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Studying the Impacts of Emerging Truck Technologies at Macroscopic and Microscopic Level.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Studying the Impacts of Emerging Truck Technologies at Macroscopic and Microscopic Level./
作者:	Sharma, Soumya.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	174 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-08, Section: B.
Contained By:	Dissertations Abstracts International85-08B.
標題:	Transportation planning. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30767454
ISBN:	9798381449730

Studying the Impacts of Emerging Truck Technologies at Macroscopic and Microscopic Level.
Sharma, Soumya.

Studying the Impacts of Emerging Truck Technologies at Macroscopic and Microscopic Level. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 174 p.

Source: Dissertations Abstracts International, Volume: 85-08, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2023.

This dissertation advances the understanding of emerging truck technologies and their implications for travel demand and transportation capacity, with a particular focus on autonomous and connected trucks and the influence of e-commerce. Four research objectives were identified, each addressing distinct aspects of this evolving landscape.The first research objective was to explore the impact of introducing autonomous trucks (TAVs) into the urban transportation network. The triangle region of North Carolina was used as a case study for this. The Triangle Regional Model (TRM), the four-step macroscopic travel demand forecasting tool for the Triangle region of North Carolina, was employed to analyze the impacts of autonomous trucks and e-commerce on travel demand. Utilizing scenarios for the year 2045, and two levels of automation i.e. SAE level 4 and level 5, the research assesses the potential of autonomous vehicles (AVs) to alleviate congestion, operational dynamics, and infrastructure implications. Findings reveal nuanced interactions between AVs and the existing travel patterns within the Triangle Region, providing insights into the future of autonomous freight trips. For both SAE level 4 and level 5 scenarios, the shift from traditional trucks (TVs) to a mix of TAVs and TVs did not have a significant impact on the urban network. The analysis indicated that the redirection of Origin-Destination (OD) flows to make more use of freeway segments for AV trips led to increased flows on freeways and additional vehicle miles traveled (VMT) on the rest of the network. However, AV segments for trips with central origins and destinations were removed, resulting in offsetting changes. The changes in VMT and vehicle hours traveled (VHT) were captured. The results showed that, depending on the scenario, there were decreases in VMT on certain segments (e.g., urban interstates) and increases on other segments, highlighting the complex interplay of TAVs and existing traffic.The second research objective focused on the impact of autonomous trucks (TCAVs) on freeway performance, specifically examining basic freeway segments and weaving segments. The analysis included varying percentages of TCAVs and traditional trucks (TTs). The Simulation of Urban Mobility (SUMO), an open-source microsimulation tool was used to examine the capacity impacts of connected and autonomous trucks (TCAVs) on freeway segments. The study investigates the integration of TCAVs into traffic flow, considering lane allocation and communication strategies. Results suggest limited changes to freeway performance due to the introduction of TCAVs, offering insights for the integration of autonomous freight vehicles within existing traffic infrastructure. The results demonstrated that, for the scenarios tested, introducing TCAVs into the traffic stream had limited effects on freeway performance, and the simulation outcomes were generally stable. Travel rates were compared for different scenarios, considering lane reservation policies and varying truck percentages. The results showed that TCAVs had a minor impact on travel rates, with little differentiation between scenarios with or without lane reservation for TCAVs. The analysis suggested that implementing lane reservation for TCAVs, particularly for the middle lane, did not adversely affect freeway operations.The third research objective involved modeling the impact of e-commerce on urban freight and transportation demand. The impacts of e-commerce on travel demand were assessed within the Triangle Region of North Carolina. For this research objective, an updated version of the TRM was utilized, namely the Triangle Regional Model Generation 2 (TRMG2), for the analysis year of 2045. By adapting trip generation factors based on the National Household Travel Survey (NHTS), the study models the shifts in trip patterns induced by e-commerce, particularly the transformation of in-store shopping trips into truck-based deliveries. The methodology identifies key factors influencing e-commerce impact and lays the foundation for future investigations into the dynamic landscape of online retail, while maintaining the fundamental model structure of TRMG2. The results revealed that the shift from in-store shopping to e-commerce led to reductions in vehicle volumes and roadway congestion, particularly during Midday and PM periods. The introduction of e-commerce-induced truck trips replaced a portion of single-occupancy vehicle (SOV) trips, resulting in changed flow patterns on major arterials and highways. The study illustrated that e-commerce's influence extended beyond truck trips, affecting overall travel patterns and congestion levels, although in a nuanced manner.The fourth research objective focused on enhancing the methodology to include ecommerce returns and various modes of delivery. To accomplish this objective, the study introduces a package-based approach to model e-commerce deliveries and returns within TRMG2, for the analysis year of 2045. This paradigm shift allows for a holistic representation of e-commerce dynamics, considering diverse delivery modes such as trucks, automobiles, and drones. The approach still maintains the core model structure while accommodating evolving ecommerce practices and incorporating returns dynamics. The results demonstrated that incorporating returns in the e-commerce scenario led to a slight increase in total SOV trips. Additionally, the analysis compared the vehicle miles traveled (VMT), vehicle hours traveled (VHT), and flow differences between the base case and the e-commerce scenario. The findings showed that while overall VMT savings were observed, the savings were slightly reduced when e-commerce returns were included. This comprehensive approach of incorporating returns and multiple delivery modes provided a more accurate depiction of the influence of e-commerce on transportation networks.The findings provide urban and transportation planners with methods to assess impacts of various emerging technologies on travel demand and transportation capacity. This research also identifies the limitations of traditional travel demand models in simulating the impacts of driverless vehicle technologies, which will help develop next-generation travel demand forecasting tools. The results showcased the nuanced impacts of autonomous trucks, ecommerce-induced freight shifts, and diverse delivery dynamics on traffic patterns, congestion, and overall urban transportation system performance. The findings collectively contribute to a better understanding of how these emerging trends can shape transportation planning and policy decisions.

ISBN: 9798381449730Subjects--Topical Terms:

3423850
Transportation planning.

Studying the Impacts of Emerging Truck Technologies at Macroscopic and Microscopic Level.
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520 $a This dissertation advances the understanding of emerging truck technologies and their implications for travel demand and transportation capacity, with a particular focus on autonomous and connected trucks and the influence of e-commerce. Four research objectives were identified, each addressing distinct aspects of this evolving landscape.The first research objective was to explore the impact of introducing autonomous trucks (TAVs) into the urban transportation network. The triangle region of North Carolina was used as a case study for this. The Triangle Regional Model (TRM), the four-step macroscopic travel demand forecasting tool for the Triangle region of North Carolina, was employed to analyze the impacts of autonomous trucks and e-commerce on travel demand. Utilizing scenarios for the year 2045, and two levels of automation i.e. SAE level 4 and level 5, the research assesses the potential of autonomous vehicles (AVs) to alleviate congestion, operational dynamics, and infrastructure implications. Findings reveal nuanced interactions between AVs and the existing travel patterns within the Triangle Region, providing insights into the future of autonomous freight trips. For both SAE level 4 and level 5 scenarios, the shift from traditional trucks (TVs) to a mix of TAVs and TVs did not have a significant impact on the urban network. The analysis indicated that the redirection of Origin-Destination (OD) flows to make more use of freeway segments for AV trips led to increased flows on freeways and additional vehicle miles traveled (VMT) on the rest of the network. However, AV segments for trips with central origins and destinations were removed, resulting in offsetting changes. The changes in VMT and vehicle hours traveled (VHT) were captured. The results showed that, depending on the scenario, there were decreases in VMT on certain segments (e.g., urban interstates) and increases on other segments, highlighting the complex interplay of TAVs and existing traffic.The second research objective focused on the impact of autonomous trucks (TCAVs) on freeway performance, specifically examining basic freeway segments and weaving segments. The analysis included varying percentages of TCAVs and traditional trucks (TTs). The Simulation of Urban Mobility (SUMO), an open-source microsimulation tool was used to examine the capacity impacts of connected and autonomous trucks (TCAVs) on freeway segments. The study investigates the integration of TCAVs into traffic flow, considering lane allocation and communication strategies. Results suggest limited changes to freeway performance due to the introduction of TCAVs, offering insights for the integration of autonomous freight vehicles within existing traffic infrastructure. The results demonstrated that, for the scenarios tested, introducing TCAVs into the traffic stream had limited effects on freeway performance, and the simulation outcomes were generally stable. Travel rates were compared for different scenarios, considering lane reservation policies and varying truck percentages. The results showed that TCAVs had a minor impact on travel rates, with little differentiation between scenarios with or without lane reservation for TCAVs. The analysis suggested that implementing lane reservation for TCAVs, particularly for the middle lane, did not adversely affect freeway operations.The third research objective involved modeling the impact of e-commerce on urban freight and transportation demand. The impacts of e-commerce on travel demand were assessed within the Triangle Region of North Carolina. For this research objective, an updated version of the TRM was utilized, namely the Triangle Regional Model Generation 2 (TRMG2), for the analysis year of 2045. By adapting trip generation factors based on the National Household Travel Survey (NHTS), the study models the shifts in trip patterns induced by e-commerce, particularly the transformation of in-store shopping trips into truck-based deliveries. The methodology identifies key factors influencing e-commerce impact and lays the foundation for future investigations into the dynamic landscape of online retail, while maintaining the fundamental model structure of TRMG2. The results revealed that the shift from in-store shopping to e-commerce led to reductions in vehicle volumes and roadway congestion, particularly during Midday and PM periods. The introduction of e-commerce-induced truck trips replaced a portion of single-occupancy vehicle (SOV) trips, resulting in changed flow patterns on major arterials and highways. The study illustrated that e-commerce's influence extended beyond truck trips, affecting overall travel patterns and congestion levels, although in a nuanced manner.The fourth research objective focused on enhancing the methodology to include ecommerce returns and various modes of delivery. To accomplish this objective, the study introduces a package-based approach to model e-commerce deliveries and returns within TRMG2, for the analysis year of 2045. This paradigm shift allows for a holistic representation of e-commerce dynamics, considering diverse delivery modes such as trucks, automobiles, and drones. The approach still maintains the core model structure while accommodating evolving ecommerce practices and incorporating returns dynamics. The results demonstrated that incorporating returns in the e-commerce scenario led to a slight increase in total SOV trips. Additionally, the analysis compared the vehicle miles traveled (VMT), vehicle hours traveled (VHT), and flow differences between the base case and the e-commerce scenario. The findings showed that while overall VMT savings were observed, the savings were slightly reduced when e-commerce returns were included. This comprehensive approach of incorporating returns and multiple delivery modes provided a more accurate depiction of the influence of e-commerce on transportation networks.The findings provide urban and transportation planners with methods to assess impacts of various emerging technologies on travel demand and transportation capacity. This research also identifies the limitations of traditional travel demand models in simulating the impacts of driverless vehicle technologies, which will help develop next-generation travel demand forecasting tools. The results showcased the nuanced impacts of autonomous trucks, ecommerce-induced freight shifts, and diverse delivery dynamics on traffic patterns, congestion, and overall urban transportation system performance. The findings collectively contribute to a better understanding of how these emerging trends can shape transportation planning and policy decisions.
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