DNA N6-methyladenine (6mA) is an important epigenetic modification, which involved in many biology regulation processes. An accurate and reliable method for 6mA identification can help us gain a better insight into the regulatory mechanism of modification. Although, experimental techniques have been proposed to identify sites genome-wide, while these are time consuming laborious. Recently, several machine learning methods developed genome-wide. However, there room improvement on their...

Bus arrival time prediction intends to improve the level of services provided by transportation agencies. Intuitively, many stochastic factors affect predictability time, e.g., weather and local events. Moreover, for a current station is closely correlated with that multiple passed stations. Motivated observations above, this paper proposes exploit long-range dependencies among steps bus via recurrent neural network (RNN). Concretely, RNN long short-term memory block used "correct" During...

The development of real-time road condition systems will better monitor network operation status. However, the weak point all these is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) floating vehicles 2) remote microwave sensors (RTMS). former consists use mobile probe as sensors, latter a set fixed detectors installed in roads. First, structure three-layer BP neural designed to achieve fusion car (FCD) detector (FDD)...

Abstract Motivation Polyadenylation plays a regulatory role in transcription. The recognition of polyadenylation signal (PAS) motif sequence is an important step polyadenylation. In the past few years, some statistical machine learning-based and deep methods have been proposed for PAS identification. Although these predict with success, there room their improvement on Results this study, we neural network-based computational method, called SANPolyA, identifying human mouse genomes. SANPolyA...

The emphasis of this research effort was on using historical GPS data to develop a dynamic model forecast long-term path travel times between bus stops origin and destination. This study brought about by the shortcomings existing real-time short-term prediction system, which have been developed utilised in Beijing. is based cluster analysis polynomial fitting for running stations. also intends test proposed real-world data. In with Beijing, China, performed effectively terms both accuracy...

Predicting the passenger flow of public transport in a newly developed area city is very urgent for designing precise and efficient network. This paper proposes new prediction model by exploring relationship between station its surrounding area's factors. First, order to obtain more accurate factors affecting flow, divided into multiple regions with similar internal traffic properties moderate spatial size using data urban road network buildings. Second, effectively solve problem fuzziness...

The procedure of matching vehicle location data onto road map is very essential for many ITS (Intelligent Transportation System) applications. However, with the boosting deployment GPS devices in vehicles, accumulation huge amount caused great challenge on efficiency and scalability traditional serial algorithm. In this paper we address by presenting a novel parallel algorithm to realize high-performance processing data. main idea adapt cloud computing environment reforming its'...

As increasing volumes of urban data are being available, new opportunities arise for data-driven analysis that can lead to improvements in the lives citizens through evidence-based policies. In particular, taxi trip is an important sensor provides unprecedented insights into many aspects a city, from economic activity, human mobility land development. However, analyzing these presents challenges, e.g., sparse fine-grained patterns, and regularity submerged by seemingly random data. Inspired...

The paper proposed a practical method to accurately estimate the vehicle delay at signalized intersections. Firstly we derived an analytical formula based on observation of queue forming/discharging process. According uniform arrivals assumption, was then simplified linear relation between travel time and observed location. However, factors in are associated with specific intersections' characteristics. So collected massive historical trajectory data using GPS instrumented transit vehicles,...

The real time prediction of bus arrival is important in public transport service. Many researches use a traditional error metrics to measure the predicting accuracy, which cannot evaluate service comprehensively. This paper proposes novel multi-index evaluation method based on Support Vector Machine (SVM) for prediction. uses three new indexes including GPS coverage, release rate, and accuracy rate service, then SVM train model evaluation. Experiment results show that our intuitive...

The lack of the historical data new rail line makes passenger flow distribution prediction be a challenge. Traditional methods always use simple factors, which can not reflect complexity OD distribution. This paper proposes novel method based on multi-factor model. obtains quantitative impact factors by analyzing existing stations, and then constructs model considers influence nature station, as well network structure, it more precision. Validation experiment results show that is reasonable.

Understanding the impact of traffic incidents, not only can help decision-makers choose a better strategy, but also provide travel recommendations for travelers. This paper uses real GPS data collected from probe vehicle to analyze incident in urban network. Queuing length and duration are used evaluate level incident. Then, pattern classification method based on queuing variation is proposed more clearly understand characteristics At last, we propose prediction model time that takes pass...

Accurate bus passenger flow prediction can provide effective data support for operators to realize intelligent scheduling and scientific network planning. This paper collects the relevant of routes in a city 2020, uses Person correlation coefficient select eigenvalue, defines sample similarity, selects training based on establishes multiple stepwise regression model, established model predict adjusted routes. The experiment shows that is suitable flow.

The boosting deployment of GPS devices in urban vehicles is leading to the collection large volumes GPS. Such massive spatial-temporal datasets challenges efficiency and scalability query process during data analysis. In this paper, we introduce MapReduce framework into analysis system. Particularly, built a graph based bi-level index accelerate spatial processing. key idea that use topological instead traditional R-tree lock space scope data, due fact are moving along road network. This...

Prediction of incident duration not only can give the traffic managers decisive support, such as releasing information in advance, but also make participants arrange travel routes and improve efficiency. In order to low prediction accuracy complexity model proposed previously, broadcasting information, including from Traffic Management Bureau, real-time data Information Committee, has been applied this paper. The incidents were divided into three categories with 3782 incidents. artificial...

Sustainable development requires businesses to manage finite resources ensure that both present and future generations can meet their needs. However, the demand for has outstripped planet’s ability regenerate them, nowhere is this more concrete salient than in local places. Our study explores “how do organizations limited sustainable a globalizing world?”. We answer question through qualitative inductive of LuxuryYak -- luxury textile enterprise uses on Tibetan Plateau sells its products...