How routing strategies impact urban emissions

Cornacchia, Giuliano; Böhm, Matteo; Mauro, Giovanni; Nanni, Mirco; Pedreschi, Dino; Pappalardo, Luca

doi:10.1145/3557915.3560977

Cited by 13 publications

(9 citation statements)

References 8 publications

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“…This factor serves to quantify emission values in connection with the various groups of visual features. Its defined as in Equation (17):…”

Section: Experiments Resultsmentioning

confidence: 99%

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Visual Eco-Routing (VER): XGBoost Based Eco-Route Selection From Road Scenes and Vehicle Emissions

Pradeep,

Khemmar,

Jendoubi

2024

IEEE Access

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Traffic-related pollution significantly contributes to environmental degradation. The escalating demand for vehicles, coupled with consumer preferences for larger utility vehicles, poses a challenge to achieving targeted carbon emission reductions in common fleet vehicles. To address the inefficiencies in existing route planning methods, this paper introduces a novel eco-routing approach known as Visual Eco-Routing (VER). VER is designed to understand the non-linear relationships between captured road scenes and emissions data across various road links, providing a comprehensive insight into the real-time dynamics of roads and their influence on vehicle performance characteristics. On-road experimental cycles are conducted to gather data, creating a new dataset called the Vehicle Activity Dataset (VAD). To assess the viability of the VER approach, a model named VER-XGB based on eXtreme Gradient Boosting (XGBoost) is proposed. Performance comparisons are made by individual training and benchmarking three selected models, both without VER association and separately with VER association. The comparison reveals significantly lower prediction errors in models with VER, with VER-XGB exhibiting enhanced reliability, yielding MAPE of 4.83% with VAD. Additionally, an aggregate factor termed the emission factor is introduced to explore the correlation between emission gases and distinct groups of visual features defined in the study. The analysis indicates a high correlation between infrastructure features such as traffic signals and stop signs on the road and vehicle emission values. Concluding the study, a qualitative examination is undertaken to evaluate the real-world applicability of the model by predicting an eco-route for a given source and destination pair. The MAPE for this route for predictions from VER-XGB is found to be 6.21%, affirming the practical utility of the proposed VER-XGB model in real-world scenarios.

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“…This factor serves to quantify emission values in connection with the various groups of visual features. Its defined as in Equation (17):…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Efforts have been in the past decade to develop eco-routing navigation systems that find a route that causes the least amount of emissions and/or requires less amount of fuel consumption. [9]- [11], [16], [17]. It has been demonstrated that these eco-routes are not always the same as the shortest duration route [10], [11].…”

Section: Background a Eco-routingmentioning

confidence: 99%

Visual Eco-Routing (VER): XGBoost Based Eco-Route Selection From Road Scenes and Vehicle Emissions

Pradeep,

Khemmar,

Jendoubi

2024

IEEE Access

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“…Prosociality is relevant when people decide to recycle, consume resources responsibly, take good care of public urban spaces, or take an active role in their communities (Santos & Bloembergen 2019;Hsu et al 2020;Arana-Catania et al 2021;Hsu et al 2022). The connection between prosociality, AI, and urban systems is also evident in the case of route recommender systems, where following AI recommendations might lead to detrimental outcomes such as higher pollution levels (Cornacchia et al 2022): will citizens be willing to accept algorithmic recommendations that are not individually optimal, yet contribute to the collective good?…”

Section: Prosociality In Urban Systemsmentioning

confidence: 99%

“…The connection between prosociality, AI, and urban systems is also evident in the case of route recommender systems, where following AI recommendations might lead to detrimental outcomes such as higher pollution levels (Cornacchia et al. 2022): will citizens be willing to accept algorithmic recommendations that are not individually optimal, yet contribute to the collective good?…”

Section: Introductionmentioning

confidence: 99%

Prosocial dynamics in multiagent systems

Santos

2024

AI Magazine

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Meeting today's major scientific and societal challenges requires understanding dynamics of prosociality in complex adaptive systems. Artificial intelligence (AI) is intimately connected with these challenges, both as an application domain and as a source of new computational techniques: On the one hand, AI suggests new algorithmic recommendations and interaction paradigms, offering novel possibilities to engineer cooperation and alleviate conflict in multiagent (hybrid) systems; on the other hand, new learning algorithms provide improved techniques to simulate sophisticated agents and increasingly realistic environments. In various settings, prosocial actions are socially desirable yet individually costly, thereby introducing a social dilemma of cooperation. How can AI enable cooperation in such domains? How to understand long‐term dynamics in adaptive populations subject to such cooperation dilemmas? How to design cooperation incentives in multiagent learning systems? These are questions that I have been exploring and that I discussed during the New Faculty Highlights program at AAAI 2023. This paper summarizes and extends that talk.

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“…Many road users rely on navigation applications [143][144][145] to choose their routes in major cities. These applications provide optimal routes based solely on real-time traffic information without necessarily seeking equilibrium for obvious computational time reasons [146,147].…”

Section: Intelligent Transportation Systemsmentioning

confidence: 99%

Review of Traffic Assignment and Future Challenges

Elimadi,

Abbas-Turki,

Koukam

et al. 2024

Applied Sciences

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The problem of traffic assignment consists of determining the routes taken by the users of transportation infrastructure. This problem has been the subject of numerous studies, particularly in analyzing scenarios for developing road infrastructure and pricing strategies. This paper reviews the major progress in the field. Accordingly, it shows that the evolution of intelligent transportation systems and the emergence of connected and autonomous vehicles present new challenges to classical approaches for solving the traffic assignment problem. It addresses two major perspectives: digital twins coupled with artificial intelligence to help decision-makers, and rule-based policy to offer users fair and efficient itineraries while respecting infrastructure capacity.

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How routing strategies impact urban emissions

Cited by 13 publications

References 8 publications

Visual Eco-Routing (VER): XGBoost Based Eco-Route Selection From Road Scenes and Vehicle Emissions

Visual Eco-Routing (VER): XGBoost Based Eco-Route Selection From Road Scenes and Vehicle Emissions

Prosocial dynamics in multiagent systems

Review of Traffic Assignment and Future Challenges

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