Alexander Y. Bigazzi

Impact of Bicycle Lane Characteristics on Exposure of Bicyclists to Traffic-Related Particulate Matter

Bicycling as a mode of transportation is increasingly seen as a healthy alternative to motorized transportation modes. However, in congested urban areas, the health benefits of bicycling can be diminished by the negative health effects associated with inhalation of particulate matter. Particles of small size (ultrafine particles <0.1 μm) are the most harmful, even during short-duration exposure. Because vehicular exhaust is the major source of ultrafine particles, the impact of traffic levels and bicycle lane characteristics on exposure of bicyclists was studied. Ultrafine particle exposure concentrations were compared in two settings: (a) a traditional bicycle lane adjacent to the vehicular traffic lanes and (b) a cycle track design with a parking lane separating bicyclists from vehicular traffic lanes. Traffic measurements were made alongside air quality measurements. The cycle track design mitigated ultrafine particle exposure concentrations for cyclists. Results showed statistically significant differences in terms of exposure levels for the two bike facilities, as well as correlations between traffic levels and exposure level differences. Results also suggested that ultrafine particle levels and spatial distribution were sensitive to proximity to signalized intersections. Findings of this research indicated that, in high traffic areas, bicycle facility design had the potential to lower air pollution exposure levels of bicyclists.

Adding Green Performance Metrics to a Transportation Data Archive

Transportation sustainability is of increasing concern to professionals and the public. This paper describes the modeling and calculation of sustainability performance measures in a transportation data archive. The purpose of these measures is to assess the sustainability of the Portland, Oregon, metropolitan freeway system. The measures were developed to be part of, and to use the data from, the Portland Oregon Regional Transportation Archive Listing (PORTAL) at Portland State University. These performance measures estimate vehicle emissions (carbon monoxide, volatile organic compounds, nitrogen oxides, and carbon dioxide), fuel consumption, cost of time delay, and person mobility (travel in person hours and person miles and delay in person hours). Methods for modeling and necessary data are described. Future plans call for integrating these measures into the PORTAL web interface to expand the types of performance measures used for regional transportation planning and operations.

Modeling the effects of congestion on fuel economy for advanced power train vehicles

Fuel-speed curves (FSC) are used to account for the aggregate effects of congestion on fuel consumption in transportation scenario analysis. This paper presents plausible FSC for conventional internal combustion engine (ICE) vehicles and for advanced vehicles such as hybrid electric vehicles, fully electric vehicles (EVs), and fuel cell vehicles (FCVs) using a fuel consumption model with transient driving schedules and a set of 145 hypothetical vehicles. The FSC shapes show that advanced power train vehicles are expected to maintain fuel economy (FE) in congestion better than ICE vehicles, and FE can even improve for EV and FCV in freeway congestion. In order to implement these FSC for long-range scenario modeling, a bounded approach is presented which uses a single congestion sensitivity parameter. The results in this paper will assist analysis of the roles that vehicle technology and congestion mitigation can play in reducing fuel consumption and greenhouse gas emissions from motor vehicles.

Freeway Sensor Spacing and Probe Vehicle Penetration: Impacts on Travel Time Prediction and Estimation Accuracy

Accurate travel time prediction–estimation is important for advanced traveler information systems and advanced traffic management systems. Traffic managers and operators are interested in estimating optimal sensor density for new construction and retrofits. In addition, with the development of vehicle-tracking technologies, they may be interested in estimating optimal probe vehicle percentage. Unlike most studies focusing on data-driven models, this paper extends some limited previous work and describes a concept developed from first principles of traffic flow. The goal is to establish analytical relationships between travel time prediction–estimation accuracy and sensor spacing by means of two basic travel time prediction–estimation algorithms, as well as to probe vehicle penetration rate. The methods are based on computing the magnitude of under- and overprediction–estimation of total travel time (TTT) during shock passages in a time–space plane by using the midpoint method for online travel time prediction and the Coifman method for offline travel time estimation. Three shock wave configurations are assessed with each method so as to consider representative traffic dynamics situations. TTT prediction–estimation errors are calculated and expressed as a function of sensor spacing and probe vehicle percentage. Optimal sensor spacing is calculated with consideration of the tradeoff between TTT estimation error and sensor deployment cost. The results from this study can provide simple and effective support for detector placement and probe vehicle deployment, especially along a freeway corridor with existing detectors. Optimal sensor spacing results are analyzed and compared for various methods of travel time estimation during different types of shock waves.

Traffic data for local emissions monitoring at a signalized intersection

In order to assist planning efforts for air pollution-responsive dynamic traffic management (DTM) systems, this research assesses the accuracy of local emissions monitoring based on traffic data and models. The study quantifies the benefits of increased data resolution for short-term emissions estimates at a signalized intersection. The emissions estimates are also compared with air quality measurements in the immediate roadside environment. Results show that traffic-based emissions estimates require detailed knowledge of the local vehicle fleet and speed profiles. Traffic-based emissions monitoring enables pollution-responsive DTM, but these results indicate that this approach only applies over long time periods. This limit is due to the inherent stochasticity of vehicle arrivals and emissions rates. Using current tools, even detailed knowledge of on-road vehicles and traffic leaves uncertainty in short-term roadway emissions.

Traffic Congestion and Air Pollution Exposure for Motorists: Comparing Exposure Duration and Intensity

This article investigates the effects of congested freeway traffic conditions on motorists’ exposure to traffic-related air pollution using real-world traffic data and a framework of established emissions and dispersion models. The intent is to isolate and compare the influences of congested traffic characteristics on exposure duration and exposure intensity. Mass inhalation of carbon monoxide (CO) and nitrogen oxides (NOx) is estimated for 45,226 simulated trips through a 14-mile congested freeway corridor. Results show that congestion increases total trip pollutant inhalation primarily through motorist delay (exposure duration), and to a lesser extent through increased concentrations (exposure intensity). The effects of varying wind and background concentration can be sufficient to obscure the influence of congestion on exposure intensity. The variability of exposure intensity due to traffic is mitigated by offsetting impacts among traffic flow, emissions rates, and pollutant dispersion. Exposure intensity increases with higher traffic flow and with lower traffic speed, but the impact of lower traffic speeds (through increased emissions rates and decreased dispersion) is smaller. The importance of exposure “hot spots” at traffic bottlenecks also increases in congestion. These findings suggest that traffic-based motorist exposure mitigation should focus on reducing travel duration on high-volume corridors through reduced vehicle flows (i.e., demand-side congestion mitigation). This analysis does not include non–congestion-based mitigation strategies such as cleaner vehicle engine technology or improvements in vehicle cabins, which can also reduce exposure intensity. On an individual scale, motorists can greatly reduce their own exposure during travel by, among other strategies, adjusting their departure time to less congested, lower volume periods.

Breath Biomarkers to Measure Uptake of Volatile Organic Compounds by Bicyclists

Breath biomarkers were used to study uptake of traffic-related volatile organic compounds (VOCs) from urban bicycling. Breath analysis was selected because it is one of the least invasive methods to assess urban traveler exposure. Research hurdles that were overcome included considering that factors other than on-road exposure can influence concentrations in the body, and absorbed doses during a trip can be small compared to baseline body burdens. Pre-trip, on-road, and post-trip breath concentrations and ambient air concentrations were determined for 26 VOCs for bicyclists traveling on different path types. Statistical analyses of the concentration data identified eight monoaromatic hydrocarbons potentially useful as breath biomarkers to compare differences in body levels brought about by urban travel choices. Breath concentrations of the biomarker compounds were significantly higher than background levels after riding on high-traffic arterial streets and on a path through a high-exposure industrial area, but not after riding on low-traffic local streets or on other off-street paths. Modeled effects of high-traffic streets on ambient concentrations were 100-200% larger than those of low-traffic streets; modeled effects of high-traffic streets on breath concentrations were 40-100% larger than those of low-traffic streets. Similar percentage increases in breath concentrations are expected for bicyclists in other cities.

Effects of Temporal Data Aggregation on Performance Measures and Other Intelligent Transportation Systems Applications

Intelligent transportation systems (ITSs) data are a valuable resource for traffic operations, transportation systems management, performance measurement, and transportation research. Historically, these data are time-aggregated for collection, transmission, and storage, with only mean values saved for traffic parameters for each arbitrary time interval. This convention of aggregation discards valuable information that is necessary for some applications. To understand whether systems should continue the practice of aggregation, this paper investigates how temporal aggregation can affect performance measures and other data applications. The investigation uses disaggregate speed data from loop detectors on a London freeway and vehicle trajectories from video imaging on a California freeway. Aggregating measured speed data greatly reduces the spread in reported vehicle speeds, which will distort estimates of emissions, fuel consumption, and travel delay. Using aggregate data for travel time estimates from sampled speeds results in errors attributable to the constant-speed assumption, group-averaged travel times, and using the arithmetic mean speed (as opposed to the harmonic mean speed) to estimate average travel time. Arithmetic mean speeds consistently underestimate aggregate delay, although estimating a harmonic mean speed from the arithmetic mean speed and speed variance can partially mitigate this effect. Temporal aggregation also affects the identification of traffic state transitions times, the estimation of shockwave speed and shockwave travel times, and the construction of fundamental diagrams. The results of this research will help increase understanding of the ability of ITS data to describe transportation systems, and improve forthcoming sustainability performance measures in the Portland Oregon Regional Transportation Archive Listing data archive at Portland State University.

Determination of active travel speed for minimum air pollution inhalation

ABSTRACT A higher active travel speed has offsetting impacts on air pollution inhalation dose through higher breathing rate but shorter exposure duration. The net effect of speed choice on inhalation dose for pedestrians and bicyclists has not been established. This paper derives equations for pedestrian and bicycle steady-state minimum-dose speed (MDS). Parameter distributions from the literature are applied to a synthetic population of travelers to calculate individual MDS. Results strongly support the existence of a definable MDS, which is near observed travel speeds for urban pedestrians and bicyclists. For a wide range of travelers, the MDS is 2–6 km/h while walking and 12–20 km/h while bicycling, decreasing with road grade at a rate similar to observed speeds. On level ground, pedestrian and bicycle MDS corresponds to a moderate-intensity physical activity level (3–6 MET). Small deviations from the MDS have little effect, but large deviations (by more than 10 km/h for bicycling) can more than double inhalation dose over a fixed distance. It appears that pedestrians and bicyclists choose travel speeds that approximately minimize pollution inhalation dose, although pollution is unlikely a primary motivation.

Role of Heavy-Duty Freight Vehicles in Reducing Emissions on Congested Freeways with Elastic Travel Demand Functions

This paper investigates the effect of heavy-duty (HD) vehicles (primarily road freight) on the traffic congestion–emissions relationship. Unlike previous studies, this research explicitly considers the effects of travel demand elasticity by vehicle class on total emissions. Modeling results show that, even as a small share of the traffic volume, HD vehicles can contribute a large share of total pollution emissions, especially for particulate matter and nitrogen oxides. HD vehicle emission rates are more sensitive to congestion than are light-duty (LD) vehicle emission rates, and thus greater emissions benefits may result from mitigating congestion for these vehicles. Potentially lower travel demand elasticity with respect to speed for HD vehicles further indicates vehicle class–specific benefits from congestion mitigation. Differences between LD and HD vehicles suggest greater air quality benefits from vehicle class–targeted congestion mitigation or lane and capacity management strategies. HD vehicle travel demand elasticity is a key parameter for predicting the net emissions effects of congestion. It is strongly recommended that analysis of emissions effects from congestion mitigation strategies include class-specific volume forecasts. However, the estimation of HD vehicle travel demand elasticity values has received scant attention in the literature.