Redundancy-based automated program repair (APR), which generates patches by referencing existing source code, has gained much attention since they are effective in repairing real-world bugs with good interpretability. However, since existing approaches either demand the existence of multiline similar code or randomly reference existing code, they can only repair a small number of bugs with many incorrect patches, hindering their wide application in practice. In this work, we aim to improve the effectiveness of redundancy-based APRs by exploring more effective source code reuse methods for improving the number of correct patches and reducing incorrect patches. Specifically, we have proposed a new repair technique named REPATT, which incorporates a two-level pattern mining process for guiding effective patch generation (i.e., token and expression levels). We have conducted an extensive experiment on the widely-used Defects4J benchmark and compared Repatt with ten state-of-the-art APR approaches. The results show that it complements existing approaches by repairing 9 unique bugs compared with the latest Large Language Model (LLM)-based and deep learning-based methods and 19 unique bugs compared with traditional repair methods when providing the perfect fault localization. In addition, when the perfect fault localization is unknown in real practice, REPATT significantly outperforms the baseline approaches by achieving much higher patch precision, i.e., 83.8 %, although it repairs fewer bugs. Moreover, we further proposed an effective patch ranking strategy for combining the strength of REPATT and the baseline methods. The result shows that it repairs 124 bugs when only considering the Top-1 patches and improves the best-performing repair method by repairing 39 more bugs. The results demonstrate the effectiveness of our approach for practical use.

In recent years, Artificial Intelligence (AI) has significantly enhanced road safety, with Explainable Artificial Intelligence (XAI) providing essential transparency and trust. Our research utilizes AI to improve Advanced Driving Assistance Systems (ADAS) by investigating the gap in Forward Collision Warning (FCW): the impact of previous negative warnings (false and nuisance warnings) on drivers’ response times to subsequent accurate FCWs. By integrating XAI methods, we offer insights into the factors affecting driver behavior and system trust. Utilizing extensive dataset that encompasses various driving scenarios and driver behaviors, we constructed a gradient-boosting machine model to forecast driver response times . To explain the underlying mechanics of the model, the Shapley Additive Explanations (SHAP) framework was employed, enabling a comprehensive interpretation of feature importance and inter-feature interactions. Key findings reveal that increased speeds heighten driver responsiveness due to amplified alertness, whereas slower speeds lead to delayed reactions. The influence of previous negative warnings, significantly extends response times to accurate warnings. Additionally, older drivers require longer response times. The relationship between the driving period and previous warning judgment profoundly affects subsequent driver responsiveness, indicating trust dynamics with FCW systems. By using interpretable machine learning, we provide insights into ADAS functionality, suggesting pathways for FCW responsiveness and contributing to the field of XAI applications. In the validation experiment, our approach improved driver response times, reducing the average time from 2.1 s to 1.6 s. The proportion of ignored warnings decreased from 12% to 6%, and the driver acceptance rate increased from 59% to 71%.

Delineating urban land use patterns and the close relation to transport has long been a core research topic, in which most are analyzed at aggregate level from a macroscopic perspective. This study tended to create a new homogeneity-related zone system from the basic grid cells and examining if the improved system outperforms than traditional zone systems when conducting aggregate analysis in transportation. Within ride-hailing schemes, the selection process of drop-off locations of each trip is fully recorded by the mobile transportation platform, which offers a promising way in addressing the spatial aggregation issue. Specifically, on the basis of destination retrieval record (DRR) data, this study explored the potential relevance among them and built homogeneity pairs. Based on the improved cluster algorithm, homogeneity pairs are used to aggregate basic grid cells into the homogeneous traffic analysis zones (HTAZs) that are high in inter-zone consistency. The third ring area of Chengdu City as a case study was divided into 260 HTAZs using the proposed partition approach. Newly developed homogeneity-related zone system performs better than traditional systems in terms of geometry and internal consistency. Findings from this study also suggest that the zone partition should maintain internal consistency, namely homogeneity, as much as possible so that the aggregate-level spatial units can reflect and summarize the features of intra-zone individuals. This study demonstrates the mining potential of trip record data within transport systems, and provides a more reliable spatial structure to reveal travel patterns and conduct transportation design.

Molecule inverse folding has been a long-standing challenge in chemistry and biology, with the potential to revolutionize drug discovery and material science. Despite specified models have been proposed for different small- or macro-molecules, few have attempted to unify the learning process, resulting in redundant efforts. Complementary to recent advancements in molecular structure prediction, such as RoseTTAFold All-Atom and AlphaFold3, we propose the unified model UniIF for the inverse folding of all molecules. We do such unification in two levels: 1) Data-Level: We propose a unified block graph data form for all molecules, including the local frame building and geometric feature initialization. 2) Model-Level: We introduce a geometric block attention network, comprising a geometric interaction, interactive attention and virtual long-term dependency modules, to capture the 3D interactions of all molecules. Through comprehensive evaluations across various tasks such as protein design, RNA design, and material design, we demonstrate that our proposed method surpasses state-of-the-art methods on all tasks. UniIF offers a versatile and effective solution for general molecule inverse folding.

Data missing phenomena and random anomalies are ubiquitous in intelligent transportation systems (ITS), resulting in poor data quality and usability, which is a major impediment to real-world ITS applications. Most studies regarding traffic data recovery either assume that the original data are clean or complete, while such two issues often coexist in reality due to inevitable data measurement errors like detector malfunctions. In this paper, we fully exploit the algebraically low-rank property of traffic spatiotemporal data and develop an innovative tensor completion approach (termed SCPN) based on the tensor Schatten capped$ p $norm, a unified representation of tensor norms with a high flexibility. Furthermore, we extend the proposed method to a robust form (termed RSCPN) by leveraging the sparsity of unstructured outliers, with the aim to reconstruct ground-truth values from corrupted and incomplete observations. Finally, associated optimization solutions based on the alternating direction multiplier method are derived. Extensive experiments on four datasets substantiate the significant superiority of our proposed models over other state-of-the-art methods on both missing data imputation and corrupted data recovery tasks with miscellaneous simulated scenarios.

Due to convenience and flexibility, online ride-hailing has become increasingly more prevalent across the world. However, many violations and road crashes involving online ride-hailing were related to the unhealthy working pace of drivers, especially inadequate rest. This paper enriches the literature by providing a first look into the latent break patterns of online ride-hailing drivers based on a one-month order record dataset. A data mining and knowledge discovery process is presented for extracting and analyzing characteristics of online ride-hailing drivers’ work and rest based on GPS trajectory data, as follows: 1) logical judging to identify non-work order-gaps; 2) dynamic topic modeling to discover latent break patterns; and 3) integrating the topic modeling results with feature analysis results of order-gaps to summarize the time-dependent characteristics of online ride-hailing drivers’ special working pace. The case study results show that the latent break patterns extracted from two cities’ online ride-hailing order records are significantly different in the strength and cycles of the topics, which is greatly related to the travel supply-demand conditions and urban characters. Furthermore, the proposed analytical framework can help mobile transportation platform companies to better understand online ride-hailing markets from the perspective of drivers and to adjust their marketing strategies in real time.

Intermodal transfer patterns could provide a better understanding of urban mobility in an integrated transit system. However, the existing literature is still limited related to in-depth data-driven investigations of transfer patterns, particularly between metro and bus. In this study, we propose a generic framework to unravel latent transfer patterns at both aggregated and disaggregated levels. K-means clustering method is first used to classify metro stations based on surrounding built environment. We then introduce the cube technique which allows hierarchical aggregations for each dimension and cross-tabulations of massive ridership data. Last, we redesign the generative mechanism of the structural topic model that is capable of capturing the variability and interdependence in transfer patterns with passenger-level attributes. An empirical study is conducted with large-scale metro and bus smart card data, as well as built environment data in Nanjing, China. The results indicate intermodal transfers exhibit significant heterogeneous patterns in different transfer types over space and time. Although transfers per station in central business district (CBD) areas rank the highest, urban sprawl helps increase the utilization rate of transfer services in inner suburbs with most types of transfer behaviors. Strong correlations are identified among commuting transfer patterns, while correlations remain weak for pattern pairs between commuting and non-commuting activities, reflecting high consistency among commuting activities. Adults play a dominant role in commuting patterns in CBD areas and inner suburbs. Yet, students and the elderly are found to influence the pattern prevalence more effectively in outer suburbs and periphery areas.

In order to reduce passenger delays and prevent severe overcrowding in the subway system, it is necessary to accurately predict the short-term passenger flow during special events. However, few studies have been conducted to predict the subway passenger flow under these conditions. Traditional methods, such as the autoregressive integrated moving average (ARIMA) model, were commonly used to analyze regular traffic demands. These methods usually neglected the volatility (heteroscedasticity) in passenger flow influenced by unexpected external factors. This paper, therefore, proposed a generic framework to analyze short-term passenger flow, considering the dynamic volatility and nonlinearity of passenger flow during special events. Four different generalized autoregressive conditional heteroscedasticity models, along with the ARIMA model, were used to model the mean and volatility of passenger flow based on the transit smart card data from two stations near the Olympic Sports Center, Nanjing, China. Multiple statistical methods were applied to evaluate the performance of the hybrid models. The results indicate that the volatility of passenger flow had significant nonlinear and asymmetric features during special events. The proposed framework could effectively capture the mean and volatility of passenger flow, and outperform the traditional methods in terms of accuracy and reliability. Overall, this paper can help transit agencies to better understand the deterministic and stochastic changes of the passenger flow, and implement precautionary countermeasures for large crowds in a subway station before and after special events.

In metro train systems, energy-saving operations can include timetable optimization and speed control. Timetable optimization aims to promote the effective utilization of regenerative energy, and speed control aims to minimize the tractive energy consumption. To effectively reduce the net energy consumption (i.e., the difference between tractive energy consumption and regenerative energy utilization), this paper introduces an integrated optimization model that incorporates timetable and speed profile optimization. A cataclysmic genetic algorithm was developed to solve the proposed optimization model. Data were collected from the No. 2 metro line in Nanjing, China; the results showed that the proposed model could reduce the practical energy consumption by 17.5% in comparison with the original timetable. In addition, compared with the cooperative scheduling model that optimized the timetable and speed separately, the proposed model reduced the practical energy consumption by 8.9% on average. Finally, sensitivity analyses were also conducted to investigate the effects of the number of stations on the energy-saving operations.

Conflicting transit signal priority (TSP) requests can significantly influence the efficiency of TSP. Determining the best serve sequence for TSP requests is important for both the transit and other vehicles. The majority of previous studies focused on resolving conflicting TSP requests at the intersection level. For urban arterials, few studies take coordination into consideration, so the benefits of TSP may be lost due to the lack of progression. In view of this, this paper proposes a bi-level optimization model to resolve the problem of conflicting TSP requests at arterial corridors. The upper level of the model is progression control and the lower level is intersection control. At the progression control level, by considering the characteristics of bus operation, this paper proposes a signal coordination method that generates a green band between the two adjacent bus stops (stop-to-stop). At the intersection control level, a linear programming model is developed to resolve conflicting TSP requests. Then, this paper uses a hybrid genetic algorithm to solve the proposed model. Finally, an arterial with seven intersections in Nanjing, China, is selected to evaluate the performance of the proposed model. Three other models (the baseline model without TSP, Model 1 with the regular TSP and the classical coordination, and Model 2 with the lower model and the classical coordination) are also utilized to conduct the comparison analysis. The results show the proposed model can significantly reduce the bus passenger delay and stops compared to the three other models and meanwhile the operation of other vehicles will not turn worse.

Resolving conflicting transit signal priority (TSP) requests has become an emerging topic in public transportation studies. The main objective of TSP is to reduce schedule deviation and enhance the reliability of bus service, whereas many previous studies have aimed at reducing priority delay. Furthermore, they did not take into account the element of waiting time experienced by passengers at bus stops. Therefore, this paper selects in-bus passenger delay and passenger waiting delay at next bus stops as the indexes to measure the priority level of TSP requests. Here, a decision model is proposed. It favors a bus with long delay and adds in-bus passenger delay and passenger waiting delay at next stops for buses requesting the same TSP actions. A case study is conducted to examine the performance of the proposed model, which was compared with the baseline model (no TSP), the conventional model (Model 1) using the first-in-first-service policy, and a typical optimization model (Model 2) developed in previous studies. Results show that the proposed model significantly outperformed the other three models. Specifically, the proposed model reduced average passenger waiting delay by 20.4%, 16.2%, and 12.2% over the baseline model, Model 1, and Model 2, respectively. The average in-bus passenger delay was reduced by 14.2% over the baseline method, by 10.3% over Model 1, and by 6.6% over Model 2. At the same time, the passenger delay of other vehicles for the proposed model was 8.8% and 4.8% lower than those of Model 1 and Model 2, respectively.