2018 Transportation Research Board Annual Meeting

Welcome UB TRB Reception.

Reception

University Transportation Center Award

Shweta Dixit.
University Transportation Center Student of the Year Award

Congratulations to the George Mason University's Shweta Dixit, TransInfo's UTC Outstanding Student of the Year!

Read Shweta's Bio

Shweta Dixit received her Bachelor's degree in Civil Engineering from Nagpur University in 2005. She worked for Aarvee consultants, an engineering firm in India, as an environmental engineer before moving to the United States in 2007. She received her Master of Science in Civil, Environmental and Infrastructure Engineering in 2010 from George Mason University (GMU). She started her Ph.D. program in 2010 as a recipient of the prestigious Presidential Scholar Award at GMU. While continuing with her PhD program, she worked for Arlington County, VA, AECOM, and Loudoun County, VA for two years until 2016, in various capacities. In 2016 she returned to Mason as a fulltime doctoral student and received a competitive award from GMU provost for completing her dissertation. She graduated with Ph.D. in August 2017.

TransInfo Partners: Posters, Papers and Awards

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

Monday, January 8th, 2018

Poster: Innovations in Adaptive Signal Control

Monday 8:00 AM- 9:45 AM

Online Traffic Signal Coordination with a Game Theoretic Approach

Xuan Han, University at Buffalo

Jun Zhuang, University at Buffalo

Qing He, University at Buffalo

Abstract: The occurrence of Connected and Automated Vehicles (CAV) technologies brings an opportunity to develop an online data-driven signal coordination method which cooperatively optimizes the traffic performance of a group intersections. This research proposes a game theoretic approach to epsilon-equilibrium (or near-Nash equilibrium) to achieve online traffic signal coordination. Each intersection of the traffic network is just like a player in a game. Different intersections pursue their own benefit maximization by changing their offsets. This paper examines the effect, applicability, and efficiency of the game theoretic approach in signal coordination. The game theoretical approach is proven to outperform the system optimum on vehicle delay at intersection level regarding delay equity. The variances of vehicle delay among different intersections are significantly decreased by the proposed game theoretic algorithm. Thus no intersection needs to sacrifice its own delay performance to achieve system optimum, and traffic delay has been widely distributed among intersections. In addition, this research also compares the network delay performances between CAVs and Human-driven Vehicle (HDV). A simulation platform is built to evaluate the proposed algorithms and models. The results also show that the CAVs can generate much less delay compared to HDVs.

 

Poster | Spotlight Theme | Practice Ready Paper: Advances in Urban Freight Transportation Research and Practices

Monday 8:00 AM- 9:45 AM

Using Local Stores for Same Day Delivery

Ming Ni, University at Buffalo

Qing He, University at Buffalo

Jose Walteros, University at Buffalo

Xuan Liu, IBM, Thomas J. Watson Research Center

Arun Hampapur, IBM, Thomas J. Watson Research Center

Abstract: This study investigates the same-day delivery planning with store fulfillment problem (SDD-SFP) that identifies a seasonal order fulfillment plan for delivering local online orders from nearby retailing stores and minimizes the overall planning and operational costs. It aims to develop optimization models and solution algorithms about store location selection, fleet-sizing for transportation, and inventory planning. In order to solve large-scale SDD-SFP with real-world datasets, this paper develops accelerated Benders decomposition methods that integrate outer search tree and local branching based on mixed-integer programming, and optimization-based algorithms for initial lifting constraints.

Poster | Spotlight Theme | Practice Ready Paper: Advances in Urban Freight Transportation Research and Practices

Monday 8:00 AM- 9:45 AM

Quantification of Freight and Service Activity Trends in Cities

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

Diana Ramirez-Rios, Rensselaer Polytechnic Institute (RPI)

Lokesh Kalahasthi, Rensselaer Polytechnic Institute (RPI)

Shama Campbell, Rensselaer Polytechnic Institute (RPI)

Carlos Gonzalez-Calderon, Universidad Nacional de Colombia

Jeffrey Wojtowicz, Rensselaer Polytechnic Institute (RPI)

Abstract: The purpose of this paper is to demonstrate the potential of freight trip generation and service trip attraction models in studying freight and service activity (FSA) patterns for cities of various sizes. To this effect a software has been developed that inputs the publically available ZIP code business patterns data and outputs the freight and service trip estimates for various industry sectors at ZIP code level. Three city sizes are considered: Small, Intermediate and Large; North American Industry Classification System (NAICS) at 2-digit level are considered for industry type, eight cities (two small, three intermediate, and three large) based on population density are analyzed for FSA patterns. The cities are compared with respect to various metrics of FSA estimates. The results show that the distribution of firms across NAICS and employment range has higher influence on the intensity of the FSA (FSA per establishment). ZIP code level FSA estimates are a great help in identifying zones with high freight or service activity. On an average FSA per employment for various NAICS are relatively stable across larger and intermediate cities compared to that of small cities.

 

Poster | Spotlight Theme | Practice Ready Paper: Advances in Urban Freight Transportation Research and Practices

Monday 8:00 AM- 9:45 AM

PUBLIC OPINION TOWARDS CROWD DELIVERIES IN NEW YORK STATE

Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

Diana Ramirez-Rios, Rensselaer Polytechnic Institute (RPI)

Carlos Rivera-Gonzalez, Rensselaer Polytechnic Institute (RPI)

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

Joshua Schmid, Rensselaer Polytechnic Institute (RPI)

Abstract: Crowd deliveries is a business model in freight deliveries that is part of the new trend of shared economies. It matches excess vehicle capacity from the common person or crowd (who act as the courier) with the demand of others geographically dispersed individuals who require their merchandize to be delivered.  In the last few years, there are more than 40 start-up companies in the U.S and more than 50 worldwide that use this new concept. This innovative model has shown to make urban freight more efficient particularly in the last-mile delivery, showing potential reductions for both transportation costs and environmental impacts. This study is intended to understand the shopping behaviors of individuals and their acceptance, or not, of these alternate delivery services, such as crowd deliveries, and their feasibility in accordance to today’s market conditions. Initial empirical findings in New York State suggest that there is still some resistance in accepting Crowd Deliveries but some segments of the population are more inclined to accept this new trend of deliveries.

 

Lectern | Practice Ready Paper: Applications and Characteristics of Probe-Based Travel Time Data

Monday 8:00 AM- 9:45 AM

PERFORMANCE MEASURES FOR CHARACTERIZING REGIONAL CONGESTION USING AGGREGATED MULTI-YEAR PROBE VEHICLE DATA

Thomas Brennan, College of New Jersey

Mohan Venigalla, George Mason University

Abstract: Probe vehicle speed data has become an important data source for evaluating the congestion performance of highways and arterial roads.  Predefined spatially located segments known as Traffic Message Channels (TMCs) are linked to commercially available, temporal anonymous probe vehicle speed data. This data has been used to develop agency-wide performance measures to better plan and manage infrastructure assets. Recent research has analyzed individual as well as aggregated TMC links on roadway systems to identify congested areas along spatially defined routes.  By understanding the typical congestion of all TMCs in a region as indicated by increased travel times, a broader perspective of the congestion characteristics can be gained. This is especially important when determining the impact of such occurrences in the region as a major crash event, special events or during extreme conditions like a natural or human-made disaster. This paper demonstrates how aggregated probe speed data can be used to characterize regional congestion. To demonstrate the methodology an analysis of vehicle speed data during Hurricane Sandy, the second costliest hurricane in the United States, is used to show the regional impact in 2012. Further, the analysis results are compared and contrasted with comparable periods of increased congestion in 2013, 2014, and 2016.  The analysis encompasses 614 TMCs, within 10-miles of the New Jersey coast. Approximately 90-million speed records covering five counties are analyzed in the study. 

Poster | AICP Certification | Practice Ready Paper: Innovations in Urban Data and Mobility Modeling

Monday 10:15 AM- 12:00 PM

Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences

Laiyun Wu

Samiul Hasan, University of Central Florida

Jee Eun Kang, University at Buffalo

Younshik Chung, Yeungnam University

Abstract: Characterizing individual mobility is critical to understand urban dynamics and develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal human mobility patterns. However, due to the limit of the underlying datasets, these studies could not investigate how mobility patterns change over user characteristics. In this paper, we analyze a large-scale Automatic Fare Collection (AFC) dataset of the main transit system of Seoul, South Korea and investigate how mobility patterns change over user characteristics and modal preferences. We identify users' commuting locations and estimate the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets.

Lectern: Research Advances in Statistical and Econometric Methods

Monday 10:15 AM- 12:00 PM

Panagiotis Anastasopoulos, University at Buffalo, presiding

Lectern: Toward Sustainable and Resilient Transportation Networks

Monday 1:30 PM- 3:15 PM

A Spectral Risk Measure in Hazardous Materials Transportation

Liu Su, University of South Florida

Longsheng Sun, University at Buffalo

Mark Karwan, University at Buffalo

Changhyun Kwon, University of South Florida

Abstract: Due to catastrophic consequences of accidents by hazardous materials (hazmat) transportation, a risk-averse approach for routing is needed.Due to catastrophic consequences of accidents by hazardous materials (hazmat) transportation, a risk-averse approach for routing is needed.In this paper, we consider spectral risk measures, which are coherent and more general than existing approaches such as conditional value-at-risk.In spectral risk measures, one may define the spectrum function to reflect the decision maker's risk preference precisely. We first consider a special class of spectral risk measures, for which the spectrum function may be represented as a weighted sum of step functions.We develop a mixed integer programming model in hazmat routing to minimize these special spectral risk measures and propose an efficient search algorithm to solve the problem.We also consider general classes of spectral risk measures, for which we propose two computational approaches.  We illustrate the usage of spectral risk measures and the proposed computational approaches via a case study in the real road network of Ravenna, Italy.

 

Lectern: Monitoring and Predicting Surface Conditions Using Road Weather Information Systems and Mobile Sensors

Monday 1:30 PM- 3:15 PM

The Optimal Location of Road Weather Information System in New York State

Julie Fetzer

Hernan Caceres Venegas, Universidad Catolica del Norte

Qing He, University at Buffalo

Rajan Batta, University at Buffalo

Abstract: Inclement weather is a threat to the safety of transportation systems as well as the efficiency of their operation. A road weather information system (RWIS) is a network of environmental sensor stations (ESS) that collect a range of real-time data about weather and pavement conditions. These systems can support highway officials and civilians in making more informed transportation safety decisions, particularly in times of adverse weather, by giving them more accurate and localized weather information. This enables the proper maintenance activities to be executed and safety to be restored while using minimum resources. However, because of the range of network characteristics and geographical factors affecting the implementation of ESS, no widely adopted guidelines exist that outline where to implement ESS in a network beyond taking into the physical criteria of an appropriate site, although several methods have been suggested. This paper aims to take a practical approach to solving the location problem of RWIS by proposing a unified multi-objective optimization methodology that takes into account vehicular accident data, vehicle miles traveled, area coverage, access to power and maintenance, and existing ESS. This study produces an exact solution method that produces a Pareto set of multiple efficient solutions. The proposed methodology is applied to a real world case study focused on the deployment of additional ESS in the existing RWIS network across New York State. Further, a sensitivity analysis is conducted to examine the effects of different parameters and a non-preference solution is proposed.

 

Lectern: Distraction Full Circle: Prevalence, Behavior, Crash Risk, Intervention

Monday 1:30 PM- 3:15 PM

Prevalence of Engagement in Single Vs. Multiple Types Of Secondary Tasks: Results From The Naturalistic Engagement In Secondary Task (Nest) Dataset

Martina Risteska, University of Toronto

Birsen Donmez, University of Toronto

Huei-Yen (Winnie) Chen, University at Buffalo

Miti Modi, University of Toronto

Abstract: We investigated engagement in single vs. multiple types of secondary tasks in distraction-affected safety-critical events (SCEs: crashes/near-crashes) and baselines reported in the Naturalistic Engagement in Secondary Tasks (NEST) dataset. NEST was created from SHRP2 data for studying distractions in detail. Early descriptive analysis on NEST found that most distraction-affected SCE and baseline epochs (10s long) include more than one type of secondary task, suggesting that a considerable number of drivers may be engaging in multiple secondary activities within a relatively short time frame, potentially being exposed to increased demands brought upon by multi-tasking and task-switching. We conducted inferential statistics on NEST focusing on engagement in single vs. multiple types of tasks across SCEs and baselines. A logit model was built to compare the odds of engaging in single vs. multiple types of tasks with the following predictors: event type (SCE, baseline), environmental demand, GPS speed, driver age. The last three predictors were included to capture the driving demands experienced, which may have impacted drivers’ task engagement behaviour. Odds of engagement in multiple types of secondary tasks was higher in SCEs than baselines. Furthermore, with marginal statistical significance, drivers 65 years-and-over were less likely to engage in multiple types of secondary tasks than younger drivers. Overall, engagement in multiple secondary task types is more prevalent in SCEs. Most crash risk studies to date reported the effects associated with one type of secondary task when it appears that in reality these effects may be confounded by the presence of other secondary tasks.

 

 

Poster: Artificial Intelligence and Machine Learning Tools for Estimation, Detection, and Prediction Applications in Transportation

Monday 1:30 PM- 3:15 PM

Detecting Traffic Accidents from Social Media Data with Deep Learning

Zhenhua Zhang, State University of New York (SUNY)

Qing He, University at Buffalo

Jing Gao

Ming Ni, University at Buffalo

Abstract: This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents related to traffic accidents in two metropolitan areas: Northern Virginia and New York City. Our results show that paired tokens can capture the association rules inherent in the accident-related tweets and further increase the accuracy of the traffic accident detection. Second, two deep learning methods: Deep Belief Network (DBN) and Long Short-Term Memory (LSTM) are investigated and implemented in extracted tokens. Results show that DBN can obtain an overall accuracy of 85% with about 24 individual token features and 20 paired token features. The classification results from DBN outperform those of support vector machine (SVM) and supervised Latent Dirichlet allocation (sLDA). Finally, based on the time and location comparison between accident-related tweets with the traffic accident record on two freeway segments: I66 and I395, it is found that half the accidents detected by Twitter can be found in the accident record.

 

Poster: Young Professional Research in Aviation

Monday 3:45 PM- 5:30 PM

Germs on a Plane: The Transmission and Risks of Airplane-Borne Diseases

Nereyda L. Sevilla, George Mason University

Lawrence Goldstein, Transportation Research Board

Abstract: This research explores the role of air travel in the spread of diseases, including a threat of 2 pneumonic plague. Background: Air travel provides means for diseases to spread internationally 3 at unprecedented rates. An outbreak of pneumonic plague, which has a high mortality rate, is 4 spread from person to person, and is endemic to the United States, may challenge the effectiveness 5 of current public health responses. Methods: This research uses a mixed methods approach to 6 evaluate the impact of aviation on the spread of infectious diseases and the effectiveness of 7 different public health strategies. Results: Modeling shows that the spread of pneumonic plague is 8 minimal and should not be a major air travel concern if an individual becomes infected. Due to the 9 rapid progression of pneumonic plague and the high likelihood of death, spread of the disease is 10 highly unlikely to progress from the initial victims. Conclusion: The threat of pneumonic plague 11 is not from the disease, but from the potential psychological impact. To contain the outbreak of 12 pneumonic plague, aviation and public health authorities should establish preventative infectious 13 disease measures at airports, streamline contact procedures for ticketed passengers, expand the 14 definition of “close contact,” and conduct widespread educational programs. The measures will 15 put in place a foundation for containing any infectious disease and ensure that a natural pneumonic 16 plague outbreak cannot be sustained.

 

Lectern: Disaster Relief: Intermodal Freight Must Get Through

Monday 3:45 PM- 5:30 PM

The Local Distribution Conundrum in Catastrophic Events

Jose Holguin-Veras, Rensselaer Polytechnic Institute (RPI)

Abstract: This session discusses the methods used to transport lifesaving goods to the rural communities affected by a devastating earthquake.  This research describes the need for adaptability in getting goods delivered when disasters destroy the transportation infrastructure.

 

Poster | Spotlight Theme: Perspectives on Economic Development Impacts of Transportation Investments and Policies

Monday 3:45 PM- 5:30 PM

Spatial Spillover Effects of High-Speed Railways: Evidence from Northeast China

Qiao Li, Northeast Normal University

Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

Guofeng Gu, Northeast Normal University

Wei Zou, Rensselaer Polytechnic Institute (RPI)

Abstract: This paper examines the spatial spillover effects of public transportation infrastructure on regional economy in Northeast China, the “rust belt” region in China. The dataset consists of socioeconomic data from 47 cities in the area during the period of years 2004 through 2014. Accessibility is used as an explanatory variable to reflect the influence of infrastructure on economic development. Distance and the shortest travel time between cities are used to define the spatial weight matrices. The results show that a significant positive spillover effect exists after the construction of high speed railways (HSR), indicating the extensive economic benefits of HSR construction, despite of the overall economic difficulty experienced by this region.

Poster | Practice Ready Paper: Transportation Safety Management: Start to Finish

Monday 3:45 PM- 5:30 PM

Hotspot Identification for Freeways Considering Difference in Single and Multi-vehicle Crashes

Xuesong Wang, Tongji University

Mingjie Feng, Tongji University

Qi Shi, University of Central Florida

Xiaokun Wang, Rensselaer Polytechnic Institute (RPI)

Yan Li, Tongji University

Abstract: Previous studies found that the spatial distributions of Single-vehicle (SV) and Multi-vehicle (MV) crashes are quite different, but there has not been much research on hotspots identification considering differences in SV and MV crashes. This study identified hotspots of SV, MV and total crashes separately, using road design data, traffic operational data and crash data collected from a 45-km freeway segment in Shanghai. Full Bayes Poisson Lognormal regression models were developed for SV, MV and total crashes and the potential for safety improvement (PSI) was used to rank hotspots. Model estimation results showed that the significant influencing factors vary in different crash types. Hotspots identification results demonstrated that hotspots of SV crashes are quite different from MV crashes. For example, only three of the top ten hotspots were shared by both SV and MV crashes. Additionally, hotspots of total crashes have a higher consistency with MV crashes than with SV crashes, indicating that a majority of SV crash hotspots may be ignored if total crashes are used to identify hotspots. These conclusions prove the necessity to differentiate SV and MV crashes for hotspot identification and conducting road safety management.

Tuesday, January 9th, 2018

Artificial Intelligence and Advanced Computing Applications Committee

Tuesday 8:00 AM- 12:00 PM 

Sherif Ishak, University of Alabama, Huntsville, presiding

Adel Sadek, University at Buffalo, presiding 

Poster: Current Issues in Environmental Analysis in Transportation

Tuesday 8:00 AM- 9:45 AM 

How a Broad Cross-Section of TRB Standing Committees Looks at “the Environment” in Transportation—Open Survey Results, Trends and Eye-Openers

Richard Record, RL RECORD LLC Consultants
Meredith Morgan, George Mason University

Abstract: This poster provides suggestions from professionals on how environment should be integrated into transportation studies. It also provides support for seeking measurable outcomes from the environmental analysis portion of transportation studies.

Poster | Practice Ready Paper: Geometric Design Research and Graduate Student Poster Session

Tuesday 10:15 AM- 12:00 PM 

Evaluation and Safety Analysis of a Two-Way Left Turn Lane in the PR-107 using a Driving Simulator 

Ricardo Garcia Rosario, University of Puerto Rico, Mayaguez

Didier Valdes, University of Puerto Rico, Mayaguez

Poster | Practice Ready Paper: The World of Transportation Planning Applications

Tuesday 3:45 PM- 5:30 PM 

Comprehensive Plug-and-play Methodology for Multimodal Travel Trend Analysis at a Metropolitan Level Utilizing only Public Domain Data 

Bo Peng, University of Maryland, College Park

Yixuan Pan, University of Maryland, College Park

Shanjiang Zhu, George Mason University

Minha Lee, University of Maryland, College Park

Weiyi Zhou, University of Maryland, College Park

Lei Zhang, University of Maryland, College Park

Abstract: Travel behavior data enable the understanding of why, how, and when people travel, and play a critical role in travel trend monitoring, transportation planning, and policy decision support. Departments of Transportation (DOTs) at both federal and state levels have strategically invested in travel behavior information gathering. While the estimation of travel trends plays a critical role in different aspects of urban development and traffic monitoring, the potential of public domain data lacks significant study. With decision makers increasingly requesting recent and up-to-date information on travel trends, establishing a sustainable and timely travel monitoring program based on available data sources from the public domain is in order. In this paper, a package of comprehensive methods that utilize all data accessible to the public is developed. This package can be applied to disaggregate state level traffic monitoring data into metropolitan statistical areas to understand the traffic pattern dynamically. Additionally, a case study of Seattle MSA is presented as a demonstration of the reliability and accuracy of the proposed methods

Poster | Practice Ready Paper: Emerging Trends and Models in Demand-Responsive Transportation

Tuesday 3:45 PM- 5:30 PM

Regularized Least Squares Approach to Fitting Paratransit Demand Models with Limited and Uncertain Data 

Daniel Rodriguez-Roman, University of Puerto Rico, Mayaguez

Sarah Hernandez, University of Arkansas, Fayetteville

Abstract: Travel demand models are useful for paratransit system planning in the face of changing demographics. Unfortunately, developing travel demand models that account for demographic information can be challenging for transit agencies that do not have the resources to collect or purchase the travel behavior data required to estimate these types of models. In response to this problem, a regularized least squares approach is proposed that can be used to fit paratransit demand models using three types of inexpensive data: publicly available sociodemographic data, basic ridership data collected by transit agencies, and general travel behavior information available from technical publications or derived from a transit agency’s in-house knowledge. The latter is used as prior information that anchors the final model parameter values obtained with the regularized least squares procedure.  The demand models fitted with the proposed methodology can be used to forecast paratransit ridership given population projections or expected changes in system attributes. In addition, a procedure is presented to incorporate input data uncertainty in the model fitting process. The uncertainty stemming from the sampling procedures used to create population estimates, as well as the uncertainty associated with the spatial boundaries of paratransit service areas, are considered. An illustrative application of the proposed methodology is presented using the Ozark Regional Transit paratransit service, in Arkansas, as a case study.

 

Wednesday, January 10th, 2018

Poster | Practice Ready Paper: Social Networks and Group Behavior

Wednesday 8:00 AM- 9:45 AM 

Travel Behavior Classification: An Approach with Social Network and Deep Learning 

Yu Cui, University at Buffalo

Qing He, University at Buffalo

Alireza Khani, University of Minnesota, Twin Cities

Abstract: Uncovering human travel behavior is crucial for not only travel demand analysis but also ridesharing opportunities. To group similar travelers, this paper develops a deep learning based approach to classify travelers’ behaviors given their trip characteristics, including time of day and day of week for trips, travel modes, previous trip purposes, personal demographics, and nearby place categories of trip ends. This study first examines the dataset of California Household Travel Survey (CHTS) between the year of 2012 and 2013. After preprocessing and exploring the raw data, we construct an activity matrix for each participant. The Jaccard similarity coefficient is employed to calculate matrix similarities between each pair of individuals. Moreover, given matrix similarity measures, we construct a community social network for all participants. We further implement a community detection algorithm to cluster travelers with similar travel behavior into the same groups. There are five clusters detected: non-working people with more shopping activities, non-working people with more recreation activities, normal commute working people, shorter working duration people, later working time people, and individuals needing to attend school. We further build an image of activity map from each participant’s activity matrix. Finally, a deep learning approach with convolutional neural network is employed to classify travelers into corresponding groups according to their activity maps. The accuracy of classification reaches up to 97%. The proposed approach offers a new perspective for travel behavior analysis and traveler classification.

 

Poster | Practice Ready Paper: Activity Patterns and Activity Scheduling

Wednesday 8:00 AM- 9:45 AM 

Multi-day Activity-Travel Pattern Sampling Based on Single-Day Data 

Anpeng Zhang, University at Buffalo

Jee Eun Kang, University at Buffalo

Kay Axhausen, ETHZ - Swiss Federal Institute of Technology

Changhyun Kwon, University of South Florida

Abstract: Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data with considerations of day-to-day intrapersonal variability. One of the key observations we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day datasets. Thus, interpersonal variability observed in cross-sectional single-day data of a large population can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented.

Poster | Practice Ready Paper: Operational Effects of Innovative Intersection and Interchange Designs

Wednesday 8:00 AM- 9:45 AM

Non-conventional Left Turn Treatment for Six-leg Intersections 

Maria Vizcarrondo, RS&H Architecture/Engineering Consulting

Ivette Cruzado, University of Puerto Rico, Mayaguez

Abstract: Congestion problems, especially at intersections, have been on the rise due to increased traffic volumes throughout mainline corridors in Puerto Rico. Although innovative intersection designs have been available for many years, such as the Michigan Left and the Superstreet, these have not been implemented in Puerto Rico. A congested intersection in the municipality of Mayagüez was chosen as the study site for this project. Initially the Michigan Left was the only intersection design considered for analysis. Simulation models indicated that this type of intersection design did not provide an acceptable level of service, thus suggesting that an alternate design was needed for the sex-leg intersection. Hence, a new intersection design that combined both Michigan Left and Superstreet concepts was modeled. The simulation model resulted in acceptable levels of service in most approaches, thus indicating that this intersection design is appropriate for six-leg intersections.

Lectern: Drivers’ Behavior as a Function of Their Characteristics and the Driving Environment

Wednesday 10:15 AM- 12:00 PM

Operational and Safety Performance of Signage and Pavement Markings Managed Lane Using a Driving Simulator 

Bryan Ruiz-Cruz, University of Puerto Rico, Mayaguez

Johnathan Ruiz Gonzalez, University of Puerto Rico, Mayaguez

Ricardo Garcia Rosario, University of Puerto Rico, Mayaguez

Enid Colón Torres, University of Puerto Rico, Mayaguez

Didier Valdes, University of Puerto Rico, Mayaguez

Benjamin Colucci, University of Puerto Rico, Mayaguez

Abstract: Managed lanes facilities are providing an opportunity to reduce travel time as well as pollution worldwide. In 2013, Puerto Rico built their first ever two lane reversible Dynamic Toll Lane (DTL) facility. The 10.4-mile managed lane, located at the median of the PR-22 freeway, is shared by Bus Rapid Transit (BRT) and passenger cars. Safety issues have been found associated with sudden lane changing and incorrect use of the designated DTL exit. An online survey was developed to gather information and knowledge of drivers in managed lanes facilities. A driving simulator was used to study the safety aspect of driving behavior inside the PR-22 DTL. The University of Puerto Rico at Mayagüez cockpit simulator was used to compare the driving behavior between two configurations of signage and pavement markings. Configuration 1 corresponds to the existing condition of signage and Configuration 2 consists of a proposed treatment of signage and in-lane pavement markings. The information gathered in the online survey was used to develop the proposed treatment. A group of 24 participants drove 6 representative scenarios changing the traffic flow and the time of the day, calculating the Average Position. The results indicated that Configuration 2 has improved the Average Position variable in at least 67% of the zones evaluated in comparison with the Configuration 1.

Poster: The Search for a Better Way, Part 2: Exploring and Refining Methods in Highway Safety (Part 1, Session 523)

Wednesday 10:15 AM- 12:00 PM 

Analysis of accident injury-severities using a time-variant correlated random parameters ordered probit approach 

Grigorios Fountas, University at Buffalo

Panagiotis Anastasopoulos, University at Buffalo

Mohamed Abdel-Aty, University of Central Florida

Abstract: This paper employs a correlated random parameters ordered probit modeling framework to explore time-variant and time-invariant factors affecting injury-severity outcomes in single-vehicle accidents.  The proposed approach extends traditional random parameters modeling, by accounting for possible correlations among the random parameters.  On the basis of an unrestricted covariance matrix for the random parameters, the proposed framework can capture the combined effect of the unobserved factors – which are captured by the random parameters – on the injury-severity mechanism.  The empirical analysis is based on traditional roadway-, traffic- and crash-specific information, and detailed weather and pavement surface disaggregate data, collected in the State of Washington, between 2011 and 2013.  The results show that accident injury-severity outcomes are affected by a number of time-variant (ice thickness or water depth on pavement surface, sub-surface temperature) and time-invariant (roadway geometrics, and    vehicle-, driver-, and collision-specific characteristics) factors, several of which result in statistically significant parameters – thus they have mixed effects on the injury-severity generation mechanism.  The findings also present statistically significant correlation effects among the random parameters, which substantiates the appropriateness of the approach.  The comparative assessment between the employed approach and its lower-order counterparts (i.e., fixed parameters, and uncorrelated random parameters ordered probit modeling approaches) shows that accounting for the unobserved heterogeneity interactions results not only in superior statistical performance (in terms of model’s fit, and explanatory and prediction performance) but also in less biased and more consistent parameter estimates.

 

Poster | Practice Ready Paper: Transportation Network Modeling

Wednesday 2:30 PM- 4:00 PM

A Probabilistic Trip Chaining Algorithm for Transit Origin-Destination Matrix Estimation using Automated Data 

Pramesh Kumar, University of Minnesota, Twin Cities

Alireza Khani, University of Minnesota, Twin Cities

Qing He, University at Buffalo

Abstract: Development of an origin-destination demand matrix is crucial for transit planning. The development process is eased with automated data collection, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a probabilistic method for trip chaining which uses Automatic Fare Collection (AFC) and General Feed Transit Specification (GTFS) data. The proposed method avoids problems resulting from errors in AFC transaction locations or selection of incorrect subroutes from GTFS data which may result in incorrect inference of the origin or destination. The method has been applied to the Twin Cities AFC data as a case study. The transit system is an open system where passengers tap smart cards once while boarding or sometimes alighting (on outbound pay-exit buses). Based on the consecutive tags of the passenger, an algorithm with different capabilities is developed for different cases based on the pay-exit property. The method is compared with a baseline algorithm which shows improvements in the quantity and quality of inferred trips. Finally, the inferred origin-destination demand matrices as well as route ridership are presented visually for planning purposes.

Poster | Practice Ready Paper: Transportation Network Modeling

Wednesday 2:30 PM- 4:00 PM

A Spectral Risk Measure in Hazardous Materials Transportation 

Liu Su, University of South Florida

Longsheng Sun, University at Buffalo

Mark Karwan, University at Buffalo

Changhyun Kwon, University of South Florida