Haul vehicle fuel and GHG emissions estimation using GPS data

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

“…First-order Model-agnostic Meta-learning (FOMAML) is simplified based on the Model-agnostic Meta-learning (MAML) by ignoring the second derivative terms to reduce the number of gradient steps in finding the best match [33]. Given that M defines the number of pre-training tasks, the objective function of MAML can be defined by (2), which is to find a set of initial parameters minimizing the learner loss (i.e., the predicting error in POP). Thereinto, θ represents the intermediate parameter related to the initial parameter φ; θ m denotes the current parameter in task m; l m (θ m ) indicates the loss with θ m in task m; and L(φ) is the total after-training loss of initial parameter φ.…”

“…A recent investigation indicated that between 9 and 56 percent of traffic was cruising for parking, and the average search time was about 6.03 min [1]. It can be said that "cruising for parking" has become a common phenomenon in areas with high travel demand, which not only increases travel costs for travelers but also causes additional congestion and emissions [2]. Therefore, optimizing the allocation of parking resources is a critical and urgent issue.…”

“…In the current literature, there are three common ways to handle data shortages: (1) feature enhancement by introducing extra heterogeneous data [8,13,14]; (2) structure optimization by incorporating spatial-temporal data into deep neural networks [15,16]; and…”

“…To overcome these shortages, this paper proposed an approach named ALL which can provide a lightweight and effective personalized model for the data-poor parking lots by integrating two novel ideas, i.e., Adaptation and Learning to Learn. As illustrated in Figure 1, the proposed algorithm consists of three modules, namely: (1) Federation: a community formed by parking lots, which adopts federated learning framework [19] to bridge data islands among the clients and support distributed computing; (2) Selector: an auto-selector used to select appropriate "guiders" automatically for the Learner module, which is trained by a Reinforcement Learning (RL) method called Asynchronous Advantage Actor-Critic (A3C) [20] with parking-related features; and (3) Learner: a meta-learner based on Federated First-order Model-agnostic Meta-learning (FedFOMAML) [21], which utilizes the aggregated gradients obtained from selected other clients (i.e., "guiders") to update the personalized parameters of the target parking lot. In summary, the proposed approach enables the POP model to support distributed computing, customize knowledge sources and learn extra knowledge by integrating the three modules (i.e., Federation, Selector, and Learner).…”