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2016 Transportation Research Board Annual Meeting

Welcome UB TRB Reception

Reception

University Transportation Center Award

Christopher Torres
University Transportation Center Student of the Year Award

 

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

Read Christopher's Bio

Christopher Torres was born and raised in the city of Bayamon, Puerto Rico. In 2012, he obtained a Bachelor’s degree in Computer Science from the University of Puerto Rico (UPR), Bayamon Campus. Right away he started pursuing a Master of Public Administration specializing in Program Administration which he completed in the summer of 2014. That same year he started pursuing another Master’s degree, but this time in Computer Engineering specializing in Computing Systems at UPR, Mayagüez Campus.

Throughout all his university preparation, both as an undergraduate and graduate student, Christopher has being active in the academic community. He was elected by the General Student Council to serve as Student Representative to the University Board of UPR for the 2011-2012 academic year, as well as Student Representative to the Board of Trustees for the following year. In April 2013 he served as the Graduate Student Representative to the Governing Board of UPR. When he started graduate school at Mayaguez, he was elected by the university’s General Student Council to serve as the Student Representative to the Discipline Board of the university; he was later elected to serve as their chair, position that he still has until this day.

Christopher has also worked as a Teaching Assistant at his current school, as well as a Reports Programmer. He is currently part of the TransInfo UTC in UPR Mayaguez, working on an information system with a client-server architecture for issuing citations for Puerto Rico’s Police Department.

TransInfo Partners: Posters, Papers and Awards

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

Sunday, January 10th, 2016

9:00 AM – 12:00 PM

Workshop: SHRP 2 C20, Part 1: Innovations in Local Freight Data Implementation Projects—Progress and Outcome (Part 2, Session 188)

IAP Project Report Out - Capital District Transportation Committee

Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

No abstract available

 

Workshop: Innovative Doctoral Research from Dwight David Eisenhower Transportation Fellowship Program

Strategies to Reduce Highway Project Delivery

Josie Bianchi Santiago, University of Puerto Rico, Mayaguez

Highway project delivery is an important issue for the U.S. government and local transport agencies. The Federal Highway Administration (FHWA) of the United States have found that major highway facilities construction take over 10 to 15 years to plan and build. This include new construction, major expansions of existing highway facilities and major reconstruction. From planning to delivery, the highway construction process is typically grouped in five phases, namely 1. Planning; 2. Preliminary design and environmental review; 3. Final design; 4. Right-of-way acquisition and utility relocation; and 5. Construction. Time extensions or delays in anyone of these phases usually generates project cost overruns and delays in project delivery. In order to address time and cost extension issues, methodologies to accelerate highway project delivery are needed. Accelerate the highway project delivery have several benefits including a reduction in total project time from conception to delivery for both government agencies and the users of the new highway, less construction time, less time impacting the area around the construction sites of the road and the opportunity to minimize crashes related to highway work zones, among others. To preserve safety and assure a cost-effective highway project is essential to identify construction operations and management practices that support accelerated project delivery. Accelerating transportation infrastructure delivery, both in government and transport agencies, assure the decrease of project construction cost and time, and experience project benefits sooner. This research is focused on the identification and recommendation of new and innovative techniques, strategies, and technologies to reduce highway construction time and costs in Puerto Rico, focusing on methodologies that can be applied to the management of the highway construction process. Different phases of design and construction will be identified and evaluated.

 

1:30 PM – 4:30 PM

Workshop: Sensing Technologies for Transportation Applications

Optimal Access Restoration in Post-Disaster Environments: An Innovative Use of Remote Sensing and Optimization Techniques

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

No abstract available

 

Workshop: On-Demand Economy and the Urban Environment: The Urban Freight Story

Sharing Economy and Warehouse Utilization

Jennifer Pazour, Rensselaer Polytechnic Institute (RPI)

No Abstract available

Urban Design and Changing Needs

Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

No abstract available

 

Workshop:  Doctoral Student Research in Transportation Modeling

Districting in Post-Disaster Environments

Johanna Amaya-Leal, Rensselaer Polytechnic Institute (RPI)

No abstract available

A Disaggregate Spatial Matching Model: Analyzing Spatial Interaction with Collaborative Agents

Dapeng Zhang, Rensselaer Polytechnic Institute (RPI)

No abstract available

 

Monday, January 11th, 2016

8:00 AM – 9:45 AM

Session:  Pedestrian and Bicycle University Education Joint Subcommittee of ANF20, ANF10

Daniel Hess, University at Buffalo, presiding

 

Lecture: World's Next Top (Choice) Model

Discrete Choice with Interdependencies: Spatial Binary Probit Model with Endogenous Weight Matrix

Yiwei Zhou, Precision Systems, Inc.

Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

Abstract: Discrete choice modeling is widely applied in transportation studies. However, the problem becomes complicated when interdependencies are introduced. Interdependencies refer to the phenomenon where an agent’s decision influences and is also influenced by other agents’ decisions. In spatial econometrics, interdependencies are often captured using a spatial lag term with a pre-defined weight matrix. In most previous studies, the weight matrix is assumed to be exogenous. However, this assumption is invalid in many cases, leading to biased and inconsistent parameter estimates. Although some attempts have been made to address the endogenous weight matrix issue, none has focused on discrete choice modeling. This paper fills the existing gap by developing a Bayesian spatial binary probit model with endogenous weight matrix (BSPEW). Markov Chain Monte Carlo (MCMC) method is used to estimate the model. Model validation experiments show that the model parameters can converge to their true values and the endogenous weight matrix can be reliably captured. The model is then applied to a simplified firm relocation choice problem to demonstrate the model result interpretation. The estimation results suggest that firms’ relocation choices are indeed influenced by their peers, especially the firms with similar sizes. The application results offer important insights into business location choice problem and provide reference for future policy making. Overall, this paper adds value to the existing literature on discrete choice modeling and spatial econometrics. It provides a powerful tool that can be applied to a wide range of transportation issues where discrete responses are subject to the influence of social and economic connections, such as land development, location choice, and various travel behavior.

 

Lecture: Transportation Network Logistics

Implications of Cost Equity Consideration in Hazmat Network Design

Longsheng Sun, University at Buffalo

Mark Karwan, University at Buffalo

Abstract: The hazmat network design problem (HNDP) aims to reduce the risk of transporting hazmat in the network by enforcing regulation policies. The goal of reducing risk can increase cost for different hazmat carriers. Since HNDP involves multiple parties, it is essential to take the cost increase of all carriers into consideration for the implementation of the regulation policy. While we can consider cost by placing upper bounds on the total increase, the actual cost increase for various OD pairs can differ, which results in unfairness among carriers. Thus we propose to consider cost equity issue as well in HNDP. Additionally, due to the existence of multiple solutions in current HNDP models and the possibility of unnecessarily closing road segments, we introduce a new objective considering the length of all the closed links. Our computational experience is based on a real network and we show results under different cost consideration cases.

 

Poster Session:  Freight Modeling

Impacts of Information Technology and Urbanization on Less-Than-Truckload Freight Flows in China: Analysis Considering Spatial Effects

Linglin Ni, Zhejiang University of Finance and Economics

Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

Dapeng Zhang, Rensselaer Polytechnic Institute (RPI)

Abstract: Understanding the relationship between socioeconomic factors and the Less-than-Truckload freight flows is the critical step for transportation planners and policy makers. This paper explores the impacts of information technology, urbanization on LTL freight flows by building a spatial autocorrelation model using freight flow data from a leading LTL company in China. The results show that all IT variables urbanization variables have positive correlation with freight flows. The distance, as expected, is a negatively correlated with the freight flow volume. The application of the spatial autocorrelation model further shows that origin dependence, destination dependence and O-D dependence are all significant, justifying the consideration of spatial interdependence. Finally, policy implications are discussed based on the calibrated model. These findings shed light on the impacts of internet and urbanization on freight transportation, and contribute to the design of freight policies and the development of the LTL industry.

Factors Influencing Freight Mode Choice: Insights from In-Depth Interviews

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

Shama Campbell, Rensselaer Polytechnic Institute (RPI)

Lokesh Kalahasthi, Rensselaer Polytechnic Institute (RPI)

Carlos Gonzalez-Calderon, Rensselaer Polytechnic Institute (RPI)

Jeffrey Wojtowicz, Rensselaer Polytechnic Institute (RPI)

This paper conducts qualitative research to gain insight into behavioral influencers of freight mode choice. To this effect, the authors conducted numerous in-depth interviews (IDIs) with shippers, carriers, and receivers of various sizes. The paper describes the approach taken to carry out the IDIs and discusses the chief results. The interviews reveal the key factors that influence mode choice and suggest initiatives to address the identified issues.

 

10:15 AM – 12:00 PM

Lecture: Resilient and Sustainable Transportation Networks

Surrogate-Based Optimization for the Design of Area Charging Schemes under Environmental Constraints

Daniel Rodriguez-Roman, University of Puerto Rico, Mayaguez

Stephen Ritchie, University of California, Irvine

A surrogate-based solution algorithm is proposed for cordon and area-based road pricing problems. Surrogate models serve as simple approximations to computationally expensive models. In the proposed algorithm, surrogate models are constructed using geometric representations of charging boundaries. The surrogates are employed as part of a screening procedure to select the most promising candidate schemes for evaluation by time-consuming models. Departing from previous elastic demand-based formulations, objective functions are presented that can be utilized along with commonly used transportation planning models. Environmental considerations are introduced to the design problem in the form of pollutant concentration constraints. Numerical tests were conducted to explore the surrogates’ predictive accuracy and degree of correlation with the model outputs. Radial basis functions with different functional forms were used as surrogate models. On average, the surrogate estimates exhibited relatively good correlation with model outputs (correlation coefficients greater than 0.70). In addition, a sample application of the proposed problem and methods is presented for illustrative purposes.

Beyond Distribution: Realistic Assessment of System-Wide Travel Time reliability in the Stochastic Traffic Network

Xiangfeng Ji, Southeast University

Xuegang Ban, Rensselaer Polytechnic Institute (RPI)

Mengtian Li, Southeast University

Jian Zhang, Southeast University

 Bin Ran, Southeast University

This paper proposes a new method to assess the system-wide travel time reliability, which belongs to a generalized moment problem (GMP). As a distribution-free method, the proposed model only requires the moment information of the uncertainty to conduct the evaluation. However, in the distribution-based methods, the distribution of the uncertainty should be given explicitly. In the GMP formulation, the upper bound of the probability that the total system travel time exceeds some predefined threshold (which is called unreliability function) is the objective and the moment information is formulated as a moment constraint. The solution of the GMP formulation consists of de-indicator, semi-definite relaxation, and solution of semi-definite programming problem. In the numerical test, it is shown that the bounds given by our model are tighter than that of current methods in most cases. In some cases, the bounds given by our methods are the tightest. Moreover, our model is more realistic.

 

Lecture:  Access Management: Science and Standards

Safety Impacts of Directional Median Openings at Downstream U-turn Locations

Yi Qi, Texas Southern University

Yubian Wang, National Academy of Sciences

Xiaoming Chen, Texas Southern University

Guanqi Liu, George Mason University

Over the past decades, many states and local transportation agencies have installed directional median openings on urban divided roadways to help avoid potential conflicts. At directional median openings, left-turn movements from driveways or minor streets have to make an alternative movement, i.e., a right-turn followed by a U-turn, which would increase the U-turn demands at the downstream openings and may increase the crash risk at the U-turn locations.  Therefore, the objective of this study was to investigate the safety impacts of directional median openings at downstream U-turn locations. For this purpose, a Poisson regression model was developed to analyze the factors that contributed to the crashes that occurred at the downstream U-turn locations of directional median openings. The results showed that 1) higher downstream U-turn volume and left-turn volume resulted in more crashes at downstream U-turn locations and 2) the closer the downstream U-turn location was to the subject opening, the more crashes that occurred at the downstream U-turn location. These findings indicated that the selection of U-turn locations is critical for the safety performance of directional openings because converting a full median opening to a directional median opening generates more U-turns at downstream openings.  Diverted left-turn traffic should not be allowed to make U-turns at a closely-spaced opening that already has significant U-turn or left-turn volumes.

 

Poster Session:  Information Technology Applications for Travel Monitoring and Connected Vehicles (14)

Data Warehouse Development and Real-Time Incident Detection

Andrew Bartlett, University at Buffalo

Adel Sadek, University at Buffalo

In the traffic engineering field, study and analysis often requires the use of multiple datasets. The nature of these data often makes them difficult to work with, especially in conjunction with one another. The overall goal of this study was to not only design a solution to this problem for the Buffalo-Niagara Region of western New York, but to demonstrate its usefulness through a specific application. To achieve this, three objectives were designed: (1) outline the structure of a data warehouse for the Buffalo-Niagara region, (2) use the combined data in the prototype warehouse to examine its usefulness in the construction of a real-time incident detection system which not only detects incidents but also tries to predict incident characteristics, and (3) show the importance of the data warehouse by comparing the results of incident detection strategies which require different combinations of data. To meet these objectives a prototype data warehouse was first created, and then used in the creation and validation of three incident detection strategies: a speed threshold detection system, a binary probit model which uses only speed data, and a binary probit model which uses a combination of speed and volume data. The prototype data warehouse showed it was possible to construct a fully fleshed-out version for transportation data in the Buffalo-Niagara region with useful results. The speed threshold model which used a 10 minute speed drop of 10 mph to detect incidents had a 62.5% detection rate, as well as favorable false alarm and classification rates. The more complex binary outcome model which used only speed data detected incidents with a success rate of 70.4%, an improvement over the speed threshold model despite worse false alarm and classification rates. It was also able to predict incident type, number of blocked lanes, and incident severity with 75.9%, 70.4%, and 75.9% accuracy, respectively. The binary outcome model which used both speed and volume data had a more impressive detection rate of 75.5% with similar false alarm and classification rates and was slightly better at predicting incident type and severity (both with 77.6% accuracy) but slightly worse at predicting the number of blocked lanes (with 69.4% accuracy). Overall, the combined data model is the best strategy for both detecting incidents and predicting their characteristics, which emphasizes the importance of a transportation data warehouse.

 

Poster Session:  Research Trends in Evacuation Transportation Modeling and Analysis

Statistical Analysis of Dynamics of Household Hurricane-Evacuation Decisions

Md Tawfiq Sarwar, University at Buffalo

 Panagiotis Anastasopoulos, University at Buffalo

Satish Ukkusuri, Purdue University

Pamela Murray-Tuite, Virginia Polytechnic Institute and State University

Fred Mannering, Purdue University

With the increasing number of hurricanes in the last decade, efficient and timely evacuation remains a significant concern. Households’ decisions to evacuate/stay and selection of departure time are complex phenomena. This study identifies the different factors that influence the decision making process, and if a household decides to evacuate, what affects the timing of the execution of that decision. While developing a random parameters binary logit model of the evacuate/stay decision, several factors, such as, socio-economic characteristics, actions by authority, and geographic location, have been considered along with the dynamic nature of the hurricane itself. In addition, taking the landfall as a base, how the evacuation timing varies, considering both the time-of-day and hours before landfall, has been analyzed rigorously. Influential factors in the joint model include the relative time until the hurricane’s landfall, height of the coastal flooding, and approaching speed of the hurricane; household’s geographic location (state); having more than one child in the household, vehicle ownership, and level of education; and type of evacuation notice received (voluntary or mandatory). Two time intervals from 30 to 42 hours and 42 to 66 hours before landfall resulted in random parameters, reflecting mixed effects on the likelihood to evacuate/stay. Possible sources of the unobserved heterogeneity captured by the random parameters include the respondents’ risk perception or other unobserved physiological and psychological factors associated with how respondents comprehend a hurricane threat. Thus the model serves the purpose of estimating evacuation decision and timing simultaneously using the data of Hurricane Ivan.

 

10:45 AM – 12:30 PM

Poster Session: Freeway Traffic Control (18)

Identifying On-Site Traffic Accidents using Both Traffic and Social Media Data

Zhenhua Zhang, University at Buffalo

Ming Ni, University at Buffalo

Qing He, University at Buffalo

Jing Gao; JIZHAN GOU; Xiaoling Li, Virginia Department of Transportation

Social media receives increasing attentions as crowdsourced information for traffic operations and management. One recent trending study is to use social media to detect on-site traffic accidents. However, it remains unknown how effective the social media based detection methods is as compared with traditional loop detector based method. In this paper, we first develop two prevailing accident detection models with traffic and tweet data, respectively. Then, we fuse such two models to jointly detect traffic accidents. The results show a promising improvement in both accuracy and precision. This study provides insights in utilizing social media data to assist accurate on-site traffic accident detection. 

 

2:00 PM – 3:45 PM

Poster Session: Current Trends in Transportation Funding and Financing

Comparative Analysis of Value-for-Money Studies: Highway Public-Private Partnerships Projects in the United States

Jonathan Gifford, George Mason University

Porter Wheeler, George Mason University

Jeong Yun Kweun, George Mason University

The objective of this study is to provide a comparative analysis of value for money (VfM) studies of U.S. highway projects. By examining the state of practice, the authors aim to develop an evaluation framework for P3 projects in future research. Among VfM studies located from project websites and through direct contact with public agencies, seven VfM studies were selected for a comparative analysis. The analysis shows that the choice of public sector comparator (PSC) model varied from one study to another, which is driven by agency experience and various models. The transferred risks from employing public-private partnership (P3) model are closely related to the choice of repayment schemes. Lastly, we found consistency in discount rates used in practice despite debates in the literature.

 

Poster Session: New Directions in Travel Surveys: Big Data, Smartphones, and Stated Preference (13)

Fast Online Travel Mode Identification using Smartphone Sensors

Authors: Xing Su, City University of New York (CUNY)

Hernan Caceres Venegas, University at Buffalo

Hanghang Tong, University at Buffalo

Qing He, University at Buffalo

Personal trips in modern urban society usually involve multiple travel modes. Recognizing a traveler's transportation mode is not only critical to applications like personal context-awareness, but also essential to urban traffic operations, transportation planning, and facility design. While most of recent practice often leverages infrastructure-based fixed sensors or GPS for traffic mode recognition, the emergence of smartphone provides an alternative promising way with its ever-growing computing, networking and sensing powers. 

In this paper, we propose a GPS and network-free method to detect a traveler’s travel mode using mobile phone sensors. Our application is based on the latest Android smartphones with multimodality sensors. By developing a hierarchical classification method with online learning model, we achieve almost 100% accuracy in the binary classification wheelers/non-wheelers travel mode, and an average of  97.1% accuracy with all of the six travel modes (buses, subways, cars, bicycling, walking, and jogging). Our system (a) performs significantly faster in computation and could adapt to each traveler's pattern by using the online learning model (b) works with a low sampling rate for sensing so that it saves the smartphone battery.

 

3:45 PM – 5:30 PM

Lecture: Urban Freight Transportation: Innovations and Best Practices

Potential Market of Freight Demand Management

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

Felipe Aros-Vera, Ohio University

The paper’s main objective is to quantify the potential market for Freight Demand Management (FDM) in metropolitan areas. To this effect, the authors define FDM, and describe the various initiatives that could be undertaken to reduce the externalities produced by freight traffic, by means of changes in the demand patterns enacted by the receivers of the supplies. The literature on freight behavior research is reviewed to identify the industry segments that are most inclined to participate in FDM. Having identified the industry sectors of interest for FDM programs, the authors used freight trip generation models, and publicly available data, to quantify the freight trip generation for the industry sectors deemed to be the ideal target of FDM initiatives. These estimates are obtained for the top ten metropolitan statistical areas, and a sample of representative metropolitan statistical areas of different sizes in the US. The paper ends with a discussion of policy implications and chief results.

 

Lecture: Innovations in Statistical Methods for Transportation Researchers and Practitioners

Panagiotis Anastasopoulos, University at Buffalo, presiding

 

Lecture: New Research on Alcohol and Driving

Drinking and Driving Behavior at Stop Signs

Jingyan Wan, University at Buffalo

Changxu Wu, University at Buffalo

YIQI ZHANG, University at Buffalo

Rebecca Houston, University at Buffalo

Chang Wen Chen, University at Buffalo

Panya Chanawangsa, University at Buffalo

Alcohol is one of the principal risk factors for motor vehicle crashes. One factor that contributes to vehicle crashes at intersections is noncompliance with stop signs. The present experiment investigated the effects of alcohol, drinking pattern and gender on driving behavior at stop signs. 30 subjects participated drinking and driving sessions during which they received a moderate dose of alcohol (0.08% BAC) or a placebo. A simulated driving tasks measured participants’ driving performance at stop signs in response to each dose. Results showed that alcohol led to impaired stop sign compliance, worse lateral and speed maintenance, and lower mean deceleration towards stop signs. More abrupt acceleration from stop signs was also observed among non-binge drinkers. Intoxicated female drivers, especially intoxicated female non-binge drinkers, exhibited longer duration of complete stop, greater stop accuracy, and more gradual and stable decelerating than other groups. In addition, under alcohol, male binge drinkers’ standard deviation of deceleration was lower compared with male non-binge drinkers. However, under placebo, binge drinkers’ complete stop duration was shorter than non-binge drinkers. Results indicated that alcohol increased willingness to take risks, impaired driving precision, and increased reaction time. It was also suggest that, intoxicated female drivers, especially intoxicated female non-binge drinkers, were more cautious than other groups, indicating that they were aware of their impairment and adjusted their behavior appropriately. Binge drinking was found increase males’ tolerance and, however, increased impulsivity of all drivers.

 

4:15 PM – 6:00 PM

Poster Session:  Numbers in Safety: Revealing What's Behind Them Through Advances in Assembling, Analyzing, and Modeling Crash Data

Factors Affecting Freeway Safety: Analysis of Single- and Multivehicle Crashes in Shanghai, China

Xuesong Wang, Tongji University

Yan Li, Tongji University

Ting Wang, Tongji University

Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

Rongjie Yu, Tongji University

The freeway mileage and traffic volume in Shanghai have been increasing fast in recent years. However, the traffic safety situation on freeways is also getting increasingly concerning. No comprehensive safety evaluation studies have been conducted so far due to the lack of reliable traffic and crash data. In order to identify the different contributing factors for single-vehicle (SV) and multi-vehicle (MV) crashes, information about the geometric design features and traffic operational characteristics on a 45km-long freeway segment in Shanghai has been collected. The Finite Mixture of Negative Binominal regression models (FMNB) were applied to analyze SV and MV crashes separately. The two-component FMNB model produced the best goodness-of-fit. The modeling results suggested that segment length had significant influence, indicating that analyzing the whole segment as one homogenous sample is inappropriate. Moreover, only segment length and the indicator variable for three-lane segments showed consistent effects on both SV and MV crashes, while all other significant variables’ effects were different. Therefore, it is necessary to investigate SV and MV crashes separately.

 

Poster Session:  Capacity and Level-of-Service Issues for Freeways and Other Facility Types (19)

An Exploratory Study on the Correlation between Twitter Concentration and Traffic Surge

Zhenhua Zhang, University at Buffalo

Ming Ni, University at Buffalo

Qing He, University at Buffalo

Jing Gao,  University at Buffalo

JIZHAN GOU; Xiaoling Li, Virginia Department of Transportation

Social media receives increasing attentions as a crowdsourced information source in traffic operations and management. The tweets, that are blogged and shared by the broad masses of people, may be associated with some major social activities. These tweets are called “Twitter concentrations” in this paper. The public activities behind Twitter concentrations potentially pose more pressure on traffic network and cause traffic surge within a specified time and location.  However, it still remains unknown how closely the Twitter concentration and traffic surge are correlated with each other. Our study fuses a set of tweets and traffic data collected during the whole year of 2014 in North Virginia Region, and mainly investigates the correlation between Twitter concentration and traffic surge in July. The results show the promise and effectiveness of our proposed methods and even provide insights in the causality of non-recurrent traffic surge.

 

Poster Session: Innovations in Rail Transit System Modeling, Optimization, and Simulation

Nonrecurrent Subway Passenger Flow Prediction from Social Media Under Event Occurrences

Ming Ni, University at Buffalo

Qing He, University at Buffalo

Jing Gao, University at Buffalo

Subway passenger flow prediction is strategically important in metro transit system management. The prediction under event occurrences turns into a very challenging task. In this paper, we adopt a new kind of data source -- social media to tackle this challenge. We develop a systematic approach to examine social media activities and identify event occurrences. Our results on real-world data demonstrate that there exists a strong positive correlation between passenger flow and the rates of social media posts. This finding motivates us to develop a novel approach for improved flow forecast. Specifically, we propose a parametric and convex optimization based approach, called Optimization and Prediction with hybrid Loss function (OPL), to fuse the linear regression and the results of seasonal autoregressive integrated moving average (SARIMA) model in the objective function jointly. The OPL hybrid model takes advantage of unique strengths of linear correlation in social media features and SARIMA model in time series prediction. Experiments on data related to a subway station show that OPL has the best forecasting performance compared with the state-of-the-art techniques. In addition, an ensemble model is developed to leverage the weighted results from OPL and support vector machine regression (SVR) together. As a result, the prediction accuracy and robustness further increases.

Tuesday, January 12th, 2016

8:00 AM – 9:45 AM

Lecture: State Maintenance Practice Case Studies

Use of Social Media by Transportation Agencies for Traffic Management

Jeffrey Wojtowicz, Rensselaer Polytechnic Institute (RPI)

William Wallace, Rensselaer Polytechnic Institute (RPI)

Social media has become an integral part of modern communication. There is however no clear consensus among transportation managers on how social media could or should be used to collect or disseminate actionable information. To provide guidance on the potential use of social media in transportation, a better understanding is needed of the content of message as well as the path taken from the sender to the potential user of actionable information during crises and other non-routine events in the transportation system. This paper assesses how social media is used to support traffic management operations during planned special events such as concerts and sporting events, and unplanned disruptive events, such as accidents and weather events. This paper examines best practices for traffic operations amongst various agencies, and presents practices used to disseminate real-time, actionable information to motorists in a useful and engaging format via social media.

 

Lecture: Transit Quality of Service Evaluation: Four International Applications

Quality of Service Analysis of the Transit System in Mayaguez, Puerto Rico: Implementing the New TCRP Report 165

Wilfredo Cordero-Cruz, University of Puerto Rico, Mayaguez

Edgardo Roman, University of Puerto Rico, Mayaguez

Performance measurement and peer comparison are valuable tools that can be used to evaluate actual performance, identify areas that need improvement, and establish feasible goals for a near future in any transportation system. During the past years, the academic community has been discussing which factors of transit systems are the most pertinent in determining the service’s quality and at what level of accuracy it should be measured.  In 2013 the third edition of the Transit Capacity and Quality of Service Manual was published as the TCRP Report 165.  A study conducted at the University of Puerto Rico at Mayagüez (UPRM) used as a guide the recent publication to evaluate the service of the Transit System of the Mayaguez Municipality.  The research took place between the year 2013 and 2014. During several weeks GPS tracking devices were temporarily installed, providing the location of vehicles at 10 second intervals.  A once a year passenger count was also included in order to provide the data needed.  The study served as an assessment of the applicability of the new manual, thus shedding some light on the changes made between editions.  The study also included an accurate description of the system’s performance, and recommendations that could lead to its improvement.

 

8:00 AM – 12:00 PM

Artificial Intelligence and Advanced Computing Applications Committee

Sherif Ishak, Louisiana State University, presiding

Adel Sadek, University at Buffalo, presiding

 

8:30 AM – 10:15 AM

Traffic Flow Theory and Characteristics, Part 2 (24) (Part 1, Session 536; Part 3, Session 589)

Link Travel Time Approximation in Double Queue Traffic Model

Xia Yang, Rensselaer Polytechnic Institute (RPI)

Xuegang Ban, Rensselaer Polytechnic Institute (RPI)

Rui Ma, University of California

Double queue concept has recently been applied in continuous-time dynamic user assignment (DUE) because it can capture queue spillbacks by presenting link dynamics with capacitated upstream and downstream queues. While directly solving such DUE problems with time-varying, state-dependent delays is extremely challenging, some approximation schemes (called first-order approximation technique in this paper) have been proposed to simplify the link travel time calculation in a recent study on double-queue-based continuous-time DUE model. This paper mainly focuses on analyzing the performance and the properties of such approximated travel times. It’s proved that with the first-order approximation, FIFO is guaranteed for network with fixed link capacities but may be violated if link capacities are time-varying. Numerical analysis is conducted on a simple stretch network with travel times computed based on accumulative inflow and exit flow curves, point queue model, the second-order approximation for comparison. The results state that if the demand is larger than the link capacities, the first-order approximation techniques lead to either overestimated or underestimated link travel times. In addition, overall the double queue model can produce relatively higher performance than the point queue model and the second-order approximated results are slightly better than the first-order approximated results.

 

Poster Session:  Advances in Adaptive Signal Control and Optimization (19)

Qing He, University at Buffalo, presiding

 

10:15 AM – 12:00 PM

Lecture:  Innovation in Passenger Rail Equipment and System Integration

Identification of Railcar Asymmetric Wheel Wear with Extreme Value Theory

Yu Cui, University at Buffalo

Qing He, University at Buffalo

Zhenhua Zhang, University at Buffalo

Zhiguo Li, IBM, Thomas J. Watson Research Center

Railcar asymmetric wheel wear results into severe wear on one wheel but little or no wear on the other wheel. The consequences of asymmetric wheel can be accelerated wear, mechanical failure and downtime and incurs high financial penalties. Therefore, identifying the asymmetric wheel wear is critical not only for cost effective maintenance, but also for safe operations.

Fortunately, increasing amount of various wayside detectors are instrumented along the railway which can monitor the health of railcar components and log large numbers of detailed information of railroad and. One can use this information to identify the asymmetric wheel wear as soon as possible. However, most elliptically contoured distributions are good at describing normal event but not good at dealing with the outliers which mainly locates in the tails of the distribution. Asymmetric wheel wear requires effective anomaly detection which mainly focused on the extreme values in the tail of a right-skewed distribution. In this paper, we employ the extreme value theory (EVT), which handles the unusually high or low data of distributions, to model the asymmetric wheel wear and derive an extreme value score to identify asymmetric wheel wear. Experiment results show that identification of asymmetric wheel wear can generate huge monetary benefit in terms of reducing yearly maintenance times of railcars.

 

1:30 PM – 3:15 PM

Lecture:  Millennials and Our Connected Multimodal Future, Part 1: Travel Behavior (Part 2, Session 674)

E-Commerce and Shopping Behavior

Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

No abstract available

 

Lecture: Dynamic Control and Assessment of Lighting for Visibility

Assessment of an Adaptive Driving Beam Headlighting System: Visibility and Glare

John Bullough, Rensselaer Polytechnic Institute (RPI)

Nicholas Skinner, Rensselaer Polytechnic Institute (RPI)

Timothy Plummer, Rensselaer Polytechnic Institute (RPI)

Recent developments in solid-state lighting, sensor and control technologies are making new configurations for vehicle forward lighting feasible. Building on systems that automatically switch from high- to low-beam headlights in the presence of oncoming vehicles, adaptive driving beam (ADB) systems can detect both oncoming headlights and preceding taillights and reduce their intensity only in the direction of the other lights while maintaining higher levels of illumination throughout the remainder of the field of view. The nominal benefit of ADB systems is the provision of high-beam levels of illumination in the forward scene while reducing glare to oncoming and preceding drivers, who perceive low-beam illumination levels. Two dynamic field experiments were conducted; one experiment measured the ability of observers to identify the walking direction of roadside pedestrian targets with and without using the ADB system, and the other experiment evaluated the discomfort glare elicited by the ADB system in comparison to conventional low- and high-beam headlights. The findings from both experiments are consistent with previous analytical and static field tests and suggest that ADB systems can offer safety benefits compared to conventional headlight systems.

 

2:00 PM – 3:45 PM

Poster Session: Urban Freight Innovations

Qian Wang, University at Buffalo, presiding

 

Poster Session:  Dwight David Eisenhower Transportation Fellowship Program, Part 2 (Part 1, Session 435)

Assessment on Use of Roundabouts in Puerto Rico: Lessons Learned and Potential Areas of Improvement

Jennifer Aponte Rivera, University of Puerto Rico, Mayaguez

No abstract available

 

3:45 PM – 5:30 PM

Lecture: Reducing Energy Consumption, Carbon Emissions, and Criteria Pollutants from Heavy-Duty Vehicles: Integrated Technology and Policy Approaches

Heavy-Duty Vehicle Logistics and Freight Demand Policies

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

No abstract available

 

Lecture: Road Illumination, Signage, and Visibility

John Bullough, Rensselaer Polytechnic Institute (RPI), presiding

 

Lecture: Technology Transfer Principles

Joseph Lane, University at Buffalo

No abstract available

 

4:15 PM – 6:00 PM

Poster Session: Innovations in Intercity Passenger Rail Transportation

Impact of High-Speed Rail Investment on Economic and Environmental Change in China: Dynamic Computable General Equilibrium Analysis

Zhenhua Chen, University of Southern California

Junbo Xue, Chinese Academy of Sciences

Adam Rose, University of Southern California

Kingsley Haynes, George Mason University

This study investigates the impact of high-speed rail investment on the economy and environment in China using a computable general equilibrium (CGE) model with a focus on the period 2002-2013. The analysis is implemented in a dynamic recursive framework capturing long-run capital accumulation and labor market equilibrium. A national level impact was simulated through direct impact drivers including land use conversion, output expansion, cost reduction, productivity increase, transport demand substitution and induced demand. The results suggest that rail investment in China over the past two decades has been a positive stimulus to both the economy and social welfare, while the effect on CO 2 emissions generation has been large. Overall, the economic and welfare contributions of rail investment are achieved primarily through induced demand and output expansion, whereas the contribution from a reduction of rail transportation costs and rail productivity increases were modest. In addition, negligible negative impacts from land use for rail development and the substitution effect among other modes were found. Emissions reduction from substitution of rail for other modes was small and overwhelmed by output expansion due to lowered rail transport costs and induced demand.

 

Timetable Optimization for High-Speed Rail with Multiple Operation Periods: Solution Method Based on Lagrangian Relaxation Decomposition Framework

Xia Yang, Rensselaer Polytechnic Institute (RPI)

Wenliang Zhou, Central South University

Aiming at providing high-speed rail (HSR) passengers with travel convenience in terms of train operation regularity, this research devotes to optimizing a novel type of multi-period train timetable in which trains operating with the same stop stations, speeds, and headways are classified into one period type. Moreover different period types of trains have various operation time periods on the HSR system. First we construct an optimization model with the aim of minimizing the total travel time of all trains, and then further reformulate the model based on a weighted digraph. Through the period-type-based decomposition to the reformulated model with Lagrangian relaxation, a solving algorithm with a multi-flow shortest-path searching algorithm included is designed for optimizing the multi-period train timetable and obtaining a lower bound of its objective to evaluate its quality. Numerical examples illustrate that the solving algorithm has a good convergence, and can obtain a satisfactory feasible solution with a small gap between the upper and lower bounds of the objective values.

 

Poster Session: Surface Transportation Weather and Its Effects on Traffic Networks

Measuring Freeway Route Travel Time Distributions Under Inclement Weather

Hernan Caceres Venegas, University at Buffalo

Ha Hwang, University at Buffalo

Qing He, University at Buffalo

This paper develops an efficient probabilistic model for estimating route travel time distributions, incorporating a variety of weather conditions with different potential traffic incident occurrence rates. Estimating the route travel time distribution from historical link travel time data is challenging due to the interactions among upstream and downstream links. Upon creating conditional probability function for each link travel time, we applied Monte Carlo Simulation to estimate the total travel time from origin to destination. A numerical example of three alternative routes in the City of Buffalo shows several implications. This study found that weather conditions, except for snow, incur minor impact on Off-Peak and Weekend travel time, whereas Peak travel time suffers great variations under different weather conditions. On top of that, inclement weather exacerbates route travel time reliability, even when mean travel time increases moderately. The computation time of the proposed model is linearly correlated to the number of links in a route. Therefore, this model can be used to obtain all the Origin-Destination travel time distributions in an urban region.

Wednesday, January 13th, 2016

8:00 AM – 9:45 AM

Lecture: New Approaches for Urban Data

Data Fusion and Information Integration for Fine-Grained Arterial Traffic Modeling

Zhanbo Sun, Western Michigan University

Peng Hao, University of California, Riverside

Xuegang Ban, Rensselaer Polytechnic Institute (RPI)

A data fusion and information integration approach is proposed to model and interpret traffic along urban arterial corridors based on heterogeneous traffic data. The method attempts to deduce the “most probable” explanation that can integrate fixed-location data and mobile sensing data and match the vehicle records at upstream with those collected at downstream sensor locations. To make the probabilistic model more realistic, traffic knowledge such as lane choice decision, traffic merging and travel time information are calibrated using the historical dataset and then integrated into the model. By doing so, the model can obtain individual travel times of the matched data pairs, which can be directly used to estimate corridor travel times of individual vehicles. The paper also explores some practical issues related to the use of heterogeneous traffic data, such as “inaccurate data”, and “detection failure” problem. Results from the method can be further applied to estimate vehicle trajectories along arterial corridors, estimate individual vehicle-based fuel consumption/emissions, and help infer real-time queuing processes at signalized intersections.

 

8:30 AM – 10:15 PM

Poster Session: Transportation Economics Topics: Cost-Benefit Analysis, Pricing, Mileage-Based User Fees, Elasticities, and More

Demand Elasticity for U.S. Toll Roads: An Aggregate Analysis of Panel Toll Data

Jeong Yun Kweun, George Mason University

Shanjiang Zhu, George Mason University

This paper examines how a choice of aggregate demand model affects the estimation results for toll price elasticity of demand and discusses its implications. This paper uses a large panel data from over 90 toll roads in 21 states from 1984 to 2013 and estimates toll price elasticity of demand using a static fixed effects model and a dynamic model with a lagged dependent variable as one of explanatory variables. The estimates from a static fixed effects model show that the toll price elasticity of demand ranges between -0.378 and -0.303. Using a dynamic demand model, this paper finds that a short-run toll price elasticity of demand is -0.1028 and a long-run elasticity is -0.1816. The findings imply that it is important for the users of toll elasticity of demand estimates to recognize implications of different models used in the estimation process and to draw policy implications with a caution.

Analyses Considering Partner Selection and Joint Decision Making: Investigation of Freight Demand with Spatial Matching Models

Dapeng Zhang, Rensselaer Polytechnic Institute (RPI)

 Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

Freight transportation plays an increasingly important role in social and economic activities. However, freight travel demand has not been comprehensively understood due to its unique features: the freight activity is a result of collaboration among multiple freight agents. This feature distinguishes freight transportation from passenger transportation where travel decisions are made mostly by individual travelers. In specific, the collaboration can be observed by two processes: partner selection and joint decision making. Using the supplier-customer collaboration as an example, the partner selection is a process for the supplier (or shipper in the supply chain) and customer (or receiver) to assess the counterparty’s characteristics, and select the best business partner based on the assessment. Joint decision making is a process for the supplier and the customer to seek common interests and compromise conflicting claims on freight activity decisions. As traditional travel demand model cannot capture these unique features of freight travel demand, this research develops an innovative econometric model, spatial matching model, to fill such void. The proposed model is specified based on freight agents’ behavioral background, validated by experiments, and explained by numerical examples. 

 

Poster Session:  Travel Demand Models R Us (59)

Metro-Based Park-and-Ride Facilities in Guangzhou City, China: Evaluation of Facility Locations and Parking Rate Policy in Central Area

Yongzhong Wu, South China University of Technology

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

Felipe Aros-Vera, Ohio University

A logit-based park-and-ride evaluation model was developed to facilitate appropriate planning decisions for a metro-based park-and-ride network in the city of Guangzhou. The decisions regard the number, location, and capacity of park-and-ride facilities and the differential pricing for park-and-ride and general parking facilities. Effects of these decisions on the users’ choice between automobile only and park-and-ride alternatives were examined. Based on the analysis, the capacity and location of the six existing park-and-ride facilities were found to be non-optimal in terms of demand maximization. Best locations for the park-and-ride facilities were identified with demand estimation. Experiments further show that there is competition among nearby park-and-ride facilities, which should be considered in locating park-and-ride facilities. The diminishing effects of additional park-and-ride facilities on demand contribution are also identified. Instead of building 26 facilities as proposed by the government, a more prudent way is to focus on providing sufficient capacity at the best locations and conduct cost-benefit analysis before building additional facilities at locations with less demand. On the other hand, increasing the parking rates in the central area was shown to be an effective way to improve overall park-and-ride demands but with significantly diminishing effects. As the parking rate regulation is a sensitive issue for both the parking facility operators and the residents, the diminishing effects should be taken into account before making a pricing decision.

 

Poster Session: New Insights into Mode Choice (12)

Disaggregate Models for Mode Choice Behavior of Transit-Oriented Developments

Arsalan Faghri, Virginia Department of Transportation

Mohan Venigalla, George Mason University

Various studies have indicated an apparent lack of analyses associated with the modal choice characteristics of TOD areas. This research presents methodologies for developing mode choice models for TODs using the travel activity data. Two discrete choice models in the 0.25-mile radius of Rosslyn-Ballston corridor transit stations in the Washington Metro area are developed and validated. First, a multinomial logit model is developed using logistic regression to show the modal split among transit, auto-driver, auto-passenger, walk, bike and other in the study area. The primary focus of this TOD mode-share model is on home-based work trips.

Secondly, a transit-share model is formulated as an innovative combination of the direct generation, urban travel factor (UTF) and logit models. This TOD transit-share model is aimed at determining transit usage in TODs based on household auto ownership as the primary input and only the transit variables (travel time, average wait time and average walk time) as secondary inputs. Results indicate that transit patronage decreases with increase in the household vehicle ownership. Since the input requirements to the TOD transit-share model are minimal, this model structure is expected to be very useful for sketch analysis of many TOD project alternatives.

The methodologies presented in this paper are applicable to TODs surrounding major transit stations and can be replicated in urban areas where location specific travel activity data are available. It is recommended that disaggregate models be developed for TODs whenever travel survey data with spatial resolution are available. The need for integrating disaggregate models that are specific to TOD land use into travel demand modeling is highlighted.

 

Poster Session:  Evaluation of Operator Performance in a Simulator

Driving Simulation in Safety and Operation Performance of Freeway Toll Plaza

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 (OST-R)

Johnathan Ruiz Gonzalez, University of Puerto Rico, Mayaguez

Enid Colón Torres, University of Puerto Rico, Mayaguez

Ricardo Garcia Rosario, University of Puerto Rico, Mayaguez

Toll plaza designs have implemented electronic toll collection and other technologies to improve toll systems.  However, with these improvements has come an increase in crashes as well.  To study safely pertinent aspects of driver behavior in toll plazas with electronic toll collection, a cockpit driving simulator housed at the University of Puerto Rico at Mayagüez (UPRM) was used. Specifically, in this study a comparison was made of two different configurations of the signs that indicate the corresponding speed limits and toll station for each lane in the area prior to the toll plaza. Configuration 1 corresponds to the current condition of the signage in Puerto Rico, with signs located at the highway roadside while Configuration 2 presents a proposed overhead signage. A representative group of 20 subjects was selected in order to test the effectiveness of the two signage configurations on the approach zone leading to the toll plaza, calculating the standard deviation of the lane position (SDLP), speed and acceleration noise in five different zones.  The behavior of drivers using Configuration 2 was safer than those using Configuration 1.  Specifically, at each of five zones where behavior was sampled on the approach to the toll plaza drivers using Configuration 2 changed lanes more smoothly and reduced their vehicle’s velocity more when approaching the toll plaza. However, there was no significant difference between configurations in acceleration noise. The study provides strong evidence that driving simulators can be used to identify efficiently and inexpensively alternative signage configurations at toll plazas.

 

10:15 AM – 12:00 PM

Lecture: Logistics of International Humanitarian Relief Efforts in Africa and Nepal

Nepal Earthquake Relief Support

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

No abstract available

 

Lecture: Pavement Performance Modeling and Measurement

Three-Stage Least-Squares Analysis of Post-Rehabilitation Pavement Performance

Md Tawfiq Sarwar, University at Buffalo

Panagiotis Anastasopoulos, University at Buffalo

Recent studies have provided improvements in the forecasting accuracy of pavement performance modeling, by statistically modeling pavement performance indicators as a system of seemingly unrelated regression equations (SURE). This approach accounts for cross-equation error correlation as a means to control for unobserved factors that lead pavements in poor condition to observe poor performance indicators. In the state of Indiana, the most common pavement performance indicators are the international roughness index (IRI), the rutting depth, and the pavement condition rating (PCR). Even though the first two can be accurately measured, the PCR is based on Engineers’ observations of the pavement surface. Therefore, it is possible that the PCR may be measured as a function of the observable IRI and rutting depth. This paper, explores this possibility by estimating a three-stage least squares (3SLS) model of IRI, rutting depth, and PCR, using data collected between 1999 and 2011 in Indiana. All three pavement performance indicators are found to be affected by traffic characteristics, prior pavement condition, treatment and surface characteristics, drainage performance, and weather conditions. In addition, the PCR is found to also be significantly affected by the IRI and rutting depth measurements. The results of the 3SLS and SURE models are counter imposed, with the 3SLS models providing significant improvements in the forecasting accuracy of the pavement performance. Pavement performance modeling with 3SLS has, therefore, the potential to help roadway Agencies cut costs through a more effective and efficient allocation of pavement management resources.

 

Lecture:  National U.S. Department of Transportation Act at Age 50: Consequences and Lessons

Bruce Seely, Michigan Technological University

Jonathan Gifford, George Mason University

No abstract available

 

2:30 PM – 4:00 PM

Lecture:  Perspectives on the Future of the Commodity Flow Survey: Findings from the Commodity Flow Survey Workshop

Rensselaer Polytechnic Institute Perspective on the Future of the Commodity Flow Survey

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

No abstract available

 

2:45 PM – 4:30 PM

Poster Session: Route Choice and Traffic Equilibrium (12)

Nonlinear-Expectation Risk-Averse User Equilibrium on Stochastic Traffic Networks

Xiangfeng Ji, Southeast University

Xuegang Ban, Rensselaer Polytechnic Institute (RPI)

Mengtian Li, Southeast University

Jian Zhang, Southeast University

Bin Ran, University of Wisconsin, Madison

In this paper, we propose the nonlinear-expectation route travel time (NERTT) model, a rank-dependent expected utility model, to solve the paradox during the route choice in a stochastic traffic network. The NERTT consists of two parts, which are the route travel time distribution and the distortion function. With the increasing and concave distortion function, we can prove that the route travel time in the proposed model is risk-averse, which is the main focus of this paper. Besides, we show two different reduction methods from the NERTT model to the travel time budget (TTB) model and mean-excess travel time (METT) model. One method is based on the properly selected distortion functions and the other method is based on a general distortion function. This indicates that the proposed model is very general. Finally, we develop a nonlinear-expectation risk-averse user equilibrium model and formulate it as a variational inequality (VI) problem. A heuristic gradient projection algorithm is used to solve the VI. The proposed model and algorithm are tested on two traffic network.

 

Poster Session: Transportation Network Modeling (60)

Implications of Cost Equity Consideration in Hazmat Network Design (16-4734)

Longsheng Sun, University at Buffalo

Mark Karwan, University at Buffalo

Changhyun Kwon, University of South Florida

No abstract available

Beyond Distribution: Realistic Assessment of System-Wide Travel Time reliability in the Stochastic Traffic Network (16-3023)

Xiangfeng Ji, Southeast University

Xuegang Ban, Rensselaer Polytechnic Institute (RPI)

Mengtian Li, Southeast University

Jian Zhang, Southeast University

Bin Ran, Southeast University

No abstract available

Thursday, January 14th, 2016

8:00 AM – 12:00 PM

Workshop: Freight Transport and Logistics in the Developing Countries: Challenges and Possible Solutions

Urban Freight Transport Initiatives in the Developing Countries

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

This presentation focuses on recent urban freight and sustaunable transport in the developing countries, with examples from Latin-America.