JointMatch is a learning-based framework that simultaneously handles request pairing and vehicle assignment for ride‑sharing using a single, sparsified heterogeneous graph neural network. By scoring all candidate decisions in one forward pass, it scales linearly with the number of vehicles and requests, outperforming classical heuristics and two‑stage GNN baselines on New York City Yellow Taxi data. The model achieves significant speedups—over 20× faster per dispatch epoch at city scale—and further improves revenue through supervised training and policy‑gradient fine‑tuning.
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.
RideSkill is a hierarchical algorithm for generalized ride sharing that uses large language models (LLMs) to automatically design and train a skill repository, a combiner, and a repositioner. The combiner assigns vehicle-specific skills for adaptive dispatch across varying scenarios and objectives, while the repositioner moves idle vehicles to emerging regions to avoid conflicts. By training all components via an LLM-based evolutionary method, RideSkill eliminates the need for real-time LLM calls, enabling high-performance deployment in large-scale systems.
The paper introduces GREAT, a Graph Edge Attention Network that operates directly on edge space to handle asymmetric energy costs in the Electric Vehicle Routing Problem (EVRP). Integrated into a reinforcement learning framework, GREAT jointly optimizes routing and charging decisions, outperforming a strong HGS-based heuristic by up to 8.78% in energy savings and reducing runtime by a factor of 60. It also shows competitive performance against a custom adaptive large neighborhood search and achieves state‑of‑the‑art results on other asymmetric routing benchmarks such as TSP, CVRP, and Orienteering.
Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convolutional networks, recurrent networks, and Transformers, achieve strong results on standard benchmarks, but their architectures are designed by hand, requiring significant expert effort and producing models that often generalize poorly across cities and datasets.
The paper introduces a deep architecture that jointly optimizes cost functions and a route-ranking model to accommodate diverse user preferences in route planning. It first generates a complete set of Pareto‑optimal routes using a multi‑objective Dijkstra algorithm, then employs a neural network that emulates shortest‑path search and ranking in an end‑to‑end differentiable framework. A novel loss function treats route preference as a constrained optimization problem, allowing a single objective to be optimized while other attributes remain constrained, and experiments on real‑world data show significant improvements over existing methods.
Bike-sharing systems are an important component of low-carbon urban mobility, but continued expansion creates challenges in both cold-start prediction and equitable resource allocation. Newly deployed...
arXiv:2607. 26467v1 Announce Type: new Abstract: Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch.
The paper introduces FairGIN, a fairness-aware graph neural network designed to predict demand for expanding bike‑sharing systems while addressing cold‑start challenges and equity concerns. It combines expansion‑simulated incremental training, attention‑based knowledge transfer, and income‑stratified regularization to improve predictive accuracy and reduce income‑based disparities. Experiments on NYC and Seattle show that FairGIN outperforms existing methods and supports more inclusive station placement without sacrificing overall efficiency.
The paper investigates why attention-based Pointer Networks, though size‑agnostic, lose performance when applied to different sized Capacitated Vehicle Routing Problems (CVRP). It identifies three causes—node‑density shifts, attention dispersion, and diluted graph context—and evaluates architectural changes on both encoder and decoder sides, including a learned permutation‑invariant graph‑context aggregation. The authors show that entropy‑based attention scaling improves large‑to‑small transfer, while the new aggregation methods best aid small‑to‑large transfer, and they combine these insights into a selector that chooses the optimal model for each train‑test size pair, reducing costs by up to 3.2% across seen and many unseen sizes.
arXiv:2606. 29725v1 Announce Type: new Abstract: In this paper, we formulate a new vehicle dispatch optimization problem, called Nursing Care Taxi Dispatch, as a variant of the Vehicle Routing Problem, considering constraints related to wheelchair use, user compatibility, pick-up and drop-off times, and vehicle limitations.
arXiv:2602. 00488v3 Announce Type: replace Abstract: Solving large-scale capacitated vehicle routing problems (CVRP) is hindered by the high complexity of classical heuristics and the limited generalization of neural solvers.