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Near-Optimal Emission-Aware Online Ride Assignment Algorithm for Peak Demand Hours

Ridesharing has experienced significant global growth over the past decade and is becoming integral to future transportation networks. These services offer alternative mobility options in many urban areas, promoting car-light or car-free lifestyles, with their market share rapidly expanding due to the convenience they offer. However, alongside these benefits, concerns have arisen about the environmental impact of ridesharing, particularly its contribution to carbon emissions. A major source of these emissions is deadhead miles that are driven without passengers between trips. This issue is especially pronounced during high-demand periods when the number of ride requests exceeds platform capacity, leading to longer deadhead miles and higher emissions. While reducing these unproductive miles can lower emissions, it may also result in longer wait times for passengers as they wait for a nearby driver, potentially diminishing the overall user experience. In this paper, we propose LARA, an online algorithm for rider-to-driver assignment that dynamically adjusts the maximum allowed deadhead miles for drivers and assigns ride requests accordingly. While LARA can be applied under any conditions, it is particularly more effective during high-demand hours, aiming to reduce both carbon emissions and rider wait times. We prove that LARA achieves near-optimal performance in online settings compared to the optimal offline algorithm. Furthermore, we evaluate LARA using both synthetic and real-world datasets, demonstrating up to 34.2% reduction in emissions and up to 42.9% reduction in rider wait times compared to state-of-the-art algorithms. While recent studies have introduced the problem of emission-aware ride assignment, LARA is the first algorithm to provide both theoretical and empirical guarantees on performance.

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