The accurate estimated time of arrival (ETA) is crucial for mobility and transportation applications. Although significant efforts have been made to improve ETA prediction, most existing approaches ignore the influence of individual driving habits and preferences, known as the driving style. Since different drivers may prefer specific routes and speeds based on their experience and familiarity with traffic conditions, driving styles play a crucial role in determining the actual ETA. To fill this gap, we present a novel approach, DSETA, which leverages deep learning to learn and then integrate driving style representations for personalized and precise ETA predictions. Our method employs a diffusion model that captures nuanced driving styles by generating driving speed distribution. We also utilize attention mechanisms to dynamically adjust the impacts of various spatio-temporal factors and driving styles on ETA predictions. Additionally, we introduce a Multi-View Multi-Task framework that incorporates auxiliary tasks, including segment-view driving style classification and route-view speed distribution prediction, to enhance the ETA learning process. A route-level speed prior regularization strategy further improves the model's generalization capabilities. Extensive experiments conducted on a large real-world trip trajectory dataset demonstrate that DSETA achieves high effectiveness and outperforms various baselines across multiple evaluation metrics.

Map matching aims to align GPS trajectories to their actual travel routes on a road network, which is an essential pre-processing task for most of trajectory-based applications. Many map matching approaches utilize Hidden Markov Model (HMM) as their backbones. Typically, HMM treats GPS samples of a trajectory as observations and nearby road segments as hidden states. During map matching, HMM determines candidate states for each observation with a fixed searching range, and computes the most likely travel route using theViterbialgorithm. Although HMM-based approaches can derive high matching accuracy, they still suffer from high computation overheads. By inspecting the HMM process, we find that the computation bottleneck mainly comes from improper candidate sets, which contain many irrelevant candidates and incur unnecessary computations. In this paper, we present$\mathtt {LiMM}$– a learned road network index structure for efficient map matching.$\mathtt {LiMM}$improves existing HMM-based approaches from two aspects. First, we propose a novel learned index for road networks, which considers the characteristics of road data. Second, we devise an adaptive searching range mechanism to dynamically adjust the searching range for GPS samples based on their locations. As a result,$\mathtt {LiMM}$can provide refined candidate sets for GPS samples and thus accelerate the map matching process. Extensive experiments are conducted with three large real-world GPS trajectory datasets. The results demonstrate that$\mathtt {LiMM}$significantly reduces computation overheads by achieving an average speedup of$11.7\times$than baseline methods, merely with a subtle accuracy loss of 1.8%.

Dynamic ridesharing has garnered significant attention in recent years due to its numerous benefits. Existing ridesharing algorithms often employ a “filter-and-refine” frame-work, where a large set of candidate vehicles is initially selected for each ride order, followed by computationally intensive route planning for each candidate. However, this process can lead to significant response delays and limit system efficiency. To address this challenge, we propose an order-vehicle pre-matching recommendation approach (PreMR) that refines the candidate set before route planning. PreMR leverages spatial-temporal intervals and a self-attention mechanism to encode diverse order and vehicle information into uniform and informative representations, enabling it to accurately identify the most suitable vehicles for each order. Extensive experiments using real-world datasets and four representative ridesharing algorithms demonstrate that PreMR significantly reduces order response time (by 46.78% on average) while maintaining high service quality, with a slight trade-off in the order completion rate.

Existing domain generalization (DG) methods for cross-person generalization tasks often face challenges in capturing intra- and inter-domain style diversity, resulting in domain gaps with the target domain. In this study, we explore a novel perspective to tackle this problem, a process conceptualized as domain padding. This proposal aims to enrich the domain diversity by synthesizing intra- and inter-domain style data while maintaining robustness to class labels. We instantiate this concept using a conditional diffusion model and introduce a style-fused sampling strategy to enhance data generation diversity. In contrast to traditional condition-guided sampling, our style-fused sampling strategy allows for the flexible use of one or more random styles to guide data synthesis. This feature presents a notable advancement: it allows for the maximum utilization of possible permutations and combinations among existing styles to generate a broad spectrum of new style instances. Empirical evaluations on a broad range of datasets demonstrate that our generated data achieves remarkable diversity within the domain space. Both intra- and inter-domain generated data have proven to be significant and valuable, contributing to varying degrees of performance enhancements. Notably, our approach outperforms state-of-the-art DG methods in all human activity recognition tasks.

In this article, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation (TOAR) with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards with different spatial locations and rental prices, our goal is to find a subset of billboards, such that the total targeted influence is maximum under a limited budget constraint. To achieve this goal, we are facing two challenges: (1) it is difficult to estimate targeted advertising influence in physical world; (2) due to NP hardness, many common search techniques fail to provide a satisfied solution with an acceptable time, especially for large-scale problem settings. Taking into account the exposure strength, advertisement matching degree, and advertising repetition effect, we first build a targeted influence model that can characterize that the advertising influence spreads along with users mobility. Subsequently, based on a divide-and-conquer strategy, we develop two effective approaches, i.e., a master–slave-based sequential optimization method, TOAR-MSS, and a cooperative co-evolution-based optimization method, TOAR-CC, to solve our studied problem. Extensive experiments on two real-world datasets clearly validate the effectiveness and efficiency of our proposed approaches.

Taxi ridesharing becomes promising and attractive because of the wide availability of taxis in a city and tremendous benefits of ridesharing, e.g., alleviating traffic congestion and reducing energy consumption. Existing taxi ridesharing schemes, however, are not efficient and practical, due to they simply match ride requests and taxis based on partial trip information and omit the offline passengers, who hail a taxi at roadside with no explicit requests to the system. In this paper, we consider the mobility-aware taxi ridesharing problem, and present mT- Share to address these limitations. mT-Share fully exploits the mobility information of ride requests and taxis to achieve efficient indexing of taxis/requests and better passenger-taxi matching, while still satisfying the constraints on passengers' deadlines and taxis' capacities. Specifically, mT-Share indexes taxis and ride requests with both geographical information and travel directions, and supports the shortest path based routing and probabilistic routing to serve both online and offline ride requests. Extensive experiments with a large real-world taxi dataset demonstrate the efficiency and effectiveness of mT-Share, which can response each ride request in milliseconds and with a moderate detour cost. Compared to state-of-the-art methods, mT-Share serves 42% and 62% more ride requests in peak and non-peak hours, respectively.