendations for future research included expanding the dataset to obtain more accurate estimates and applying the duration model to optimize operations at a business or logistics firm.

Direct Impacts of Off-Hour Deliveries on Freight Emissions 

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)
Trilce Encarnacion, Rensselaer Polytechnic Institute (RPI)
Carlos Gonzalez-Calderon, Rensselaer Polytechnic Institute (RPI)
James Winebrake, Rochester Institute of Technology (RIT)
Sofia Kyle, Rensselaer Polytechnic Institute (RPI)
Nilson Herazo Padilla, Rensselaer Polytechnic Institute (RPI)
Lokesh Kalahasthi, Rensselaer Polytechnic Institute (RPI)
Wilson Adarme, Universidad Nacional de Colombia
Victor Cantillo, Universidad del Norte
Hugo Yoshizaki, Universidade de Sao Paulo
Rodrigo Garrido, Universidad Diego Portales

Abstract:  A large portion of the negative environmental impacts from trucking in urban areas is the result of their travel during congested traffic conditions. To gain insight the potential of innovative solutions to this problem, this paper presents new research on the emission reductions associated with off-hour freight deliveries. To this effect, the paper uses fine-level GPS data of delivery vehicles operating during regular-hours (6AM to 7PM) and off-hours (7PM to 6AM), to quantify their emissions in five major cities in the Americas. Using second-by-second emissions modeling, the paper compares vehicle emissions under both delivery schedules for: reactive organic gases, total organic gases, carbon monoxide, carbon dioxide, oxides of nitrogen, and particulate matter. The results indicate that a switch to off-hour deliveries can reduce emissions by 48% in average, ranging from 9-82% depending on the pollutant, location, and improvements in traffic dynamics. The chief implication is that public policy should foster off-hour deliveries, and other forms of Freight Demand Management, where practicable.

 

8:00 AM- 9:45 AM

Lecture Program: Alternative and Recycled Materials in MSE Walls

Challenges and Opportunities When Considering Using Recycled Concrete Aggregate to Construct MSE Walls 

Burak Tanyu, George Mason University

Abstract: This talk will focus on physical, chemical, engineering, and hydraulic properties of recycled concrete aggregate in the context of considering this material to be used as backfill for the MSE walls. The talk will also point out the opportunities and challenges that need to be considered/addressed when dealing with recycled concrete aggregate to construct MSE walls.

8:00 AM- 9:45 AM

Poster Program: P3 Transportation Solutions: Issues and Considerations

Allowing Flexible Commercial Scopes for P3 ’Financial Success: Case Study of Japanese Expressway Revenue Generation Activities 

Motoki Murayama, Nexco Central
Nobuhiko Daito, George Mason University

No Abstract Available.

Impact of Political Risk on the Efficiency of Public-Private Partnerships 

Lisardo Bolanos, George Mason University
Lauren McCarthy, George Mason University
Jeong Yun Kweun, George Mason University
Jonathan Gifford, George Mason University

Abstract: This paper examines the impact of political risk on the delivery of surface transportation infrastructure projects using a public-private partnership (P3) approach. The goal is to assess the probability of completion of P3 projects and what role political risk plays. To understand how political risks impact P3 projects at each stage of project delivery, this paper first develops a data structure illustrating stages of P3 project delivery. Data points are collected from multiple sources, including the Federal Highway Administration, state transportation agencies and private sector websites, Public Works Financing Database, and journalistic accounts. Using the developed database, the paper analyzes the probability of a project making it to the opening of the project to the public and the average time it takes for this. The empirical findings are then used to draw implications on how political risk affects the delivery of P3 projects. The data can be useful to practitioners and academics to properly calibrate the risks premium in their analysis.

8:00 AM- 9:45 AM

Lecture Program: Social Media and Travel: Explorations Using New Data and Methods

Exploring Travel Behavior with Social Media: An Empirical Study of Abnormal Movements Using High-Resolution Tweet Trajectory Data

Zhenhua Zhang, State University of New York (SUNY)
Qing He, University at Buffalo
Shanjiang Zhu, George Mason University

 Abstract: This study reveals the characteristics of travel behavior using high-resolution Twitter data through a series of empirical studies and further explains the abnormal movements by the tweet trajectories. First, this paper explores the characteristics of individual travel behavior especially the location geo-distribution, movement scale and the clustering features of undirected travel. Second, this paper proposes a geo-mobility clustering method that groups the tweet locations driven by the same travel motif. This clustering method captures the clustering features of traveler’s hourly locations and detects the abnormal travel behavior. Third, the tweet posts are examined to identify the social activities behind these abnormal movements. The results of our algorithm shows that 46.2% of the abnormal movements can be tied with social activities by the keywords of the tweets.henhua Zhang,

State University of New York (SUNY).

10:15 AM- 12:00 PM

Poster Program: Statistical Methods in Transportation

Panagiotis Anastasopoulos, University at Buffalo, presiding

Modeling Multinomial Outcomes from Partner Selection and Joint Decision-Making Processes

 Dapeng Zhang, Hyperloop One
Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

Abstract: Burgeoning information technology innovations and wide adoptions of global positioning system (GPS) devices have greatly changed the transportation system. For travelers, the real-time ridesharing platforms allow drivers and riders to interact, pair up, and jointly decide on departure time and routes. In freight transportation, the prosperity of e-commerce leads to individualized real-time seller-buyer matching and their joint decisions on delivery modes and time windows. Transportation agents mutually select, or get matched with their counter-partners, and jointly make decisions on a set of matters. Existing econometric models are not able to behavioral-consistently capture these new phenomena which include intricate matching networks, mutual selection, and intensive joint decision making. This paper develops an innovative econometric model to fill the void. The proposed model consists of two parts: The first part explains the matching process in a many-to-one matching structure; the second part characterizes the joint decision making process of mutually-selected decision makers. The two parts are integrated by recognizing their dependency by a sample selection process: a joint response is only observed for matched decision makers. The proposed model is estimated using a Bayesian Markov-Chain Monte-Carlo approach with data augmentation. The likelihood functions and posterior distributions are derived, followed by a set of simulation studies to test parameter recovery capability at different parameter settings.

 

10:15 AM- 12:00 PM

Lecture Program: Railroad Track Performance Metrics

Data-Driven Optimization of Railway Track Inspection and Maintenance Using Markov Decision Process

Siddhartha Sharma, University at Buffalo
Yu Cui, University at Buffalo
Qing He, University at Buffalo

Abstract: Railway big data technologies are transforming the existing track inspection and maintenance policy deployed for Class I railroad in North America. This paper develops a data-driven condition-based policy for the track geometry inspection and maintenance. Both preventive maintenance and corrective maintenance are taken into account by the investigation on a 33-month inspection dataset which contains a variety of geometry measurements for every foot of track. First, this study separates the data based on the time interval of inspection run, calculates the aggregate TQI for each track section, and predicts the track spot geo-defect arrival probability with Random Forests. Then a Markov Chain is built for modeling aggregated track deterioration, while the spot geo-defect is modeled by a Bernoulli process. Finally, a Markov Decision Process (MDP) is developed for track maintenance decision making and optimized by using value iteration algorithm. By comparing with existing maintenance policy with Markov Chain Monte Carlo (MCMC) simulation, the new maintenance policy developed in this paper results in a saving around 10% of total maintenance costs for every 1 mile of track.

 

10:15 AM- 12:00 PM

Poster Program: Multimodal Operations at Signalized Intersections

Multimodal Hierarchically Responsive Signal Control with Lexicographical Dynamic Programming Approach

Qing He, University at Buffalo
Hernan Caceres Venegas, Universidad Catolica del Norte (Chile)
Manoj Reddy Kandukuri, University at Buffalo
Zhenhua Zhang, State University of New York (SUNY)

Abstract: This paper develops a Multi-modal Hierarchically Responsive Signal control model called MARS for trajectory-based signal control, by assuming that high penetration of floating sensors (e.g. Connected Vehicles, Smartphones, etc.) is available. First, this study conducts a comprehensive survey with traffic signal professionals, who bring up existing state-of-practice, open issues and future challenges in multi-modal traffic signal control. This survey also identifies the issues of current weight-based modeling for multi-modal control. It is found that assigning weights cannot be tied with delay of each mode in a straight forward manner. Second, by using multi-modal trajectory data, this paper develops a hierarchically multi-modal signal control model, in which each travel mode is solved by a dynamic programming hierarchically with the consideration of the delay and budget from upper-level modes. Further, the proposed control model is evaluated by microscopic simulation tool VISSIM at an isolated intersection, including three competing travel modes: light rail, buses and passenger cars (with trucks).

10:15 AM- 12:00 PM

Poster Program: Geometric Design Research

Ivette Cruzado, University of Puerto Rico, Mayaguez, presiding

10:15 AM- 12:00 PM

Lecture Program: Facing Freight Gridlock in the City of the Future: Importance of Understanding Urban Freight Stakeholders’ Behavior

Role of Freight Behavior Research in Fostering Sustainability and Urban Quality of Life 

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

 

1:30 PM- 3:15 PM

Lecture Program: Advances in Transportation Network Equilibrium Analysis

Statistical Meta-modeling of Dynamic Network Loading 

Wenjing Song, Pennsylvania State University
Ke Han, Pennsylvania State University
Yiou Wang, Pennsylvania State University
Terry Friesz, George Mason University
Enrique del Castillo, Pennsylvania State University

 

1:30 PM- 3:15 PM

Poster Program: New Research on Travel Time, Speed, and Reliability Data

Analyzing Travel Time Reliability and Its Influential Factors on Emergency Vehicles with Generalized Extreme Value Theory

Zhenhua Zhang, State University of New York (SUNY)
Qing He, University at Buffalo
JIZHAN GOU
Xiaoling Li, Virginia Department of Transportation

Abstract: Travel time reliability is very critical for emergency vehicle (EV) service and operation. The travel time characteristics of EVs are quite different from those of ordinary vehicles (OVs). Although EVs own highest road privilege, they may still experience unexpected delay that results in massive loss to the society. In this study, we employ the generalized extreme value (GEV) theory to measure and analyze extremely prolonged travel time. Among three GEV distributions, Weibull distributions are found to be the best distribution model according to several goodness-of-fit test. A new reliability index is derived to measure travel time reliability. Numerical examples demonstrate the advantages of GEV-based reliability index over variance and percentile value in the applications of EV. We further investigate the potential influential factors on EV travel time reliability. Results show that link length and number of lanes may have a negative impact on the link reliability. To some extent, left-turn traffic volume, and the number of left-turn lanes, also have negative impacts on travel time reliability.

 

1:30 PM- 3:15 PM
Poster Program: User Perspectives and Impact Assessments of Vehicle-Highway Automation Systems

Public Acceptance of Driver-Assist and Warning Technologies

Andrew Bartlett, University at Buffalo
Adel Sadek, University at Buffalo

Abstract: The last decade has seen a surge in the implementation of new onboard technologies in personal vehicles. While the emergence of fully autonomous vehicles (AVs) on the market may occur in the near future, the use of lower levels of automation in vehicles, such as driver-assist systems (e.g. self-parking, adaptive cruise control, collision avoidance) and driver warning systems (e.g. collision avoidance, blind spot, lane departure, and congestion/incident warnings) already exist and are becoming more prominent. The primary objective of this study was to determine what factors make individuals more or less comfortable with using or owning vehicles which incorporate a variety of driver-assist and warning technologies. Survey results revealed that a small majority said they would be comfortable with driver-assist technologies, such as self-parking (57%), adaptive cruise control (61%), and collision avoidance (58%). A much larger majority felt comfortable with warning technologies, including warnings for congestion/incidents (87%), lane departures (81%), blind spot occupancy (89%), and collisions (81%). A series of well-fitting models led to the identification of a variety of relationships between demographic information, driving behavior, and opinions about driving technologies. Those who stated they understood the benefits of AVs were found to be more comfortable with all driver-assist and warning technologies across all models. In general, those who were older, had higher incomes, and had higher levels of education were more comfortable with the technologies described. Those who had recently been ticketed for a driving violation were less comfortable with driver-assist technologies but this was not seen to impact comfort with warning technologies. Drivers with longer commutes were less comfortable with some warnings, but more comfortable with self-parking. These results could be applied to help shape the marketing of these technologies to certain demographics, as well as aid in forming public policy related to their implementation.

1:30 PM- 3:15 PM

Poster Program: Simulation and Measurement of Vehicle and Operator Behavior

Comparative Analysis of Toll Plaza Safety Features in Puerto Rico and Massachusetts Using a Driving Simulator 

Didier Valdes, University of Puerto Rico, Mayaguez
Benjamin Colucci, University of Puerto Rico, Mayaguez
Donald Fisher, Office of the Assistant Secretary for Research and Technology
Michael Knodler, University of Massachusetts, Amherst
Bryan Ruiz-Cruz, University of Puerto Rico, Mayaguez
Johnathan Ruiz Gonzalez, University of Puerto Rico, Mayaguez
Enid Colón Torres, University of Puerto Rico, Mayaguez
Ricardo Garcia Rosario, University of Puerto Rico, Mayaguez
Foroogh Sadat Hajiseyedjavadi, University of Massachusetts, Amherst

Abstract: Driving simulators have been widely used in transportation research and have potential applications for toll plaza safety research. The University of Puerto Rico at Mayagüez (UPRM) and the University of Massachusetts Amherst (UMass-Amherst) performed a collaborative investigation using driving simulators to evaluate drivers’ behavior in two toll plazas with different signage and lane configurations that operate under the USA jurisdiction. The studied toll roads were the Caguas South Toll Plaza in Puerto Rico (participants from Puerto Rico) and the West Springfield Toll Plaza in Massachusetts (participants from Massachusetts). The major safety issues identified in both toll roads were unexpected lane changes, sudden vehicle stops and variable speed patterns. The purpose of this study was to exchange research scenarios between UPRM and UMass-Amherst to test drivers that are unfamiliar with the area of study and enlarge our scope. Assuming that the patterns of behavior are similar, this will suggest that drivers’ behaviors from different regions depends largely on the geometry of the toll plaza and not the driving culture particular to a region. This will greatly add to the utility of driving simulator studies since the results reported from one region and one toll plaza arrangement should generalize to other regions around the country. The results indicated that there was no significant difference in the behaviors of drivers familiar and not familiar with a given toll plaza. Moreover, the proposed treatments for each toll plaza improved road safety for both familiar and unfamiliar drivers.

1:30 PM- 3:15 PM

Poster Program: Innovative Big Data Solutions for Transportation Challenges

Big Data Analysis-Based Decision-Making Tool for Applying Adaptive Traffic Control Systems 

Wan Li, University of Washington
Xuegang Ban, Rensselaer Polytechnic Institute (RPI)

Abstract: Adaptive signal control technologies have been increasingly deployed in real world situation. The objective of this study was to develop a decision making tool to guide traffic engineers and decision-makers who must decide whether or not adaptive control is better suited for a given traffic corridor and/or intersections than the existing actuated control system. The tool we develop here is based on big data analysis methods using a large amount of data from various sources such as detectors, 511 systems, weather, and special events. Support vector machine (SVM) is particularly applied to distinguish “good” and “bad” signal performances, as well as adaptive and actuated control. Results show satisfactory performances of the SVM methods and a decision-making procedure was developed to guide the deployment of adaptive control. Limitations of the proposed tool and potential pitfalls of big data methods were also summarized and discussed.

1:30 PM- 5:30 PM 
Visibility Committee

John Bullough, Rensselaer Polytechnic Institute (RPI), presiding

3:45 PM- 5:30 PM

Poster Program: Current Research on User Information Systems

Validation of Design Criteria for In-Vehicle Collision Warnings 

Bridget Lewis, George Mason University
Carryl Baldwin, George Mason University

Abstract: Despite recent physical and technological advances in vehicle safety, automobile accidents remain one of the leading causes of death, particularly among young people.  As automation allows for more control of non-critical functions by the vehicle, the potential for distraction among drivers also increases.  It is, therefore, as important as ever that in-vehicle safety-critical interfaces, such as collision avoidance systems, are intuitive and unambiguous when presenting information to the driver.  To this end, the current study aimed to validate previously defined acoustic criteria for use in forward collision warning systems.  The current study used a motion-base driving simulator to assess the effectiveness of two warnings, one which met all criteria and one which did not, as well as a no warning control in the context of a lead-vehicle following task in conjunction with a distractor task, and collision event.  Results indicate that participants receiving a warning which meets all criteria had improved outcomes relative to participants receiving no warning on their first exposure.  Responses to consistently good warnings improved with subsequent exposures whereas results suggest that participants receiving the warning which met only some criteria performed more poorly with increased exposure, and that the inclusion of a poor warning or variable warnings may negate the effects of learning shown by participants who received consistently good warnings or even no warning at all.  Findings from this study show support for previous, rating-based, subjective warning design studies.  Implications for collision avoidance system design are discussed.

 

3:45 PM- 5:30 PM

Poster Program: Driver Behavior and Inattention

Exploratory Empirical Analysis of Measured and Perceived Aggressive Driving Behavior in a Driving Simulation Environment

Nima Golshani, University of Illinois, Chicago
Md Tawfiq Sarwar, NRC Research Associateship
Panagiotis Anastasopoulos, University at Buffalo
Kevin Hulme

Abstract: In this paper, driving simulation data and surveys collected in the spring of 2014 in Buffalo, NY, are used to explore the factors that can affect measured (through driving simulation experiments) and perceived (self-reported, based on surveys) aggressive driving behavior.  To simultaneously account for unobserved heterogeneity, panel data effects, and cross equation error correlation, a random parameters with within-panel heterogeneity bivariate probit model is estimated.  The results show that a number of factors affect the measured and perceived aggressive driving behavior, such as: driving experience and exposure (frequency and willingness to drive, accident history, and driver experience); socio-demographic characteristics (family status, household income, level of education, and grow up area), and behavioral and other characteristics (traffic violation warning, music preference, and alcohol consumption tendency).  The results imply that some drivers may drive aggressively when they perceive their driving behavior as non-aggressive (or the opposite), and that different factors play in how aggressive driving behavior is measured and perceived.

6:00 PM- 7:30 PM 

Lecture Program: Parking 201: How Much Is Enough?

Freight and Service Parking Needs and the Role of Demand Management 

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)
Shama Campbell, Rensselaer Polytechnic Institute (RPI)
Diana Ramirez-Rios, Rensselaer Polytechnic Institute (RPI)
Carlos Gonzalez-Calderon, Rensselaer Polytechnic Institute (RPI)
Lokesh Kalahasthi, Rensselaer Polytechnic Institute (RPI)
Catherine Lawson, State University of New York, Albany
Jeffrey Wojtowicz, Rensselaer Polytechnic Institute (RPI)

Abstract: The research reported in this paper assess the parking needs of freight and service related commercial activities and identifies the role of demand management in mitigating these needs. To provide a context for the analyses, the authors selected two small commercial areas of about the same number of commercial establishments—one in Troy, NY, and the other in New York City—and applied freight and service trip generation models to estimate the total freight and service traffic generated at these sites. Then, using different assumptions of the amount of time these vehicles spend at a parking location, the authors estimated the number of parking spaces required by time of day under different assumptions of demand management. The paper describes the analyses of the results, and discusses key findings and policy implications. 

Tuesday, January 10th, 2017

8:00 AM- 12:00 PM

Program: Artificial Intelligence and Advanced Computing Applications Committee

Sherif Ishak, Louisiana State University, presiding
Adel Sadek, University at Buffalo, presiding

8:00 AM- 12:00 PM

Program: International Cooperation Committee

O. A. Elrahman, Rensselaer Polytechnic Institute (RPI), presiding
George Giannopoulos, Hellenic Institute of Transport (HIT), presiding

8:00 AM- 12:00 PM

Poster Program: Crash-Based Research: Wrong-Way Driving, Work Zones, Debris, Taxis, and Other Crash Situations

Analysis of Stationary and Dynamic Factors Affecting Highway Accident Occurrence

Grigorios Fountas, University at Buffalo
Md Tawfiq Sarwar, NRC Research Associateship
Panagiotis Anastasopoulos, University at Buffalo
Alan Blatt, CUBRC
Kevin Majka, CUBRC

Abstract: Traditional accident analysis typically explores non-time-varying (stationary) factors that affect accident occurrence on roadway segments.  However, the impact of time-varying (dynamic) factors is not thoroughly investigated.  This paper seeks to simultaneously identify pre-crash stationary and dynamic factors of accident occurrence, while accounting for unobserved heterogeneity.  Using highly disaggregate information for the potential dynamic factors, and aggregate data for the traditional stationary elements, a dynamic binary random parameters (mixed) logit approach is adopted.  With this approach, the dynamic nature of weather-related, and driving- and pavement-condition information is jointly investigated with traditional roadway geometrics and traffic characteristics.  The analysis is based on crash and non-crash observations between 2011 and 2013, drawn from urban and rural highway segments in the state of Washington.  The findings show that the proposed methodological framework can account for both stationary and dynamic factors affecting accident occurrence probabilities, and for unobserved heterogeneity.  The results also demonstrate the potential of random parameters modeling, in terms of forecasting accuracy and explanatory power. 

8:00 AM- 9:45 AM 

Poster Program: Emergency Evacuation Poster Presentations

Modeling multimodal transportation network emergency evacuation considering evacuees’ cooperative behavior 

Xia Yang, Rensselaer Polytechnic Institute (RPI)
Xuegang Ban, Rensselaer Polytechnic Institute (RPI)
John Mitchell

Abstract: Modeling emergency evacuation could help reduce the losses and damages from disasters. In this paper, based on the system optimum principle we develop a multi-modal evacuation model which captures the proper traffic dynamics, the cooperative behavior of evacuees, the interactions among different transportation modes, and the capacities of shelters. The proposed model can achieve a balance between computation efficiency and modeling details. Further, we have developed an MSA-based sequential optimization algorithm for large-scale evacuation problems. Both the proposed model and the solution algorithm are tested and validated through numerical analysis and a case study on Lower Manhattan. The modeling results can provide constructive guidance in evacuation planning and help reduce system evacuation time.  

8:00 AM- 9:45 AM 

Poster Program: Route Choice Behavior

Impact of Target on Traveler's Multiple-Criteria Route Choice Decision 

Xiangfeng Ji, Southeast University
Xuegang Ban, Rensselaer Polytechnic Institute (RPI)
Xu Qu, Southeast University
Jiang Zhang, Southeast University
Bin Ran, University of Wisconsin, Madison

Abstract: Traveler's route choice behavior is influenced by many criteria, e.g., route travel time, route late arrival penalty and route distance. In this paper, we propose a novel target-oriented route choice model, where multiple different criteria are involved. The potential benefit of this model is that the targets for the route choice criteria are often natural to specify. In the proposed model, we simultaneously consider the dependence between different route choice criteria and the interaction among different targets on the stochastic traffic network. On one hand, combining the marginal distribution for each route choice criterion, we propose to use the copula to capture the dependence between different route choice criteria, and the exact form of copula can be found via the proved comonotonicity. On the other hand, we capture the interaction among different targets, i.e., the complementarity and substitutability relationship, which can show traveler's behavioral insights. Furthermore, we extend the proposed route choice model into the user equilibrium model, which can be a novel method for the transportation planning. We formulate the user equilibrium as the variational inequality (VI) problem and prove its properties. Finally, the VI problem is solved with the proposed method of successive average algorithm, and the performance of the proposed model is illustrated with numerical example, which can bring some inspirations to the practical policy design and implementation.

8:00 AM- 9:45 AM 

Lecture Program: Surveillance and Control of Sustainable and Resilient Transportation Networks

Ridesharing User Equilibrium and Its Implications for High-Occupancy-Toll Lane Pricing 

Xuan Di
Henry Liu, University of Michigan, Ann Arbor
Xuegang Ban, Rensselaer Polytechnic Institute (RPI)
Hai Yang, Hong Kong University of Science and Technology

 

 

10:15 AM- 12:00 PM

Poster Program: Current Issues in Alternative Transportation Fuels and Technologies

Incorporating Demand Dynamics in Multi-period Capacitated Recharging Location Planning for Electric Vehicles

Anpeng Zhang, University at Buffalo
Jee Eun Kang, University at Buffalo
Changhyun Kwon, University of South Florida

Abstract: We develop a multi-period capacitated flow refueling location problem for electric vehicles (EVs) as EV market responds to the charging infrastructure. The optimization model will help us deter mine the optimal location of chargers as well as the number of charging modules at each station over multiple time periods. We define a number of demand dynamics (varying degree of sensitivity to path specific charging opportunities and general charging opportunities) with two objective functions (one maximizing flow coverage and the other maximizing electric vehicle demand). A case 7 study based on a road network around Washington, D.C., New York City, and Boston is presented to provide numerical experiments related to demand dynamics, illustrating the potential outcomes of multi-period charging infrastructure planning.

10:15 AM- 12:00 PM
Lecture Program: Thinking Outside the Bulb: Innovations in Airfield Lighting Strategies

Human Factors Impacts of Light-Emitting-Diode Airfield Lighting 

John Bullough, Rensselaer Polytechnic Institute (RPI)

Abstract: Light emitting diodes (LEDs) differ from incandescent light sources in several ways that are relevant to energy and maintenance requirements of airfield lighting systems. They have higher luminous efficacy and when designed properly, have longer useful operating lives; both factors make LEDs attractive candidates for airfield lighting. The photometric, colorimetric and temporal characteristics of LEDs also differ from those of incandescent light sources, and these can have important implications for the appearance of runway and taxiway lighting systems. The present paper reviews publications summarizing experimental and analytical investigations designed to assess these implications in terms of the following human factors impacts: color identification, brightness and glare, visibility in fog and haze, response to onset of flashing lights, and stroboscopic effects such as the phantom array. Overall, this review of experimental evidence suggests that in addition to their reduced energy use and maintenance requirements, LED airfield lighting can be advantageous in comparison to incandescent lighting systems used to delineate airport runways and taxiways.

 

10:15 AM- 12:00 PM
Lecture Program: Vulnerable Road Users: Analysis of Pedestrian and Bicycle Crashes

Preliminary Investigation of the Effectiveness of High Visibility Crosswalks on Pedestrian Safety Using Crash Surrogates

Md Tawfiq Sarwar, NRC Research Associateship
Grigorios Fountas, University at Buffalo
Courtney Bentley, University at Buffalo
Panagiotis Anastasopoulos, University at Buffalo
Alan Blatt, CUBRC
John Pierowicz, CUBRC
Kevin Majka, CUBRC
Robert Limoges, New York State Department of Transportation

Abstract: Using the SHRP2 naturalistic driving study (NDS) data, this paper provides a preliminary evaluation of the effectiveness of high-visibility crosswalks (HVC) in terms of improving pedestrian safety at uncontrolled locations.  This is accomplished by analyzing the driving behavior of SHRP2 participants at three uncontrolled locations in the Erie County, New York test site.  In this context, crash surrogates (i.e., speed, acceleration, throttle pedal actuation, and brake application) are used, in order to evaluate the participants’ driving behavior, primarily on the basis of data before and after the HVC installation.  The before/after analysis allows the assessment of HVC effectiveness in terms of driver behavior modification.  Mixed logit and random parameters linear regression models are estimated, and panel effects and unobserved heterogeneity are accounted for.  A number of factors are explored and controlled for (e.g., vehicle and driver characteristics, roadside environment, weather conditions, etc.), and the preliminary exploratory results show that HVCs have the potential to improve pedestrian safety and positively modify driving behavior.

10:15 AM- 12:00 PM
Poster Program: Evaluating the Performance of Traffic Signal Systems

Traffic State Estimation Based on Vehicle Trajectory Segmentation 

Choudhury Siddique, University of Washington
Xuegang Ban, Rensselaer Polytechnic Institute (RPI)

Abstract: Identification of Traffic condition in a motorway is essential for traffic management and control. GPS-enabled cell phones provide new opportunities for location-based services and traffic estimation. When traveling on-board vehicles, these phones can accurately provide position and velocity of the vehicle, and therefore can be used as probe traffic sensors. The In this paper, a methodology for the identification of the traffic state from GPS sensor data found from a single probe vehicle is presented. The methodology combines Hidden Markov Model (HMM) and Support Vector Machine (SVM) techniques to classify individual data points. Later, an algorithm is developed to identify stop and go segments in a trajectory. An application of the methodology for traffic state dependent sampling in a freeway network in Albany, NY shows encouraging results.

 

1:30 PM- 3:15 PM
Lecture Program: Artificial Intelligence Methods and Modeling Tools for Classification, Estimation,

Interval Prediction of Short-Term Traffic Volume Based on Extreme Learning Machine and Particle Swarm Optimization

Lei Lin, PARC Research
John Handley, Xerox Corporation
Adel Sadek, University at Buffalo

Abstract: Short-term traffic volume prediction models have been extensively studied in the past few decades. However, most of the previous studies only focus on single-value prediction. Considering the uncertain and chaotic nature of the transportation system, an accurate and reliable prediction interval with upper and lower bounds may be better than a single point value for transportation management. In this paper, we introduce a neural network model called Extreme Learning Machine (ELM) for interval prediction of short-term traffic volume and improve it with the heuristic particle swarm optimization algorithm (PSO). The hybrid PSO-ELM model can generate the prediction intervals under different confidence levels and guarantee the quality by minimizing a multi-objective function which considers two criteria reliability and interval sharpness. The PSO-ELM models are built based on an hourly traffic dataset and compared with ARMA and Kalman Filter models. The results show that ARMA models are the worst for all confidence levels, and the PSO-ELM models are comparable with Kalman Filter from the aspects of reliability and narrowness of the intervals, although the parameters of PSO-ELM are fixed once the training is done while Kalman Filter is updated in an on-line approach. Additionally, only the PSO-ELMs are able to produce intervals with coverage probabilities higher than or equal to the confidence levels. For the points outside of the prediction levels given by PSO-ELMs, they lie very close to the bounds.

1:30 PM- 3:15 PM

Poster Program: Dwight David Eisenhower Transportation Fellowship Program, Part 2 (Part 1, Session 613)

Load Rating of Reinforced Concrete Box Culverts Without Plans Using Statistical Parameters in Their Steel Reinforcements 

Ricardo Perez-Gracia, University of Puerto Rico, Mayaguez

1:30 PM- 3:15 PM
Lecture Program: Transportation Workforce Needs for Critical Infrastructure Resilience for Physical Security and Related Cyber-Security Challenges

Resilience and Business Continuity 

Mark Troutman, George Mason University
Christie Jones, George Mason University

1:30 PM- 3:15 PM

Poster Program: Pedestrian Planning and Design

Real-World Demonstrations of Novel Pedestrian Crosswalk Lighting 

John Bullough, Rensselaer Polytechnic Institute (RPI)
Nicholas Skinner, Rensselaer Polytechnic Institute (RPI)

Abstract: Outdoor urban pedestrian lighting serves multiple purposes, and should do so in the most efficient and economic manner. An important purpose of outdoor urban pedestrian lighting is to support the safety of pedestrians, particularly those who interact with adjacent vehicle traffic, while enhancing pedestrians' perceptions of personal safety and security. A review of published literature as well as the demonstration activities summarized in this paper indicate the potential for bollard-level crosswalk lighting to enhance pedestrian visibility and improve safety at crosswalks, particularly at locations where the presence of a crosswalk might not be expected by approaching drivers. Such locations include midblock crossings, roundabouts and locations near schools and other public venues that might experience high levels of pedestrian traffic at sporadic or unexpected times.

 

1:30 PM- 3:15 PM

Poster Program: Current Issues in Aviation

Uncertainty Analysis for Event Sequence Diagrams in Aviation Safety 

Azin Zare Noghabi, George Mason University
John Shortle, George Mason University

Abstract: The Integrated Safety Assessment Model (ISAM) is being developed by the FAA for analysis and assessment of risk in the National Airspace System (NAS). ISAM includes a collection of Event Sequence Diagrams (ESDs) and their supporting fault trees and hazards. Historical incident and accident data provide point estimates to quantify the probabilities in the event trees. However, because accident occurrences are rare, there is some uncertainty in the point estimates. In particular, many accident event sequences have never been observed and thus are quantified as having zero probability of occurrence, but this does not mean that such events could never occur. Because a large number of the quantified parameters in ISAM are rare-events, it is important to characterize the uncertainty in these estimates in order to estimate the uncertainty in the output produced by model. The objective of this paper is to quantify the uncertainty of the point estimates in the model and to infer the resulting uncertainty in the intermediate pivoting event probabilities. Results indicate that the uncertainty in the pivoting events is driven by the number of accident end states with no historical observations.

1:30 PM- 5:30 PM 

Program: Geo-Environmental Processes Committee

Burak Tanyu, George Mason University, presiding

 

3:45 PM- 5:30 PM 

Lecture Program: Ensuring Public Transport Networks Are Connected: For Everyone, At All Times

Travel Behavior Reactions to Transit Service Disruptions: Case Study on Washington, D.C., Metro SafeTrack Project 

Shanjiang Zhu, George Mason University
Hamza Masud, George Mason University
Chenfeng Xiong, University of Maryland, College Park
Zhuo Yang, George Mason University
Yixuan Pan, University of Maryland, College Park
Lei Zhang, University of Maryland, College Park

Abstract: Major transit infrastructure disruptions have become more frequent due to increasing maintenance needs for an aging infrastructure, system failures, and disasters. Understanding travel behavior reactions to service disruptions based on empirical observations is a fundamental step toward planning and operating an efficient and reliable transportation system. Few studies in the literature investigated the behavioral and system impact of transit service disruptions. To bridge this gap in literature, this research investigated travel behavioral reactions to transit service disruptions during the Washington D.C. Metro SafeTrack projects using a unique panel survey. This study will offer new insights on multi-modal, multi-dimensional travel behavioral responses to major transit network disruptions, a critically theoretical prerequisite toward developing and implementing effective strategies (e.g., how to optimally deploy the reserved bus fleet) that minimize system impact and improve transit system reliability and resiliency.

3:45 PM- 5:30 PM 

Poster Program:  Travel and Land Development: Research and Practice Insights

Spatial Econometric Model for Travel Flow Analysis and Real-World Applications with Massive Mobile Phone Data 

Linglin Ni, Zhejiang University of Finance and Economics
Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)
Xiqun (Michael) Chen, Zhejiang University

Abstract: Cellular signaling data provide a massive and emerging source to acquire urban origin-destination (OD) travel flows for transportation planners, support decision-making of large-scale mobility enhancement, and make it possible to explore underling influence factors of travel demand considering spatial autocorrelation. The effects of population, facilities, and transit accessibility on the travel flow between traffic analysis zones are revealed with empirical evidence. This paper employs the spatial econometric model for the OD travel flow analysis by coping massive mobile data with other related explanatory features of different urban regions. The results of real-world applications with Hangzhou, China show that: (I) all of the origin dependence, destination dependence and OD dependence are statistically significant, which verifies the consideration of spatial interdependence; (II) permanent population, facility number and transit accessibility all have positive correlation with travel flows; (III) distance, as expected, is negatively correlated with the travel flow volume. Finally, policy implications are discussed based on the estimated coefficients, marginal effects of explanatory variables, and future urban development plans by 2020. These findings contribute to the design of urban land use and transportation policies.

3:45 PM- 5:30 PM 

Lecture Program:  Addressing Concerns About LED Street Lighting

               John Bullough, Rensselaer Polytechnic Institute (RPI), presiding

Beyond Illuminance and CCT: How Do We Measure the Health Impacts of Roadway Lighting? 

Mark Rea, Rensselaer Polytechnic Institute (RPI)

Abstract: The most important reason to install roadway lighting is to help drivers reduce collisions with other cars, bicycles and pedestrians. These intended safety benefits may, however, have unintended negative consequences, such as creating sky glow in regions where astronomical observations are important.  Recently the AMA expressed concerns about potential negative effects of LED roadway lighting on human health.  These concerns are extremely important, but roadway lighting metrics like photopic illuminance and CCT are of no value for characterizing health impacts and therefore of no value for addressing these concerns.  The AMA report raised health concerns about three phenomena, blue light hazard (BLH), circadian disruption and nocturnal melatonin suppression, and glare.  There is sufficient literature and proper metrics available to assess the risks associated with LED roadway lighting as it might affect human health.  When properly characterized, one must conclude that LED roadway lighting as typically applied and experienced represents little risk for BLH, circadian disruption and melatonin suppression, and glare.

3:45 PM- 5:30 PM 

Poster Program: Current Research in Freight Planning and Logistics

GENERALIZED NOORTMAN AND VAN ES’ EMPTY TRIPS MODEL 

Carlos Gonzalez-Calderon, Rensselaer Polytechnic Institute (RPI)
Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)
Johanna Amaya Leal, Iowa State University
Ivan Sanchez-Diaz, Chalmers University of Technology
Ivan Sarmiento, National University of Colombia

Abstract: This paper presents a generalized Noortman and Van Es’ empty trip model that considers commodity groups and vehicle types. The model allows the modeler to identify the commodities that produce empty trips per se along with the commodities that contribute differently to the generation of empty trips. The method was validated using data collected in Colombia as part of the national Freight Origin-Destination Survey. The results highlight that the not all commodities contribute in the same way and specialized goods impact more in the generation of empty trips. In the same context, it was also found that for the same commodity, the contributions differ across vehicle types. The key contribution of the model is that it produces more precise estimates as it includes the impacts of the cargo type in the generation of empty trips.

 


8:00 AM- 12:00 PM

Program: Artificial Intelligence and Advanced Computing Applications Committee

Sherif Ishak, Louisiana State University, presiding
Adel Sadek, University at Buffalo, presiding

8:00 AM- 12:00 PM

Program: International Cooperation Committee

O. A. Elrahman, Rensselaer Polytechnic Institute (RPI), presiding
George Giannopoulos, Hellenic Institute of Transport (HIT), presiding

8:00 AM- 12:00 PM

Poster Program: Crash-Based Research: Wrong-Way Driving, Work Zones, Debris, Taxis, and Other Crash Situations

Analysis of Stationary and Dynamic Factors Affecting Highway Accident Occurrence

Grigorios Fountas, University at Buffalo
Md Tawfiq Sarwar, NRC Research Associateship
Panagiotis Anastasopoulos, University at Buffalo
Alan Blatt, CUBRC
Kevin Majka, CUBRC

Abstract: Traditional accident analysis typically explores non-time-varying (stationary) factors that affect accident occurrence on roadway segments.  However, the impact of time-varying (dynamic) factors is not thoroughly investigated.  This paper seeks to simultaneously identify pre-crash stationary and dynamic factors of accident occurrence, while accounting for unobserved heterogeneity.  Using highly disaggregate information for the potential dynamic factors, and aggregate data for the traditional stationary elements, a dynamic binary random parameters (mixed) logit approach is adopted.  With this approach, the dynamic nature of weather-related, and driving- and pavement-condition information is jointly investigated with traditional roadway geometrics and traffic characteristics.  The analysis is based on crash and non-crash observations between 2011 and 2013, drawn from urban and rural highway segments in the state of Washington.  The findings show that the proposed methodological framework can account for both stationary and dynamic factors affecting accident occurrence probabilities, and for unobserved heterogeneity.  The results also demonstrate the potential of random parameters modeling, in terms of forecasting accuracy and explanatory power. 

8:00 AM- 9:45 AM 

Poster Program: Emergency Evacuation Poster Presentations

Modeling multimodal transportation network emergency evacuation considering evacuees’ cooperative behavior 

Xia Yang, Rensselaer Polytechnic Institute (RPI)
Xuegang Ban, Rensselaer Polytechnic Institute (RPI)
John Mitchell

Abstract: Modeling emergency evacuation could help reduce the losses and damages from disasters. In this paper, based on the system optimum principle we develop a multi-modal evacuation model which captures the proper traffic dynamics, the cooperative behavior of evacuees, the interactions among different transportation modes, and the capacities of shelters. The proposed model can achieve a balance between computation efficiency and modeling details. Further, we have developed an MSA-based sequential optimization algorithm for large-scale evacuation problems. Both the proposed model and the solution algorithm are tested and validated through numerical analysis and a case study on Lower Manhattan. The modeling results can provide constructive guidance in evacuation planning and help reduce system evacuation time.  

8:00 AM- 9:45 AM 

Poster Program: Route Choice Behavior

Impact of Target on Traveler's Multiple-Criteria Route Choice Decision 

Xiangfeng Ji, Southeast University
Xuegang Ban, Rensselaer Polytechnic Institute (RPI)
Xu Qu, Southeast University
Jiang Zhang, Southeast University
Bin Ran, University of Wisconsin, Madison

Abstract: Traveler's route choice behavior is influenced by many criteria, e.g., route travel time, route late arrival penalty and route distance. In this paper, we propose a novel target-oriented route choice model, where multiple different criteria are involved. The potential benefit of this model is that the targets for the route choice criteria are often natural to specify. In the proposed model, we simultaneously consider the dependence between different route choice criteria and the interaction among different targets on the stochastic traffic network. On one hand, combining the marginal distribution for each route choice criterion, we propose to use the copula to capture the dependence between different route choice criteria, and the exact form of copula can be found via the proved comonotonicity. On the other hand, we capture the interaction among different targets, i.e., the complementarity and substitutability relationship, which can show traveler's behavioral insights. Furthermore, we extend the proposed route choice model into the user equilibrium model, which can be a novel method for the transportation planning. We formulate the user equilibrium as the variational inequality (VI) problem and prove its properties. Finally, the VI problem is solved with the proposed method of successive average algorithm, and the performance of the proposed model is illustrated with numerical example, which can bring some inspirations to the practical policy design and implementation.

8:00 AM- 9:45 AM 

Lecture Program: Surveillance and Control of Sustainable and Resilient Transportation Networks

Ridesharing User Equilibrium and Its Implications for High-Occupancy-Toll Lane Pricing 

Xuan Di
Henry Liu, University of Michigan, Ann Arbor
Xuegang Ban, Rensselaer Polytechnic Institute (RPI)
Hai Yang, Hong Kong University of Science and Technology

10:15 AM- 12:00 PM

Poster Program: Current Issues in Alternative Transportation Fuels and Technologies

Incorporating Demand Dynamics in Multi-period Capacitated Recharging Location Planning for Electric Vehicles

Anpeng Zhang, University at Buffalo
Jee Eun Kang, University at Buffalo
Changhyun Kwon, University of South Florida

Abstract: We develop a multi-period capacitated flow refueling location problem for electric vehicles (EVs) as EV market responds to the charging infrastructure. The optimization model will help us deter mine the optimal location of chargers as well as the number of charging modules at each station over multiple time periods. We define a number of demand dynamics (varying degree of sensitivity to path specific charging opportunities and general charging opportunities) with two objective functions (one maximizing flow coverage and the other maximizing electric vehicle demand). A case 7 study based on a road network around Washington, D.C., New York City, and Boston is presented to provide numerical experiments related to demand dynamics, illustrating the potential outcomes of multi-period charging infrastructure planning.

10:15 AM- 12:00 PM
Lecture Program: Thinking Outside the Bulb: Innovations in Airfield Lighting Strategies

Human Factors Impacts of Light-Emitting-Diode Airfield Lighting 

John Bullough, Rensselaer Polytechnic Institute (RPI)

Abstract: Light emitting diodes (LEDs) differ from incandescent light sources in several ways that are relevant to energy and maintenance requirements of airfield lighting systems. They have higher luminous efficacy and when designed properly, have longer useful operating lives; both factors make LEDs attractive candidates for airfield lighting. The photometric, colorimetric and temporal characteristics of LEDs also differ from those of incandescent light sources, and these can have important implications for the appearance of runway and taxiway lighting systems. The present paper reviews publications summarizing experimental and analytical investigations designed to assess these implications in terms of the following human factors impacts: color identification, brightness and glare, visibility in fog and haze, response to onset of flashing lights, and stroboscopic effects such as the phantom array. Overall, this review of experimental evidence suggests that in addition to their reduced energy use and maintenance requirements, LED airfield lighting can be advantageous in comparison to incandescent lighting systems used to delineate airport runways and taxiways.

 

10:15 AM- 12:00 PM
Lecture Program: Vulnerable Road Users: Analysis of Pedestrian and Bicycle Crashes

Preliminary Investigation of the Effectiveness of High Visibility Crosswalks on Pedestrian Safety Using Crash Surrogates

Md Tawfiq Sarwar, NRC Research Associateship
Grigorios Fountas, University at Buffalo
Courtney Bentley, University at Buffalo
Panagiotis Anastasopoulos, University at Buffalo
Alan Blatt, CUBRC
John Pierowicz, CUBRC
Kevin Majka, CUBRC
Robert Limoges, New York State Department of Transportation

Abstract: Using the SHRP2 naturalistic driving study (NDS) data, this paper provides a preliminary evaluation of the effectiveness of high-visibility crosswalks (HVC) in terms of improving pedestrian safety at uncontrolled locations.  This is accomplished by analyzing the driving behavior of SHRP2 participants at three uncontrolled locations in the Erie County, New York test site.  In this context, crash surrogates (i.e., speed, acceleration, throttle pedal actuation, and brake application) are used, in order to evaluate the participants’ driving behavior, primarily on the basis of data before and after the HVC installation.  The before/after analysis allows the assessment of HVC effectiveness in terms of driver behavior modification.  Mixed logit and random parameters linear regression models are estimated, and panel effects and unobserved heterogeneity are accounted for.  A number of factors are explored and controlled for (e.g., vehicle and driver characteristics, roadside environment, weather conditions, etc.), and the preliminary exploratory results show that HVCs have the potential to improve pedestrian safety and positively modify driving behavior.

10:15 AM- 12:00 PM
Poster Program: Evaluating the Performance of Traffic Signal Systems

Traffic State Estimation Based on Vehicle Trajectory Segmentation 

Choudhury Siddique, University of Washington
Xuegang Ban, Rensselaer Polytechnic Institute (RPI)

Abstract: Identification of Traffic condition in a motorway is essential for traffic management and control. GPS-enabled cell phones provide new opportunities for location-based services and traffic estimation. When traveling on-board vehicles, these phones can accurately provide position and velocity of the vehicle, and therefore can be used as probe traffic sensors. The In this paper, a methodology for the identification of the traffic state from GPS sensor data found from a single probe vehicle is presented. The methodology combines Hidden Markov Model (HMM) and Support Vector Machine (SVM) techniques to classify individual data points. Later, an algorithm is developed to identify stop and go segments in a trajectory. An application of the methodology for traffic state dependent sampling in a freeway network in Albany, NY shows encouraging results.

 

1:30 PM- 3:15 PM
Lecture Program: Artificial Intelligence Methods and Modeling Tools for Classification, Estimation,

Interval Prediction of Short-Term Traffic Volume Based on Extreme Learning Machine and Particle Swarm Optimization

Lei Lin, PARC Research
John Handley, Xerox Corporation
Adel Sadek, University at Buffalo

Abstract: Short-term traffic volume prediction models have been extensively studied in the past few decades. However, most of the previous studies only focus on single-value prediction. Considering the uncertain and chaotic nature of the transportation system, an accurate and reliable prediction interval with upper and lower bounds may be better than a single point value for transportation management. In this paper, we introduce a neural network model called Extreme Learning Machine (ELM) for interval prediction of short-term traffic volume and improve it with the heuristic particle swarm optimization algorithm (PSO). The hybrid PSO-ELM model can generate the prediction intervals under different confidence levels and guarantee the quality by minimizing a multi-objective function which considers two criteria reliability and interval sharpness. The PSO-ELM models are built based on an hourly traffic dataset and compared with ARMA and Kalman Filter models. The results show that ARMA models are the worst for all confidence levels, and the PSO-ELM models are comparable with Kalman Filter from the aspects of reliability and narrowness of the intervals, although the parameters of PSO-ELM are fixed once the training is done while Kalman Filter is updated in an on-line approach. Additionally, only the PSO-ELMs are able to produce intervals with coverage probabilities higher than or equal to the confidence levels. For the points outside of the prediction levels given by PSO-ELMs, they lie very close to the bounds.

1:30 PM- 3:15 PM

Poster Program: Dwight David Eisenhower Transportation Fellowship Program, Part 2 (Part 1, Session 613)

Load Rating of Reinforced Concrete Box Culverts Without Plans Using Statistical Parameters in Their Steel Reinforcements 

Ricardo Perez-Gracia, University of Puerto Rico, Mayaguez

1:30 PM- 3:15 PM
Lecture Program: Transportation Workforce Needs for Critical Infrastructure Resilience for Physical Security and Related Cyber-Security Challenges

Resilience and Business Continuity 

Mark Troutman, George Mason University
Christie Jones, George Mason University

1:30 PM- 3:15 PM

Poster Program: Pedestrian Planning and Design

Real-World Demonstrations of Novel Pedestrian Crosswalk Lighting 

John Bullough, Rensselaer Polytechnic Institute (RPI)
Nicholas Skinner, Rensselaer Polytechnic Institute (RPI)

Abstract: Outdoor urban pedestrian lighting serves multiple purposes, and should do so in the most efficient and economic manner. An important purpose of outdoor urban pedestrian lighting is to support the safety of pedestrians, particularly those who interact with adjacent vehicle traffic, while enhancing pedestrians' perceptions of personal safety and security. A review of published literature as well as the demonstration activities summarized in this paper indicate the potential for bollard-level crosswalk lighting to enhance pedestrian visibility and improve safety at crosswalks, particularly at locations where the presence of a crosswalk might not be expected by approaching drivers. Such locations include midblock crossings, roundabouts and locations near schools and other public venues that might experience high levels of pedestrian traffic at sporadic or unexpected times.

 

1:30 PM- 3:15 PM

Poster Program: Current Issues in Aviation

Uncertainty Analysis for Event Sequence Diagrams in Aviation Safety 

Azin Zare Noghabi, George Mason University
John Shortle, George Mason University

Abstract: The Integrated Safety Assessment Model (ISAM) is being developed by the FAA for analysis and assessment of risk in the National Airspace System (NAS). ISAM includes a collection of Event Sequence Diagrams (ESDs) and their supporting fault trees and hazards. Historical incident and accident data provide point estimates to quantify the probabilities in the event trees. However, because accident occurrences are rare, there is some uncertainty in the point estimates. In particular, many accident event sequences have never been observed and thus are quantified as having zero probability of occurrence, but this does not mean that such events could never occur. Because a large number of the quantified parameters in ISAM are rare-events, it is important to characterize the uncertainty in these estimates in order to estimate the uncertainty in the output produced by model. The objective of this paper is to quantify the uncertainty of the point estimates in the model and to infer the resulting uncertainty in the intermediate pivoting event probabilities. Results indicate that the uncertainty in the pivoting events is driven by the number of accident end states with no historical observations.

1:30 PM- 5:30 PM 

Program: Geo-Environmental Processes Committee

Burak Tanyu, George Mason University, presiding

 

3:45 PM- 5:30 PM 

Lecture Program: Ensuring Public Transport Networks Are Connected: For Everyone, At All Times

Travel Behavior Reactions to Transit Service Disruptions: Case Study on Washington, D.C., Metro SafeTrack Project 

Shanjiang Zhu, George Mason University
Hamza Masud, George Mason University
Chenfeng Xiong, University of Maryland, College Park
Zhuo Yang, George Mason University
Yixuan Pan, University of Maryland, College Park
Lei Zhang, University of Maryland, College Park

Abstract: Major transit infrastructure disruptions have become more frequent due to increasing maintenance needs for an aging infrastructure, system failures, and disasters. Understanding travel behavior reactions to service disruptions based on empirical observations is a fundamental step toward planning and operating an efficient and reliable transportation system. Few studies in the literature investigated the behavioral and system impact of transit service disruptions. To bridge this gap in literature, this research investigated travel behavioral reactions to transit service disruptions during the Washington D.C. Metro SafeTrack projects using a unique panel survey. This study will offer new insights on multi-modal, multi-dimensional travel behavioral responses to major transit network disruptions, a critically theoretical prerequisite toward developing and implementing effective strategies (e.g., how to optimally deploy the reserved bus fleet) that minimize system impact and improve transit system reliability and resiliency.

3:45 PM- 5:30 PM 

Poster Program:  Travel and Land Development: Research and Practice Insights

Spatial Econometric Model for Travel Flow Analysis and Real-World Applications with Massive Mobile Phone Data 

Linglin Ni, Zhejiang University of Finance and Economics
Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)
Xiqun (Michael) Chen, Zhejiang University

Abstract: Cellular signaling data provide a massive and emerging source to acquire urban origin-destination (OD) travel flows for transportation planners, support decision-making of large-scale mobility enhancement, and make it possible to explore underling influence factors of travel demand considering spatial autocorrelation. The effects of population, facilities, and transit accessibility on the travel flow between traffic analysis zones are revealed with empirical evidence. This paper employs the spatial econometric model for the OD travel flow analysis by coping massive mobile data with other related explanatory features of different urban regions. The results of real-world applications with Hangzhou, China show that: (I) all of the origin dependence, destination dependence and OD dependence are statistically significant, which verifies the consideration of spatial interdependence; (II) permanent population, facility number and transit accessibility all have positive correlation with travel flows; (III) distance, as expected, is negatively correlated with the travel flow volume. Finally, policy implications are discussed based on the estimated coefficients, marginal effects of explanatory variables, and future urban development plans by 2020. These findings contribute to the design of urban land use and transportation policies.

3:45 PM- 5:30 PM 

Lecture Program:  Addressing Concerns About LED Street Lighting

               John Bullough, Rensselaer Polytechnic Institute (RPI), presiding

Beyond Illuminance and CCT: How Do We Measure the Health Impacts of Roadway Lighting? 

Mark Rea, Rensselaer Polytechnic Institute (RPI)

Abstract: The most important reason to install roadway lighting is to help drivers reduce collisions with other cars, bicycles and pedestrians. These intended safety benefits may, however, have unintended negative consequences, such as creating sky glow in regions where astronomical observations are important.  Recently the AMA expressed concerns about potential negative effects of LED roadway lighting on human health.  These concerns are extremely important, but roadway lighting metrics like photopic illuminance and CCT are of no value for characterizing health impacts and therefore of no value for addressing these concerns.  The AMA report raised health concerns about three phenomena, blue light hazard (BLH), circadian disruption and nocturnal melatonin suppression, and glare.  There is sufficient literature and proper metrics available to assess the risks associated with LED roadway lighting as it might affect human health.  When properly characterized, one must conclude that LED roadway lighting as typically applied and experienced represents little risk for BLH, circadian disruption and melatonin suppression, and glare.

3:45 PM- 5:30 PM 

Poster Program: Current Research in Freight Planning and Logistics

GENERALIZED NOORTMAN AND VAN ES’ EMPTY TRIPS MODEL 

Carlos Gonzalez-Calderon, Rensselaer Polytechnic Institute (RPI)
Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)
Johanna Amaya Leal, Iowa State University
Ivan Sanchez-Diaz, Chalmers University of Technology
Ivan Sarmiento, National University of Colombia

Abstract: This paper presents a generalized Noortman and Van Es’ empty trip model that considers commodity groups and vehicle types. The model allows the modeler to identify the commodities that produce empty trips per se along with the commodities that contribute differently to the generation of empty trips. The method was validated using data collected in Colombia as part of the national Freight Origin-Destination Survey. The results highlight that the not all commodities contribute in the same way and specialized goods impact more in the generation of empty trips. In the same context, it was also found that for the same commodity, the contributions differ across vehicle types. The key contribution of the model is that it produces more precise estimates as it includes the impacts of the cargo type in the generation of empty trips. 

Wednesday, January 11th, 2017

8:00 AM- 9:45 AM
Lectern Program: Technology Use in Travel Surveys

Inferring Activity-Mobility Behavior of College Students Based on Smartcard Transaction Data

Negin Ebadi, University at Buffalo
Jee Eun Kang, University at Buffalo
Samiul Hasan, University of Central Florida

Abstract: Understanding individual activity-mobility behavior at a finer spatio-temporal resolution has various applications including urban planning, traffic management, spread of biological and mobile viruses, and disaster management. In recent years, proliferation of modern data sources such as GPS observations, mobile phone call records, smart card transactions, and social media activities significantly improved the quality of the activity-mobility pattern observations and reduced the cost of data collection. In this research, we propose to use UB card as a convenient source of combined data in order to define a campus-wide model for constructing students' activity-mobility trajectories in time-space dimension. UB Card is a student's official ID at the University at Buffalo and is used across campus for various activities including Stampedes and Shuttles (on-campus bus system), facilities access, library services, dining and shopping. Therefore, it could be a reliable source of data to identify time, location and activity types of individual students.

In this paper, we present two activity-mobility trajectory reconstruction algorithms. The base algorithm constructs students' activity-mobility patterns in space-time dimension using a set of smart card transaction data points as the only inputs. Then we modified the base algorithm to construct activity-mobility patterns with prior knowledge of students' previous patterns as they have similar patterns for certain days of the week. A database of 37 students' travel survey and UB card transactions that contains a period of 5 days have been used to illustrate the results of the study. These Travel surveys contain detailed information of the students' daily routine from home to school and back as well as other activities such as social, shopping, exercise, etc, that is used to validate the performance of these algorithms.
Three measures of errors have been proposed to capture the time allocation, location deviation, and activity sequences. These errors present an acceptable accuracy (12-25\% error ranges for activity types and average 0.04-0.16 miles of error for location predictions) and show the potential of inferring activity-mobility behaviors based on transaction type data sets.

8:00 AM- 9:45 AM
Poster Program: ICT and Travel Behavior: A Potpourri of Findings

Relationship Between Online Shopping and Shopping Trips: Comparison Across Major U.S. Metropolitan Areas 

Joshua Schmid, Rensselaer Polytechnic Institute (RPI)
Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

Abstract: Online shopping has greatly changed the methods by which goods are obtained, resulting in significant changes to the transportation system. The effect of e-commerce on passenger travel is not as well-studied as the impact on freight travel, with differing hypotheses as to how passenger travel and shopping patterns are affected. With the 2009 National Household Travel Survey data, this study used a structural equation model to analyze variables affecting the propensity to shop online or in person on the level of metropolitan statistical areas (MSAs). In some MSAs, online and in-person shopping are complementary, while in others they are substitutes. This behavior can differ greatly even within a single state or region, with widely-differing factors determining shopping patterns in each MSA. The advent of same-day delivery necessitates updates to estimates as soon as updated data is available in order to ensure that transportation planning and policy remains current.

10:15 AM- 12:00 PM

Poster Program: Transportation Demand Forecasting Poster Mega-session, Part 2 (Part 1, Session 829)

A Random Utility Based Estimation Framework for the Household Activity Pattern Problem

Zhiheng Xu, University at Buffalo
Jee Eun Kang, University at Buffalo
Roger Chen, Rochester Institute of Technology (RIT)

Abstract: This paper develops a random utility based estimation framework for the Household Activity Pattern Problem (HAPP). Based on the realization that outputs of complex activity-travel decisions form a continuous pattern in space-time dimension, the estimation framework is treated as a pattern selection problem. In particular, we define a variant of HAPP that has capabilities of forecasting activity selection and durations in addition to activity sequencing. The framework is comprised of three steps, (i) choice set generation, (ii) choice set individualization and (iii) multinomial logit estimation. The estimation results show that utilities for work, shopping and dis-utilities for travel time, time outside home, and average tour delay are found to be significant in activity-travel decision making.

Temporal Origin-Destination Matrix Estimation of Passenger Car Trips in Medellin, Colombia 

Carlos Gonzalez-Calderon, Rensselaer Polytechnic Institute (RPI)
John Posada-Henao, Universidad Nacional de Colombia
Susana Restrepo-Morantes, National University of Colombia

Abstract: This paper develops a demand synthesis model based on entropy maximization to estimate origin-destination matrices for passenger cars trips using traffic counts for different years in an urban area. The model discussed in this paper improve current Origin-Destination matrix estimation techniques incorporating the total number of trips in the network as a constraint, and secondary data sources for different years, giving a point of comparison for the results. The performance of the formulation for different years (2005-2008) is tested in the Medellín (Colombia) network.

 

10:15 AM- 12:00 PM

Poster Program: Transportation Network Modeling

Elise Miller-Hooks, George Mason University, presiding

Statistical Meta-modeling of Dynamic Network Loading 

Wenjing Song, Pennsylvania State University
Ke Han, Imperial College London
Yiou Wang, Pennsylvania State University
Terry Friesz, George Mason University
Enrique del Castillo, Pennsylvania State University

Abstract: Dynamic traffic assignment models rely on a network performance module known as dynamic network loading (DNL), which expresses the dynamics of flow propagation, flow conservation, and travel delay at a network level. The DNL defines the so-called network delay operator, which maps a set of path departure rates to a set of path travel times. It is widely known that the delay operator is not available in closed form, and has undesirable properties that severely complicate DTA analysis and computation, such as discontinuity, nondifferentiability, nonmonotonicity, and computational inefficiency. This paper proposes a fresh take on this important and difficult problem, by providing a class of surrogate DNL models based on a statistical learning method known as Kriging. We present a metamodeling framework that systematically approximates DNL models and is flexible in the sense of allowing the modeler to make trade-offs among model granularity, complexity, and accuracy. It is shown that such surrogate DNL models yield highly accurate approximations (with errors below 7%) and superior computational efficiency (9 to 100 times faster than conventional DNL procedures). Moreover, these approximate DNL models admit closed-form delay operators, which are Lipschitz continuous and infinitely differentiable, while possessing closed-form Jacobians. The implications of these desirable properties for DTA research and model applications are discussed in depth.

 

10:15 AM- 12:00 PM

Poster Program: Travel Behavior and Route Choice

Elise Miller-Hooks, George Mason University, presiding

10:15 AM- 12:00 PM

Lecture Program: Emerging Bridge Technologies, Part 1: Unmanned Equipment (Part 2, Session 873)

Integrating Unmanned Aerial Vehicles and Photogrammetry to Support Bridge Inspections 

David Lattanzi, George Mason University
Ali Khaloo, George Mason University
Keith Cunningham, University of Alaska, Fairbanks
Rodney Dell’Andrea, US Forest Service
Mark Riley, US Forest Service

10:15 AM- 12:00 PM

Lecture Program: Performance of Recycled Materials in Geotransportation

Burak Tanyu, George Mason University, presiding

2:30 PM- 6:00 PM 
Program: Transportation Network Modeling Committee

Elise Miller-Hooks, George Mason University, presiding 

Thursday, January 12th, 2017

8:00 AM- 12:00 PM 

Workshop: Sensing Technologies for Transportation Applications

Optimal Access Restoration in Post-Disaster Environments 

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)
Paul Salasznyk, Rensselaer Polytechnic Institute (RPI)
Jeffrey Wojtowicz, Rensselaer Polytechnic Institute (RPI)
Trilce Encarnacion, Rensselaer Polytechnic Institute (RPI)