Quantifying the impacts of dynamic control in connected and automated vehicles on greenhouse gas emissions and urban NO2 concentrations

Tu, Ran; Alfaseeh, Lama; Djavadian, Shadi; Farooq, Bilal; Hatzopoulou, Marianne

doi:10.1016/j.trd.2019.06.008

Cited by 54 publications

(26 citation statements)

References 15 publications

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“…With reference to the NOx variable, it has been found that the relationship between NOx and speed is also quasi-convex (Djavadian et al, 2020). Moreover, previous studies have confirmed that at high speeds NOx is sensitive to aggressive driving (Tu et al, 2019). Figure 5B shows that when GHG was part of the routing objective (GHG or TT&GHG), the produced NOx was less compared to when TT was the routing objective regardless of the routing protocol, reactive or proactive.…”

Section: Routing Strategies Analysismentioning

confidence: 80%

“…This is because vehicles were directed to longer paths, but of higher speed to minimize the travel time. NOx is sensitive to aggressive driving (Tu et al, 2019), which makes higher speed links unfavorable. However, the reduction in the experienced TT and VKT in the network in the case of proactive routing means higher throughput over time.…”

Section: Network Level Analysismentioning

confidence: 99%

“…The employment of ICT and connected and automated vehicles (CAVs) has been suggested as a potential solution to alleviate the undesirable social and environmental impact of transportation systems (Zegeye et al, 2009). In particular, routing schemes for CAVs have an explicit impact on traffic (Yang and Recker, 2006;Alfaseeh et al, 2018;Djavadian and Farooq, 2018) and environmental (Tu et al, 2019) characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles

Alfaseeh¹,

Farooq²

2020

Front. Future Transp.

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This study exploited the advancements in information and communication technology (ICT), connected and automated vehicles (CAVs), and sensing to develop proactive multi-objective eco-routing strategies for travel time and Greenhouse Gas (GHG) emissions reduction on urban road networks. For a robust application, several GHG costing approaches were examined. The predictive models for link level traffic and emission states were developed using the long short-term memory (LSTM) deep network with exogenous predictors. It was found that proactive routing strategies outperformed the reactive strategies regardless of the routing objective. Whether reactive or proactive, the multi-objective routing, with travel time and GHG minimization, outperformed the single objective routing strategies. Using a proactive multi-objective (travel time and GHG) routing strategy, we observed a reduction in average travel time (17%), average vehicle kilometer traveled (22%), total GHG (18%), and total nitrogen oxide (20%) when compared with the reactive single-objective (travel time).

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Section: Routing Strategies Analysismentioning

confidence: 80%

Section: Network Level Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles

Alfaseeh¹,

Farooq²

2020

Front. Future Transp.

Self Cite

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“…45 Also, Taiebat et al's comprehensive review suggests that CAVs can yield higher energy ef f iciency through a variety of technology-enabled f eatures, such as optimizing driving cycle, platooning, minimizing idling time, reducing cold starts, reducing speed fluctuations, self-parking, and eco-routing. 46 As the share of vehicles on the road that are CAVs increases, we can expect greater reductions in GHG emissions because of improved end-to-end dynamic routing of the vehicles. 47 In a CAV energy savings study by Tu et al, the authors did not f ind statistical differences between electric CAV and human-driven electric connected vehicle-though both had signif icantly better energy use than internal combustion engines.…”

Section: A Synthesis Of Major Findings From the Literaturementioning

confidence: 99%

Local Climate Action Planning as a Tool to Harness the Greenhouse Gas Emissions Mitigation and Equity Potential of Autonomous Vehicles and On-Demand Mobility

Alexander

Agrawal

Clark

2021

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This report focuses on how cities can use climate action plans (CAPs) to ensure that on-demand mobility and autonomous vehicles (AVs) help reduce, rather than increase, green-house gas (GHG) emissions and inequitable impacts from the transportation system. We employed a three-pronged research strategy involving: (1) an analysis of the current literature on on-demand mobility and AVs; (2) a systematic content analysis of 23 CAPs and general plans developed by municipalities in California; and (3) a comparison of findings from the literature and content analysis of plans to identify opportunities for GHG emissions reduction and mobility equity. Findings indicate that maximizing the environmental and social benefits of AVs and on-demand mobility requires proactive and progressive planning; yet, most cities are lagging behind in this area. Although municipal CAPs and general plans in California have adopted a few strategies and programs relevant to AVs and on-demand mobility, many untapped opportunities exist to harness the GHG emissions reduction and social benefits potential of AVs and on-demand mobility. Policy and planning discussions should consider the synergies between AVs and on-demand mobility as two emerging mobility trends, as well as the key factors (e.g., vehicle electrification, fuel efficiency, use and ownership, access and distribution, etc.) that determine whether deployment of AVs would help reduce GHG emissions from transportation. Additionally, AVs and on-demand mobility can potentially contribute to a more equitable transportation system by improving independence and quality of life for individuals with disabilities and the elderly, enhancing access to transit, and helping alleviate the geographic gap in public transportation services.

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“…Connected and Automated Vehicles (CAVs) are burgeoning. With the advanced control systems and wireless communication technologies, CAVs have the potential to progressively revolutionise intelligent transportation system (ITS) with increased efficiency, enhanced safety, reduced emissions, saved energies and improved environmental effects (Vahidi and Sciarretta, 2018;Gawron et al, 2018;Tu et al, 2019). These potential benefits are confirmed by many exploratory studies in which the characteristics of CAVs are modeled or simulated, assuming that CAVs have become a reality and are allowed to drive on public roads.…”

Section: Chaptermentioning

confidence: 99%

Intersection management strategies to realise the benefits of new generation vehicles

Wu¹

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xix the CAV penetration rate and the three core platooning parameters (intra-platoon gap, inter-platoon gap and platoon size) are captured, but also the individual willingness of CAVs to form platoons is involved to consider the behavioural aspect of the problem. The influences of platooning parameters and platooning willingness are in line with expectations, while the effect of CAVs penetration rate is not intuitive. It is not certain that higher penetration rate will lead to higher intersection capacity, but further depends on the other analysed parameters.

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Quantifying the impacts of dynamic control in connected and automated vehicles on greenhouse gas emissions and urban NO2 concentrations

Cited by 54 publications

References 15 publications

Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles

Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles

Local Climate Action Planning as a Tool to Harness the Greenhouse Gas Emissions Mitigation and Equity Potential of Autonomous Vehicles and On-Demand Mobility

Intersection management strategies to realise the benefits of new generation vehicles

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