Advancements in Multi-Objective Eco-Routing Solutions Using Connectivity

The emerging connected and automated vehicle (CAV) technology is likely to bring significant changes to the transportation world. This study helps understand how the anticipated emergence of CAVs will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. We design a set of future system configurations under the California Statewide Travel Demand Model framework to simulate scenarios for the deployment of passenger CAVs in California by 2050. These scenarios consider the ownership and operational type (e.g., private and shared) of CAVs, as well as additional policies such as pricing and the use of zero-emission vehicles (ZEVs) to curb potential impacts. The scenarios are: 0. Baseline (no automation); 1. Private CAV; 2. Private CAV + Pricing; 3. Private CAV + ZEV; 4. Shared CAV; 5. Shared CAV + Pricing; 6. Shared CAV + ZEV. Impacts of the introduction of CAVs across the transportation system are quantitatively estimated. The results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled (VMT) and emissions will likely increase, because of the relative convenience of CAVs. The results also show that VMT could be substantially affected by a modification in auto travel costs. This means that the implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help reduce tailpipe pollutant emissions in future scenarios.

Greenhouse gas emission prediction on road network using deep sequence learning

Intelligent Transportation Systems (ITS) are increasingly shifting toward data-driven and artificial intelligence-based frameworks that integrate sensing, prediction, and control to enhance mobility efficiency, safety, and adaptability. This dissertation contributes to the development of next-generation ITS through three complementary studies focusing on flexible traffic monitoring, real-time traffic forecasting, and stability-aware vehicle control. The first study develops a multi-drone framework for wide-view corridor traffic analysis and origin-destination (OD) estimation. By coordinating multiple unmanned aerial vehicles, as demonstrated with two drones in this study, the framework enables continuous vehicle tracking across extended corridors. A deep learning-based pipeline combining vehicle detection and tracking automatically reconstructs vehicle trajectories and matches them across drone views. Field experiments conducted at a freeway weaving section achieved over 91% accuracy in OD flow estimation compared with ground-truth data, demonstrating the framework’s effectiveness for scalable and high-resolution traffic monitoring. The second study proposes a day-specific spatial-temporal graph convolutional network (Day-STGCN) for real-time traffic forecasting. Building upon the classical STGCN architecture, the model integrates rolling historical time-dependent features with day-type categorization to capture both short-term variations and long-term recurring patterns. Using real-world sensor data, the model significantly improves prediction accuracy over baseline graph neural networks, providing robust day-aware forecasting that reflects realistic daily traffic dynamics. The third study investigates cooperative adaptive cruise control (CACC) and its effects on traffic string stability under varying market penetration rates of connected and automated vehicles (CAVs). Analytical derivations establish explicit stability conditions linking controller parameters, time headway, and control intervals in both homogeneous and heterogeneous traffic flows. The results reveal that appropriately designed CACC controllers enhance string stability even in mixed environments with different types of vehicles. Collectively, these studies present an integrated framework that advances ITS through data-driven sensing, learning, and control, offering theoretical and practical foundations for more intelligent, responsive, and stable transportation systems.

Life cycle greenhouse gas impacts of a connected and automated SUV and van

Author(s): Hao, Peng; Wei, Zhensong; Bai, Zhengwei; Barth, Matthew | Abstract: Connected and automated vehicle technology could bring about transformative reductions in traffic congestion, greenhouse gas emissions, air pollution, and energy consumption. Connected and automated vehicles (CAVs) can directly communicate with other vehicles and road infrastructure and use sensing technology and artificial intelligence to respond to traffic conditions and optimize fuel consumption. An eco-approach and departure application for connected and automated vehicles has been widely studied as a means of calculating the most energy-efficient speed profile and guiding a vehicle through signalized intersections without unnecessary stops and starts. Simulations using this application on roads with fixed-timing traffic signals have produced 12% reductions in fuel consumption and greenhouse gas emissions. But real-world traffic conditions are much more complex—uncertainties and the limited sensing range of automated vehicles create challenges for determining the most energy-efficient speed.To account for this uncertainty, researchers from the University of California, Riverside, propose a prediction-based, adaptive connected eco-driving strategy. The proposed strategy analyzes the possible upcoming traffic and signal scenarios based on historical data and live information collected from communication and sensing devices, and then chooses the most energy-efficient speed. This approach can be extended to accommodate different vehicle powertrains and types of roadway infrastructure. This research brief summarizes findings from the research and provides research implications.View the NCST Project Webpage

Connected and automated vehicles (CAVs) hold great promise in enhancing transportation operations and roadway capacity. However, there is still limited guidance in the field of highway capacity analysis to help agencies account for the potential benefits. This paper investigates the impact of CAVs on the capacity of two-way-stop-controlled (TWSC) intersections and quantifies capacity adjustments under different CAV scenarios. An isolated TWSC intersection is considered in this research to analyze the CAV impacts on various intersection movement capacities under different CAV market penetration rates (MPRs) and conflict flow rates (CFRs). To model CAVs, cooperative adaptive cruise control (CACC) is considered because it directly impacts the vehicle’s longitudinal car-following behavior and therefore the capacity. Additionally, enhanced gap-acceptance behavior based on vehicle-to-vehicle (V2V) communication is included because it can benefit minor road movements via advanced notifications. This paper collects data from customized, well-calibrated TWSC simulation models constructed in commercially available software. The results show that with the increase in MPRs, CAVs can substantially enhance the capacity of the stop or yield controlled movements, and the improvement is more significant under low CFR scenarios. CAVs can also reduce the follow-up headway and thus improve the potential capacity based on the existing HCM TWSC capacity estimation method. Finally, based on the data collected from the simulation, a capacity adjustment factor (CAF) table is proposed for HCM implementation.

Summary The positive and negative environmental impacts of information and communication technology (ICT) are widely debated. This study assesses the electricity use and greenhouse gas (GHG) emissions related to the ICT and entertainment & media (E&M) sectors at sector level, including end users, and thus complements information on the product level. GHGs are studied in a life cycle perspective, but for electricity use, only the operational use is considered. The study also considers which product groups or processes are major contributors. Using available data and extrapolating existing figures to the global scale for 2007 reveals that the ICT sector produced 1.3% of global GHG emissions in 2007 and the E&M sector 1.7%. The corresponding figures for global electricity use were 3.9% and 3.2%, respectively. The results indicate that for the ICT sector, operation leads to more GHG emissions than manufacture, although impacts from the manufacture of some products are significant. For the E&M sector, operation of TVs and production of printed media are the main reasons for overall GHG emissions. TVs as well as printed media, with the estimations made here, led to more GHG emissions on a global level in 2007 than PCs (manufacture and operation). A sector study of this type provides information on a macro scale, a perspective easily lost when considering, for example, the product‐related results of life cycle assessments. The macro scale is essential to capture changes in total consumption and use. However, the potential of the ICT sector to help decrease environmental impacts from other sectors was not included in the assessment.

PACC: A platoon-based adaptive cruise control strategy based on leader-following information topology to mitigate traffic oscillations under CAV environment

Autonomous vehicles in the sustainable cities, the beginning of a green adventure

Life cycle assessment of the energy consumption and GHG emissions of state-of-the-art automotive battery cell production

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

Knowing that today's transportation system accounts for 28% of our Nation's greenhouse gases, second only to electrical energy production requires the transportation profession to take very seriously operational management policies and construction techniques to reduce our carbon footprint. This means using transportation techniques to minimize greenhouse gas emissions by reducing travel delays and congestion as well as promoting non-single occupant vehicle travel such as transit and carpools or the use of non-motorized transportation such as bicycles and walking. Roadway construction techniques are available to further reduce greenhouse gas emissions to construct sustainable roadway infrastructure. The transportation industry needs to develop policies that rate and/or provide incentives for a sustainable transportation system. This paper provides a summary of the state of the practice to reduce fuel consumption and greenhouse gas emissions related to today's transportation systems and a recommendation on national policy for transportation sustainability. INTRODUCTION Transportation greenhouse gas (GHG) emissions are attributable to vehicle operation as well as construction process/design requirements of the transportation infrastructure. Research appears to have focused on how our transportation system operates and what strategies can be undertaken to improve the operation of the transportation system. This paper focuses on both transportation system operation efficiency and infrastructure design. Transportation operation emissions are related to travel system efficiency as well as vehicle efficiency. Vehicle emission standards are not included in this paper discussion on transportation system sustainability. Background Transportation plays a major role in generating GHG. According to the US Environmental Protection Agency (EPA), US transportation contributes 28% of the country's GHG as shown in Figure 1.

The advent of vehicle-to-everything (V2X) communication technology, as well as driving automation that is happening at a rapid pace, enables the potential for transforming the transportation system. The near future will, therefore, bring the coexistence of human-driven vehicles (HVs), connected vehicles (CVs), and connected and automated vehicles (CAVs). Correspondingly, platoon management based on V2X communication is expected to improve traffic capacity and fuel efficiency. However, there is a lack of analysis of the impact of platoon management on heterogeneous traffic combined with CVs. Moreover, V2X communication limitation is usually dismissed in existing studies. To address these limitations, this paper explores the impact of platoon management on the capacity of heterogeneous-traffic environments under different CV and CAV market penetration rates (MPRs) with the fundamental diagram considering V2X communication limitations, and further studies the changes in capacity and fuel efficiency of traffic combined with different platoon sizes. Firstly, in light of platoon management, we propose the heterogeneous traffic configurations through distribution characteristics of nine types with simulated models. Secondly, the fundamental diagram considering platoon management with V2X communication limitation is proposed under different CV and CAV MPRs. Thirdly, numerical simulations are conducted to demonstrate the impact of platoon size on the changes in capacity and fuel efficiency of traffic. Conclusions are drawn from the results of the simulations: with the cooperation of CVs and CAVs, the traffic capacity and the fuel efficiency will be promoted, particularly under high CV MPR or high CAV MPR environments.

Rivers play an important role in greenhouse gas emissions. Over the past decade, because of global urbanization trends, rapid land use changes have led to changes in river ecosystems that have had a stimulating effect on the greenhouse gas production and emissions. Presently, there is an urgent need for assessments of the greenhouse gas concentrations and emissions in watersheds. Therefore, this study was designed to evaluate river-based greenhouse gas emissions and their spatial-temporal features as well as possible impact factors in a rapidly urbanizing area. The specific objectives were to investigate how river greenhouse gas concentrations and emission fluxes are responding to urbanization in the Liangtan River, which is not only the largest sub-basin but also the most polluted one in Chongqing City. The thin layer diffusion model method was used to monitor year-round concentrations of pCO2, CH4, and N2O in September and December 2014, and March and June 2015. The pCO2 range was (23.38±34.89)-(1395.33±55.45) Pa, and the concentration ranges of CH4 and N2O were (65.09±28.09)-(6021.36±94.36) nmol·L-1 and (29.47±5.16)-(510.28±18.34) nmol·L-1, respectively. The emission fluxes of CO2, CH4, and N2O, which were calculated based on the method of wind speed model estimations, were -6.1-786.9, 0.31-27.62, and 0.06-1.08 mmol·(m2·d)-1, respectively. Moreover, the CO2 and CH4 emissions displayed significant spatial differences, and these were roughly consistent with the pollution load gradient. The greenhouse gas concentrations and fluxes of trunk streams increased and then decreased from upstream to downstream, and the highest value was detected at the middle reaches where the urbanization rate is higher than in other areas and the river is seriously polluted. As for branches, the greenhouse gas concentrations and fluxes increased significantly from the upstream agricultural areas to the downstream urban areas. The CO2 fluxes followed a seasonal pattern, with the highest CO2 emission values observed in autumn, then successively winter, summer, and spring. The CH4 fluxes were the highest in spring and the lowest in summer, while N2O flux seasonal patterns were not significant. Because of the high carbon and nitrogen loads in the basin, the CO2 products and emissions were not restricted by biogenic elements, but levels were found to be related to important biological metabolic factors such as the water temperature, pH, DO, and chlorophyll a. The carbon, nitrogen, and phosphorus content of the water combined with sewage input influenced the CH4 products and emissions. Meanwhile, N2O production and emissions were mainly found to be driven by urban sewage discharge with high N2O concentrations. Rapid urbanization accelerated greenhouse gas emissions from the urban rivers, so that in the urban reaches, CO2/CH4 fluxes were twice those of the non-urban reaches, and all over the basin N2O fluxes were at a high level. These findings illustrate how river basin urbanization can change aquatic environments and aggravate allochthonous pollution inputs such as carbon, nitrogen, and phosphorus, which in turn can dramatically stimulate river-based greenhouse gas production and emissions; meanwhile, spatial and temporal differences in greenhouse gas emissions in rivers can lead to the formation of emission hotspots.

A dynamic temporal and spatial speed control strategy for partially connected automated vehicles at a signalized arterial