Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

The Ride-Pool Matching Problem (RMP) is central to on-demand ride-pooling services, where vehicles must be matched with multiple requests while adhering to service constraints such as pickup delays, detour limits, and vehicle capacity. Most existing RMP solutions assume passengers are picked up and dropped off at their original locations, neglecting the potential for passengers to walk to nearby spots to meet vehicles. This assumption restricts the optimization potential in ride-pooling operations. In this paper, we propose a novel matching method that incorporates extended pickup and drop-off areas for passengers. We first design a tree-based approach to efficiently generate feasible matches between passengers and vehicles. Next, we optimize vehicle routes to cover all designated pickup and drop-off locations while minimizing total travel distance. Finally, we employ dynamic assignment strategies to achieve optimal matching outcomes. Experiments on city-scale taxi datasets demonstrate that our method improves the number of served requests by up to 13\% and average travel distance by up to 21\% compared to leading existing solutions, underscoring the potential of leveraging passenger mobility to significantly enhance ride-pooling service efficiency.

We present a method for road inference from GPS trajectories to map construction sites. This task introduces a unique challenge due to the erratic and non-standard movement patterns of construction machinery, which diverge significantly from typical vehicular traffic on established roads. Our method first identifies intersections in the road network that serve as critical decision points, and later connects them with edges, producing a graph, which subsequently can be used for planning and task-allocation. We demonstrate the effectiveness of our approach by mapping roads at a real-life construction site in Norway. In Norway, the building and construction sector contributes directly and indirectly to 15% of total greenhouse gas emissions (2019), with construction vehicles accounting for 1.5% of the total emissions (2021) [1, 2].

In this article, we propose a reactive task allocation architecture for a multi-agent system for scenarios where the tasks arrive at random times and are grouped into multiple queues. Two stage tasks are considered where every task has a beginning, an intermediate and a final part, typical in pick-and-drop and inspect-and-report scenarios. A centralized auction-based task allocation system is proposed, where an auction system takes into consideration bids submitted by the agents for individual tasks, current length of the queues and the waiting times of the tasks in the queues to decide on a task allocation strategy. The costs associated with these considerations, along with the constraints of having unique mappings between tasks and agents and constraints on the maximum number of agents that can be assigned to a queue, results in a Linear Integer Program (LIP) that is solved using the SCIP solver. For the scenario where the queue lengths are penalized but not the waiting times, we demonstrate that the auction system allocates tasks in a manner that all the queue lengths become constant, which is termed balancing. For the scenarios where both the costs are considered, we qualitatively analyse the effect of the choice of the relative weights on the resulting task allocation and provide guidelines for the choice of the weights. We present simulation results that illustrate the balanced allocation of tasks and validate the analysis for the trade-off between the costs related to queue lengths and task waiting times.

A new report suggests Uber and Lyft are bias. Researchers analyzed data from millions of trips in Chicago, uncovering those traveling to and from low-income and non-white areas were overcharged per mile. However, the prejudice does not stem from the drive, but derives from the decision-making algorithms used to determine pricing. Aylin Caliskan, with George Washington University, said: 'Given a type of data, AI algorithms learn the patterns in that data--so if you give an algorithm data from the social domain, they end up learning the patterns of society.' 'Since our society is biased, these models end up learning these biases as well.' 'Then, when they're used in the social context to make decisions about human beings, they use that biased data to make decisions that not only perpetuates societal biases but even amplifies them.' Researchers created a map showing that some areas were charged as much as $5.10 per mile, compared to just 85 cents.

Conditional distributions provide a versatile tool for capturing the relationship between a target variable and a conditioning variable (or covariate). The last few decades has seen a broad range of modeling applications across multiple disciplines including econometrics in particular [30, 42], machine learning [14, 40], among others. In many cases, estimating a conditional density function from the observed data is a one of the first crucial steps in the data analysis pipeline. While the task of conditional density estimation has received a considerable attention in the literature, fewer works have investigated the equally important task of evaluating the goodness of fit of a given conditional density model. Several approaches that address the task of conditional model evaluation take the form of a hypothesis test. Given a conditional model, and a joint sample containing realizations of both target variables and covariates, test the null hypothesis stating that the model is correctly specified, against the alternative stating that it is not. The model does not specify the marginal distribution of the covariates. We refer to this task as conditional goodness-of-fit testing. One of the early nonparametric tests is [1], which extended the classic Kolmogorov test to the conditional case.

Conditional Density Estimation (CDE) models deal with estimating conditional distributions. The conditions imposed on the distribution are the inputs of the model. CDE is a challenging task as there is a fundamental trade-off between model complexity, representational capacity and overfitting. In this work, we propose to extend the model's input with latent variables and use Gaussian processes (GP) to map this augmented input onto samples from the conditional distribution. Our Bayesian approach allows for the modeling of small datasets, but we also provide the machinery for it to be applied to big data using stochastic variational inference. Our approach can be used to model densities even in sparse data regions, and allows for sharing learned structure between conditions. We illustrate the effectiveness and wide-reaching applicability of our model on a variety of real-world problems, such as spatio-temporal density estimation of taxi drop-offs, non-Gaussian noise modeling, and few-shot learning on omniglot images.

Conditional Density Estimation (CDE) models deal with estimating conditional distributions. The conditions imposed on the distribution are the inputs of the model. CDE is a challenging task as there is a fundamental trade-off between model complexity, representational capacity and overfitting. In this work, we propose to extend the model's input with latent variables and use Gaussian processes (GP) to map this augmented input onto samples from the conditional distribution. Our Bayesian approach allows for the modeling of small datasets, but we also provide the machinery for it to be applied to big data using stochastic variational inference. Our approach can be used to model densities even in sparse data regions, and allows for sharing learned structure between conditions. We illustrate the effectiveness and wide-reaching applicability of our model on a variety of real-world problems, such as spatio-temporal density estimation of taxi drop-offs, non-Gaussian noise modeling, and few-shot learning on omniglot images.

In this paper, we consider the problem of feasibly integrating new pick-up and delivery requests into existing vehicle itineraries in a dynamic, dial-a-ride problem (DARP) setting. Generalizing from previous work in oversubscribed task scheduling, we define a controlled iterative repair search procedure for finding an alternative set of vehicle itineraries in which the overall solution has been feasibly extended to include newly received requests. We first evaluate the performance of this technique on a set of DARP feasibility benchmark problems from the literature. We then consider its use on a real-world DARP problem, where it is necessary to accommodate all requests and constraints must be relaxed when a request cannot be feasibly integrated. For this latter analysis, we introduce a constraint relaxation post processing step and consider the performance impact of using our controlled iterative search over the current greedy search approach.