Yanzhi Xu

Safety impacts of bicycle infrastructure: A critical review

PROBLEM AND METHOD This paper takes a critical look at the present state of bicycle infrastructure treatment safety research, highlighting data needs. Safety literature relating to 22 bicycle treatments is examined, including findings, study methodologies, and data sources used in the studies. Some preliminary conclusions related to research efficacy are drawn from the available data and findings in the research. RESULTS AND DISCUSSION While the current body of bicycle safety literature points toward some defensible conclusions regarding the safety and effectiveness of certain bicycle treatments, such as bike lanes and removal of on-street parking, the vast majority treatments are still in need of rigorous research. Fundamental questions arise regarding appropriate exposure measures, crash measures, and crash data sources. PRACTICAL APPLICATIONS This research will aid transportation departments with regard to decisions about bicycle infrastructure and guide future research efforts toward understanding safety impacts of bicycle infrastructure.

Idle Monitoring, Real-Time Intervention, and Emission Reductions from Cobb County, Georgia, School Buses

Georgia Institute of Technology researchers developed an idle detection and warning notification system that features Global Positioning System–based real-time tracking and a web-based user interface. Four hundred and eighty buses in the Cobb County (Georgia) School District were equipped with the idle detection system, and the research team provided bus dispatchers with a web-based system to track vehicle activity and provide notification of idle events exceeding 5 min. The idle detection and warning notification system can differentiate idling with engine on from key-on events with engine off, an important capability that sets it apart from previous systems that only detected key-on events. Idle reductions were monitored, and emissions and fuel savings were evaluated with the Environmental Protection Agencys MOVES (Motor Vehicle Emission Simulator) model. The idle reduction that resulted from implementing the system was statistically significant—more than 6 min of idle reduction per bus per day. Greater idle reduction could be achieved with more stringent implementation of the system. The anti-idle program reduced total annual emissions of criteria pollutants (oxides of nitrogen, particulate matter, and carbon monoxide) by 1.82 tons and annual emissions of carbon dioxide by 53.3 tons. Implementation throughout the school district would conserve 6,400 gal of diesel fuel. Approximately 41,100 children riding the buses or attending schools served by the buses were positively affected by the idle reduction system.

Estimating Project-Level Vehicle Emissions with Vissim and MOVES-Matrix

Estimating transportation network emissions requires multiplying estimates of on-road vehicle activity (by source type and operating mode) by applicable emission rates for the observed source type and operating conditions. Coupling microsimulation model runs with emissions modeling can make fast assessments possible in transportation air quality planning. This research developed a tool with automated linkage between the Vissim microsimulation model and the Motor Vehicle Emission Simulator (MOVES) model. To link the two models, the research team used MOVES-Matrix, which was prepared by iteratively running MOVES across all possible iterations of vehicle source type, fuel, environmental and operating conditions, and other parameters (hundreds of millions of model runs) to create a multidimensional emission rate lookup matrix. A Vissim simulation of the major arterial roads and freeways at I-85 and Jimmy Carter Boulevard in Gwinnett County, Georgia, provided the case study for this MOVES-matrix application. The researchers present predicted emissions and the results of a sensitivity analysis to identify the potential impacts of various internal Vissim modeling parameters (such as minimum headway, maximum deceleration rate for cooperative braking, and emergency stop distance) on a case study’s emissions outputs. The sensitivity analysis found that internal Vissim parameters impacted emissions and that proper care should be taken in using Vissim for emissions analysis at the corridor and link level. The case study demonstrates that Vissim coupled with MOVES-Matrix can be an effective tool for emissions analysis.

Developing Vehicle Classification Inputs for Project-Level MOVES Analysis

The motor vehicle emission simulator (MOVES) model is the primary regulatory model for estimating automobile emissions in the United States. The model requires refined input data; otherwise, internal model assumptions that are not necessarily representative of the project being modeled can dominate the outputs. For example, project-level on-road fleet composition is highly dependent on local vehicle use; hence, MOVES default inputs and regional distributions are not likely to apply (and MOVES estimates for project-level analyses are especially sensitive to vehicle source type distribution). Unfortunately, developing project-level source type distributions can be challenging for model users. This research proposes a procedure for developing MOVES vehicle source type distribution inputs that uses the FHWA vehicle classification scheme, Environmental Protection Agency certification data, state registration data, along with on-road license plate and video data. A case study of I-85 near Atlanta, Georgia, is presented to illustrate the importance of distinguishing within light-duty vehicle classes for hydrocarbon and carbon monoxide estimations, and between the single-unit heavy-duty truck (HDT) and combination HDT classes for nitrogen oxide and particulate matter estimation. The analysis suggests that the most important work is to generate on-road distributions of HDTs with respect to single-unit and combination trucks rather than to use regional defaults. The case study results show the need for locally derived vehicle class inputs for MOVES for project-level analysis and calls for an alternative MOVES vehicle class input option that uses regulatory class distributions because the default vehicle class distribution embedded in MOVES may sometimes be unrealistic.

Demonstrating a Bottom-Up Framework for Evaluating Energy and Emissions Performance of Electric Rail Transit Options

Current frameworks for analyzing emissions performance of public transportation systems use top-down approaches that can often provide useful information at the network level but can be uninformative at the project level at which the influence of route and vehicle characteristics can significantly impact emission profiles of candidate transit options. This paper describes an alternative bottom-up framework that uses second-by-second travel activity data to estimate total power consumption and related emissions for propulsion purposes with application to electric rail transit systems. The model was developed and calibrated with data from Portland, Oregon, and was supplemented with activity data from Chicago, Illinois. The results showed a predicted 1% to 8% difference in expected power consumption relative to estimates derived from the national transit database. In addition, the results highlighted how the speed profile, configuration of the train in number of cars, and mix of power generation sources could significantly vary emissions performance across different service routes. The developed framework can serve as an important tool for a transit planner or policy maker to evaluate the emissions performance of electric rail transit options. This framework has the advantage of relevance at both the network and project levels. At the project level, users can easily perform detailed sensitivity analysis on aspects of transit services such as vehicle and fuel technologies, passenger loading profiles, train size, and track profile. This framework gives transportation planners a flexible and efficient tool for emissions performance analysis.

Load-Based Life Cycle Greenhouse Gas Emissions Calculator for Transit Buses: An Atlanta, GA, Case Study

Using the Public Transit Greenhouse Gas (GHG) Emissions Management Calculator (hereafter the Calculator), this paper presents a case study of transit bus GHG emissions using Atlanta, Georgia, data. The Calculator, developed by Georgia Tech researchers, is the first load-based life cycle emissions model for transit buses. The modal modeling approach of the Calculator estimates emissions as an indirect function of engine load, which in turn is a function of transit service parameters such as driving cycle (idling and speed-acceleration profile), road grade, and passenger loading. Direct emissions are calculated based on the scaled tractive power (STP) operating mode bins employed in the Motor Vehicle Emissions Simulator (MOVES) model, and life cycle emissions are calculated using the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The case study compares life cycle greenhouse gas emissions of five vehicle technologies, conventional compression ignition, parallel hybrid electric, series hybrid electric, battery electric, and fuel-cell electric, in combination with three fuel types, conventional diesel, compressed natural gas (CNG), and 20% biodiesel. The comparisons are carried out for two public transit route types, e.g. an urban transit route vs. an express bus route. The Atlanta case study showcases the practice-ready capabilities of the GHG emissions calculator in assessing the differences in technology and fuel performances under different operating conditions. The results illustrate that the decision as to which bus technology-fuel combination produces the least greenhouse gas emissions is a function of location and route characteristics. The Calculator will support transit agencies in evaluating bus technologies for GHG emissions within the context of local conditions.

Use of Web-Based Rider Feedback to Improve Public Transit Services

This report provides a practical and easy-to-use toolkit of best practices, emerging platforms, and promising approaches for customer web-based and electronic feedback to help improve public transit services. The report is separated into two parts: Part I identifies best practices among transit agencies and other industries using in-house or third-party web-based and mobile platforms to engage customers and provides guidance on managing web-based feedback; and Part II includes a Tool Selection Guide that helps transit agencies select the most appropriate web-based feedback tool based on their needs. The results of this research may be used by a variety of transportation professionals, including policymakers, operations and maintenance managers, customer service managers, marketers, and safety and security personnel to assist with implementing structured feedback systems and utilizing the feedback both internally and externally with customers.

Longitudinal Global Positioning System Travel Data and Breach of Privacy via Enhanced Spatial and Demographic Analysis

Longitudinal Global Positioning System (GPS) travel data provide a wealth of information related to travel behavior and on-road vehicle behavior that is very valuable to researchers. Sharing the data publicly allows researchers to explore the data and create new knowledge beyond the initial research objectives. However, if any data are to be used outside a secure server, the data must be processed in such a manner that ensures that the confidentiality of the data will not be breached. High-resolution GPS data (e.g., second-by-second speed and location information), when associated with the individual households or drivers, compromise privacy and have a significant potential to harm human subjects. This paper explores how data from the Commute Atlanta study in Georgia could be processed to make it useful to researchers while participants’ privacy is protected. The research developed and assessed methodologies designed to identify the individual participants home location from processed data and then tested analytical data sets for breach of privacy. The research effort found that the home location could be identified to within reasonably small neighborhoods; when the household demographic information was included in the data sets (which was necessary for researchers), exact households could be identified. Although some new data-processing approaches might be used to eliminate privacy concerns, until such systems are developed and proved to be unbreachable through rigorous analysis, the Georgia Institute of Technology team has determined that researchers should access the high-resolution data in controlled secure labs and that the data sets should not be made public without additional efforts to ensure that home locations cannot be identified when external data sources are leveraged in the analyses.

Comparison of Fuel-Cycle Emissions per Passenger Mile from Multiple Bus and Rail Technologies

This paper examines the fuel-cycle passenger-mile emissions from multiple bus and rail technologies using a travel-activity-based bottom-up approach. There is abundant literature on transits emissions savings, but most prior studies rely on the top-down fuel consumption approach. While the top-down approach paints a broad picture of transit emissions on a national or regional level, it cannot reflect changes in emissions as a function of operational characteristics such as passenger loading. It is also difficult to estimate passenger-mile emissions of new vehicle/fuel technologies for which fuel economy data are hard to obtain using the top-down approach. This paper develops a unified load-based methodology framework that compares fuel-cycle passenger-mile emissions across transit technologies, including alternative fuels and advanced technologies. The methodology reflects the intricate trade-offs between the increased total emissions and decreased passenger-mile emissions as passenger load increases under specific local meteorological and route settings. Using the load-based methodology, the paper demonstrates the varying levels of emissions savings of transit fuels and technologies giving different passenger loadings scenarios. The methodology presented in this paper serves as the foundation of a transit emissions calculator currently under development. The calculator will prove instrumental for local transit agencies to compare fuel/technology alternatives in terms of emissions savings.

Energy Consumption and Emissions Modeling of Individual Vehicles

This study demonstrated an approach to modeling individual vehicle second-by-second fuel consumption and emissions on the basis of vehicle operations. The approach used the Motor Vehicle Emission Simulator (MOVES)–Matrix, a high-performance vehicle emissions modeling system consisting of a multidimensional array of vehicle emissions rates (pulled directly from EPA’s MOVES emissions model) that could be quickly queried by other models to generate an applicable emissions rate for any specified on-road fleet and operating conditions. For this project, the research team developed a spreadsheet-based MOVES-Matrix calculator to simplify connecting vehicle activity data with multidimensional emissions rates from MOVES-Matrix. This paper provides a walk-through of the calculation procedures, from basic vehicle information and driving cycles to second-by-second emissions rates. The individual vehicle emissions modeling framework was incorporated into Commute Warrior, a trademarked travel survey application for Android smartphones, to provide real-time fuel consumption and emissions rate estimates from concurrently obtained GPS-based speed data.