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Transportation Research Board 2015

TransINFO Partners: Posters, Papers and Presentations

Sunday, January 11th, 2015

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Monday, January 12th, 2015

8:00 AM - 9:45AM

Modeling Mixed Equilibria in Transportation Networks with Link Constraints

Xia Yang, Rensselaer Polytechnic Institute

Rui Ma, University of California, Davis

Xuegang (Jeff) Ban, Rensselaer Polytechnic Institute

We study the mixed equilibrium problem in transportation networks, considering possible interactions among the control variables of different players. We first formulate the problem as a generalized Nash equilibrium problem (GNEP), with user equilibrium, system optimum and Cournot-Nash players. The interactions are mainly captured by common link constraints that can account for internal and/or external flow restrictions. The study shows that, under certain assumptions, the proposed GNEP is jointly convex and feasible, which can be reformulated and solved as a variational inequality problem. Numerical tests are conducted on a small network to illustrate the model and solution method. The results show that external flow restrictions may be implemented to enhance system performance. The results also provide some insight on link capacity planning and congestion policy making

 

8:30AM - 10:15AM

Review of Infrastructure Public-Private Partnership Data: Meta-analysis

Zhenhua Chen, George Mason University

Nobuhiko Daito, George Mason University

Jonathan L. Gifford, George Mason University

The limited understanding of public-private partnerships (P3s) for infrastructure finance has been generally attributed to the lack of data. The more fundamental question of how P3 data are utilized in the literature is more relevant and critical, but remains unclear. This study investigates this question by examining the linkages between research objectives and data characteristics through a meta-analysis of infrastructure P3 studies. Using multinomial logit regression. it analyzes 90 empirical studies that adopt actual data, selected from a P3 research database that includes over 340 studies and are classified into five categories including performance, contract, risk, value for money (VFM) and institutional factors. Results show that the case study method is more frequently utilized to analyze institutional issues regarding P3s than studies that focus on performance and VFM, whereas survey data are more frequently used to analyze issues related to P3 performance and risk. The review shows that ex ante data are more likely to be adopted to understand P3 performance whereas ex post data are more often adopted to understand P3 contract specifics such as renegotiation.

Understanding Interaction of Multiple Jurisdictions for Highway Investment: Viability of Public-Private Partnership Alternatives

Nobuhiko Daito, George Mason University

Shanjiang Zhu, George Mason University

Jonathan L. Gifford, George Mason University

Observing that highway public-private partnerships (P3s) have been rare for projects that cross jurisdictional boundaries, this study attempts to evaluate strategic interactions of multiple governments in terms of their road network investment, with the prospect to partner with a private firm. There is a gap in the literature regarding network level analyses of investment behavior in partnership with the private sector. In undertaking this complex analysis, this paper presents a model with which to investigate how governments strategically interact with each other in making these decisions. Numerical analysis of the model on a highly stylized highway network was conducted to serve as the foundation for investigating various complex policy alternatives not only on stylized networks but also on real highway networks. The analysis demonstrated that two jurisdictions, in maximizing respective welfare given the best response of the counterpart, raised toll and increased the capacity for the link that served inter-jurisdictional commuters, relative to the other links. Welfare losses and negative profitability resulted. The analysis suggested the analytical framework could be a tool to inform decision makers on possible consequences of various policy scenarios, including the use of P3s for such contexts.

Managing Complexity for Small Highway Projects

Carla Lopez del Puerto, University of Puerto Rico, Mayaguez

Douglas D. Gransberg, Iowa State University

Carlos F. Figueroa, Federal Highway Administration

To meet the growing demand of the nation’s infrastructure, transportation projects are becoming more complex. Complex projects involve an unusual degree of uncertainty and instability. Decisions must be made in an environment where the project team does not have direct control over many of the critical factors. This paper discusses a five-dimensional project management model (5DPM) and the use of complexity maps as tools to identify and manage the sources of complexity. This paper also details how complexity was managed in four projects valued between $8.0 million and $50 million that were not classified as Federal Highway (FHWA) Major Projects and demonstrates how an agency can determine if a seemingly routine small project is indeed complex. Based on the results of the case study analysis, it can be concluded that both small and large projects benefit from 5DPM and the use of complexity mapping to identify sources of complexity and develop action plans that allow them to address the sources of complexity proactively. The complexity maps also indicate that in small projects, context is the dimension in which the most complexity is observed.

 

10:15AM - 12:00PM

Repeal of Minimum Parking Requirements in the Green Code in Buffalo: New Directions for Land Use and Development

Daniel Baldwin Hess, State University of New York, Buffalo

Minimum parking requirements, which mandate off-street parking by law for all land uses, have been a staple of zoning codes in American cities for more than 80 years. Realizing the harm caused by minimum parking requirements—such as distorted land value, incentives for driving, disruption of urban form, higher development costs, bundled parking costs with other goods and services—planners in various cities have sought to make changes to the use of minimum parking requirements in city ordinances. This article reports on the development of a new Unified Development Ordinance in Buffalo, known as “The Green Code”, which completely removes minimum parking requirements from zoning mandates, relieving developers and property owners from the burden of providing off-street parking. This is the first time that a U.S. city has completely repealed minimum parking requirements. The article discusses expected outcomes related to the change, including a need to better manage on-street parking, a planning focus on multi-modal transport (given mode shift away from driving and parking), and the potential to establish more complete streets. Opportunities are outlined for evaluating the response to the removal of minimum parking requirements in Buffalo.

 

10:45AM - 12:30PM

Research in Statistical Methods in Transportation

Panagiotis Ch. Anastasopoulos, State University of New York, Buffalo, presiding

A Fixed Effects Bivariate Ordered Probit Analysis of Perceived and Observed Aggressive Driving Behavior: A Driving Simulation Study

Nima Golshani, State University of New York, Buffalo

Panagiotis Ch. Anastasopoulos, State University of New York, Buffalo

Kevin Hulme, State University of New York, Buffalo

This paper uses driving simulation data and surveys conducted in the Spring of 2014 at Buffalo, NY, to study the factors that affect perceived (self-reported, based on surveys) and actual (as measured, based on driving simulation experiments) aggressive driving behavior. To that end, a fixed effects bivariate ordered probit model is estimated. The model simultaneously accounts for panel data effects, and for cross equation error correlation. The results show that a number of socio-demographic (age, race, level of education, household income level, and growing up in a suburban or rural area), driving experience and exposure (accident history, driver experience, and willingness to drive), and behavioral and other characteristics (speeding habits, listening to music while driving, caffeine usage, and fatigue) affect how drivers perceive their driving behavior, and how they actually drive. In fact, the findings reveal that different variables play in the perceived and the actual aggressive driving behavior. And it is found that young individuals and experienced drivers can potentially perceive their driving as non-aggressive, when in fact they may be possibly driving aggressively.

An Empirical Exploratory Analysis of Factors Affecting Highway Segment Hazard-Level Likelihood Using Random Parameters Ordered Probit Regression

SeyedAta Nahidi, State University of New York, Buffalo

Nima Golshani, State University of New York, Buffalo

Panagiotis Ch. Anastasopoulos, State University of New York, Buffalo

This paper explores a possible alternative to the traditional approach of identifying safety countermeasures and forecasting hot spots, by studying directly the likelihood of a location being hazardous. To that end, a random parameters ordered probit model of highway segment hazard-level likelihood is estimated. The hazard level for every highway segment (i.e., low hazard, hazard, and extreme hazard) is defined based on the segment’s excess-over-the-norm accident frequency and using scientific judgment and physical data barriers evident in the data distribution. Using five-year accident data from rural highways in Indiana, the results show that a number of road geometrics, pavement condition and traffic characteristics affect the highway segment hazard-level likelihood. The identified safety countermeasures are found to be in line with past traditional accident analysis research. Finally, the proposed approach is evaluated in terms of predicting the most hazardous locations, and the results are counter-imposed to a traditional accident rates model prediction. The results indicate that the proposed approach performs equally well to the traditional approach in predicting extremely hazardous locations.

Real–Time Seismic Damage Assessment Method for Bridges Using Nonlinear Regression: Exploratory Analysis

Ioannis Anastasopoulos, Dundee University, United Kingdom

Panagiotis Ch. Anastasopoulos, State University of New York, Buffalo

Athanasios Agalianos, Dundee University, United Kingdom

Lampros Sakellariadis, Dundee University, United Kingdom

Seismic damage of bridges may pose a severe threat to motorway users, and preventive closure until post–seismic inspection may be viewed as the only safe option. However, such a measure may incur pronounced losses by obstructing transportation of rescue teams. On the other hand, allowing traffic on earthquake–damaged bridges is a difficult decision with potentially dire consequences. Hence, the main dilemma for the motorway administrator is whether to interrupt the operation of the network, calling for timely development and implementation of a RApid REsponse (RARE) system. The development of such a RARE system requires an effective means to estimate the seismic damage of motorway structures in real time. This paper contributes towards such a direction by exploring a simple method for real time seismic damage assessment of motorway bridges. The proposed method requires nonlinear dynamic time history analyses using multiple seismic records as seismic excitation. Based on the results of the analyses, statistical models are estimated, and nonlinear regression equations are developed to express seismic damage as a function of statistically significant intensity measures (IMs). Such equations are easily programmable and can be employed for real-time damage assessment, as part of an online expert system. In the event of an earthquake, the nearest seismic motion(s), recorded by an online accelerograph network, will be used in real time to estimate the damage state of motorway structures, employing the developed equations. The efficiency of the proposed method is demonstrated using a single bridge pier as an illustrative example. Based on finite element (FE) analysis results, three nonlinear regression models are estimated correlating three damage indices (DIs) with statistically significantly IMs.

 

10:45AM - 12:30PM

Freight Deliveries Directly Generated by Residential Units: Analysis with 2009 NHTS Data

Yiwei Zhou, Rensselaer Polytechnic Institute

Xiaokun (Cara) Wang, Rensselaer Polytechnic Institute

As a result of the rapid growth of online shopping, more goods and services are delivered directly to residential units. The door-to-door deliveries improve residents’ accessibility to retailing sector, and at the same time create truck delivery trips. However, partially due to the data limitation, most existing freight research focuses on freight trips generated by business establishments, little is known about freight trips generated by residential units. As more and more urban areas are pushing for dense and mixed development, it is necessary to understand the pattern of truck freight trips directly generated by residential units. For this paper, dataset from NHTS is used to investigate the freight trips generated by residential units. NHTS 2009 provides accurate, comprehensive and timely information on trips, land use, household characteristics and social economic factors. It is the first time NHTS data is used to estimate freight trips. A set of right censored negative binomial models are used to identify the impacts of influential factors such as housing density, type of house and house ownership. A case study for the Capital District in New York State is then presented. The predicted freight trips generated by residential units are also compared to freight trips generated by business establishments. Such a study will supplement city logistics studies that traditionally focus on business behaviors, help reconstruct the complete picture of freight activities in urban areas.

 

1:30PM - 3:15PM

Using Dynamic Flashing Yellow for Traffic Signal Control under Emergency Evacuation

Charles Amoateng Asamoah, State University of New York - Buffalo

Qing He, State University of New York, Buffalo

Effective signal timing plan for emergency evacuation is very crucial for public safety. Two conflicting objectives of emergency evacuation in a corridor are to increase throughput on the main street (evacuation route) and decrease delay on side streets. Some studies have proven the effectiveness of the static flashing yellow (SFY) signal timing plan in evacuating high number of vehicles [1]. However, SFY plan also yields extremely high delay on side streets. This paper investigates a variant of the SFY plan called dynamic flashing yellow (DFY) signal timing plan under a few reasonable assumptions. DFY plan basically consists of two signal phases. Phase 1 is flashing yellow on the main street and flashing red on the side street, whereas phase 2 is red signal on the main street and green signal on the side street. Three different types of the DFY plan are proposed, including Fixed DFY (DFY-F), Actuated DFY (DFY-A) and Actuated and Coordinated DFY (DFY-AC). This paper demonstrates that DFY provides a high volume of evacuated vehicles with relatively lower delay to side street traffic. Moreover, the proposed DFY is adjustable to favor different weights between network throughput and average delay. To compare DFY with SFY and PM peak plan, VISSIM is implemented to model a 4.1 mile corridor in Buffalo, NY. The DFY plan is further analyzed under different methods and minimal cycle lengths. According to Pareto frontier, it is realized that DFY-AC with minimal cycle length of 60 seconds and 120 seconds produces more desirable results (Pareto non-dominated solutions) than others.

 

2:00PM - 3:45PM

Economic Analysis of Welfare and Equity Effects of Vehicle Ownership Rationing Policies

Lei Zhang, University of Maryland, College Park

Yu Ding, Tsinghua University, China

Shanjiang Zhu, George Mason University

Longuyuan Du, University of Maryland, College Park

Huapu Lu, Tsinghua University, China

As traffic congestion has become a serious issue in many cities, vehicle ownership rationing has been a demand management policy implemented to directly control the number of motorized vehicles. This paper follows the indirect utility approach and quantitatively measures the welfare impact of vehicle ownership rationing, including both lottery rationing and auction. It is shown through a numerical example that under the circumstance of small remaining ratio, auction rationing enjoys a higher welfare gain than lottery, and vice versa. The equity issues are also discussed with this framework allowing the consideration of different household income. It is suggested that although both policies perform badly in the equity aspect, lottery rationing shows an even larger inequity compared to auction. Both social welfare gain and equity should be combined to determine the optimal quota ratio.

Multiclass Vehicle Classification Using GPS Data

Zhanbo Sun, Western Michigan University

Xuegang (Jeff) Ban, Rensselaer Polytechnic Institute

It has been previously proved that GPS data can be used to distinguish passenger cars from large trucks. In this study, a machine learning approach is proposed to use GPS data to identify multiple vehicle classes: including passenger cars, single unit trucks, and multi-trailer trucks. The method is acceleration and deceleration-based since it considers the variations of accelerations and decelerations as the most effective features to classify vehicles. The overall classification result for the three vehicle classes is about 75%. The major challenge is to distinguish single unit trucks from multi-trailer trucks. It is found that the classification results are not very sensitive to the sampling frequency of GPS data, as long as the data are collected frequent enough to capture major acceleration and deceleration processes of the vehicles.

 

3:45PM - 5:30PM

Continuous-time Instantaneous Dynamic User Equilibria on A Real World Traffic Network

Rui Ma, University of California, Davis

Xuegang (Jeff) Ban, Rensselaer Polytechnic Institute

This paper proposes an evaluation procedure for the results of a recent established instantaneous dynamic user equilibrium (IDUE) model with the real-world dynamic data collected in Kichijoji area. A summary of the IDUE model is provided. The real-world data set is utilized with necessary revisions on the network structure. Network characteristics are obtained to solve the IDUE model. Comparisons are made between the IDUE results and the real-world data, using two micro simulation model calibration criteria.

Luminous Intensity Requirements for Service Vehicle Warning Beacons

John D. Bullough, Rensselaer Polytechnic Institute

Mark S. Rea, Rensselaer Polytechnic Institute

Flashing yellow warning beacons are used on a wide variety of service vehicles including highway construction vehicles, dump trucks, delivery vehicles, snow plows, and tow trucks. These lights serve as an important line of defense for several million U.S. workers in the transportation, construction and utilities sectors, who are over-represented in terms of workplace fatalities. In order to understand visual responses to warning beacons varying in their luminous intensity characteristics, flashing yellow warning beacons were presented under laboratory conditions simulating daytime and nighttime roadway scenes. Response times to the onset of flashing and subjective ratings of beacon visibility and of the visibility of low-contrast objects in the scenes were measured. The results provide a preliminary basis for the development of quantitative specifications of warning beacon luminous intensity characteristics that ensure high levels of visibility under both daytime and nighttime conditions, while minimizing glare, especially at night.

 

4:15PM - 6:00PM

Landscape of Motor Freight Transportation and Warehousing: Analysis and Findings from Six Large Metropolitan Areas in the U.S.

Qian Wang, State University of New York, Buffalo

Tao Zhou, State University of New York, Buffalo

Shuai Tang, State University of New York, Buffalo

Li Yin, State University of New York, Buffalo

The new trends in supply chain and logistics have led to new spatial patterns of the motor freight transportation and warehousing industry in mega cities. This study is among the few to systematically and empirically explore the landscapes of the industry, using the six large metropolitan areas as the case studies, namely Chicago (Illinois), Houston (Texas), Los Angeles (California), Miami (Florida), and New York (New York). A kernel density map was created for each area to visually demonstrate the distinct spatial distribution patterns. In addition, several landscape metrics were also created to statistically verify the existence of spatial clusters and the compactness of the establishment distribution in an area. The empirical results show three main characteristics of the landscapes, including: (1) that landscapes of freight and warehousing vary by area due to the differences in physical boundaries, transportation network structures, and spatial distributions of major freight activity generators; (2) that the freight establishments tend to cluster to take advantage of the economies of scale; and (3) that they tend to be located nearby highways and railroads.

Freight Deliveries Directly Generated by Residential Units: An Analysis with the 2009 NHTS Data

Yiwei Zhou, Rensselaer Polytechnic Institute

Xiaokun (Cara) Wang, Rensselaer Polytechnic Institute

As a result of the rapid growth of online shopping, more goods and services are delivered directly to residential units. The door-to-door deliveries improve residents’ accessibility to retailing sector, and at the same time create truck delivery trips. However, partially due to the data limitation, most existing freight research focuses on freight trips generated by business establishments, little is known about freight trips generated by residential units. As more and more urban areas are pushing for dense and mixed development, it is necessary to understand the pattern of truck freight trips directly generated by residential units. For this paper, dataset from NHTS is used to investigate the freight trips generated by residential units. NHTS 2009 provides accurate, comprehensive and timely information on trips, land use, household characteristics and social economic factors. It is the first time NHTS data is used to estimate freight trips. A set of right censored negative binomial models are used to identify the impacts of influential factors such as housing density, type of house and house ownership. A case study for the Capital District in New York State is then presented. The predicted freight trips generated by residential units are also compared to freight trips generated by business establishments. Such a study will supplement city logistics studies that traditionally focus on business behaviors, help reconstruct the complete picture of freight activities in urban areas.

The Role Of Classification Systems In Freight Trip Attraction Modeling

Ivan Sanchez-Diaz, Chalmers University of Technology, Sweden

Shama Campbell, Rensselaer Polytechnic Institute

Miguel Jaller, University of California, Davis

Jose Holguin-Veras, Rensselaer Polytechnic Institute

Catherine Theresa Lawson, State University of New York, Albany

This paper describes different classification systems that have been used in the literature for Freight Trip Attraction (FTA) modeling, and explores the implications of using industry and land use classification systems for FTA modeling. The paper places a special focus on the North American Classification System (NAICS) and the City of New York Zoning Resolution (NYCZR) to analyze their similarities, and assess the performance of the FTA models derived based on those classification systems. The models ability to replicate FTA is assessed in both the calibration dataset and a validation dataset from receivers based in New York City (NYC). The application to NYC shows that in spite of grouping establishments using very different criteria, the NAICS-based and the NYCZR-based FTA models produce similar estimates.

Tuesday, January 13th, 2015

8:30AM - 10:15AM

Modeling Multi-modal Manual Signal Control under Event Occurrences

Nan Ding, State University of New York, Buffalo

Qing He, State University of New York, Buffalo

Changxu Wu, State University of New York, Buffalo

Julie Fetzer, State University of New York, Buffalo

Traffic control agencies (TCAs), including police officers, firefighters or other traffic law enforcement officers, can override automatic traffic signal control and manually control the traffic at an intersection. TCA-based traffic signal control is crucial to mitigate non-recurrent traffic congestions caused by planned and unplanned events. Understanding and predicting of TCA behaviors is significant to optimize event traffic management and operations. In this study, we propose a pressure-based human behavior model to mimic TCA’s decision making behavior. The model calculates TCA’s pressure based on two attributes: vehicle and pedestrian queue dynamics and the red time duration for each phase. When TCA’s pressure on each phase meet certain criteria and the minimal green is satisfied, TCA will terminate the current phase and switch to another phase. In order to study TCA behavior systematically, we first build a manual signal control simulator based on a microscopic traffic simulation tool. Supported by the manual control simulator, a series of human subject experiments have been conducted with real-world TCAs. Experiment data are divided into training data and test data. The proposed behavior model is then calibrated by training data, and the model is validated by both offline segment-based phase and duration prediction and online VISSIM-based simulation. Both validation results support the effectiveness of proposed behavior model.

8:30AM - 10:15AM

Signal Control at Alternative Intersection Designs: Poster Session | Practice Ready Papers

Qing He, State University of New York, Buffalo, presiding

A Validation of Inclement Weather Traffic Models in Buffalo, New York

Andrew Bartlett, State University of New York, Buffalo

Anna Racz, State University of New York, Buffalo

Adel W. Sadek, State University of New York, Buffalo

The impact of inclement weather on traffic conditions has long been a concern to drivers and transportation agencies alike. Inclement weather conditions, such as fog, rain, snow, and ice, are known to negatively impact the operational efficiency of networks, as well as user safety. Recently, the effect of inclement weather on freeway operating conditions near the city of Buffalo, NY has been the topic of multiple studies. Specifically, two studies have attempted to model average operating speed and hourly traffic volume, respectively, as a function of weather conditions. The objective of the current study is twofold. First, the current paper will attempt to determine if most recent winter (2013-2014) has been harsher compared to the previous couple of winters in Buffalo, which have tended to be milder than normal. The second objective is to test the accuracy of the models developed during the previous two studies using more recent data, which includes the most recent 2013-2014 winter. The winter of 2013-2014 was found to have significantly lower minimum and average temperatures than other years examined. In terms of the previous models’ accuracy, the speed model performed reasonably well, usually achieving results within 5 mph of the observed speed, but accuracy somewhat suffered when inclement weather conditions were harsh or when observed speeds were below 40 mph. The volume model’s accuracy was usually within 1500 vehicles, and often within 1000 vehicles, of the observed volume. It was also observed that the volume model tended to overestimate hourly volumes. 

Modeling the Impacts of Inclement Weather on Freeway Traffic Speed: An Exploratory Study Utilizing Social Media Data

Lei Lin, State University of New York, Buffalo

Ming Ni, State University of New York at Buffalo

Qing He, State University of New York, Buffalo

Jing Gao, State University of New York at Buffalo

Adel W. Sadek, State University of New York, Buffalo

Recently, there has been an increased interest in quantifying and modeling the impact of inclement weather on transportation system performance. One problem that the majority of previous research studies on the topic have faced is that they largely depended on weather data merely from atmospheric weather stations, which lacked information about road surface condition. The emergence of social media platforms, such as Twitter and Facebook, provides a new opportunity to extract more weather related data from such platforms. The current study has two primary objectives; first, to examine if real world weather events can be inferred from social media data, and secondly, to determine whether including weather variables, extracted from social media data, can improve the predictive accuracy of models developed to quantify the impact of inclement weather on freeway traffic speed. To achieve those objectives, weather data, Twitter data, and traffic information were compiled for the Buffalo-Niagara metropolitan area as a case study. A method called the Twitter Weather Events observation was then applied to the Twitter data, and the sensitivity and false alarm rate for the method was evaluated against real world weather data. Following this, linear regression models for predicting the impact of inclement weather on freeway speed were developed with and without the Twitter-based weather variables incorporated. The results indicate that Twitter data has a relatively high sensitivity for predicting inclement weather (i.e., snow) especially during the daytime and for areas with significant snowfall. They also show that the incorporation of Twitter-based weather variables can help improve the predictive accuracy of the models.

Performance Measure of Travel Time Reliability of Emergency Vehicles in an Urban Region

Zhenhua Zhang, University at Buffalo, State University of New York

Qing He, State University of New York, Buffalo

Jizhan Gou, Sabra, Wang & Associates, Inc.

XiaoLing Li, Virginia Department of Transportation

Travel time is very critical for emergency vehicle (EV) service and operations. Due to EVs’ high road privileges, the characteristics of travel times of EVs, the study of which draws relatively less attention, are different from that of ordinary vehicles (OVs). This study obtains 3-year EV travel time data in Northern Virginia region using 13,000 preemption records at the signalized intersections. First, the features of travel time, which are extracted from emergency vehicle preemption records, are revealed including the mean, median and standard deviation. Second, a utility model is proposed to model and quantify the travel time reliability of EVs. Third, study continues with two important components of the utility model: benchmark travel time and standardized travel time. An empirical analysis is conducted on the relationship between the link distance and benchmark travel time. The characteristics of standardized travel time are unveiled, and Inv. Gaussian distribution is used to model the standardized travel time. Finally, to validate proposed models, a utility model is implemented in a case study on both links and routes. Moreover, the proposed models can support EV route choice and eventually improve EV service and operations in the society.

 

8:30AM - 10:15AM

Various Aspects of User Information Research: Poster Session

Android Smartphone Application for Collecting, Sharing, and Predicting Border Crossing Wait Time

Lei Lin, State University of New York, Buffalo

Qian Wang, State University of New York, Buffalo

Adel W. Sadek, State University of New York, Buffalo

Gregory Kott, Xerox Corporation

This paper introduces an Android smartphone application called the Toronto Buffalo Border Wait Time (TBBW) app, designed to collect, share and predict waiting time at the three Niagara Frontier border crossings, namely the Lewiston-Queenston Bridge, the Rainbow Bridge, and the Peace Bridge. The innovative app offers the user three types of waiting time estimates: (1) current waiting times collected at the crossings; (2) historical waiting times; and (3) future waiting time predicted for the next 15 minutes and updated every five minutes. For the current waiting time, the app can provide both the data collected by border crossing authorities as well as user-reported or “crowd-sourcing” data shared by the community of the app’s users. Reporting of the data could be done either manually or automatically through a GPS tracking function provided by the smartphone. For the historical waiting time, the app provides statistical charts and tables to help users choose the crossing with the likely shortest wait time. Future waiting times are predicted by a real-time stepwise traffic delay prediction model which consists of a short-term traffic volume forecasting model and a multi-server queueing model. To validate the prediction functionality of the app, its predictions were compared against real-world delay measurements for the entire month of May, 2014. The comparison showed that the model offered predictions with a mean absolute difference of 9.22 minutes. When considering only delays that are greater than 10 minutes, the model has a mean absolute difference of only 6.95 minutes. The ability to integrate officially reported delay estimates with crowd-sourcing data, and the ability to provide future border wait times clearly distinguish the TBBW app from others on the market.

 

8:30AM - 10:15AM

Fine-Grained Modeling of Arterial Traffic: A Data Fusion and Information Integration Approach

Zhanbo Sun, Western Michigan University

Peng Hao, University of California, Riverside

Xuegang (Jeff) Ban, Rensselaer Polytechnic Institute

In this study, 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 concerns with the “most probable” explanation that can validate fixed-location data and mobile sensing data against each other and match the vehicle records at upstream with those collected at downstream sensor locations. To make the stochastic 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 time of individual vehicles. Results from the method can be directly 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.

 

1:30PM - 3:15PM

Modeling the Safety Impacts of Off-Hour Delivery Programs in Urban Areas

Kun Xie, New York University

Kaan Mehmet Ali Ozbay, New York University

Hong Yang, New York University

Jose Holguin-Veras, Rensselaer Polytechnic Institute

Ender Faruk Morgul, New York University

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

 

3:45PM - 5:30PM

A GIS-Based Performance Measurement System for Assessing Transportation Sustainability and Community Livability

Qian Wang, State University of New York, Buffalo

Shuai Tang, State University of New York, Buffalo

Jinge Hu, State University of New York, Buffalo

Xiao Chen, State University of New York, Buffalo

Le Wang, State University of New York, Buffalo

Sustainability and livability in transportation, as the concepts indicating the capability of transportation systems to maintain the well-being of our society, have been widely accepted as the critical principles to improve the quality of life and health of our communities. The research introduced a GIS-based performance measurement system for assessing the two goals from the standpoint of transportation systems. Using the City of Buffalo, New York as the case study, we collected various data and developed twenty sustainability and livability related performance measures (PMs), including the transportation attributes, land use measures, living condition indicators, and system-wide indices. The analysis on PMs derives several policy implications and suggestions. Lessons and challenges learnt from the PM development process were also summarized to help other relevant initiatives. The PMs, supporting database, case study and findings produced by the research are expected to help a wide range of audience such as policy makers, planners and transportation engineers to gain more insights about the transportation oriented sustainability and livability performance measurement.

 

4:15PM - 6:00PM

Geography of Warehousing in Urban Areas: Spatial Analysis and Findings of Transportation Warehouses and Distribution Centers in New York Metropolitan Region

Qian Wang, State University of New York, Buffalo

Shuai Tang, State University of New York, Buffalo

Tao Zhou, State University of New York, Buffalo

Li Yin, State University of New York, Buffalo

The new trends in supply chain and logistics have led to a new geography of warehousing in urban areas. This study is among the first to systematically and empirically explore the geography of warehousing, using the New York metropolitan region, one of the largest cities and the busiest freight hubs in the world, as the study area. Various spatial analyses are conducted to explore the spatial distribution patterns of the warehousing establishments. The empirical results show three major geographic characteristics of the warehouses, including: (1) clustered establishments to take advantage of the economies of scale; (2) concentration of establishments in the main market of freight activities and end customers; and (3) proximity to transportation networks being a significant factor affecting location decisions. Several policy implications are also suggested for warehousing and logistics oriented planning and decision making.

Empirical Investigation of Commercial Vehicle Parking Violations in New York City

Qian Wang, State University of New York, Buffalo

Satyavardhan Gogineni, State University of New York, Buffalo

This paper looks at the parking violation behavior of commercial vehicles that has been widely overlooked in the literature. Two types of analyses were conducted based on a geo-coded violation location data obtained in New York City (NYC) for March 2010. The spatial intensity indices of violations were calculated to identify the “hot spots” of commercial vehicle violations in terms of road function class and land use type. As found, road segments with special characteristics are more likely to have violations, followed by highways and arterials. Commercial and industrial areas, as the main generators of freight and service activities, are more prone to intensive violations. Individual vehicles’ violation trajectories and frequency patterns were also examined to gain behavioral insights. As both the average behavior and the extreme case confirm, commercial vehicles, particularly the frequent violators, tend to made repetitive violations with the same types in the same areas. These results, combined, indicate the “inevitable” nature of commercial vehicle violations and the imbalance between demand and supply relation as the fundamental contribution factor. As suggested, advanced parking management strategies are urgently needed, such as the metering strategies and price incentive mechanisms to foster the fast turnover in parking space.

Multinomial Logistic Regression for Land Use Classification with Remote Sensing

Qian Wang, State University of New York, Buffalo

Shuai Tang, State University of New York, Buffalo

Xiao Chen, State University of New York, Buffalo

Le Wang, State University of New York, Buffalo

In the era of big data, harnessing remote sensing data for transportation decision making has become an achievable task. This paper focuses on the land use classification on the finest parcel scale by using the remote sensing data as the input. Different from other relevant research, we utilized the multinomial logistic regression, or called multinomial logit (MNL) models, whose great potentials have been overlooked for remote sensing based land use classification. In addition, we also suggest using transportation related attributes, such as the distances from a parcel of land to the nearest road or intersection, as the ancillary attributes to improve classification performance, in addition to spectral features collected by remote sensing. The MNL models were tested on the land use data collected in the City of Buffalo, New York. The best model achieves an average prediction accuracy of 83.7%. For the residential and commercial parcels, the prediction accuracy reaches up to 94.5%. In addition, the suggested transportation attributes were also found significant in discriminating land use classes. Two main conclusions were raised from the research, including remote sensing as a reliable data source for timely updating land use and land cover, and the applicability of the MNL models for land use classification with remote sensing.

OFF-HOUR DELIVERIES: LESSONS LEARNED FROM THE OUTREACH IN NEW YORK CITY

Jeffrey Wojtowicz, Rensselaer Polytechnic Institute

Stacey Darville Hodge, New York City Department of Transportation

Shama Campbell, Rensselaer Polytechnic Institute

Jose Holguin-Veras, Rensselaer Polytechnic Institute

Xiaokun (Cara) Wang, Rensselaer Polytechnic Institute

Such a complex and dynamic program as the Off-Hour Delivery (OHD) Program requires extensive outreach to both the public and private sectors. This paper describes key lessons learned, primarily from the institutional coordination and outreach effort undertaken through the entirety of the project, in order to maximize participation in both staffed and unassisted off-hour deliveries. The paper provides background on the launch phase of the Off-Hour Delivery Program completed by Rensselaer Polytechnic Institute (RPI) and the New York City Department of Transportation (NYCDOT), a program that induced over 400 receivers to switch to the off-hours. Insights were gained and lessons learned from the outreach, branding and recruiting efforts, all of which are presented in this paper.

Speed Variability Along Horizontal Curves on Two-Lane Rural Highways in Puerto Rico

Alex Bermudez Arbona, University of Puerto Rico, Mayaguez

The Federal Highway Administration (FHWA) states that rural highways move approximately 40% of the total traffic volume in the United States. However, 57% of traffic fatalities every year occur along this highway type. Twenty-five percent of traffic fatalities are associated with horizontal curves; this being one of the reasons why they are considered to be one of the most dangerous road design elements. Another factor that has a great effect in the frequency and severity of the crashes is the operational speed along highways, especially when it exceeds both the posted and design speeds. This research focuses on collecting speed data of free flow vehicles in two-lane rural highways in the west area of Puerto Rico in order to identify high risk horizontal curves and predict the speed of drivers through these highway elements. Five speed sensors were placed along several horizontal curves: at the entry tangent, at the point of curvature, at the midpoint of the curve, at the point of tangency, and at the exit tangent. Also, geometric data were measured, such as curve radius, curve length, width of cross section elements, and longitudinal slope, among others. Speed data will be used to estimate the operational speeds as indicated by its 85th percentile; then it will be compared to the posted speed limit as well as the inferred design speed by applying hypothesis tests. Research has shown that high speed changes in road segments are related to crash frequency. Based on this information, the speed and geometric data will be used to develop prediction models for speed reductions between entry tangent and point of curvature that can assist the decision making process in roadway design and direct efforts to improve the level of safety for drivers when traversing high risk horizontal curves.

Wednesday, January 14th, 2015

8:00AM - 9:45 AM

Inverted Classroom and Its Influence on Students’ Attitudes Across Learning Styles: Before-and-After Study

Ivette Cruzado, University of Puerto Rico, Mayaguez

Edgardo M. Román, University of Puerto Rico, Mayaguez

Students learn in different ways and any mismatch between the learning preference of a student and the teaching style of the instructor could lead to a poor performance in the classroom. In order to improve the education process, instructors may modify their courses, such as requiring a group project or changing the teaching method. These changes can be considered as an advantage for some students and a disadvantage to others if these modifications are not compatible with their learning styles. Among the literature on learning styles, two models have been commonly identified to describe how students gather and process information during the learning process: Experiential Learning Theory (ELT) and the Visual-Auditory-Kinesthetic (VAK) framework. A study was done at the University of Puerto Rico at Mayagüez (UPRM), which involved students enrolled in the Introduction to Transportation Engineering course. The study took place during two academic semesters and a before-and-after study was performed in which the instructor use the Inverted Classroom teaching method during the second study period. The purpose of the study was to determine if this teaching method is appropriate for all students regardless of their learning preferences. The results of the study indicate that learning preferences are not influential on students’ grades, but rather influential on other measures of students’ attitude towards the classroom, such as attendance record. In addition, the Inverted Classroom concept was neither beneficial nor disadvantageous to the students, as the average grade remained the same across both study periods.

8:30AM - 10:15AM

Travel Mode Identification with Smartphones

Xing Su, The City College of the City University of New York/CUNY

Hernan Andres Caceres Venegas, State University of New York, Buffalo

Hanghang Tong, Arizona State University

Qing He, State University of New York, Buffalo

Personal trips in modern urban society usually involve multiple travel modes. To recognize a user’s transportation mode is not only critical to the applications in personal context-awareness, but also contributes to the urban traffic operations, transportation planning and city design. While most of current practice often leverages infrastructure based fixed sensors or GPS for traffic mode recognition, the emergence of smartphone provides an alternative appealing way with its ever-growing computing, networking and sensing powers. In this paper we propose a GPS and network- free method to detect user’s travel mode using mobile phone sensors. Our application is built on the latest Android smartphone with multimodality sensors. By applying a hierarchical classification method, we achieve 100% accuracy in a binary classification wheelers/non-wheelers travel mode, and an average of 96.4% accuracy in a 10-fold cross validation with all six travel modes (buses, subways, cars, bicycling, walking, and jogging).

Effects of Jobs-Residence Balance on Commuting Patterns: Differences in Employment Sectors and Urban Forms

Dapeng Zhang, Rensselaer Polytechnic Institute

Xiaokun (Cara) Wang, Rensselaer Polytechnic Institute

Jobs-residence balance is believed as an important way to reduce commuting distance and related externalities. Although literature of jobs-residence balance is rich, relatively fewer studies investigated it in terms of employment industry sectors. However, it is necessary to consider the unique features of different industry sectors such as workers’ ability and working hours. Besides, the commuting distance variability is also an important factor often neglected in previous study. Low variability implies a high potential for car sharing, and for utilization of transportation services. Thus, this paper fills the voids of the existing literature by investigating three employment industry sectors in terms of both average and standard deviation of commuting distances. A spatial error seemingly unrelated regression model is applied to examine the relationship between jobs-residence balance and commuting patterns. The investigation also considers the effects of urban forms, which have been found influential in commuting patterns. Two groups of cities, compact and sprawled, are used as the cases of different urban forms. Results show that jobs-residence balance is more sensitive to the good producing sector and compact regions. Meanwhile, an efficient commuting pattern is associated with a high jobs-residence ratio, clustered industry distribution, and dense road networks with few intersections.

Investigating Temporal Effects on Truck Accident Occurrences in Manhattan

Robyn Marquis, Rensselaer Polytechnic Institute

Xiaokun (Cara) Wang, Rensselaer Polytechnic Institute

This research analyzes the truck-related crash occurrences in Manhattan, New York over four time blocks: the morning peak (AM, 6:00-10:00 AM), the mid-day (MD, 10:00 AM-3:00 PM), the afternoon peak (PM, 3:00-7:00 PM), and the night time (NT, 7:00 PM-6:00 AM). Using zero-inflated negative binomial models, the study finds that both the built environment and the traffic flows contribute to the temporal variation of truck-related crash occurrence. More specifically, it is found that tracts with larger populations and higher employment in finance, insurance, and the health care sector tend to have fewer crashes at night. Larger household sizes and the industry sectors of retail, professional services, education, and accommodation are all associated with increases in crashes during the night time. Additionally, it was found that if 1,000 trucks are shifted from the AM to the NT, the average tract would experience a net increase of 0.2160 truck crashes. The number is 0.1948 if the trucks are shifted from the PM to the NT. Shifting from the MD to the NT reduces the count by 0.0267, suggesting that this may be the best strategy in terms of the safety benefit. When accounting for possible induced demand of non-trucks, even the largest impact on safety (during the PM) only increased crashes by 3.56%. The major contribution of this analysis is to fill the void of studies which focus on the temporal effects that influence truck crash occurrences in congestion urban settings.

Truck Accident Severity in New York City: Investigation of Spatiotemporal Effects and Vehicle Weight

Wei Zou, Rensselaer Polytechnic Institute

Xiaokun (Cara) Wang, Rensselaer Polytechnic Institute

Dapeng Zhang, Rensselaer Polytechnic Institute

This paper uses a flexible econometric structure for truck injury severity analysis in New York City, accounting for both spatial dependency, time of day effect, and the heterogeneous effect of truck weight. The results show that heterogeneity does exist in the truck weight, but crash severity for individual crashes are spatially isolated events. The time of day effect, after other environmental factors are controlled, is also insignificant.

 

2:45PM - 4:30PM

Indifference Bands for Route Switching

Xuan Di, University of Minnesota

Henry X. Liu, University of Michigan

Shanjiang Zhu, George Mason University

David M. Levinson, University of Minnesota

The replacement I-35W bridge in Minneapolis saw less traffic than the original bridge though it provided substantial travel time saving for many travelers. This observation cannot be explained by the classical route choice assumption that travelers always take the shortest path. Accordingly, a boundedly rational route switching model is proposed assuming that travelers will not switch to the new bridge unless travel time saving goes beyond a threshold or “indifference band”. To validate the boundedly rational route switching assumption, route choices of 78 subjects from a GPS travel behavior study were analyzed before and after the addition of the new I-35W bridge. Indifference bands are estimated for both commuters who were previously bridge users and those who never had the experience of using the old bridge. This study offers the first empirical estimation of bounded rationality parameters from GPS data and provides guidelines for traffic assignment.

Continuous-time Instantaneous Dynamic User Equilibria on A Real World Traffic Network (15-1652)

Rui Ma, University of California, Davis

Xuegang (Jeff) Ban, Rensselaer Polytechnic Institute

No abstract available

Modeling Mixed Equilibria in Transportation Networks with Link Constraints (15-5300)

Xia Yang, Rensselaer Polytechnic Institute

Rui Ma, University of California, Davis

Xuegang (Jeff) Ban, Rensselaer Polytechnic Institute

No abstract available

Pictures

TransINFO / UB TRB Reception
TransINFO / UB TRB Reception