Hi!
I'm Ran

Communication between vehicles and road infrastructure can enable more efficient use of the road network and hence reduce congestion in urban areas. This improvement can be enhanced by distributed control due to lighter computational load and higher reliability. Despite favourable impacts on traffic, little is known about the effects of such systems on near-road air quality. In this study, an End-To-End (E2E) dynamic distributed routing algorithm in Connected and Automated Vehicles (CAVs) was applied in downtown Toronto, to identify whether benefits to network throughput were associated with lower near-road NO2 concentration. We observe significant reductions in the emissions of Greenhouse Gases (GHGs) with increased penetration of CAVs. Nonetheless, emissions of nitrogen oxides (NOx) increased with higher penetration of CAVs under certain circumstances. Besides, a higher frequency and severity of NO2 hot-spots were observed under a 100% CAV scenario, while it led to a lower long-term average concentration. Impacts of the proposed system on electrical energy consumption in a full electric vehicle network were also investigated, indicating that the addition of CAVs that are electric did not contribute to high energy savings. We propose that such new transformative technologies in transportation should be designed with air pollution and public health goals.

This study presents a novel method for integrating the output of a microscopic emission modelling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first classifies road links based on aggregate traffic characteristics and traffic condition clusters, then assigns representative emission factors (EFs) calibrated from microscopic emission modelling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach shows that GHG emissions of uncongested links were overestimated by the average speed model, while at intersections and ramps with more congested links and more interrupted traffic flow, CLEVER offers higher and more accurate GHG estimates. This indicates its ability to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation.

Traffic emission inventories have been under development for decades, often relying on data from traffic assignment models, ranging from macroscopic models generating average link speeds, to more detailed microscopic models with instantaneous speed profiles. Policy testing within such frameworks has often focused on identifying changes in total emissions, or in emissions aggregated at a zonal or street level. Emissions from specific trips or trajectories are seldom analyzed, although reductions in greenhouse gas (GHG) emissions can be achieved more efficiently when targeting high emitters. In this paper, we propose a different approach to reducing transportation GHG emissions, by catering policies to specific trips based on their emission burden. We focus on the City of Toronto downtown. Using second-by-second speed data for entire trajectories, GHGs (in CO2eq) and nitrogen oxides (NOx) emissions were estimated. We observe that the destinations attracting the highest trip emissions tend to be in the hospital and financial districts. Trips originating and ending in the downtown area are responsible for a small share of total emissions, although they have high emission intensity. Removing trips with high total emissions and high emission intensity led to significant reductions in CO2eq and NOx emissions, whereas removing shorter trips, did not have a significant influence on total emissions nor emission intensities.

The development of accurate emission inventories at an urban scale is of utmost importance for cities in light of climate change commitments and the need to identify the emission reduction potential of various strategies. Emission inventories for on-road transportation are sensitive to the network models used to generate traffic activity data. For large networks (cities or regions), average-speed models have been relied upon extensively in research and practice, primarily due to their computational attractiveness. Nevertheless, these models are myopic to traffic states and driving cycles and therefore lack in accuracy. The aim of this study is to improve the quality of regional on-road emission inventories without resorting to computationally-intensive traffic microsimulation of an entire region. For this purpose, macroscopic, mesoscopic, and microscopic emission models are applied and compared, using average speed, average speed and its standard deviation, and instantaneous speeds. We also propose a hybrid approach called the CLustEr-based Validated Emission Re-calculation (CLEVER), which bridges between the microscopic and mesoscopic approaches. CLEVER defines unsupervised traffic conditions using a combination of mesoscopic traffic characteristics for selected road segments, and identifies a representative emission factor (EF) for each condition based on the microscopic driving cycle of the sample. Regional emissions can then be estimated by classifying segments in the regional network into these conditions, and applying corresponding EFs. The results of the CLEVER method are compared with the results of microsimulation and of mesoscopic approaches revealing a robust methodology that improves the emission inventory while reducing computational burden.

Electric vehicles (EVs) are one of the most important paths for achieving low carbon transportation. Despite zero on-road greenhouse gas (GHG) emissions, their upstream emissions from electricity generation cannot be ignored. Taking GHG minimization as the objective, while fulfilling regional passenger travel demand, this study designs an optimization algorithm for EV charging using travel survey data of the Greater Toronto and Hamilton Area (GTHA). GHG emissions from charging demand were estimated by a marginal emission factor model calibrated with historical data for electricity generation in Ontario. The results show that the optimized plan can reach around 97% GHG reduction compared to a base case where vehicles are powered by gasoline. Four other charging scenarios that do not entail optimization were also investigated. The scenario where charging is only allowed after 3am generates the lowest GHG emissions among all four scenarios but its emissions are 50% higher than the optimized scenario. Charging at the end of each trip was observed to generate the highest GHG emissions. Besides GHG emissions, the number and cost effectiveness (GHG emissions reduced per cost) of non-residential charging stations were evaluated. The optimized plan requires the lowest number of charging stations thus offering the highest cost effectiveness (around 4.6 tons of GHG emissions reduced per million US dollars invested in the charging infrastructure).

In dense downtown cores within major cities, traffic congestion is on the rise and it is associated with air quality concerns. Recently, distributed dynamic routing algorithms integrated with connected autonomous vehicles have been developed, revealing substantial improvements in terms of reductions in vehicle travel times and delay. However, there is a paucity of studies that have focused on the environmental impacts of such control schemes. In this study, an End-To-End dynamic distributed routing algorithm in a Connected Autonomous Vehicles environment (E2ECAV), developed by the Laboratory of Innovations in Transportation (LiTrans) at Ryerson University is adopted to identify whether benefits on traffic are associated with improvement on environment. Our study is set in downtown Toronto whereby scenarios with different demand levels and market penetration rates (MPRs) of E2ECAVs are investigated. The results demonstrate that compared to the base case (no CAVs), total vehicle kilometers travelled (VKT) slightly increase by at most 4% among all scenarios with CAVs while travel times are significantly reduced. Despite neglectable changes in VKT, network Greenhouse Gas (GHG) emissions and average segment level GHG factor (g/veh/km) and its intensity (g/km) decrease with higher MPRs—especially under high traffic demand: total GHG emissions decrease by 40% with a 100%CAV, compared to a base scenario of no CAVs. In contrast, nitrogen oxides (NOx) does not show a continuously decreasing trend with increasing MPRs. With 100%CAV in the network, average segment NOx emission factor as well as its intensity increase from the base case, under all demand levels.

Various studies have been conducted to integrate microscopic emission models with aggregated traffic conditions in order to achieve more accurate greenhouse gas (GHG) emission estimates for transportation in large metropolitan regions. Despite the range of approaches published in the recent literature, limited number studies have conducted validation of the proposed approaches or assessed their transferability within different parts of an urban region. In this paper, we present the CLustEr-based Validated Emission Recalculation (CLEVER) method, which makes use of instantaneous speed data and aggregated traffic characteristics in order to refine the inventory for transportation GHG emissions in the Greater Toronto Area (GTA). The CLEVER approach uses unsupervised traffic condition clustering, representative EF calibration, road segment classification, and segment level EF assignment. In this paper, cluster parameters including cluster numbers and cluster centers were calibrated based on the City of Toronto network. Moreover, CLEVER was calibrated with different sets of road segment samples within the GTA, demonstrating the spatial transferability of the CLEVER algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated as 2,692 tons, which is lower than the estimate generated by a traditional, average-speed approach (3,245 tons). CLEVER generally estimated lower results than the average-speed model on all roads, while at intersections where more traffic condition uncertainties exist, emissions estimated by CLEVER were higher, indicating its ability to capture the variability of traffic conditions compared to a traditional average-speed approach.