Vehicle trajectory data are essential in smart mobility applications, yet often incomplete, necessitating systematic reconstruction for effective use. Existing methods often overlook traffic rules and vehicle interactions in their reconstruction process, a research gap that becomes critical for fine-grained reconstruction of incomplete and irregular microscopic traffic data. To address this limitation, this paper introduces a novel fine-grained trajectory reconstruction (FTR) framework, particularly for urban signalized intersections, considering both traffic rules and vehicle interactions through a microscopic traffic simulation (MTS) model. This is motivated by challenging missing patterns in real-world data from Alibaba City Brain Lab and limitations in existing reconstruction approaches. To this end, the FTR problem is first formulated as an MTS-based optimization problem. Then, to solve this problem effectively under a limited computing budget, an advanced dynamic data-driven evolutionary optimization technique, D3GA++, is proposed. Through the validation involving two real-world datasets, D3GA++ has demonstrated superior performance under various missing data scenarios consistently surpassing baselines such as brute-force random search and standard evolutionary algorithm in terms of reconstruction accuracy. Our work can have crucial implications for traffic management, urban planning, and autonomous vehicle technology development.

Just-in-Tim e (JIT) defect prediction has been proposed to help teams prioritize the limited resources on the most risky commits (or pull requests), yet it remains largely a black box, whose predictions are not explainable or actionable to practitioners. Thus, prior studies have applied various model-agnostic techniques to explain the predictions of JIT models. Yet, explanations generated from existing model-agnostic techniques are still not formally sound, robust, and actionable. In this article, we propose FoX , a Fo rmal e X plainer for JIT Defect Prediction, which builds on formal reasoning about the behavior of JIT defect prediction models and hence is able to provide provably correct explanations, which are additionally guaranteed to be minimal. Our experimental results show that FoX is able to efficiently generate provably correct, robust, and actionable explanations, while existing model-agnostic techniques cannot. Our survey study with 54 software practitioners provides valuable insights into the usefulness and trustworthiness of our FoX approach; 86% of participants agreed that our approach is useful, while 74% of participants found it trustworthy. Thus, this article serves as an important stepping stone towards trustable explanations for JIT models to help domain experts and practitioners better understand why a commit is predicted as defective and what to do to mitigate the risk.

The rise of AI methods to make predictions and decisions has led to a pressing need for more explainable artificial intelligence (XAI) methods. One common approach for XAI is to produce a post-hoc explanation, explaining why a black box ML model made a certain prediction. Formal approaches to post-hoc explanations provide succinct reasons for why a prediction was made, as well as why not another prediction was made. But these approaches assume that features are independent and uniformly distributed. While this means that “why” explanations are correct, they may be longer than required. It also means the “why not” explanations may be suspect as the counterexamples they rely on may not be meaningful. In this paper, we show how one can apply background knowledge to give more succinct “why” formal explanations, that are presumably easier to interpret by humans, and give more accurate “why not” explanations. In addition, we show how to use existing rule induction techniques to efficiently extract background information from a dataset.

The rapid rise of Artificial Intelligence (AI) and Machine Learning (ML) has invoked the need for explainable AI (XAI). One of the most prominent approaches to XAI is to train rule-based ML models, e.g. decision trees, lists and sets, that are deemed interpretable due to their transparent nature. Recent years have witnessed a large body of work in the area of constraints- and reasoning-based approaches to the inference of interpretable models, in particular decision sets (DSes). Despite being shown to outperform heuristic approaches in terms of accuracy, most of them suffer from scalability issues and often fail to handle large training data, in which case no solution is offered. Motivated by this limitation and the success of gradient boosted trees, we propose a novel anytime approach to producing DSes that are both accurate and interpretable. The approach makes use of the concept of a generalized formal explanation and builds on the recent advances in formal explainability of gradient boosted trees. Experimental results obtained on a wide range of datasets, demonstrate that our approach produces DSes that more accurate than those of the state-of-the-art algorithms and comparable with them in terms of explanation size.

Decision sets and decision lists are two of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, both of these machine learning models are becoming increasingly attractive, as they combine small size and clear explainability. In this paper, we define size as the total number of literals in the SAT encoding of these rule-based models as opposed to earlier work that concentrates on the number of rules. In this paper, we develop approaches to computing minimum-size “perfect” decision sets and decision lists, which are perfectly accurate on the training data, and minimal in size, making use of modern SAT solving technology. We also provide a new method for determining optimal sparse alternatives, which trade off size and accuracy. The experiments in this paper demonstrate that the optimal decision sets computed by the SAT-based approach are comparable with the best heuristic methods, but much more succinct, and thus, more explainable. We contrast the size and test accuracy of optimal decisions lists versus optimal decision sets, as well as other state-of-the-art methods for determining optimal decision lists. Finally, we examine the size of average explanations generated by decision sets and decision lists.

Traffic signal is the key method for city traffic control. Existing signal control systems use the loop detector data as the main input which is nearsighted in terms of both space and time. Lacking of effective data collection methods has hindered the development of more sophisticated models. It is therefore very hard to develop an optimization model considering all signals in a region or even a city. In this paper, we will introduce our method for large scale traffic signal optimization, which is the major module of Alibaba's city brain solution. By integrating multiple data sources to sense the whole city's traffic conditions, a layered model is developed based on the divide-and-conquer paradigm to gradually apply different types of data-driven optimization algorithms. It is a paradigm shift to use big data to improve a traditionally closed signal control system, and its effectiveness has been proven in a field test in Shanghai city.

In Wireless Mesh Networks (WMNs), Mesh Access Points (MAPs) forward the packets from their associated mobile stations (STAs) to other stations or to the portal through a wireless multi-hop backhaul. The wireless backhaul has limited bandwidth and may become the bottleneck of the WMN easily when network load is heavy. Inspired by the fact that transmission from MAPs with good backhaul condition consumes less backhaul network resource, we propose a Mobility-Aware ReAssociation (MARA) control scheme that makes better use of the network resource by prolonging mobile stations association period with good backhaul MAPs. In MARA, STAs adjust their scan intervals and make association decisions based on the estimated moving directions and the association cost of nearby MAPs. Our simulation results show that the proposed scheme achieves improved end-to-end performance consistently under different network scenarios.