Traffic state estimation (TSE) refers to the process of the inference of traffic state variables (i.e., flow, density, speed and other equivalent variables) on road segments using partially observed traffic data. It is a key component of traffic control and operations, because traffic variables are measured not everywhere due to technological and financial limitations, and their measurement is noisy. Therefore, numerous studies have proposed TSE methods relying on various approaches, traffic flow models, and input data. In this review article, we conduct a survey of highway TSE methods, a topic which has gained great attention in the recent decades. We characterize existing TSE methods based on three fundamental elements: estimation approach, traffic flow model, and input data. Estimation approach encompasses methods that estimate the traffic state, based on partial observation and a priori knowledge (assumptions) on traffic dynamics. Estimation approaches can be roughly classified into three according to their dependency on a priori knowledge and empirical data: model-driven, data-driven, and streaming-data-driven. A traffic flow model usually means a physics-based mathematical model representing traffic dynamics, with various solution methods. Input data can be characterized by using three different properties: collection method (stationary or mobile), data representation (disaggregated or aggregated), and temporal condition (real-time or historical). Based on our proposed characterization, we present the current state of TSE research and proposed future research directions. Some of the findings of this article are summarized as follows. We present model-driven approaches commonly used. We summarize the recent usage of detailed disaggregated mobile data for the purpose of TSE. The use of these models and data will raise a challenging problem due to the fact that conventional macroscopic models are not always consistent with detailed disaggregated data. Therefore, we show two possibilities in order to solve this problem: improvement of theoretical models, and the use of data-driven or streaming-data-driven approaches, which recent studies have begun to consider. Another open problem is explicit consideration of traffic demand and route-choice in a large-scale network; for this problem, emerging data sources and machine learning would be useful.

a b s t r a c tProbe vehicles provide some of the most useful data for road traffic monitoring because they can acquire wide-ranging and spatiotemporally detailed information at a relatively low cost compared with traditional fixed-point observation. However, current GPS-equipped probe vehicles cannot directly provide us volume-related variables such as flow and density. In this paper, we propose a new probe vehicle-based estimation method for obtaining volume-related variables by assuming that a probe vehicle can measure the spacing to its leading one. This assumption can be realized by utilizing key technologies in advanced driver assistance systems that are expected to spread in the near future. We developed a method of estimating the flow, density, and speed from the probe vehicle data without exogenous assumptions on traffic flow characteristics, such as a fundamental diagram. In order to quantify the characteristics of the method, we performed a field experiment at a real-world urban expressway by employing prototypes of the probe vehicles with spacing measurement equipment. The result showed that the proposed method could accurately estimate the 5 min and hourly traffic volumes with probe vehicle penetration rate of 3.5% and 0.2%, respectively.

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Smart card data, Travel behavior, Railway passenger, Train timetable,

The aim of this study is to assess whether fucoidan modulates the expression of chemokine ligand 12 (CXCL12)/chemokine receptor 4 (CXCR4) and exerts antitumor activity toward Huh7 hepatoma cells. According to 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays, fucoidan inhibited the growth of Huh7 cells and HepG2 cells in a dose-dependent manner, with a 50% inhibition of cell growth (IC50) of 2.0 and 4.0 mg/ml, respectively. alpha-fetoprotein levels in medium collected from fucoidan-treated cells were significantly decreased in Huh7 cells but not in HepG2 cells. Western blotting revealed that the amount of alpha-fetoprotein was decreased by 1.0 mg/ml of fucoidan in Huh7 cells, whereas it was unchanged in HepG2 cells. In Huh7 cells, CXCL12 mRNA expression was significantly downregulated by 1.0 mg/ml of fucoidan, whereas CXCR4 mRNA expression was unchanged by fucoidan. CXCL12 and CXCR4 mRNA were barely expressed in HepG2 cells. In addition, 1.0 mg/ml of fucoidan mildly arrested the cell cycle and induced apoptosis in Huh7 cells. The findings suggest that fucoidan exhibits antitumor activity toward Huh7 cells through the downregulation of CXCL12 expression.

The purpose of the present study is to identify genes that contribute to cell proliferation or differentiation of breast cancers independent of signalling through the oestrogen receptor (ER) or human epidermal growth factor receptor 2 (HER2). An oligonucleotide microarray assayed 40 tumour samples from ER(+)/HER2(−), ER(+)/HER2(+), ER(−)/HER2(+), and ER(−)/HER2(−) breast cancer tissues. Quantitative reverse transcriptase PCR detected overexpression of a cell cycle-related transcription factor, E2F-5, in ER-negative breast cancers, and fluorescence in situ hybridisation detected gene amplification of E2F-5 in 5 out of 57 (8.8%) breast cancer samples. No point mutations were found in the DNA-binding or DNA-dimerisation domain of E2F-5. Immunohistochemically, E2F-5-positive cancers correlated with a higher Ki-67 labelling index (59.5%, P =0.001) and higher histological grades ( P =0.049). E2F-5-positive cancers were found more frequently in ER(−)/progesterone receptor (PgR)(−)/HER2(−) cancer samples (51.9%, P =0.0049) and in breast cancer samples exhibiting a basal phenotype (56.0%, P =0.0012). Disease-free survival in node-negative patients with E2F-5-positive cancers was shorter than for patients with E2F-5-negative cancers. In conclusion, we identify, for the first time, a population of breast cancer cells that overexpress the cell cycle-related transcription factor, E2F-5. This E2F-5-positive breast cancer subtype was associated with an ER(−)/PgR(−)/HER2(−) status, a basal phenotype, and a worse clinical outcome.

a b s t r a c tThis paper proposes a method of estimating a traffic state based on probe vehicle data that contain spacing and position of probe vehicles. The probe vehicles were assumed to observe spacing by utilizing an advanced driver assistance system, that has been implemented in practice and is expected to spread in the near future. The proposed method relies on the conservation law of the traffic flow but is independent of a fundamental diagram. The conservation law is utilized for reasonable aggregation of the spacing data to acquire the traffic state, i.e., a flow, density and speed. Its independence from a fundamental diagram means that the proposed method does not require predetermined nor exogenous assumptions with regard to the traffic flow model parameters. The proposed method was validated through a simulation experiment under ideal conditions and a field experiment conducted under actual traffic conditions; and empirical characteristics of the proposed method were investigated.

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