Transportation Research Board 2015

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 This is the first time that a U.S. city has completely repealed minimum parking requirements.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.

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.

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.

8:30AM - 10:15AM

Multimodal Traffic Control: Poster Session | Practice Ready Papers

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.

10:45AM - 12:30PM

Modeling Intrahousehold Interactions for Use of Battery Electric Vehicles

Jee Eun Kang, State University of New York, Buffalo

This study assesses the potential use of Battery Electric Vehicles (BEVs) in place of conventional Internal Combustion Engine Vehicles (ICEVs) at household level. The assumption is that BEVs would be adopted as a secondary household vehicle and household members would operate the vehicle through intra-household interactions to overcome the limitations of BEVs such as limited range and long charging times and to utilize its cheaper operating cost. Two scenarios are applied on sample households from California Statewide Travel Survey since there is no data about how travelers would adapt their travel behavior by operating BEVs. In the first scenario, the reported pattern sequences are held but the BEV can be charged for obtaining sufficient range covering capability. In the second scenario, activity participation sequences, intra-household interactions of vehicle and activity allocations among household member are allowed to be changed on account of decreasing household travel disutility. These changes in their travel behavior are simulated by Household Activity Pattern Problem – Charging (HAPPC) model which is a variant of the well-known Household Activity Pattern Problem (HAPP). A sequential activity allocation and insertion heuristic is developed for implementing on HAPPC. The results show that if BEVs would be used at household level the travel disutility of households can be decreased about $30 per day in average.

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.

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).

Constructing Full-Day Feasible Space-Time Polyhedron Using Activity-Based Space-Time Prisms

Jee Eun Kang, State University of New York, Buffalo

In this paper, space-time prism from time geography is applied to the decision making scheme of the Household Activity Pattern Problem (HAPP), a full-day activity-based travel demand model. An activity-based space-time prism with no prior decisions of paths (Origin-Destination) is defined as supposed to the classical path-based space-time prism with known Origin-Destination. Then the new concept of full-day feasible space-time polyhedron is defined as an intersection of a set of activity-based space-time prisms. This approach is a generalization of the classical space-time prism since the full-day feasible polyhedron often decomposes into the classical space-time prism by peg activities, activities that delimit space and time by participating in those activities. The concept of activity-based space-time prism and the full-day feasible space-time polyhedron also expands the horizon of classical space-time prism in that it can be used to define accessibility for travelers with no peg activities. A computational algorithm is developed for the construction of activity-based space-time prism and feasible polyhedron for network-based travel environment. The algorithm generates a set of time windows for each activity for each node. For a full-day activity agenda, intersections of these windows are derived. Then these node-based time windows are converted to link-based time windows of accessibility. This algorithm works for a traveler that contains peg(s) of activities as well as a traveler with no peg activity for the travel day.