Exploring Large Language Models for Human Mobility Prediction under Public Events

Public events, such as concerts and sports games, can be major attractors for large crowds, leading to irregular surges in travel demand. Accurate human mobility prediction for public events is thus crucial for event planning as well as traffic or crowd management. While rich textual descriptions about public events are commonly available from online sources, it is challenging to encode such information in statistical or machine learning models. Existing methods are generally limited in incorporating textual information, handling data sparsity, or providing rationales for their predictions. To address these challenges, we introduce a framework for human mobility prediction under public events (LLM-MPE) based on Large Language Models (LLMs), leveraging their unprecedented ability to process textual data, learn from minimal examples, and generate human-readable explanations. Specifically, LLM-MPE first transforms raw, unstructured event descriptions from online sources into a standardized format, and then segments historical mobility data into regular and event-related components. A prompting strategy is designed to direct LLMs in making and rationalizing demand predictions considering historical mobility and event features. A case study is conducted for Barclays Center in New York City, based on publicly available event information and taxi trip data. Results show that LLM-MPE surpasses traditional models, particularly on event days, with textual data significantly enhancing its accuracy. Furthermore, LLM-MPE offers interpretable insights into its predictions. Despite the great potential of LLMs, we also identify key challenges including misinformation and high costs that remain barriers to their broader adoption in large-scale human mobility analysis

Deep trip generation with graph neural networks for bike sharing system expansion

Bike sharing is emerging globally as an active, convenient, and sustainable mode of transportation. To plan successful bike-sharing systems (BSSs), many cities start from a small-scale pilot and gradually expand the system to cover more areas. For station-based BSSs, this means planning new stations based on existing ones over time, which requires prediction of the number of trips generated by these new stations across the whole system. Previous studies typically rely on relatively simple regression or machine learning models, which are limited in capturing complex spatial relationships. Despite the growing literature in deep learning methods for travel demand prediction, they are mostly developed for short-term prediction based on time series data, assuming no structural changes to the system. In this study, we focus on the trip generation problem for BSS expansion, and propose a graph neural network (GNN) approach to predicting the station-level demand based on multi-source urban built environment data. Specifically, it constructs multiple localized graphs centered on each target station and uses attention mechanisms to learn the correlation weights between stations. We further illustrate that the proposed approach can be regarded as a generalized spatial regression model, indicating the commonalities between spatial regression and GNNs. The model is evaluated based on realistic experiments using multi-year BSS data from New York City, and the results validate the superior performance of our approach compared to existing methods. We also demonstrate the interpretability of the model for uncovering the effects of built environment features and spatial interactions between stations, which can provide strategic guidance for BSS station location selection and capacity planning

RouteKG: A knowledge graph-based framework for route prediction on road networks

Short-term route prediction on road networks allows us to anticipate the future trajectories of road users, enabling a plethora of intelligent transportation applications such as dynamic traffic control or personalized route recommendation. Despite recent advances in this area, existing methods focus primarily on learning sequential transition patterns, neglecting the inherent spatial structural relations in road networks that can affect human routing decisions. To fill this gap, this paper introduces RouteKG, a novel Knowledge Graph-based framework for route prediction. Specifically, we construct a Knowledge Graph on the road network, thereby learning and leveraging spatial relations, especially moving directions, which are crucial for human navigation. Moreover, an n-ary tree-based algorithm is introduced to efficiently generate top-K routes in a batch mode, enhancing scalability and computational efficiency. To further optimize the prediction performance, a rank refinement module is incorporated to fine-tune the candidate route rankings. The model performance is evaluated using two real-world vehicle trajectory datasets from two Chinese cities, Chengdu and Shanghai, under various practical scenarios. The results demonstrate a significant improvement in accuracy over baseline methods.We further validate our model through a case study that utilizes the pre-trained model as a simulator for real-time traffic flow estimation at the link level. The proposed RouteKG promises wide-ranging applications in vehicle navigation, traffic management, and other intelligent transportation tasks

GJB2 mutation spectrum in 2063 Chinese patients with nonsyndromic hearing impairment

Background: Mutations in GJB2 are the most common molecular defects responsible for autosomal recessive nonsyndromic hearing impairment (NSHI). The mutation spectra of this gene vary among different ethnic groups. Methods: In order to understand the spectrum and frequency of GJB2 mutations in the Chinese population, the coding region of the GJB2 gene from 2063 unrelated patients with NSHI was PCR amplified and sequenced. Results: A total of 23 pathogenic mutations were identified. Among them, five (p.W3X, c.99delT, c.155_c.158delTCTG, c.512_c.513insAACG, and p.Y152X) are novel. Three hundred and seven patients carry two confirmed pathogenic mutations, including 178 homozygotes and 129 compound heterozygotes. One hundred twenty five patients carry only one mutant allele. Thus, GJB2 mutations account for 17.9% of the mutant alleles in 2063 NSHI patients. Overall, 92.6% (684/739) of the pathogenic mutations are frame-shift truncation or nonsense mutations. The four prevalent mutations; c.235delC, c.299_c.300delAT, c.176_c.191del16, and c.35delG, account for 88.0% of all mutantalleles identified. The frequency of GJB2 mutations (alleles) varies from 4% to 30.4% among different regions of China. It also varies among different sub-ethnic groups. Conclusion: In some regions of China, testing of the three most common mutations can identify at least one GJB2 mutant allele in all patients. In other regions such as Tibet, the three most common mutations account for only 16% the GJB2 mutant alleles. Thus, in this region, sequencing of GJB2 would be recommended. In addition, the etiology of more than 80% of the mutant alleles for NSHI in China remains to be identified. Analysis of other NSHI related genes will be necessary

Impacts of Local Effects and Surface Loads on the Common Mode Error Filtering in Continuous GPS Measurements in the Northwest of Yunnan Province, China

While seasonal hydrological mass loading, derived from Gravity Recovery and Climate Experiment (GRACE) measurements, shows coherent spatial patterns and is an important source for the common mode error (CME) in continuous global positioning system (cGPS) measurements in Yunnan, it is a challenge to quantify local effects and detailed changes in daily GPS measurements by using GRACE data due to its low time and spatial resolutions. In this study, we computed and compared two groups of CMEs for nine cGPS sites in the northwest Yunnan province; rCMEs were computed with the residual cGPS time series having high inter-station correlations, while oCMEs were computed with all the GPS time series. The rCMEs-filtered time series had smaller variances and larger root mean square (RMS) reductions than those that were oCMEs-filtered, and when the stations local effects were not removed, spurious transient-like signals occurred. Compared with hydrological mass loading (HYDL), its combination with non-tidal atmosphere pressure and ocean mass reached a better agreement with the CME in the vertical component, with the Nash–Sutcliffe efficiency (NSE) increasing from 0.28 to 0.55 and the RMS reduction increasing from 15.19% to 33.4%, respectively. Our results suggest that it is necessary to evaluate the inter-station correlation and remove the possible noisy stations before conducting CME filtering, and that one should carefully choose surface loading models to correct the raw cGPS time series if CME filtering is not conducted

2011): The role of EDTA on Cadmium phytoextraction in a Cadmiumhyperaccumulator Rorippa globosa

Enhanced phytoextraction technologies have been proposed as an effective approach to the decontamination of heavy metals in soils. In this study, the application of ethylene diamine tetraacetic acid (0.5 and 1.0 g/kg EDTA) at preflowering stage depressed Rorippa globosa growth and Cd uptake, the dry biomass, Cd concentration and total metal accumulation (TMC) of shoots at the concentration of 1.0 g/kg EDTA resulted in 39.6, 3.1 and 41.0% reduction, respectively, relative to the control. In contrast, when EDTA was added at flowering and mature stages, it facilitated plant production and Cd absorption. Especially for 1.0 g/kg EDTA applied at mature stage, the maximum of shoot dry biomass, Cd concentration, TMC and remediation ratio (RR) were obtained, which were 4.7 g/pot, 210.3 mg/kg, 982.4 µg/pot and 1.6, respectively. Therefore, the moderate concentration of EDTA (1.0 g/kg) applied at optimal growing stage (mature stage) of R. globosa was more effective in increasing phytoextraction of Cd from contaminated soils

Phytoremediation of heavy metal contaminated saline soils using halophytes: current progress and future perspectives

Soil salinity is a destructive environmental stressor that greatly reduces plant growth and productivity. In recent years, large tracts of farmland in arid and semiarid regions have been simultaneously affected by salinity and heavy metal pollution, arousing widespread environmental concern. Phytoremediation, defined as the use of plants to remove pollutants from the environment and/or to render them harmless, is a low cost, environmentally friendly, and effective method for the decontamination of soils polluted by heavy metals. Halophytes, which can survive and reproduce in high salt environments, are potentially ideal candidates for phytoremediation of heavy metal-contaminated saline soils. In this review, we discuss the current progress on the use of halophytes, their tolerance mechanisms to salt and heavy metal toxicity, and their potential for phytoremediation in heavy metal-contaminated saline soils. The relative mechanisms are discussed and the future perspectives are proposed.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author