Traffic Emission Modelling For Robust Policy Design In Connected And Electric Transportation
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| Author | : Ran Tu |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
Traffic emissions such as greenhouse gases (GHGs, in CO2eq), nitrogen oxides (NOx), and traffic-related air pollution lead to global problems including climate change and public health issues. In order to mitigate the impacts of growing urban traffic on emissions and air pollution, travel demand management, driving operation control, and advanced technology initiatives have been implemented in many cities. The aim of this research is to realize robust transportation policy decisions with improved emission estimation approaches at local and regional levels. In the first module of the thesis, the emission factor (EF, in grams of traffic emissions per unit distance) within one traffic condition, which is commonly defined as a single value, is expanded to a distribution. A regional emission distribution is therefore established using the EF distribution, enhancing the robustness of policy analysis. Meanwhile, the identification of the EF variation leads to the development of a machine-learning based emission estimation approach, CLustEr-based Validated Emission Re-calculation (CLEVER). The CLEVER approach can accurately estimate regional traffic emissions without heavy data collection burden through refined traffic condition categories and representative EFs using traffic data of multiple resolutions. In the second module, several traffic emission control strategies are tested from perspectives of emissions, air pollution, and energy consumption. First, a travel demand management targeting on high-emitting trips is tested. Compared to a short-distance trip management, the proposed strategy is more effective on reducing GHG emissions and improving traffic conditions. Second, a travel-time minimized routing algorithm with connected automated vehicles is applied in an urban road network and the application causes potential increases of near-road NO2 concentrations. Lastly, electric vehicle charging schedules are optimized to minimize GHG emissions from electricity generation. The optimized plan demonstrates high potentials for reducing GHG emissions. However, trade-offs between emission reductions and charging facility costs are identified by comparing the optimized plan with non-optimized plans. This research achieves a reliable regional traffic emission estimation with much less data requirement. Based on that, innovative control strategies proposed in this research and their comprehensive evaluation process can contribute to a robust transportation policy decision.
| Author | : Nikolas Thomopoulos |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 162 |
| Release | : 2024-06-04 |
| Genre | : Transportation |
| ISBN | : 1803823518 |
This volume is a valuable source of ACT information for developing holistic research methods and global policies for making progress towards the SDGs.
| Author | : Ti Zhang |
| Publisher | : |
| Total Pages | : 402 |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
The adoption of plug-in electric vehicles (PEV) requires research for models and algorithms tracing the vehicle assignment incorporating PEVs in the transportation network so that the traffic pattern can be more precisely and accurately predicted. To attain this goal, this dissertation is concerned with developing new formulations for modeling travelling behavior of electric vehicle drivers in a mixed flow traffic network environment. Much of the work in this dissertation is motivated by the special features of PEVs (such as range limitation, requirement of long electricity-recharging time, etc.), and the lack of tools of understanding PEV drivers traveling behavior and learning the impacts of charging infrastructure supply and policy on the network traffic pattern. The essential issues addressed in this dissertation are: (1) modeling the spatial choice behavior of electric vehicle drivers and analyzing the impacts from electricity-charging speed and price; (2) modeling the temporal and spatial choices behavior of electric vehicle drivers and analyzing the impacts of electric vehicle range and penetration rate; (3) and designing the optimal charging infrastructure investments and policy in the perspective of revenue management. Stochastic traffic assignment that can take into account for charging cost and charging time is first examined. Further, a quasi-dynamic stochastic user equilibrium model for combined choices of departure time, duration of stay and route, which integrates the nested-Logit discrete choice model, is formulated as a variational inequality problem. An extension from this equilibrium model is the network design model to determine an optimal charging infrastructure capacity and pricing. The objective is to maximize revenue subject to equilibrium constraints that explicitly consider the electric vehicle drivers' combined choices behavior. The proposed models and algorithms are tested on small to middle size transportation networks. Extensive numerical experiments are conducted to assess the performance of the models. The research results contain the author's initiative insights of network equilibrium models accounting for PEVs impacted by different scenarios of charging infrastructure supply, electric vehicles characteristics and penetration rates. The analytical tools developed in this dissertation, and the resulting insights obtained should offer an important first step to areas of travel demand modeling and policy making incorporating PEVs.
| Author | : Xiang Song (S.M.) |
| Publisher | : |
| Total Pages | : 101 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
One of the main challenges of making strategic decisions in transportation is that we always face a set of possible future states due to deep uncertainty in traffic demand. This thesis focuses on exploring the application of model-based decision support techniques which characterize a set of future states that represent the vulnerabilities of the proposed policy. Vulnerabilities here are interpreted as states of the world where the proposed policy fails its performance goal or deviates significantly from the optimum policy due to deep uncertainty in the future. Based on existing literature and data mining techniques, a computational model-based approach known as scenario discovery is described and applied in an empirical problem. We investigated the application of this new approach in a case study based on a proposed transit policy implemented in Marina Bay district of Singapore. Our results showed that the scenario discovery approach performs well in finding the combinations of uncertain input variables that will result in policy failure.
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 395 |
| Release | : 2013-04-14 |
| Genre | : Science |
| ISBN | : 0309268524 |
For a century, almost all light-duty vehicles (LDVs) have been powered by internal combustion engines operating on petroleum fuels. Energy security concerns about petroleum imports and the effect of greenhouse gas (GHG) emissions on global climate are driving interest in alternatives. Transitions to Alternative Vehicles and Fuels assesses the potential for reducing petroleum consumption and GHG emissions by 80 percent across the U.S. LDV fleet by 2050, relative to 2005. This report examines the current capability and estimated future performance and costs for each vehicle type and non-petroleum-based fuel technology as options that could significantly contribute to these goals. By analyzing scenarios that combine various fuel and vehicle pathways, the report also identifies barriers to implementation of these technologies and suggests policies to achieve the desired reductions. Several scenarios are promising, but strong, and effective policies such as research and development, subsidies, energy taxes, or regulations will be necessary to overcome barriers, such as cost and consumer choice.
| Author | : John Marshall Taggart |
| Publisher | : |
| Total Pages | : |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
This dissertation examines the potential for electric vehicles to act as a viable technology pathway to deep decarbonization of the transportation sector. Taking an integrative and multi-disciplinary approach, this research applies theories of technology development and diffusion, social welfare optimization, life cycle analysis, and empirical modeling to better understand the processes and prospects for widespread adoption, as well as the emissions benefits that could accrue from a full market transition. The first chapter develops an endogenous model of market diffusion which incorporates positive feedback effects such as learning-by-doing and network externalities, then uses it to consider what an optimal subsidy policy regime could look like in different representative scenarios. The next chapter goes more in-depth on emissions, presenting the first comparative full life cycle analysis of greenhouse gas emissions for mass market, long-range battery electric vehicles, as compared to internal combustion engine vehicles. Lastly, data from real-world trips are utilized to explore heterogeneity in vehicle efficiency and range under different trip conditions, factors which could have significant implications for the scalability of the electric vehicle technology. Broadly, results show very large electric vehicle emissions reductions across all markets, robustness of key performance metrics to local climate conditions, and positive social welfare impacts of subsidies from accelerating early market adoption.
| Author | : Gunwoo Lee |
| Publisher | : |
| Total Pages | : 233 |
| Release | : 2011 |
| Genre | : |
| ISBN | : 9781267079824 |
Due to environmental concerns, transportation studies have extensively evaluated emission impacts associated with traffic operational strategies and transportation policies. However, the impact studies mainly relied on emission impacts found using demand forecasting models. Such planning models cannot capture individual vehicles. interactions (i.e., lane changes or stop-and-go movements) or detailed traffic operations such as with traffic signals. These limitations often lead to under-estimated emissions while evaluating several policies. Even though many studies utilized microscopic traffic models to better estimate emissions, the studies have not considered further steps such as air quality estimation and health impact studies. This research develops an integrated framework for evaluating air quality and health impacts of transportation corridors using a microscopic traffic model, a micro-scale emissions model, a non-steady state dispersion model, and a health impact model. The main advantage of this approach is to better estimate air quality and health impacts from vehicle interactions and detailed traffic management strategies. As a case study, we evaluate air quality and health impacts of several scenarios associated with major transportation corridors accessing the San Pedro Bay Ports (SPBP) complex, California. The study context consists of two 20 miles-long major freight freeway corridors and nearby arterials, as well as line-haul rail along the Alameda corridor and several rail yards associated with the SPBP complex. For the scenarios, we consider a clean truck program, cleaner locomotives, and modal shifts compared to the 2005 baseline. All scenarios performed with the integrated framework have provided larger improvements of air quality and health impacts associated with transportation corridors than conventional frameworks using transportation planning models. However, the difference in air quality and health impacts from modal shift scenarios between clean trucks and locomotives are minor. As exploratory research, pollution response surface models are developed. The main objective of the pollution response surface model is to avoid the high computational cost of the microscopic traffic model, which makes it difficult to estimate traffic for multiple days needed for evaluating emissions and health impacts over longer periods such a climate season. A conceptual framework for estimating pollution response surface models is proposed. Using a hypothetical network, response surfaces of NOX and PM are estimated.
| Author | : Matthew Muresan |
| Publisher | : |
| Total Pages | : 95 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
The need to understand the effect of policy decisions on environmental indicators is strong. The emergence of new technologies brought about by connected vehicle technologies, which are difficult to evaluate in field settings, means that policies must often be evaluated with software models. In these cases, however, the transportation model and the emissions model are often separate, and multiple different ways to connect these models are possible. Although the estimations provided by each model will vary, each method also differs in terms of the computational time. This research is motivated by the need to understand the consequences of choosing a particular method to link a traffic and emissions model. Within the literature, aggregated approaches that simply use average speeds and volumes are often selected for their convenience and lower data needs. A number of different scenarios were therefore constructed to compare the estimates of these aggregated approaches to other methods that use disaggregated data, such as the use of individual discrete trajectories, the use of a velocity binning scheme that characterises networks based on their velocity profile or the use of a clustering algorithm developed for this study. This research presents a clustering algorithm that can be used to reduce the computational loads of an emissions estimation process without loss of accuracy. The results of the analysis highlight the consequences of choosing each approach. Aggregated approaches produce unreliable estimates as they are backed by assumptions that may not be valid in every case. Using individual trajectories creates high computational loads and may not be feasible in all cases. The wealth of data available from a traffic microsimulation mean that using an aggregated approach neglects to utilise the full potential of the model; however, the hybrid approaches presented in this research (clustering and velocity binning) were found to make excellent use of this data while still minimizing computational demands.
| Author | : |
| Publisher | : |
| Total Pages | : 782 |
| Release | : 1995 |
| Genre | : Traffic engineering |
| ISBN | : |
| Author | : James M. Anderson |
| Publisher | : Rand Corporation |
| Total Pages | : 215 |
| Release | : 2014-01-10 |
| Genre | : Transportation |
| ISBN | : 0833084372 |
The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.
