Temporal disaggregation of travel demand for high resolution emissions inventories

The commonly used photochemical air quality model, the Urban Airshed Model (UAM), requires emission estimates with grid-based, hourly resolution. In contrast, travel demand models, used to simulate the travel activity model inputs for the transportation-related emissions estimation, typically only provide traffic volumes for a specific travel period (e.g. the a.m. and p.m. peak periods). A few transportation agencies have developed procedures to allocate period-based travel demand data into hourly emission inventories for regional grid cells. Because there was no theoretical framework for disaggregating period-based volumes to hourly volumes, application of these procedures frequently relied upon a single hypothetical hourly distribution of travel volumes. This study presents a new theoretical modeling framework that integrates traffic count data and travel demand model link volume estimates to derive intra-period hourly volume estimates by trip purpose. We propose a new interpretation of the model coefficients and define hourly allocation factors by trip purpose. These allocation factors can be used to disaggregate the travel demand model ‘period-based’ simulation volumes into hourly resolution, thereby improving grid-based, hourly emission estimates in the UAM.     

The impact of speed post-processing methods on regional mobile emissions estimation

Average roadway segment travel speeds play an important role in estimating stabilized running vehicle emissions. Currently stabilized, or hot, running emissions are computed based on speeds produced during the travel demand modeling process. Speed data from the travel forecasting models are widely recognized as being insufficiently accurate for air quality purposes. Frequently post-processing techniques are seen as the most cost-effective means of improving the accuracy of the speed estimates. Using the Sacramento Metropolitan area, this paper focuses on the impacts of different speed post-processors on regional peak period emissions inventories. The results indicated that most post-processed speeds produce consistently and significantly higher running emissions, particularly in locations with heavy traffic. The observed differences in emissions between different types of post-processed speeds vary with congestion level, pollutant type and the underlying approach encapsulated in the speed post-processor calculations. The Sacramento case study suggests that the post-processor used to develop speeds for the purposes of calculating on-road emissions inventories can significantly influence the emissions inventories.     

Preparing highway emissions inventories for urban scale modeling: A case study in Philadelphia

Highway emissions represent a major source of many pollutants. Use of local data to model these emissions can have a large impact on the magnitude and distribution of emissions predicted and can significantly improve the accuracy of local scale air quality modeling assessments. This paper provides a comparison of top–down and bottom–up approaches for developing emission inventories for modeling in one urban area, Philadelphia, in calendar year 1999. A bottom–up approach relies on combining motor vehicle emission factors and vehicle activity data from a travel demand model estimated at the road link level to generate hourly emissions data. This approach can result in better estimates of levels and spatial distribution of on-road motor vehicle emissions than a top–down approach that relies on more aggregated information and default modeling inputs.     

Allocation of onroad mobile emissions to road segments for air toxics modeling in an urban area

Dispersion models are useful tools for setting emission control priorities and developing strategies for reducing air toxics emissions. Previous methodologies for modeling hazardous air pollutant emissions for onroad mobile sources are based on using spatial surrogates to allocate county level emissions to grid cells. A disadvantage of this process is that it spreads onroad emissions throughout a grid cell instead of along actual road locations. High local concentrations may be underestimated near major roadways, which are often clustered in urban centers. Here, we describe a methodology which utilizes a Geographic Information System to allocate benzene emissions to major road segments in an urban area and model the segments as elongated area sources. The Industrial Source Complex Short Term dispersion model is run using both gridded and link-based emissions to evaluate the effect of improved spatial allocation of emissions on ambient modeled benzene concentrations. Allocating onroad mobile emissions to road segments improves the agreement between modeled concentrations when compared with monitor observations, and also results in higher estimated concentrations in the urban center.     

A comparative study on data segregation for mesoscopic energy modeling

On-road vehicles have been considered as one of the major contributors to energy consumption and air pollutant emissions. In order to quantify the corresponding environmental impacts, great efforts have been dedicated to the microscopic and macroscopic modeling for vehicle energy consumption and emissions. However, the mesoscopic modeling research that is focused on estimating trip-based energy consumption and is critical to some ITS applications (e.g., environmentally-friendly navigation), is relatively deficient. This study aims to investigate the effects of different data segregation methods on the mesoscopic modeling for vehicle energy consumption. A variety of novel methods, including the so-called conditional operating mode based method, have been proposed and evaluated using field data. Based on real-world data, statistical analyses have demonstrated the superior performance of enhanced models (i.e., conditional operating mode/VSP based models) in estimating vehicle energy consumption on a trip basis, compared to the other four models (velocity binning, time snipping, distance snipping and VSP based models) tested in this study.     

Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data

Ambient concentrations of pollutants are correlated with emissions, but the contribution to ambient air quality of on-road mobile sources is not necessarily equal to their contribution to regional emissions. This is true for several reasons such as the distribution of other pollution sources and regional topology, as well as meteorology. In this paper, using a dataset from a travel demand model for the Sacramento metropolitan area for 2005, regional vehicle emissions are disaggregated into hourly, gridded emission inventories, and transportation-related concentrations are estimated using an atmospheric dispersion model. Contributions of on-road motor vehicles to urban air pollution are then identified at a regional scale. The contributions to ambient concentrations are slightly higher than emission fractions that transportation accounts for in the region, reflecting that relative to other major pollution sources, mobile sources tend to have a close proximity to air quality monitors in urban areas. The contribution results indicate that the impact of mobile sources on PM₁₀ is not negligible, and mobile sources have a significant influence on both NO_x and VOC pollution that subsequently results in secondary particulate matter and ozone formation.     

Modeling regional mobile source emissions in a geographic information system framework

Suburban sprawl, population growth, and automobile dependency contribute directly to air pollution problems in US metropolitan areas. As metropolitan regions attempt to mitigate these problems, they are faced with the difficult task of balancing the mobility needs of a growing population and economy, while simultaneously lowering or maintaining levels of ambient pollutants. Although ambient air quality can be directly monitored, predicting the amount and fraction of the mobile source components presents special challenges. A modeling framework that can correlate spatial and temporal emission-specific vehicle activities is required for the complex photochemical models used to predict pollutant concentrations. This paper discusses the GIS-based modeling approach called the Mobile Emission Assessment System for Urban and Regional Evaluation (MEASURE). MEASURE provides researchers and planners with a means of assessing motor vehicle emission reduction strategies. Estimates of spatially resolved fleet composition and activity are combined with activity-specific emission rates to predict engine start and running exhaust emissions. Engine start emissions are estimated using aggregate zonal information. Running exhaust emissions are predicted using road segment specific information and aggregate zonal information. The paper discusses the benefits and challenges related to mobile source emissions modeling in a GIS framework and identifies future GIS mobile emissions modeling research needs.     

Evaluating unintended outcomes of regional smart-growth strategies: Environmental justice and public health concerns

Many urban areas are perusing infill, transit oriented, and other “smart-growth” strategies to address a range of important regional goals. Denser and more mixed use urban development may increase sustainability and improve public health by reducing vehicle travel and increasing the share of trips made by transit, walking and bicycling. Fewer vehicle trips results in fewer greenhouse gas and toxic vehicle emissions, and more trips made by walking and bicycle increases physical activity. Prior research has largely focused on modeling and estimating the potential size of these and other smart-growth strategy benefits. A largely overlooked area is the potential for unexpected public health costs and environmental justice concerns that may result from increasing density. We evaluate regional land-use and transportation planning scenarios developed for the year 2040 by a metropolitan planning organization with a newly developed regional air quality modeling framework. Our results find that a set of regional plans designed by the MPO to promote smart-growth that are estimated to result in less vehicle use and fewer vehicle emissions than a more typical set of plans results in higher population exposure to toxic vehicle emissions. The smart-growth plans also result in greater income-exposure inequality, raising environmental justice concerns. We conclude that a more spatially detailed regional scale air quality analysis can inform the creation of smarter smart-growth plans.     

Effect of roadway network aggregation levels on modeling of traffic-induced emission inventories in Beirut

Traffic-induced emissions pose a serious threat to air quality in heavily congested urban centers. While air quality can be characterized through field measurements and continuous monitoring, forecasting future conditions depends largely on estimating vehicle-emission factors coupled with mathematical modeling. Traffic and environmental planners have relied on overall average network speed in conjunction with speed-based emission factor models to estimate traffic emissions. This paper investigates the effect of three levels of roadway network aggregation, macro-scale (overall network basis), meso-scale (roadway functional class basis) and micro-scale (link-by-link basis) on emission inventories. A traffic model and an emission factor model were integrated to determine total emissions in the future Beirut Central District area for these three modeling approaches.     

Challenges and opportunities for transportation: Implementation of the clean air act amendments of 1990 and the intermodal surface transportation efficiency act of 1991

The Federal Clean Air Act Amendments of 1990 (CAAA) may be the most powerful of all environmental laws affecting transportation. They are intended to significantly affect transportation decision-making, not only to achieve air quality goals but also to affect broader environmental goals related to land use, travel mode choice, and reductions in vehicle miles traveled. The CAAA require greater integration of transportation and air quality planning, and assign a greater responsibility to transportation plans and programs for reducing mobile source emissions. By expanding the requirements for determining the conformity of transportation plans, programs, and projects with State Implementation Plans for air quality, and by expanding the use of highway funding sanctions to enforce those requirements, the CAAA ensure a continuing linkage between transportation and environmental goals.While the CAAA give transportation and air quality decision-makers the mandate to better coordinate their respective planning processes, the Intermodal Surface Transportation Efficiency Act of 1991 offers the tools to help carry out that mandate. Consequently, this paper summarizes the transportation and air quality provisions of both of these Acts and their relationships.     

Link travel time inference using entry/exit information of trips on a network

This paper studies link travel time estimation using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation–Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times.     

Improving estimates of transportation emissions: Modeling hourly truck traffic using period-based car volume data

We estimate hourly truck traffic using period-based car volumes that are usually available from travel demand models. Due to the lack of local or regional data, default vehicle-miles traveled mix by vehicle class in mobile emission inventory models is usually used in transportation emissions inventory estimates. Results from such practice, however, are often far from accurate. Heavy-duty trucks generate orders of magnitudes higher emission rates than light duty vehicles. Vehicle classification data collected from weigh-in-motion stations in California are used to examine the performance of various forms of the method across days of week and geographic areas. We find that the models identified provide satisfactory and statistically robust estimates of truck traffic.     

Short trips: An opportunity for reducing mobile-source emissions?

This paper examines how conversion of automobile trips of less than 3 miles to other transportation modes reduces emissions. Short trips contribute disproportionately to emissions because of cold starts. An analysis is conducted of short-trip behavior across the US using the 1995 Nationwide Personal Transportation Survey. The data is used to develop likely scenarios of mode conversions for short trips, which are then applied to estimate emission savings using MOBILE6 cold start and running emission factors for volatile organic compounds, nitrogen oxides, carbon monoxide, and carbon dioxide. The results suggest that reducing short auto trips would modestly reduce mobile source air pollution, but emission reductions are high compared to most federally-funded surface transportation interventions aimed at improving air quality. Enhanced the community pedestrian environment to encourage short trip mode conversion also produces co-benefits such as increased physical activity and subsequent reductions in chronic diseases.     

Quantifying the effects of input aggregation and model randomness on regional transportation emission inventories

Accurate road-traffic emission inventories are of great interest to metropolitan planning agencies especially in the appraisal of regional transport policies. Integrated road transport emission models are an effective means of establishing emission estimates, yet their development requires significant investments in data and resources. It is therefore important to investigate which data inputs are the most critical to inventory accuracy. To address this issue, an integrated transport and emissions model is developed using the Montreal metropolitan region as a case-study. Daily regional hydrocarbon (HC) emissions from private individual travel are estimated, including the excess emissions due to engine starts. The sensitivity of emission estimates is then evaluated by testing various levels of input aggregation common in practice and in previous research. The evaluated inputs include the effect of start emissions, ambient weather conditions, traffic speed, path choice, and vehicle registry information. Inherent randomness within the integrated model through vehicle selection and path allocation is also evaluated. The inclusion of start emissions is observed to have the largest impact on emission inventories, contributing approximately 67 % of total on-road HC emissions. Ambient weather conditions (season) and vehicle registry data (types, model years) are also found to be significant. Model randomness had a minimal effect in comparison with the impact of other variables.     

Retrospect and prospect for subarea travel demand modeling: an empirical study

Abstract

At present, customized subarea models have been widely used in local transportation planning throughout the USA. A subarea model's biggest strengths lie in its more detailed and accurate modeling outputs which better meet local planning requirements. In addition, a subarea model can substantially reduce database size and model running time. In spite of these advantages, subarea models remain quite weak in modeling transit projects, smart growth measures, air quality conformity, and other areas. In addition to evaluating subarea models, this paper uses the Irvine Transportation Analysis Model (ITAM) as an empirical case of subarea model to illustrate the remedial procedures in maintaining its consistency with the regional model of the Orange County Transportation Analysis Model (OCTAM). Looking into the future, subarea models face both opportunities and challenges. More GIS applications, travel surveys, micro-simulation software utilization, and modeling improvements are expected to be incorporated into the subarea modeling process.     

Vehicular emissions prediction with CART-BMARS hybrid models

Vehicular emission models play a key role in the development of reliable air quality modeling systems. To minimize uncertainties associated with these models, it is essential to match the high-resolution requirements of emission models with up-to-date information. However, these models are usually based on average trip speed, not on environmental parameters like ambient temperature, and vehicle’s motion characteristics, such as speed, acceleration, load and power. This contributes to the degradation of its predictive performance. In this paper, we propose to use the non-parametric Classification and Regression Trees (CART), the Boosting Multivariate Adaptive Regression Splines (BMARS) algorithm and a combination of them in hybrid models to improve the accuracy of vehicular emission prediction using on-board measurements and the chassis dynamometer testing. The experimental comparison between the proposed CART-BMARS hybrid model with the BMARS and artificial neural networks (ANNs) algorithms demonstrates its effectiveness and efficiency in estimating vehicular emissions.     

Trip generation and distribution: the inconsistency problem and a possible remedy

There are many shortcomings commonly associated with the conventional urban transportation modeling process. This paper focuses on one of the more important problems — the inconsistency between trip generation and distribution components — and suggests a possible way of alleviating it. The suggested approach involves sorting out the independent effects on tripmaking of origin, destination and travel cost characteristics, and introducing accessibility measures explicitly into the modeling process. The resulting modeling framework can be used to obtain consistent estimates of trip generation and distribution quantities which are responsive to changes in the transportation and spatial systems.     

Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM_2.5 emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM_2.5 pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM_2.5 pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. EPA’s MOVES and AERMOD models. The heavy-duty truck released PM_2.5 pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using our methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM_2.5 emissions are caused by trucks and also 21 percent of the urban overall ambient PM_2.5 concentrations can be attributed to trucks in Cincinnati urban area.     

Ridership estimation for short-range transit planning

The objective of this research was to develop a simple transit ridership estimation model system for short-range planning. The main feature of the model system is that it exploits knowledge of transit link volumes which are obtained readily from on-off counts. Extensive use is made of default values for model parameters, taken directly from the transportation literature. The remaining parameters can be derived easily from generally available land-use and socioeconomic data. Expensive household surveys and time-consuming model calibrations are not required. A sequence of simple trip generation, trip distribution and modal split models generate trip-purpose specific transit trip tables, denoted as “trial” trip tables. These trip tables and observed transit link volumes are used in a linear programming model which serves as a correction mechanism. The gain in accuracy is achieved by using the ridership information contained in the transit link volumes. The corrected trip tables may be used in a pivot-point analysis to estimate changes in ridership and revenue. The results of a test application of the model system indicate that it can generate accurate ridership estimates when reliable transit link volumes are available from on-off counts, and when the trial transit trip tables as derived from the first three component models are reasonably accurate.