A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

Knowledge, Attitudes and Practices Relating to HIV among Motorbike Taxi Drivers in Parakou, Benin, in 2021

Background and Objective: HIV infection is a major global Public Health threat worldwide, particularly in Sub-Saharan Africa of which Benin. The level of knowledge determines the attitudes and behaviors of the populations towards this infection. The study objective was to assess knowledge, attitudes and practices related to HIV infection among motorbike taxi drivers (MTD) in Parakou in 2021. Methods: This was a descriptive cross-sectional study targeting MTD in Parakou in 2021. Participants were selected by cluster sampling. Pretested Digitized questionnaire using KoboCollect@ applicationserved as a data collection tool. Knowledge, attitudes and practices variable were treated on a score scale. A knowledge score was considered to reflect a good knowledge of HIV if at least two-thirds of the knowledge statements had been correctly answered provided the subject recognized the sexual route as one of modes of HIV transmission, identified at least one preventive measure and meant the incurability of the disease. Quantitative and qualitative variables were appropriately described using the EPI Info 7.1.3.3 software. The participant was classified at positive attitude/practice for HIV prevention, when it has a score of at least 80% and suggests a good preventive measure face a risk of exposure to HIV. Results: A total of 374 subjects were recruited into the study. The mean age was 31.51 ± 7.76 years. Most participants (86.06%) had good knowledge of condom use as an HIV prevention method. The sources of information mentioned were mainly the media (77.07%), relatives or friends (63.38%), and field-workers from non-governmental organizations (37.26%). Routine HIV testing was 50.53%. Among participants, 76.10% reported at least two different sexual partners. Condom use was 59.18 % during the casual sexual intercourse. Within the client-provider relationship with female sex workers, 33.17% had had sexual intercourse with them. The sexual route was the most cited (92.99%), and 90.23% stated that HIV infection can be stabilized by medication in a health structure. Conclusion: The level of knowledge of motorbike taxi drivers in Parakou does not match their behavior with regard to HIV prevention. Appropriate strategies are needed to develop prevention skills in this population. To effectively comb at HIV, it will be necessary to strengthen the targeted HIV preventive interventions at key and bridge populations including motorbike taxi drivers in Benin.

Taxi origin and destination demand prediction based on deep learning:a review

Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.

Higher Plasma Potassium Level Reduces 10-Year Cardiovascular Disease Risk Predicted by the Framingham Risk Score among Taxi-Motorbike Drivers Residing and Working in Cotonou, Benin

Hypertension, obesity, smoking, dyslipidemia, and type 2 diabetes (T2D) are the major risk factors for developing cardiovascular diseases (CVD). Recent studies revealed that taxi-motorbike drivers (TMDs) in Cotonou had higher rates of CVD risk factors, but their impacts on cardiovascular events have rarely been studied. The Framingham risk score (FRS) is an algorithm that considers CVD risk factors and estimates the risk of developing CVD in the next 10 years. Our objectives were to assess the 10-year CVD risk predicted by the FRS, and to examine the relationships of 10-year CVD risk with plasma iron and potassium levels among TMDs. We included 134 TMDs (22 - 59 years old) who had no prior diagnosis of CVD or T2D, and not taking medications affecting iron and potassium homeostasis. Conventional cardiovascular risk factors were used to calculate the 10-year CVD risk, which was categorized as low (20%). FRS > 2%, which corresponded to the 75th percentile of FRS distribution in our study population, was used as a cut-off value to classify participants into two groups. Plasma iron and potassium levels were segregated into tertiles and their associations with 10-year CVD risk were quantified by multivariate-adjusted logistic regression to calculate the odd ratios (ORs) to being above the 75th percentile of 10-year CVD risk with the corresponding 95% confidence intervals (CIs). We found that 62.0% of participants had at least one of cardiovascular risk factors. Approximately 97.8% of TMDs had 10-year CVD risk 4.8 mmol/L led to an 83% risk reduction of having 10-year CVD risk > 2% (OR = 0.17, 95% CI: 0.04 - 0.82, P = 0.027). In conclusion, our findings showed that high plasma potassium levels associate with reduced 10-year CVD risk among TMDs. Interventions focused on monitoring of plasma potassium, particularly in those with existing cardiovascular risk factors, may help prevent CVD.

木聚糖酶TAXI型抑制蛋白的研究进展

木聚糖酶是一种重要的半纤维素酶,广泛用于饲料工业。近年来在小麦中发现一类能抑制木聚糖酶活性的蛋白,这类抑制蛋白可能会对木聚糖酶的应用效果产生影响。目前发现在不同的谷物中共存在2种类型的木聚糖酶抑制蛋白,根据其结构的不同,分为TAXI型(triticum aestivum xylanase inhibitor)和XIP型(xylanase inhibi-tor protein)。本文主要简述了TAXI型抑制蛋白的分子结构、抑制特性和活性影响因素等方面的研究进展,对于深入研究木聚糖酶及其活性影响因子,充分开发利用我国饲料资源和促进畜牧业生产可持续发展具有重要现实意义。

Research on development of urban taxi supply based on influence factors classification

In order to determine the regulations of the development of taxi supply under entry regulations in Chinese cities, an improved neural network model is applied to find the particular years when the government artificially puts new taxis into the market, and then extract the political influence from the taxi supply. The model is also utilized to study the relationships between the adjusted taxi supply and non-policy factors. A case study of Nanjing city is conducted. The results show that 2001 and 2007 are the particular years that the Nanjing government artificially put new taxis into its taxi market, which is in accordance with the five-year plan of China and the local development plans. The results also show that the improved neural network model has a good performance in expositing the evolution of adjusted taxi supply related to non-policy factors.

OPTIMIZATION OF AIRPORT TAXIING PLANNING DURING CONGESTED HOURS BASED ON IMMUNE CLONAL SELECTION ALGORITHM

In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm（ICSA） is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served（FCFS） strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations.

木聚糖酶TAXI型抑制剂及基因工程研究

谷物中存在2种类型的木聚糖酶抑制剂,根据其结构的不同可分为TAXI型和XIP型。本文主要简述了木聚糖酶TAXI型抑制剂对木聚糖酶的抑制特性及其基因工程研究。

4-D Trajectory Prediction and Dynamic Planning of Aircraft Taxiing Considering Time and Fuel

Most of the traditional taxi path planning studies assume that the aircraft is in uniform speed,and the optimization goal is the shortest taxi time.Although it is easy to solve,it does not consider the changes in the speed profile of the aircraft when turning,and the shortest taxi time does not necessarily bring the best taxi fuel consumption.In this paper,the number of turns is considered,and the improved A*algorithm is used to obtain the P static paths with the shortest sum of the straight-line distance and the turning distance of the aircraft as the feasible taxi paths.By balancing taxi time and fuel consumption,a set of Pareto optimal speed profiles are generated for each preselected path to predict the 4-D trajectory of the aircraft.Based on the 4-D trajectory prediction results,the conflict by the occupied time window in the taxiing area is detected.For the conflict aircraft,based on the priority comparison,the waiting or changing path is selected to solve the taxiing conflict.Finally,the conflict free aircraft taxiing path is generated and the area occupation time window on the path is updated.The experimental results show that the total taxi distance and turn time of the aircraft are reduced,and the fuel consumption is reduced.The proposed method has high practical application value and is expected to be applied in real-time air traffic control decision-making in the future.

Structure identification for compound I separated and purified from taxoids-produced endophytic fungi (Alternaria. alternata var. taxi 1011)

Endophytic fungi are widely found in almost all kinds of plants. Many endophytic fungi can produce some physio-logical active compounds, which are same to or analog to those isolated from their hosts. Producing physiological active com-pounds through microbial fermentation can give a new way to resolve resource limitation and to find out alternative source. Through the methods of organic solvent extraction, thin layer chromatography (TLC) and column chromatography, compound I was isolated, purified from the liquid fermentation metabolites of the taxoids-produced endophytic fungi (Alternaria. alternata var. taxi 1011 Y. Xiang et LU An-guo) that was screened from the bark of Taxus. cuspidata Sieb.et Zucc.. Compound I was identified as one kind of taxoids type III, based on the analyzing results by using the methods of ultraviolet spectroscopy (UV), infrared spectroscopy (IR), mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR). This study provides a com-pleted method for separation and purification of the endophytic fungi as well as structure identification of its fermentation me-tabolite

Green Aircraft Taxiing Strategy Based on Multi-scenario Joint Optimization

The issue of green aircraft taxiing under various taxi scenarios is studied to improve the efficiency of aircraft surface operations and reduce environmental pollution around the airport from aircraft emissions.A green aircraft taxi programming model based on multi-scenario joint optimization is built according to airport surface network topology modeling by analyzing the characteristics of aircraft operations under three different taxiing scenarios:all-engine taxi,single-engine taxi,and electronic taxi.A genetic algorithm is also used in the model to minimize fuel consumption and pollutant emissions.The Shanghai Pudong International Airport is selected as a typical example to conduct a verification analysis.Compared with actual operational data,the amount of aircraft fuel consumption and gas emissions after optimization are reduced significantly through applying the model.Under an electronic taxiing scenario,fuel consumption can be lowered by 45.3%,and hydrocarbon(HC)and carbon dioxide(CO)emissions are decreased by 80%.The results show that a green aircraft taxiing strategy that integrates taxiway optimization and electronic taxiing can effectively improve the efficiency of airport operations and reduce aircraft pollution levels in an airport′s peripheral environment.

Prediction of Departure Aircraft Taxi Time Based on Deep Learning

With the continuous increase in the number of flights,the use of airport collaborative decision-making(ACDM)systems has been more and more widely spread.The accuracy of the taxi time prediction has an important effect on the A-CDM calculation of the departure aircraft’s take-off queue and the accurate time for the aircraft blockout.The spatial-temporal-environment deep learning(STEDL)model is presented to improve the prediction accuracy of departure aircraft taxi-out time.The model is composed of time-flow sub-model(airport capacity,number of taxiing aircraft,and different time periods),spatial sub-model(taxiing distance)and environmental sub-model(weather,air traffic control,runway configuration,and aircraft category).The STEDL model is used to predict the taxi time of departure aircraft at Hong Kong Airport and the results show that the STEDL method has a prediction accuracy of 95.4%.The proposed model also greatly reduces the prediction error rate compared with the other machine learning methods.

Effects of behavioral characteristics of taxi drivers on safety and capacity of signalized intersections

Comparative analyses were conducted to compare the effects of the behavioral characteristics of the drivers of taxis and private cars on the capacity and safety of signalized intersections. Data were collected at sixteen signalized intersections in the Nanjing area in China. The risk-taking behaviors of the drivers of taxis and private cars were compared. The results suggest that 19.9% of taxi drivers have committed at least one of the identified risky behaviors, which is 2.37 times as high as that of the drivers of private cars(8.4%). The traffic conflicts technique was used to estimate the safety effects of taxis and private cars. The overall conflict rate for taxis is 21.4% higher than that for private cars, implying that taxis are more likely to be involved in conflicts. Almost all of the identified traffic conflicts can be attributed to certain levels of risk-taking behaviors committed by either taxi drivers or the drivers of private cars, and taxi drivers are more likely to be at fault in a conflict. Failure to yield to right-of-way and improper lane change is the leading causes of the conflicts in which taxis are at-fault. The research team further studied the effects of taxis on the queue discharge characteristics at signalized intersections. The results show that the presence of taxis significantly reduces both start-up lost time and saturation headways. The effects of taxis on saturation flow rates are dependent on the proportion of taxis in the discharge flow, and the saturation flow rates increase with the increase in the proportion of taxis. The adjustment factors for various proportions of taxis for different turning movements are then calculated to quantitatively evaluate the effects of taxis on the capacity of signalized intersections.

Big data assessment on the operating characteristics of on-line taxis and thoughts on relevant policies

A double-dimensional big data assessment method on the characteristics of on-line taxi traffic operation is proposed to provide a scientific basis for carrying out the taxi industry reform and standardizing the on-line taxi hailing management work. Taking Shenzhen as an example, multi- source data such as on-line taxi license plate data, plate identification data and taxi （including on-line taxis） operation data are combined with the results of the stated preference （SP） survey on taxi operating characteristics to assess the overall operation characteristics of on-line taxis. The results show that the current on-line taxis in Shenzhen can be divided into three categories, that is, full-time on-line taxis, non- active on-line taxis and part-time on-line taxis, accounting for 4%, 55%, and 41%, respectively, of the total quantity. In terms of the characteristics of space-time operations, full-time on-line taxis have similar operating characteristics as those of traditional taxis; the operation of non-active on-line taxis and part-time on-line taxis coincides with commuting requirements during morning and evening peak hours. However, part-time on-line taxis operate for a much longer time period at night. Due to the convenient hailing and favorable price, on-line taxis have a significant impact on trip modes of citizens; and the substitution eflbct of on-line taxis on traditional buses and cruising taxis is obvious. It is beneficial for helping the government departments to objectively understand the development law of the on-line taxi industry and providing decision reference for the formulation of relevant management policies during the critical development stage of on-line taxi industry.

Understanding taxi driver's cruising behavior with ZIP model

The taxi drivers' cruising pattern was learned using GPS trajectory data collected in Shenzhen,China.By employing zero-inflated Poisson model,the impacts of land use and previous pick-up experience on cruising decision were measured.The cruising strategies of different types of drivers as well as the top one driver were examined.The results indicate that both land use and previous pick-up experience affect travel behavior with the former's influence(7.07×10-4 measured by one of the coefficients in zero-inflated Poisson model) being greater than the latter's(4.58×10-5) in general,but the comparison also varies across the types of drivers.Besides,taxi drivers' day-to-day learning feature is also proved by the results.According to comparison of the cruising behavior of the most efficient and inefficient driver,an efficient cruising strategy was proposed,that is,obeying the distribution of land use in choice of cruising area,while learning from pick-up experience in selection of detailed cruising location.By learning taxi drivers' cruising pattern,the development of measures of regulating travel behaviors is facilitated,important factor for traffic organization and planning is identified,and an efficient cruising strategy for taxi drivers is provided.

High-Level Circulating Total Calcium and Low Phosphate as Predictors of Insulin Resistance among Non-Diabetic Taxi-Motorbike Drivers Living and Working in Cotonou, Benin

Insulin resistance (IR) is a well-recognized marker of increased cardiovascular diseases (CVDs) and type 2 diabetes (T2D) risk. Therefore, screening for IR predictors would help reduce the likelihood of progression from early stage of IR to T2D or CVDs. However, the knowledge of association between IR and circulating total calcium (CTCa) and phosphate levels among non-diabetic patients in Benin is lacking. We investigated whether CTCa and phosphate levels within the normal ranges are associated with IR risk among taxi-motorbike drivers (TMDs) living and working in Cotonou. We evaluated 134 non-diabetic TMDs (aged 22 - 59 years) based on CTCa, phosphate, glucose, fasting insulin, and IR levels. IR was assessed using the homeostatic model assessment-insulin resistance (HOMA-IR). IR was defined as the 75th percentile of HOMA-IR value. Cardiometabolic factors were analyzed by tertiles of CTCa and phosphate levels (low, middle, and high groups). Logistic regression models evaluated the relationships between IR and CTCa and phosphate levels. Our results showed that participants with high CTCa levels had the highest prevalence of IR, elevated total cholesterol and high-density lipoprotein cholesterol. In a fully adjusted model, the odd ratio (OR) of having IR comparing the highest (>2.50 mmol/L) to the lowest CTCa levels (1.23 mmol/L) and the lowest levels (<1.10 mmol/L) of phosphate was 0.28 (p = 0.037). In conclusion, our study demonstrates that elevated CTCa and low phosphate levels are significant predictors of IR in non-diabetic patients. Continuous monitoring of these markers may help identify earlier individuals at greatest IR risk.

Control Research for a Small Fixed-Wing UAV DuringGround Taxiing

Ground taxiing is the key process of take-off and landing for a tricycle-undercarriage unmanned aerial vehicle( UAV). Nonlinear model of a sample UAV is established based on stiffness and damping model of landing gears and tires taken into account. Then lateral nonlinear model is linearized and state space equations are deduced by using nose wheel and ruder as inputs and lateral states as outputs. Adaptive internal model control( AIMC) is proposed and applied to lateral control based on decoupled and linearized dynamic model during ground taxiing process. Different control strategies are analyzed and compared by simulations,and then a combined control strategy of nose wheel steering with holding and rudder control is given. Hardware in loop simulations( HILS) proves the validity of the controller designed.

Learning taxis' cruising patterns with Ripley's K function

Taxi drivers' cruising patterns are learnt with GPS trajectory data collected in Shenzhen, China. By employing Ripley's K function, the impacts of land use and pick-up experience on taxis' cruising behavior are investigated concerning about both intensity of influence and radius of influence. The results indicate that, in general, taxi drivers tend to learn more from land use characteristics than from pick-up experience. The optimal radius of influence of land use points and previous pick-up points is 14.18 km and 9.93 km, respectively. The findings also show that the high-earning drivers or thorough drivers pay more attention to land use characteristics and tend to cruise in high-density area, while the low-earning drivers or focus drivers prefer to learn more from previous pick-up experience and select the area which is far away from the high-density area. These findings facilitate the development of measures of managing taxi's travel behavior by providing useful insights into taxis' cruising patterns. The results also provide useful advice for taxi drivers to make efficient cruising decision, which will contribute to the improvement of cruising efficiency and the reduction of negative effects.

A Comprehensive Taxi Assessment Index Using Floating Car Data

With the expansion of urban area and development of taxi system,problems arise,such as low operation efficiency,high taxi idling rate,and long passenger waiting-time. Although various studies have been conducted,only limited overview of the factors towards urban taxi system has been provided. Consequently,a comprehensive evaluation of taxi system is essential for the urban planner to analyze the current situation and take effective measures. This paper,by using Floating Car Data( FCD),proposes a Comprehensive Taxi Assessment Index( CTAI) to quantify the quality of existing urban taxi system with the assistance of Geographic Information System( GIS) technology. The proposed index system extracts and classifies key factors,reflecting the taxi system from the perspectives of operation efficiency,customer and taxi-driver satisfaction. The system contributes to improving the organization and operation of urban taxi system. Based on the data obtained from the city of Shenzhen,Guangdong Province,China,for both weekday and weekends( Dec.,2011),the proposed CTAI was illustrated by using the Principal Component Analysis( PCA) with ArcGIS 10. 0 platform. The results indicate that the system provides a good multi-dimensional view to delve into the existing urban taxi operation, thus to point out the most sensitive indices towards the entire system,which consequently provides guidelines for future improvement and management of urban taxi system.

Developing a Comprehensive Taxi Strategy for Dubai: Based on System Analysis &Structured Assessment

Taxi plays an important role in providing passenger mobility. Taxi services are among the most frequently used passenger transport services in many cities in the world. Taxis are an integral part of a city’s image. In a recent IRU/EU report taxis are considered as part of the collective public transport chain. In this context taxis role is a complementary mobility option to public transport rather than a competitive one. In the growing city of Dubai taxis are utilized by many local and foreign residences as well as by tourists and visitors. The annual number of taxi trips reached 104 million trips in 2016, while the fleet is growing currently at 9613 and is expected to reach around 12,765 by 2020. Riding a taxi in Dubai is known to be pleasant, comfortable, convenient and relatively affordable experience. The Roads and Transport Authority (RTA) is the Dubai government authority responsible for the planning and governance of the taxi sector in Dubai. The RTA through its agency namely the Public Transport Agency (PTA) exercises such roles. This research is meant to provide a system analysis and structured assessment of the taxi sector in Dubai with the objective of identifying strengths and gaps as well as reviewing the literature and conducting stakeholder consultation hence culminating on developing a comprehensive taxi strategy in Dubai. Such strategy would be targeted to achieve customer satisfaction leading eventually to customer happiness. All in all this is expected to provide an added value and benefit to the business planning for PTA as well as for the overall strategic and futuristic planning by RTA.