Search

Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.

With the rapid development of urbanization and the popularization of the vehicle, the frequent occurrence of traffic jams results in idling fuel waste, environmental pollution, and other issues. In order to alleviate these problems, engine start-stop technology has been widely used in different types of vehicles in recent years. However, current start-stop trigger technology has many deficiencies, such as mistaken triggering and frequent engine start-stop, which greatly reduces user driving experience, causing most of them to deactivate this system. The intelligent engine start-stop trigger (IEST) system based on the actual road running status was established by building the image recognition model and the digital traffic analysis model in order to solve this problem. A system test shows that IEST can avoid frequently engine starting and stopping. The results show that IEST could effectively improve the driving experience and reduce engine fuel consumption, and it promotes conventional engine start-stop technology.

BackgroundAim of this meta-analysis was to evaluate the overall diagnostic value of circulating mini miRNAs for papillary thyroid carcinoma (PTC) and to find the possible molecular marker with higher diagnostic value for PTC.MethodsWe searched the Pubmed, Cochrane and Embase database until June 2020. We selected relevant literatures associated with the diagnosis of PTC with circulating miRNAs. The number of cases in experimental group and the control group, sensitivity and specificity could be extracted from the literatures.ResultsWe got 9 literatures including 2114 cases of PTC. Comprehensive sensitivity was 0.79, comprehensive specificity was 0.82, positive likelihood ratio was 4.3, negative likelihood ratio was 0.26, diagnostic advantage ratio was 16. The summary receiver operating characteristic curve was drawn and the Area Under the Curve was 0.87.ConclusionsCirculating microRNAs may be promising molecular markers for the diagnosis of papillary thyroid carcinoma. Combined detection of certain serum microRNAs can improve the diagnostic accuracy of papillary thyroid carcinoma. Especially MiR-222 and miR-146b may be prime candidates for the diagnosis of PTC in Asian population.

The travel trajectory data of mobile intelligent terminal users are characterized by clutter, incompleteness, noise, fuzzy randomness. The accuracy of original data is an essential prerequisite for better results of trajectory data mining. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most effective trajectory data mining methods, but the selection of input parameters often limits it. The Sage-Husa adaptive filtering algorithm effectively controls the error range of mobile phone GPS data, which can meet the positioning accuracy requirements for DBSCAN spatial clustering having the advantages of low cost and convenient use. Then, a novel cluster validity index was proposed based on the internal and external duty cycle to balance the influence of the distance within-cluster, the distance between clusters, and the number of coordinate points in the process of clustering. The index can automatically choose input parameters of density clustering, and the effective clustering can be formed on different data sets. The optimized clustering method can be applied to the in-depth analysis and mining of traveler behavior trajectories. Experiments show that the Sage -Husa adaptive filtering algorithm proposed further improves the positioning accuracy of GPS, which is 17.34% and 15.24% higher eastward and northward, 14.25%, and 18.17% higher in 2D and 3D dimensions, respectively. The number of noise points is significantly reduced. At the same time, compared with the traditional validity index, the evaluation index based on the duty cycle proposed can optimize the input parameters and obtain better clustering results of traveler location information.

The scientific evaluation of passenger satisfaction for public transport is helpful to enhance the attraction of public transport. To improve the accuracy of passenger satisfaction evaluation for public transport and the scientificity and objectivity of the index weighting, combining the characteristics of analytic hierarchy process (AHP), entropy weight method (EWM) and fuzzy comprehensive evaluation(FCE) method, the passenger satisfaction evaluation system for Ningbo's urban public transportation was built. The paper analyzed 5046 questionnaires on conventional bus transit and 1682 questionnaires on rail transit in Ningbo city, Passenger satisfaction for Ningbo city's public transport was evaluated comprehensively, and the evaluation results showed that the overall passenger satisfaction of the public transport in Ningbo was 91.2 in 2019, The case study shows that the application of the AHP-EWM-FCE model on the multi-mode public transport system can objectively quantify passengers' feelings about urban public transport service, and thus provide a theoretical basis for the improvement of passenger satisfaction in Ningbo.

The center of gravity of China’s new cropland has shifted from Northeast China to the Xinjiang oasis areas where the ecological environment is relatively fragile. However, we currently face a lack of a comprehensive review of the cropland expansion in oasis areas of Xinjiang, which is importantly associated with the sustainable use of cropland, social stability and oasis ecological security. In this study, the land use remote sensing monitoring data in 1990, 2000, 2010 and 2018 were used to comprehensively analyze the process characteristics, different modes and driving mechanisms of the cropland expansion in Xinjiang, as well as its spatial heterogeneity at the oasis area level. The results revealed that cropland in Xinjiang continued to expand from 5803 thousand hectares in 1990 to 8939 thousand hectares in 2018 and experienced three stages of expansion: steady expansion, rapid expansion, and slow expansion. The center of gravity of cropland showed the characteristic of shifting to the South. Edge expansion and encroachment on grassland were the dominant spatial pattern mode and land use conversion mode of Xinjiang’s cropland expansion, respectively. The expansion of cropland in Xinjiang was affected by multiple factors. Irrigation conditions played a dominant role. Topography indirectly affected cropland expansion by affecting the suitability of agricultural production and development. Population growth and farmers’ income were important driving forces. There was significant spatial heterogeneity in the intensity, mode and driving force of cropland expansion among different oasis areas in Xinjiang. The spatial shift of China’s new cropland has occupied a large amount of water resources and ecological land in Xinjiang and exacerbated the vulnerability of the ecosystem in arid regions. The key to sustainable management of cropland in Xinjiang in the future lies in maintaining an appropriate scale of cropland and promoting the coordinated development of cropland, population, water resources and industry.