Blog Posts


Workshop Papers

Venue Title
NeurIPS 2023 Reinforcement Learning in agent-based modeling to reduce carbon emissions in transportation (Papers Track)
Abstract and authors: (click to expand)

Abstract: This paper explores the integration of reinforcement learning (RL) into transportation simulations to explore system interventions to reduce greenhouse gas emissions. The study leverages the Behavior, Energy, Automation, and Mobility (BEAM) transportation simulation framework in conjunction with the Berkeley Integrated System for Transportation Optimization (BISTRO) for scenario development. The main objective is to determine optimal parameters for transportation simulations to increase public transport usage and reduce individual vehicle reliance. Initial experiments were conducted on a simplified transportation scenario, and results indicate that RL can effectively find system interventions that increase public transit usage and decrease transportation emissions.

Authors: Yuhao Yuan (UC Berkeley); Felipe Leno da Silva (Lawrence Livermore National Laboratory); Ruben Glatt (Lawrence Livermore National Laboratory)

NeurIPS 2023 Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale? (Papers Track)
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Abstract: Cooperative Logistics studies the setting where logistics companies pool their resources together to improve their individual performance. Prior literature suggests carbon savings of approximately 22%. If attained globally, this equates to 480,000,000 tonnes of CO2-eq. Whilst well-studied in operations research – industrial adoption remains limited due to a lack of trustworthy cooperation. A key remaining challenge is fair and scalable gain sharing (i.e., how much should each company be fairly paid?). We propose the use of deep reinforcement learning with a neural reward model for coalition structure generation and present early findings.

Authors: Stephen Mak (University of Cambridge); Tim Pearce (Microsoft Research); Matthew Macfarlane (University of Amsterdam); Liming Xu (University of Cambridge); Michael Ostroumov (Value Chain Lab); Alexandra Brintrup (University of Cambridge)

NeurIPS 2023 Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction (Papers Track)
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Abstract: Electrifying heavy-duty trucks offers a substantial opportunity to curtail carbon emissions, advancing toward a carbon-neutral future. However, the inherent challenges of limited battery energy and the sheer weight of heavy-duty trucks lead to reduced mileage and prolonged charging durations. Consequently, battery-swapping services emerge as an attractive solution for these trucks. This paper employs a two-fold approach to investigate the potential and enhance the efficacy of such services. Firstly, spatial-temporal demand prediction models are adopted to predict the traffic patterns for the upcoming hours. Subsequently, the prediction guides an optimization module for efficient battery allocation and deployment. Analyzing the heavy-duty truck data on a highway network spanning over 2,500 miles, our model and analysis underscore the value of prediction/machine learning in facilitating future decision-makings. In particular, we find that the initial phase of implementing battery-swapping services favors mobile battery-swapping stations, but as the system matures, fixed-location stations are preferred.

Authors: Linyu Liu (Tsinghua University); Zhen Dai (Chongqing Expressway Group Company); Shiji Song (Department of Automation, Tsinghua University); Xiaocheng Li (Imperial College London); Guanting Chen (The University of North Carolina at Chapel Hill)

NeurIPS 2023 Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion (Papers Track)
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Abstract: Identifying stop-and-go events (SAGs) in traffic flow presents an important avenue for advancing data-driven research for climate change mitigation and sustainability, owing to their substantial impact on carbon emissions, travel time, fuel consumption, and roadway safety. In fact, SAGs are estimated to account for 33-50\% of highway driving externalities. However, insufficient attention has been paid to precisely quantifying where, when, and how much these SAGs take place– necessary for downstream decision making, such as intervention design and policy analysis. A key challenge is that the data available to researchers and governments are typically sparse and aggregated to a granularity that obscures SAGs. To overcome such data limitations, this study thus explores the use of traffic reconstruction techniques for SAG identification. In particular, we introduce a kernel-based method for identifying spatio-temporal features in traffic and leverage bootstrapping to quantify the uncertainty of the reconstruction process. Experimental results on California highway data demonstrate the promise of the method for capturing SAGs. This work contributes to a foundation for data-driven decision making to advance sustainability of traffic systems.

Authors: Edgar Ramirez Sanchez (MIT); Shreyaa Raghavan (MIT); Cathy Wu ()

NeurIPS 2023 Zero-Emission Vehicle Intelligence (ZEVi): Effectively Charging Electric Vehicles at Scale Without Breaking Power Systems (or the Bank) (Tutorials Track)
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Abstract: Transportation contributes to 29% of all greenhouse gas (GHG) emissions in the US, of which 58% are from light-duty vehicles and 28% from medium-to-heavy duty vehicles (MHDVs) [1]. Battery electric vehicles (EVs) emit 90% less life cycle GHGs than their internal combustion engine (ICEV) counterparts [2], but currently only comprise 2% of all vehicles in the U.S. EVs thus represent a crucial step in decarbonizing road transportation. One major challenge in replacing ICEVs with EVs at scale is the ability to charge a large number of EVs within the constraints of power systems in a cost-effective way. This is an especially prominent problem for MHDVs used in commercial fleets such as shuttle buses and delivery trucks, as they generally require more energy to complete assigned trips compared to light-duty vehicles. In this tutorial, we describe the myriad challenges in charging EVs at scale and define common objectives such as minimizing total load on power systems, minimizing fleet operating costs, as well as maximizing vehicle state of charge and onsite photovoltaic energy usage. We discuss common constraints such as vehicle trip energy requirements, charging station power limits, and limits on vehicles’ time to charge between trips. We survey several different methods to formulate EV charging and energy dispatch as a mathematically solvable optimization problem, using tools such as convex optimization, Markov decision process (MDP), and reinforcement learning (RL). We introduce a commercial application of model-based predictive control (MPC) algorithm, ZEVi (Zero Emission Vehicle intelligence), which solves optimal energy dispatch strategies for charging sessions of commercial EV fleets. Using a synthetic dataset modeled after a real fleet of electric school buses, we engage the audience with a hands-on exercise applying ZEVi to find the optimal charging strategy for a commercial fleet. Lastly, we briefly discuss other contexts in which methods originating from process control and deep learning, like MPC and RL, can be applied to solve problems related to climate change mitigation and adaptation. With the examples provided in this tutorial, we hope to inspire the audience to come up with their own creative ways to apply these methods in different fields within the climate domain. References [1] EPA (2023). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2021. U.S. Environmental Protection Agency, EPA 430-R-23-002. [2] Verma, S., Dwivedi, G., & Verma, P. (2022). Life cycle assessment of electric vehicles in comparison to combustion engine vehicles: A review. Materials Today: Proceedings, 49, 217-222.

Authors: Shasha Lin (NextEra Mobility); Jonathan Brophy (NextEra Mobility); Tamara Monge (NextEra Mobility); Jamie Hussman (NextEra Mobility); Michelle Lee (NextEra Mobility); Sam Penrose (NextEra Mobility)

ICLR 2023 Coregistration of Satellite Image Time Series Through Alignment of Road Networks (Papers Track)
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Abstract: Due to climate change, thawing permafrost affects transportation infrastructure in northern regions. Tracking deformations over time of these structures can allow identifying the most vulnerable sections to permafrost degradation and implement climate adaptation strategies. The Sentinel-2 mission provides data well-suited for multitemporal analysis due to its high temporal resolution and multispectral coverage. However, the geometrical misalignment of Sentinel-2 imagery makes this analysis challenging. Towards the goal of estimating the deformation of linear infrastructure in northern Canada, we propose an automatic subpixel coregistration algorithm for satellite image time series based on the matching of binary masks of roads produced by a deep learning model. We demonstrate the feasibility of achieving subpixel coregistration through alignment of roads on a small dataset of high-resolution Sentinel-2 images from the region of Gillam in northern Canada. This is the first step towards training a road deformation prediction model.

Authors: Andres Felipe Perez Murcia (University of Manitoba); Pooneh Maghoul (University of Manitoba); Ahmed Ashraf (University of Manitoba)

ICLR 2023 Predicting Cycling Traffic in Cities: Is bike-sharing data representative of the cycling volume? (Proposals Track)
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Abstract: A higher share of cycling in cities can lead to a reduction in greenhouse gas emissions, a decrease in noise pollution, and personal health benefits. Data-driven approaches to planning new infrastructure to promote cycling are rare, mainly because data on cycling volume are only available selectively. By leveraging new and more granular data sources, we predict bicycle count measurements in Berlin, using data from free-floating bike-sharing systems in addition to weather, vacation, infrastructure, and socioeconomic indicators. To reach a high prediction accuracy given the diverse data, we make use of machine learning techniques. Our goal is to ultimately predict traffic volume on all streets beyond those with counters and to understand the variance in feature importance across time and space. Results indicate that bike-sharing data are valuable to improve the predictive performance, especially in cases with high outliers, and help generalize the models to new locations.

Authors: Silke K. Kaiser (Hertie School)

NeurIPS 2022 Machine Learning for Activity-Based Road Transportation Emissions Estimation (Papers Track)
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Abstract: Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives towards meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework.

Authors: Derek Rollend (JHU); Kevin Foster (JHU); Tomek Kott (JHU); Rohita Mocharla (JHU); Rodrigo Rene Rai Munoz Abujder (Johns Hopkins Applied Physics Laboratory); Neil Fendley (JHU/APL); Chace Ashcraft (JHU/APL); Frank Willard (JHU); Marisa Hughes (JHU)

NeurIPS 2022 Learning Surrogates for Diverse Emission Models (Papers Track)
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Abstract: Transportation plays a major role in global CO2 emission levels, a factor that directly connects with climate change. Roadway interventions that reduce CO2 emission levels have thus become a timely requirement. An integral need in assessing the impact of such roadway interventions is access to industry-standard programmatic and instantaneous emission models with various emission conditions such as fuel types, vehicle types, cities of interest, etc. However, currently, there is a lack of well-calibrated emission models with all these properties. Addressing these limitations, this paper presents 1100 programmatic and instantaneous vehicular CO2 emission models with varying fuel types, vehicle types, road grades, vehicle ages, and cities of interest. We hope the presented emission models will facilitate future research in tackling transportation-related climate impact. The released version of the emission models can be found here.

Authors: Edgar Ramirez Sanchez (MIT); Catherine H Tang (Massachusetts Institute of Technology); Vindula Jayawardana (MIT); Cathy Wu (MIT)

NeurIPS 2022 Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics (Proposals Track)
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Abstract: Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics has been put forward as a high impact candidate for replacing LGVs, with experts estimating over half of urban van deliveries being replaceable by cargo bikes, due to their faster speeds, shorter parking times and more efficient routes across cities. By modelling the relative delivery performance of different vehicle types across urban micro-regions, machine learning can help operators evaluate the business and environmental impact of adding cargo-bikes to their fleets. In this paper, we introduce two datasets, and present initial progress in modelling urban delivery service time (e.g. cruising for parking, unloading, walking). Using Uber’s H3 index to divide the cities into hexagonal cells, and aggregating OpenStreetMap tags for each cell, we show that urban context is a critical predictor of delivery performance.

Authors: Max C Schrader (University of Alabama); Navish Kumar (IIT Kharagpur); Nicolas Collignon (University of Edinburgh); Maria S Astefanoaei (IT University of Copenhagen); Esben Sørig (Kale Collective); Soonmyeong Yoon (Kale Collective); Kai Xu (University of Edinburgh); Akash Srivastava (MIT-IBM)

NeurIPS 2021 EcoLight: Reward Shaping in Deep Reinforcement Learning for Ergonomic Traffic Signal Control (Papers Track)
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Abstract: Mobility, the environment, and human health are all harmed by sub-optimal control policies in transportation systems. Intersection traffic signal controllers are a crucial part of today's transportation infrastructure, as sub-optimal policies may lead to traffic jams and as a result increased levels of air pollution and wasted time. Many adaptive traffic signal controllers have been proposed in the literature, but research on their relative performance differences is limited. On the other hand, to the best of our knowledge there has been no work that directly targets CO2 emission reduction, even though pollution is currently a critical issue. In this paper, we propose a reward shaping scheme for various RL algorithms that not only produces lowers CO2 emissions, but also produces respectable outcomes in terms of other metrics such as travel time. We compare multiple RL algorithms --- sarsa, and A2C --- as well as diverse scenarios with a mix of different road users emitting varied amounts of pollution.

Authors: Pedram Agand (Simon Fraser University); Alexey Iskrov (Breeze Labs Inc.); Mo Chen (Simon Fraser University)

NeurIPS 2021 Learning to Dissipate Traffic Jams with Piecewise Constant Control (Papers Track)
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Abstract: Greenhouse gases (GHGs), particularly carbon dioxide, are a key contributor to climate change. The transportation sector makes up 35% of CO2 emissions in the US and more than 70% of it is due to land transport. Previous work shows that simple driving interventions have the ability to significantly improve traffic flow on the road. Recent work shows that 5% of vehicles using piecewise constant controllers, designed to be compatible to the reaction times of human drivers, can prevent the formation of stop-and-go traffic congestion on a single-lane circular track, thereby mitigating land transportation emissions. Our work extends these results to consider more extreme traffic settings, where traffic jams have already formed, and environments with limited cooperation. We show that even with the added realism of these challenges, piecewise constant controllers, trained using deep reinforcement learning, can essentially eliminate stop-and-go traffic when actions are held fixed for up to 5 seconds. Even up to 10-second action holds, such controllers show congestion benefits over a human driving baseline. These findings are a stepping-stone for near-term deployment of vehicle-based congestion mitigation.

Authors: Mayuri Sridhar (MIT); Cathy Wu ()

ICML 2021 Commercial Vehicle Traffic Detection from Satellite Imagery with Deep Learning (Papers Track)
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Abstract: Road freight traffic is a major greenhouse gas emitter: commercial vehicles (CVs) contribute ∼7% to the global CO 2 emission budget, a fraction that is likely to increase in the future. The quantitative monitoring of CV traffic rates, while essential for the implementation of targeted road emission regulations, is costly and as such only available in developed regions. In this work, we investigate the feasibility of estimating hourly CV traffic rates from freely available Sentinel-2 satellite imagery. We train a modified Faster R-CNN object detection model to detect individual CVs in satellite images and feed the resulting counts into a regression model to predict hourly CV traffic rates. This architecture, when trained on ground-truth data for Switzerland, is able to estimate hourly CV traffic rates for any freeway section within 58% (MAPE) of the actual value; for freeway sections with historic information on CV traffic rates, we can predict hourly CV traffic rates up to within 4% (MAPE). We successfully apply our model to freeway sections in other coun tries and show-case its utility by quantifying the change in traffic patterns as a result of the first CoVID-19 lockdown in Switzerland. Our results show that it is possible to estimate hourly CV traffic rates from satellite images, which can guide civil engineers and policy makers, especially in developing countries, in monitoring and reducing greenhouse gas emissions from CV traffic.

Authors: Moritz Blattner (University of St. Gallen); Michael Mommert (University of St. Gallen); Damian Borth (University of St. Gallen)

ICML 2021 Reducing Carbon in the Design of Large Infrastructure Scheme with Evolutionary Algorithms (Papers Track)
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Abstract: The construction and operations of large infrastructure schemes such as railways, roads, pipelines and power lines account for a significant proportion of global carbon emissions. Opportunities to reduce the embodied and operational carbon emissions of new infrastructure schemes are greatest during the design phase. However, schedule and cost constraints limit designers from assessing a large number of design options in detail to identify the solution with the lowest lifetime carbon emissions using conventional methods. Here, we develop an evolutionary algorithm to rapidly evaluate in detail the lifetime carbon emissions of thousands of possible design options for new water transmission pipeline schemes. Our results show that this approach can help designers in some cases to identify design solutions with more than 10% lower operational carbon emissions compared with conventional methods, saving more than 1 million tonnes in lifetime carbon emissions for a new water transmission pipeline scheme. We also find that this evolutionary algorithm can be applied to design other types of infrastructure schemes such as non-water pipelines, railways, roads and power lines.

Authors: Matt Blythe (Continuum Industries)

ICML 2021 EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations (Papers Track)
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Abstract: The nexus between transportation, the power grid, and consumer behavior is much more pronounced than ever before as the race to decarbonize intensifies. Electrification in the transportation sector has led to technology shifts and rapid deployment of electric vehicles (EVs). The potential increase in stochastic and spatially heterogeneous charging load presents a unique challenge that is not well studied, and will have significant impacts on grid operations, emissions, and system reliability if not managed effectively. Realistic scenario generators can help operators prepare, and machine learning can be leveraged to this end. In this work, we develop generative adversarial networks (GANs) to learn distributions of electric vehicle (EV) charging sessions and disentangled representations. We show that this model successfully parameterizes unlabeled temporal and power patterns and is able to generate synthetic data conditioned on these patterns. We benchmark the generation capability of this model with Gaussian Mixture Models (GMMs), and empirically show that our proposed model framework is better at capturing charging distributions and temporal dynamics.

Authors: Robert Buechler (Stanford University); Emmanuel O Balogun (Stanford University); Arun Majumdar (Stanford University); Ram Rajagopal (Stanford University)

ICML 2021 Deep Spatial Temporal Forecasting of Electrical Vehicle Charging Demand (Papers Track)
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Abstract: Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this requirement, accurate forecasting of the charging demand is vital. Short and long-term charging demand forecasting will allow for better optimisation of the power grid and future infrastructure expansions. In this paper, we propose to use publicly available data to forecast the electric vehicle charging demand. To model the complex spatial-temporal correlations between charging stations, we argue that Temporal Graph Convolution Models are the most suitable to capture the correlations. The proposed Temporal Graph Convolutional Networks provide the most accurate forecasts for short and long-term forecasting compared with other forecasting methods.

Authors: Frederik B Hüttel (Technical University of Denmark (DTU)); Filipe Rodrigues (Technical University of Denmark (DTU)); Inon Peled (Technical University of Denmark (DTU)); Francisco Pereira (DTU)

NeurIPS 2020 Electric Vehicle Range Improvement by Utilizing Deep Learning to Optimize Occupant Thermal Comfort (Papers Track)
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Abstract: Heating, ventilation and air-conditioning (HVAC) systems can have a significant impact on the driving range of battery electric vehicles (EV’s). Predicting thermal comfort in an automotive vehicle cabin’s highly asymmetric and dynamic thermal environment is critical for developing energy-efficient HVAC systems. In this study we have coupled high-fidelity Computational Fluid Dynamics (CFD) simulations and Artificial Neural Networks (ANN) to predict vehicle occupant thermal comfort for any combination of steady-state boundary conditions. A vehicle cabin CFD model, validated against climatic wind tunnel measurements, was used to systematically generate training and test data that spanned the entire range of boundary conditions which impact occupant thermal comfort in an electric vehicle. Artificial neural networks (ANN) were applied to the simulation data to predict the overall Equivalent Homogeneous Temperature (EHT) comfort index for each occupant. An ensemble of five neural network models was able to achieve a mean absolute error of 2 ºC or less in predicting the overall EHT for all occupants in the vehicle on unseen or test data, which is acceptable for rapid evaluation and optimization of thermal comfort energy demand. The deep learning model developed in this work enables predictions of thermal comfort for any combination of steady-state boundary conditions in real-time without being limited by time-consuming and expensive CFD simulations or climatic wind tunnel tests. This model has been deployed as an easy-to-use web application within the organization for HVAC engineers to optimize thermal comfort energy demand and, thereby, driving range of electric vehicle programs.

Authors: Alok Warey (General Motors Global Research and Development); Shailendra Kaushik (General Motors Global Research and Development); Bahram Khalighi (General Motors Global Research and Development); Michael Cruse (Siemens Digital Industries Software); Ganesh Venkatesan (Siemens Digital Industries Software)

NeurIPS 2020 Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution (Papers Track)
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Abstract: Monitoring air pollution plays a key role when trying to reduce its impact on the environment and on human health. Traditionally, two main sources of information about the quantity of pollutants over a city are used: monitoring stations at ground-level (when available), and satellites' remote sensing. In addition to these two, other methods have been developed in the last years that aim at understanding how traffic emissions behave in space and time at a finer scale, taking into account the human mobility patterns. We present a simple and versatile framework for estimating the quantity of four air pollutants (CO2, NOx, PM, VOC) emitted by private vehicles moving on a road network, starting from raw GPS traces and information about vehicles' fuel type, and use this framework for analyses on how such pollutants distribute over the road networks of different cities.

Authors: Matteo Bohm (Sapienza University of Rome); Mirco Nanni (ISTI-CNR Pisa, Italy); Luca Pappalardo (ISTI)

NeurIPS 2020 Using attention to model long-term dependencies in occupancy behavior (Papers Track)
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Abstract: Over the past years, more and more models have been published that aim to capture relationships in human residential behavior. Most of these models are different Markov variants or regression models that have a strong assumption bias and are therefore unable to capture complex long-term dependencies and the diversity in occupant behavior. This work shows that attention based models are able to capture complex long-term dependencies in occupancy behavior and at the same time adequately depict the diversity in behavior across the entire population and different socio-demographic groups. By combining an autoregressive generative model with an imputation model, the advantages of two data sets are combined and new data are generated which are beneficial for multiple use cases (e.g. generation of consistent household energy demand profiles). The two step approach generates synthetic activity schedules that have similar statistical properties as the empirical collected schedules and do not contain direct information about single individuals. Therefore, the presented approach forms the basis to make data on occupant behavior freely available, so that further investigations based on the synthetic data can be carried out without a large data application effort. In future work it is planned to take interpersonal dependencies into account in order to be able to generate entire household behavior profiles.

Authors: Max Kleinebrahm (Karlsruhe Institut für Technologie); Jacopo Torriti (University Reading); Russell McKenna (University of Aberdeen); Armin Ardone (Karlsruhe Institut für Technologie); Wolf Fichtner (Karlsruhe Institute of Technology)

NeurIPS 2020 A Way Toward Low-Carbon Shipping: Improving Port Operations Planning using Machine Learning (Proposals Track)
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Abstract: Despite being the most carbon-efficient way of transportation, shipping is an important contributor to air pollution especially in coastal areas. The sector’s impact on the environment still need mitigation, through different measures undertaken so far. Operational optimization of ports and ships is a step in shipping progress towards reducing the pollution. The main purpose of this research is to reduce the degree of error and uncertainty of some operational parameters using Machine Learning models, and provide port managers with accurate information to assist them in their decision-making process. Therefore, they will be able to manage ships speed and port times for a better monitoring of ships emissions during sea voyage and port stay.

Authors: Sara El Mekkaoui (EMI Engineering School); Loubna Benabou (UQAR); Abdelaziz Berrado (EMI Engineering School)

NeurIPS 2020 ACED: Accelerated Computational Electrochemical systems Discovery (Proposals Track)
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Abstract: Large-scale electrification is vital to addressing the climate crisis, but many engineering challenges remain to fully electrifying both the chemical industry and transportation. In both of these areas, new electrochemical materials and systems will be critical, but developing these systems currently relies heavily on computationally expensive first-principles simulations as well as human-time-intensive experimental trial and error. We propose to develop an automated workflow that accelerates these computational steps by introducing both automated error handling in generating the first-principles training data as well as physics-informed machine learning surrogates to further reduce computational cost. It will also have the capacity to include automated experiments ``in the loop'' in order to dramatically accelerate the overall materials discovery pipeline.

Authors: Rachel C Kurchin (CMU); Eric Muckley (Citrine Informatics); Lance Kavalsky (CMU); Vinay Hegde (Citrine Informatics); Dhairya Gandhi (Julia Computing); Xiaoyu Sun (CMU); Matthew Johnson (MIT); Alan Edelman (MIT); James Saal (Citrine Informatics); Christopher V Rackauckas (Massachusetts Institute of Technology); Bryce Meredig (Citrine Informatics); Viral Shah (Julia Computing); Venkat Viswanathan (Carnegie Mellon University)

ICLR 2020 MobilityNet: Towards a Public Dataset for Multi-modal Mobility Research (Papers Track)
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Abstract: Influencing transportation demand can significantly reduce CO2 emissions. Individual user mobility models are key to influencing demand at the personal and structural levels. Constructing such models is a challenging task that depends on a number of interdependent steps. Progress on this task is hamstrung by the lack of high quality public datasets. We introduce MobilityNet: the first step towards a common ground for multi-modal mobility research. MobilityNet solves the holistic evaluation, privacy preservation and fine grained ground truth problems through the use of artificial trips, control phones, and repeated travel. It currently includes 1080 hours of data from both Android and iOS, representing 16 different travel contexts and 4 different sensing configurations.

Authors: K. Shankari (UC Berkeley); Jonathan Fürst (NEC Laboratories Europe); Mauricio Fadel Argerich (NEC Laboratories Europe); Eleftherios Avramidis (DFKI GmbH); Jesse Zhang (UC Berkeley)

NeurIPS 2019 Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning (Papers Track)
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Abstract: New forms of on-demand transportation such as ride-hailing and connected autonomous vehicles are proliferating, yet are a challenging use case for electric vehicles (EV). This paper explores the feasibility of using deep reinforcement learning (DRL) to optimize a driving and charging policy for a ride-hailing EV agent, with the goal of reducing costs and emissions while increasing transportation service provided. We introduce a data-driven simulation of a ride-hailing EV agent that provides transportation service and charges energy at congested charging infrastructure. We then formulate a test case for the sequential driving and charging decision making problem of the agent and apply DRL to optimize the agent's decision making policy. We evaluate the performance against heuristic policies and show that our agent learns to act competitively without any prior knowledge.

Authors: Jon Donadee (LLNL); Jacob Pettit (LLNL); Ruben Glatt (LLNL); Brenden Petersen (Lawrence Livermore National Laboratory)

NeurIPS 2019 Helping Reduce Environmental Impact of Aviation with Machine Learning (Papers Track) Best Paper Award
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Abstract: Commercial aviation is one of the biggest contributors towards climate change. We propose to reduce environmental impact of aviation by considering solutions that would reduce the flight time. Specifically, we first consider improving winds aloft forecast so that flight planners could use better information to find routes that are efficient. Secondly, we propose an aircraft routing method that seeks to find the fastest route to the destination by considering uncertainty in the wind forecasts and then optimally trading-off between exploration and exploitation. Both these ideas were previously published in [5] and [8] and contain further technical details.

Authors: Ashish Kapoor (Microsoft)

ICML 2019 Unsupervised Temporal Clustering to Monitor the Performance of Alternative Fueling Infrastructure (Research Track)
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Abstract: Zero Emission Vehicles (ZEV) play an important role in the decarbonization of the transportation sector. For a wider adoption of ZEVs, providing a reliable infrastructure is critical. We present a machine learning approach that uses unsupervised temporal clustering algorithm along with survey analysis to determine infrastructure performance and reliability of alternative fuels. We illustrate this approach for the hydrogen fueling stations in California, but this can be generalized for other regions and fuels.

Authors: Kalai Ramea (PARC)

ICML 2019 Truck Traffic Monitoring with Satellite Images (Research Track)
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Abstract: The road freight sector is responsible for a large and growing share of greenhouse gas emissions, but reliable data on the amount of freight that is moved on roads in many parts of the world are scarce. Many low- and middle-income countries have limited ground-based traffic monitoring and freight surveying activities. In this proof of concept, we show that we can use an object detection network to count trucks in satellite images and predict average annual daily truck traffic from those counts. We describe a complete model, test the uncertainty of the estimation, and discuss the transfer to developing countries.

Authors: Lynn Kaack (ETH Zurich); George H Chen (Carnegie Mellon University); Granger Morgan (Carnegie Mellon University)

ICML 2019 Finding Ship-tracks Using Satellite Data to Enable Studies of Climate and Trade Related Issues (Deployed Track)
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Abstract: Ship-tracks appear as long winding linear features in satellite images and are produced by aerosols from ship exhausts changing low cloud properties. They are one of the best examples of aerosol-cloud interaction experiments, which is currently the largest source of uncertainty in our understanding of climate forcing. Manually finding ship-tracks from satellite data on a large-scale is prohibitively costly while a large number of samples are required to better understand aerosol-cloud interactions. Here we train a deep neural network to automate finding ship-tracks. The neural network model generalizes well as it not only finds ship-tracks labeled by human experts, but also detects those that are occasionally missed by humans. It increases our sampling capability of ship-tracks by orders of magnitude and produces a first global map of ship-track distributions using satellite data. Major shipping routes that are mapped by the algorithm correspond well with available commercial data. There are also situations where commercial data are missing shipping routes that are detected by our algorithm. Our technique will enable studying aerosol effects on low clouds using ship-tracks on a large-scale, which will potentially narrow the uncertainty of the aerosol-cloud interactions. The product is also useful for applications such as coastal air pollution and trade.

Authors: Tianle Yuan (NASA)

ICML 2019 ML-driven search for zero-emissions ammonia production materials (Ideas Track)
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Abstract: Ammonia (NH3) production is an industrial process that consumes between 1-2% of global energy annually and is responsible for 2-3% of greenhouse gas emissions (Van der Ham et al.,2014). Ammonia is primarily used for agricultural fertilizers, but it also conforms to the US-DOE targets for hydrogen storage materials (Lanet al., 2012). Modern industrial facilities use the century-old Haber-Bosch process, whose energy usage and carbon emissions are strongly dominated by the use of methane as the combined energy source and hydrogen feedstock, not by the energy used to maintain elevated temperatures and pressures (Pfromm, 2017). Generating the hydrogen feedstock with renewable electricity through water electrolysis is an option that would allow retrofitting the billions of dollars of invested capital in Haber-Bosch production capacity. Economic viability is however strongly dependent on the relative regional prices of methane and renewable energy; renewables have been trending lower in cost but forecasting methane prices is difficult (Stehly et al., 2018; IRENA, 2017; Wainberg et al., 2017). Electrochemical ammonia production, which can use aqueous or steam H2O as its hydrogen source (first demonstrated ̃20years ago) is a promising means of emissions-free ammonia production. Its viability is also linked to the relative price of renewable energy versus methane, but in principle it can be significantly more cost-effective than Haber-Bosch (Giddeyet al., 2013) and also downscale to developing areas lacking ammonia transport infrastructure(Shipman & Symes, 2017). However to date it has only been demonstrated at laboratory scales with yields and Faradaic efficiencies insufficient to be economically competitive. Promising machine-learning approaches to fix this are discussed.

Authors: Kevin McCloskey (Google)

ICML 2019 Low-carbon urban planning with machine learning (Ideas Track)
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Abstract: Widespread climate action is urgently needed, but current solutions do not account enough for local differences. Here, we take the example of cities to point to the potential of machine learning (ML) for generating at scale high-resolution information on energy use and greenhouse gas (GHG) emissions, and make this information actionable for concrete solutions. We map the existing relevant ML literature and articulate ML methods that can make sense of spatial data for climate solutions in cities. Machine learning has the potential to find solutions that are tailored for each settlement, and transfer solutions across the world.

Authors: Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change (MCC)); Felix Creutzig (Mercator Research Institute on Global Commons and Climate Change (MCC))