Identifying Hidden Visits from Sparse Call Detail Record Data

Despite a large body of literature on trip inference using call detail record (CDR) data, a fundamental understanding of their limitations is lacking. In particular, because of the sparse nature of CDR data, users may travel to a location without being revealed in the data, which we refer to as a "hidden visit". The existence of hidden visits hinders our ability to extract reliable information about human mobility and travel behavior from CDR data. In this study, we propose a data fusion approach to obtain labeled data for statistical inference of hidden visits. In the absence of complementary data, this can be accomplished by extracting labeled observations from more granular cellular data access records, and extracting features from voice call and text messaging records. The proposed approach is demonstrated using a real-world CDR dataset of 3 million users from a large Chinese city. Logistic regression, support vector machine, random forest, and gradient boosting are used to infer whether a hidden visit exists during a displacement observed from CDR data. The test results show significant improvement over the naive no-hidden-visit rule, which is an implicit assumption adopted by most existing studies. Based on the proposed model, we estimate that over 10% of the displacements extracted from CDR data involve hidden visits. The proposed data fusion method offers a systematic statistical approach to inferring individual mobility patterns based on telecommunication records

Automatic Data for Applied Railway Management

In 2009, London Overground management implemented a new tactical plan for a.m. and p.m. peak service on the North London Line (NLL). This paper documents that tactical planning intervention and evaluates its outcomes in terms of certain aspects of service delivery (the operator's perspective on system performance) and service quality (the passenger's perspective). Analyses of service delivery and quality and of passenger demand contributed to the development, proposal, and implementation of the new tactical plan. NLL trains were delayed routinely en route, with excessive dwell time a major cause. Near-random passenger incidence behavior suggested that an even headway service for NLL might have been more appropriate. The confluence of these analyses was confirmed by the corresponding excess journey time results. On the basis of longitudinal analysis, an evaluation showed that on-time performance increased substantially and observed journey time decreased with the introduction of the new plan. Overall, the effects of this implementation appeared to have been positive on balance. This case study thus demonstrated the applicability of automatic data generally, and certain measures and techniques in the London Overground specifically, to support the tactical planning of an urban railway.Transport for London (Organization

Quantifying the uneven efficiency benefits of ridesharing market integration

Ridesharing is recognized as one of the key pathways to sustainable urban mobility. With the emergence of Transportation Network Companies (TNCs) such as Uber and Lyft, the ridesharing market has become increasingly fragmented in many cities around the world, leading to efficiency loss and increased traffic congestion. While an integrated ridesharing market (allowing sharing across TNCs) can improve the overall efficiency, how such benefits may vary across TNCs based on actual market characteristics is still not well understood. In this study, we extend a shareability network framework to quantify and explain the efficiency benefits of ridesharing market integration using available TNC trip records. Through a case study in Manhattan, New York City, the proposed framework is applied to analyze a real-world ridesharing market with 3 TNCs$-$Uber, Lyft, and Via. It is estimated that a perfectly integrated market in Manhattan would improve ridesharing efficiency by 13.3%, or 5% of daily TNC vehicle hours traveled. Further analysis reveals that (1) the efficiency improvement is negatively correlated with the overall demand density and inter-TNC spatiotemporal unevenness (measured by network modularity), (2) market integration would generate a larger efficiency improvement in a competitive market, and (3) the TNC with a higher intra-TNC demand concentration (measured by clustering coefficient) would benefit less from market integration. As the uneven benefits may deter TNCs from collaboration, we also illustrate how to quantify each TNC's marginal contribution based on the Shapley value, which can be used to ensure equitable profit allocation. These results can help market regulators and business alliances to evaluate and monitor market efficiency and dynamically adjust their strategies, incentives, and profit allocation schemes to promote market integration and collaboration

Synthesis, Optical Properties, and Photocatalytic Activity of One-Dimensional CdS@ZnS Core-Shell Nanocomposites

One-dimensional (1D) CdS@ZnS core-shell nanocomposites were successfully synthesized via a two-step solvothermal method. Preformed CdS nanowires with a diameter of ca. 45 nm and a length up to several tens of micrometers were coated with a layer of ZnS shell by the reaction of zinc acetate and thiourea at 180 °C for 10 h. It was found that uniform ZnS shell was composed of ZnS nanoparticles with a diameter of ca. 4 nm, which anchored on the nanowires without any surface pretreatment. The 1D CdS@ZnS core-shell nanocomposites were confirmed by XRD, SEM, TEM, HR-TEM, ED, and EDS techniques. The optical properties and photocatalytic activities of the 1D CdS@ZnS core-shell nanocomposites towards methylene blue (MB) and 4-chlorophenol (4CP) under visible light (λ > 420 nm) were separately investigated. The results show that the ZnS shell can effectively passivate the surface electronic states of the CdS cores, which accounts for the enhanced photocatalytic activities of the 1D CdS@ZnS core-shell nanocomposites compared to that of the uncoated CdS nanowires

Measuring Regularity of Individual Travel Patterns

Regularity is an important property of individual travel behavior, and the ability to measure it enables advances in behavior modeling, mobility prediction, and customer analytics. In this paper, we propose a methodology to measure travel behavior regularity based on the order in which trips or activities are organized. We represent individuals' travel over multiple days as sequences of 'travel events' - discrete and repeatable behavior units explicitly defined based on the research question and the available data. We then present a metric of regularity based on entropy rate, which is sensitive to both the frequency of travel events and the order in which they occur. The methodology is demonstrated using a large sample of pseudonymised transit smart card transaction records from London, U.K. The entropy rate is estimated with a procedure based on the Burrows-Wheeler transform. The results confirm that the order of travel events is an essential component of regularity in travel behavior. They also demonstrate that the proposed measure of regularity captures both conventional patterns and atypical routine patterns that are regular but not matched to the 9-to-5 working day or working week. Unlike existing measures of regularity, our approach is agnostic to calendar definitions and makes no assumptions regarding periodicity of travel behavior. The proposed methodology is flexible and can be adapted to study other aspects of individual mobility using different data sources.Transport for London (Organization

Evolution and vulnerability analysis of the global trade pattern in the lithium industry chain

[Objective] This study aims to simulate the vulnerability of the lithium industry trade network in the event of interruption risks. The goal is to effectively identify key nodes and potential risks in the network, providing decision support for optimizing trade patterns and avoiding interruption risks. [Methods] Analyzing the evolution of the lithium industry trade pattern based on trade flow methods, intentional attack simulations were conducted to assess the vulnerability of the lithium industry trade network after trade interruptions occurred in the top 10% of nodes by PageRank centrality. [Results] The research reveals: (1) The global trade pattern of the lithium industry chain is undergoing profound restructuring and transformation, with China’s position highlighted in the global trade network. (2) Invulnerability in the upstream network of the lithium industry chain has improved during the sample period, while the risk resistance capabilities of the midstream and downstream networks are relatively stable. (3) The vulnerability ranking of the lithium industry chain is downstream < midstream < upstream. When trade interruptions occur in the top 10% of global key nodes, the overall performance of the upstream, midstream, and downstream trade networks decreases by an average of 60%, 35%, and 23.5%, respectively. [Conclusion] To maintain the security and stability of China’s and the global lithium industry, the following measures should be implemented: enhance and refine safety risk warning and emergency support mechanisms within the lithium industry chain; establish a cooperative, win-win framework among key stakeholders in the lithium industry chain to bolster positive response capabilities across the industry, supply chain, and value chain; and improve domestic self-sufficiency and global allocation capabilities for lithium resources

Synthesis of Starch-Stabilized Ag Nanoparticles and Hg2+Recognition in Aqueous Media

The starch-stabilized Ag nanoparticles were successfully synthesized via a reduction approach and characterized with SPR UV/Vis spectroscopy, TEM, and HRTEM. By utilizing the redox reaction between Ag nanoparticles and Hg2+, and the resulted decrease in UV/Vis signal, we develop a colorimetric method for detection of Hg2+ion. A linear relationship stands between the absorbance intensity of the Ag nanoparticles and the concentration of Hg2+ion over the range from 10 ppb to 1 ppm at the absorption of 390 nm. The detection limit for Hg2+ions in homogeneous aqueous solutions is estimated to be ~5 ppb. This system shows excellent selectivity for Hg2+over other metal ions including Na+, K+, Ba2+, Mg2+, Ca2+, Fe3+, and Cd2+. The results shown herein have potential implications in the development of new colorimetric sensors for easy and selective detection and monitoring of mercuric ions in aqueous solutions

H5N1 avian influenza re-emergence of Lake Qinghai: phylogenetic and antigenic analyses of the newly isolated viruses and roles of migratory birds in virus circulation

Highly pathogenic avian influenza H5N1 virus has swept west across the globe and caused serious debates on the roles of migratory birds in virus circulation since the first large-scale outbreak in migratory birds of Lake Qinghai, 2005. In May 2006, another outbreak struck Lake Qinghai and six novel strains were isolated. To elucidate these QH06 viruses, the six isolates were subjected to whole-genome sequencing. Phylogenetic analyses show that QH06 viruses are derived from the lineages of Lake Qinghai, 2005. Five of the six novel isolates are adjacent to the strain A/Cygnus olor/Croatia/1/05, and the last one is related to the strain A/duck/Novosibirsk/02/05, an isolate of the flyway. Antigenic analyses suggest that QH06 and QH05 viruses are similar to each other. These findings implicate that QH06 viruses of Lake Qinghai may travel back via migratory birds, though not ruling out the possibility of local circulation of viruses of Lake Qinghai