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Adam Moore: Bus Stop Air Quality: An Empirical Analysis of Exposure to Particulate Matter at Bus Stop Shelters

Congested traffic corridors in dense urban areas are key contributors to the degradation of urban air quality. While waiting at bus stops, transit patrons may be exposed to greater amounts of vehicle-based pollution, including particulate matter, due to their proximity to the roadway. Current guidelines for the location and design of bus stops do not take into account air quality or exposure considerations. This study compares the exposure of transit riders waiting at three-sided bus stop shelters that either: 1) face the roadway traffic or 2) face away from the roadway traffic. Shelters were instrumented with air quality monitoring equipment, sonic anemometers, and vehicle counters. Data were collected for two days at three shelters during both the morning and afternoon peak periods. Bus shelter orientation is found to significantly affect concentration of four sizes of particulate matter: ultrafine particles, PM1, PM2.5, and PM10. Shelters with an opening oriented towards the roadway were consistently observed to have higher concentrations inside the shelter than outside the shelter. In contrast, shelters oriented away from the roadway were observed to have lower concentrations inside the shelter than outside the shelter. The differences in particulate matter concentration were statistically significant across all four sizes of particulate matter studied. Traffic flow was shown to have a significant relationship with all sizes of particulate concentration levels inside bus shelters. Micro-scale anemometer measurements were made next to bus shelters. Both wind speed and direction were shown to affect particulate concentrations differently depending on shelter orientation.

Alex Bigazzi: Do Mobility-Based Performance Measures Reflect Emissions Trends?

Given the commonly assumed association between traffic congestion and emissions, this paper addresses the question of whether mobility-based performance measures are associated with emissions performance measures. We address two facets of the roadway congestion-emissions relationship by investigating: (a) whether congestion performance measures are good indicators of trends in roadway emissions and (b) what transportation performance measures are better suited to portray macroscopic trends in emissions. In order to answer these research questions we estimate macroscopic transportation and emissions performance measures at metropolitan and corridor levels. Comparing several measures, we calculate the correlation between transportation performance measures and emissions. We also present an analytical framework to understand emissions trends as a function of mobility and travel demand variables. Results show that Vehicle Miles Traveled (VMT) and Vehicle Hours Traveled (VHT) are key factors to understanding emissions trends. Mobility measures (such as travel speed and delay) and related congestion measures (such as percent of travel in congestion) are only weakly correlated with emissions.

Carl Olson: A Framework for Multimodal Arterial Data Archiving

Multimodal data are necessary if agencies seek to implement the policies described in transportation planning documents. The Portland, Oregon metropolitan region has set targets for encouraging pedestrian and bicycle travel and for making the existing roadway infrastructure operate as effectively and efficiently as possible. This paper presents the framework that has been established to support a multimodal transportation data archive in Portland, Oregon. The current archived data user service – PORTAL (Portland Regional Transportation Archive Listing) —presently houses primarily freeway based vehicle data. In seeking to expand the coverage, arterial signal system data, transit service, bicycle counts, pedestrian actuations, and matched media-access control data are being added to PORTAL.  Whenever possible, existing schema and code have been leveraged from the freeway archive development. This paper serves to describe the framework we have implemented. A short description of the database schema and a brief data dictionary is presented. To illustrate both the scale and usefulness of the data, sample visualizations are presented with narratives for context.

Kristi Currans: Context-Based Approach for Adjusting Institute of Transportation Engineers Trip Generation Rates

The purpose of this research is to develop a methodology for utilizing household travel surveys (HTS) as a means for producing a regional-scale policy model for predicting context-based vehicle-trip reductions applied to Institute of Transportation Engineers (ITE) Trip Generation (TG) at a site-level development. This methodology may be used as a supplement to ITE TG rates, providing justification to vehicle-trip reductions based on known contextual vehicle mode splits. Using the Puget Sound Regional Council HTS of 2006, non-home-based trip ends are selected and common built environment (BE) measure are extracted. A clustering analysis is applied to all trip ends, determining clustered groups, or contexts. Using contexts as model variables, in addition to socio-demographic (SDC) and trip characteristics (TC), a binary logistic model is developed to predict the mode split. Mode splits are then calculated for all context types using average BE, SDC and TC variables. External establishment survey rates and mode splits, published from reports located in California, are then used as a validity check to verify the predictor power of the model based on the type of establishment’s context. For each establishment survey, the same BE measures are extracted, and the location is classified into contexts using a linear discriminate analysis. In general, mode splits predicted across contexts show variation expected for areas with greater residential or employment density, land use mix and connectivity. Establishment data shows predicted values fall within observed range or fall on the conservative side of estimation. Future applications of this research are discussed.