Airport Taxi Research Articles

Research on Airport Taxi Driver Decision Based on RBF Neural Network

Affected by the combination of different factors, taxi drivers leaving the airport will have different decision-making schemes. This paper establishes mathematical models to optimize the decision of airport taxi drivers and the optimization of passenger-carrying problems between taxis and passengers. First take Shuangliu Airport as an example, and take the number of taxis and the number of passengers in the data into the RBF neural network decision model. The results obtained are compared with the real data. Finally, the cyclic test shows the changes in taxi driver decisions is more dependent on the travel season, number of flights, and holidays. Then a taxi passenger model based on M/M/S is established. There are two arrangements for the two parallel lanes of the airport “ride zone”: the final solution is concluded that when the length of the boarding area is 50 meters, setting three “boarding points” has the highest total boarding efficiency. By introducing the average mileage division index of taxi drivers, the short- and long-haul taxi drivers are finally obtained after the optimized scheme. The returns have basically reached equilibrium.

Work-related COVID-19 transmission in six Asian countries/areas: A follow-up study.

There is limited evidence of work-related transmission in the emerging coronaviral pandemic. We aimed to identify high-risk occupations for early coronavirus disease 2019 (COVID-19) local transmission. In this observational study, we extracted confirmed COVID-19 cases from governmental investigation reports in Hong Kong, Japan, Singapore, Taiwan, Thailand, and Vietnam. We followed each country/area for 40 days after its first locally transmitted case, and excluded all imported cases. We defined a possible work-related case as a worker with evidence of close contact with another confirmed case due to work, or an unknown contact history but likely to be infected in the working environment (e.g. an airport taxi driver). We calculated the case number for each occupation, and illustrated the temporal distribution of all possible work-related cases and healthcare worker (HCW) cases. The temporal distribution was further defined as early outbreak (the earliest 10 days of the following period) and late outbreak (11th to 40th days of the following period). We identified 103 possible work-related cases (14.9%) among a total of 690 local transmissions. The five occupation groups with the most cases were healthcare workers (HCWs) (22%), drivers and transport workers (18%), services and sales workers (18%), cleaning and domestic workers (9%) and public safety workers (7%). Possible work-related transmission played a substantial role in early outbreak (47.7% of early cases). Occupations at risk varied from early outbreak (predominantly services and sales workers, drivers, construction laborers, and religious professionals) to late outbreak (predominantly HCWs, drivers, cleaning and domestic workers, police officers, and religious professionals). Work-related transmission is considerable in early COVID-19 outbreaks, and the elevated risk of infection was not limited to HCW. Implementing preventive/surveillance strategies for high-risk working populations is warranted.

Improvement of Short-distance Subsidy Policy for Airport Taxi Based on Cluster Analysis

The income of airport taxis is related to the mileage. Due to the difference in passenger destinations, the mileage is long and short, and the taxi driver cannot pick or refuse to load. Therefore, the difference between short-haul passengers and long-haul passengers is significant. Based on such realistic problems, a relatively reasonable priority policy has been proposed to balance the income of taxi drivers. Under the existing policy based on the short-distance ticket of Shanghai Pudong Airport, combined with the large number of drop-off point data of taxis, the data is pre-processed and then visually analyzed on Bigemap, and then cluster analysis is carried out in matlab environment. The mileage distribution makes a more reasonable division of short-distance long-distance definition and proposes new ideas for solving the problem.

The Taxi Driver’s Decision Model about Picking up Passengers at the Airport

The airport taxi plays a very important role in connecting the airport traffic with the city traffic, and its queuing and derivative problems have been paid much attention by drivers, passengers and airport management. In this paper, the factors which influence the taxi driver decision are analyzed, and the time cost driver and profits of contradictory perspective, by calculating the driver “line” and “return to the city” in both cases the net income per unit time, establish a “limitation interests”, at the same time, combined with the number of flight arrival at a different time and may choose the number of the car, with the driver received benefit maximization for the principle, the use of recursive thought to establish “recursive model”, make the driver arrive at the airport at any point in time, is obtained by the analysis of the model is able to interest the biggest selection strategy.

A Mobile Phone App to Improve the Mental Health of Taxi Drivers: Single-Arm Feasibility Trial.

BackgroundPsychological distress among taxi drivers is 5 times higher than that in the general population, and more than half of all drivers have experienced 3 or more potentially traumatic events in their lifetime. Nevertheless, help-seeking for mental health problems in this male-dominated, predominately immigrant workforce is low. Mobile technologies have the potential to increase mental health awareness, teach self-help skills, and encourage help-seeking in this hard-to-reach population.ObjectiveThis study aimed to assess the feasibility, acceptability, and potential efficacy of Driving to Health, a mobile phone–friendly mental health website app designed for people working as taxi drivers.MethodsDrivers (n=46) were recruited from the Melbourne Airport Taxi Holding Yard to participate in a single-arm trial. Self-reported, paper-based assessments were completed at baseline and at 1 month. Feasibility was measured by completion rates, representativeness of study participants, and levels of use. Acceptability was assessed by measuring users’ perception of the quality of the app and anticipated levels of future use. The efficacy of Driving to Health to increase awareness, self-help behaviors, and intentions to seek help was assessed using the user version of the Mobile App Rating Scale (uMARS) and the General Help-Seeking Questionnaire (GHSQ). Psychological symptoms were measured using the short form of the Depression, Anxiety, and Stress Scale (DASS-21). Data were analyzed using complete case analysis.ResultsIn total, 42 participants comprising drivers from 10 different countries of origin, and 14 different languages, completed pre- and poststudy measures (42/46, 91% completion rate). Just under half (45%) of all users used the app more than once with an average visit of 4 min 8 seconds. Responding to the uMARS, 62% (26/42) of the participants said that they would recommend the app to many people. Nearly all (40/42, 95%) participants said that Driving to Health increased awareness of their own mental health; 86% (36/42) said that it increased their mental health knowledge; and 76% (32/42) said that it increased their self-help behaviors. Increases in help-seeking intentions on the GHSQ were not significant, and increases on all 3 scales of DASS-21 were not reliable or meaningful.ConclusionsThis study suggests that Driving to Health is an acceptable and feasible electronic health intervention for a hard-to-reach population. Our findings also suggest that Driving to Health results in increases in mental health awareness, behaviors, and willingness to seek help.

Airport Taxi Management Based on Dynamic Decision and Multi-objective Programming Model

Nowadays major airports often have a large number of passengers and vehicles stranded, resulting in inefficient airport operations. In order to solve this problem, this article aims at the decision-making problems that airport taxi drivers face after sending passengers to the airport, and the problem of setting up the boarding point of the airport by analyzing the influencing mechanism of factors related to taxi driver decisions, and considering the changing rules of airport passengers and the benefits of taxi drivers, we have established Driver’s choice decision model based on time periods and Multi-objective programming model based on queuing theory. The model established in this paper can greatly alleviate the airport’s load-bearing pressure, improve the airport’s overall boarding efficiency under the condition that the taxi driver’s income is balanced, and provide a theoretical basis for the systematization of airport taxi queuing.

Airport Taxi Dispatching Based on VISSIM and Multi-objective Programming Model

This paper solved the problem of how to manage the distribution of airport taxis and balance the revenue of long- and short-haul passenger taxis. In this research, we established a multi-objective programming model, which was solved using genetic algorithms to obtain a reasonable distribution scheme in airport with the highest riding efficiency: set up a pick-up location in the middle of the pick-up area, requiring all cars to leave uniformly when fully loaded, and release an average of 78 taxis per batch in every single boarding location. In addition, with the queuing theory we set the basic parameters of the road. Taking the income balance difference as the objective function, we used the VISSIM software to simulate the simulation. Then the short-term “priority” arrangement plan was: Calculate the ratio of the travel time of the short-distance taxi to the distance from the airport to the city center. If the ratio is less than 0.0659, the taxis that meet the conditions are allowed to be given priority after return. The results have some guidance and strong practical significance.

Assessment of Airport Taxi Dispatching Based on Psychological Account Principle Decision Model — A Case Study of Shanghai pudong Airport

In this paper, a decision model based on psychological account principle and a queuing theory model based on (M/ M/1) system are established to solve the problem of providing more reasonable choices for airport taxi drivers. Combined with reverse test method, control variable method and other methods to analyze the problem. Taking Shanghai pudong airport as an example, the error coefficient of critical decision value is calculated to be around 0.13, and the model is reasonable. It is concluded that weather has great influence on decision making and seasonal change has little influence on decision making.

Research on Airport Taxi Resource Allocation Based on Information Asymmetry

Most passengers will go to the city or the surrounding destinations when they get off the plane. Taxi is one of the main means of transportation. Therefore, taxi drivers will face two options: queuing in the arrival area to wait for passengers to return to the urban area and directly returning to the urban area. In this paper, through the collection of relevant airport data and analysis and calculation methods, a fitting model is established, and a reasonable scheme is designed for the allocation of taxi resources. Based on the study and analysis of the influencing mechanism of taxi drivers’ decision-making factors, and considering the changing law of airport passenger number and the income of taxi drivers, this paper puts forward the reasonable allocation scheme of airport taxi.

基于多指标分析模型解决机场出租车排队问题——以重庆江北国际机场为例

基于多指标分析模型解决机场出租车排队问题——以重庆江北国际机场为例

Using Fuzzy Evaluation Decision Model to Assess the Operation Scheme of Taxi in Shanghai Pudong Airport

In order to investigate the decision of the airport taxi driver whether to return to carry passengers, we established a fuzzy evaluation decision model based on the analytic hierarchy process, combined with the application analysis of Shanghai Pudong Airport. Based on the selection and quantification of decision indicators, we used the AHP method to calculate the weights to eliminate indicators and optimize the indicator system. Then, a two-level fuzzy evaluation model was established, and the Bayesian discriminant verification model was more reasonable. Finally, based on the data analysis of Shanghai Pudong Airport, combined with 16 different airport decision-making situations, the model was used to obtain the no-load return trips and the membership of waiting passengers. We compared and selected the larger membership value as the decision. Besides, the dependence of the model was analyzed, and it was found that the main dependent factors for decision-making were the number of flight arrivals and the driver’s arrival time. After solving the above, we provide relevant basis for the decision of airport taxi drivers.

基于排队论的机场出租车司机决策模型——以杭州市为例

基于排队论的机场出租车司机决策模型——以杭州市为例

机场出租车的司机决策与管理优化研究

机场出租车的司机决策与管理优化研究

STRATEGI PEMASARAN TAKSI BANDARA NGURAH RAI DENGAN METODE BGC (BOSTON CONSULTING GROUP)

The purpose of this research is to find out the right marketing strategies and competitiveness by identifying and analyzing the internal and external environment that affect the Ngurah Rai Airport Taxi Cooperative. In this study using marketing strategy analysis, namely BCG/Boston Consulting Group Matrix and SWOT (Strength, Weakness, Opportunity, Threats) analysis. BCG matrix to determine the level of market growth and relative market share. SWOT analysis to analyze strengths, weaknesses, opportunities and threats, then using segmenting, targeting, positioning/STP and 7P (marketing mix) results of the data obtained and then analyzed with IFAS and EFAS. The types of data used in this study are qualitative and quantitative data, primary and secondary data. Determination of samples in this study using purposive sampling method as many as 50 respondents. Data collection techniques by observation, questionnaires, structured interviews, and literature study. The results of the BCG matrix research, based on the calculation of the market growth rate is 1.35% and the result of the calculation of the relative market share is 1.45 x> 1 so that it is in the position of Cash Cow. The marketing strategy that can be carried out by the Ngurah Rai Airport Taxi Cooperative is to maintain the market, create new products, find new businesses. The strategy offered in the Cash Cow quadrant the company must always keep the company in a stable condition. The results of the SWOT analysis study from IFAS assessment were 2.66 and EFAS was 2.28. Then the position of the SWOT diagram is Growth Stability which is the company must determine efforts to strengthen market share, increase sales, pursue large profits by diversifying/expanding the market.

Structural characteristics and form analysis of taxiway bridge in military airport

The scale of modern airport continues to expand, considering the needs of future use of multiple aircraft types and models, in order to improve the relevant design and planning of taxiways in the current military airport, it is necessary to consider the structure of airport taxiways and Bridges. According to the experience of the civil airport taxi road and bridge construction, the paper analyzes the military airfield taxiing bridge structural characteristics and load characteristic, proposed for use in military airfield taxiing bridge structure form, the slide to the width of the bridge, span layout, section forms, high beam and the size, the details for the military airport taxi provides the reference for the simulation modeling and the analysis of internal force of bridge.

Computational Platform for Analyzing the Safety of the National Airspace System

Safety analysis of complex systems can be carried out using computational models of the subsystems and their interactions. A modeling framework for analyzing the safety of the national airspace operations is described. The framework enables investigators to collaboratively formulate models of the subsystems of the National Airspace System and insert off-nominal operating conditions to assess their impact on the overall system safety. The software is designed to allow popular computational frameworks such as Python and Java to interact with it in a seamless manner. Interfaces to one of the popular commercial computational package are also provided. The software incorporates detailed aspects of the national airspace system infrastructure such as airport taxi ways and runways, approach-departure procedures, jet routes, air traffic control Sector and Center boundaries. It also includes simplified human performance models of controllers, pilots and ground vehicle operators. Performance data for over 400 different aircraft types, derived from a well-known database have also been incorporated. Airspace flight operations data is obtained from the Federal Aviation Administration data feed, and the weather data is derived from the National Oceanic and Atmospheric Administration web site. Two example applications of the software are given.

A two-stage taxi scheduling strategy at airports with multiple independent runways

Long taxiing times at large airports lead to fuel wastage and dissatisfied passengers. This paper investigates the 4D taxi scheduling problem in airports to minimize the taxiing time. We propose an iterative two-stage scheduling strategy. In the first stage, all aircrafts in a current schedule period are assigned initial 4D routes. In the second stage, landing aircrafts that are unavailable to fulfil their initially assigned routes are rescheduled using a shortest path algorithm based approach. In this paper, the simplified model used in most existing literature, that depicts a runway as having a single entrance and a single exit or even sets only one point to represent both of them has been discarded. Instead, we model the fact that a runway has multiple entrance and exit points and use an emerging concept—Runway Exit Availability (REA)—to measure the probability of clearing a runway from a specific exit during a specific time interval so that the taxiing scheduling model can be much higher approximation to the practical operation. An integer programming (IP) model factoring REA is proposed for assigning 4D taxiing routes in the first stage. The IP model covers most practical constraints faced in airport taxiing procedures, such as the rear-end/head-on conflict constraint, runway-crossing constraint, take-off/landing separation constraint, and taxi-out constraint. Besides, flight holding patterns at intersections are much more realistically modelled. Furthermore, to accelerate the solving process of the IP model, we have refined the formulation using several tricks. Simulation results by proposed scheduling approach for operations at the Beijing Capital International Airport (PEK) for an entire day demonstrate a surprising taxiing time saving against the empirical data and simulation results based on a strategy similar to what being used now days while showing an acceptable running time of our approach, which supports that our approach may help in real operation in the future.

Pengaruh Penghargaan terhadap Perceived Organizational Support dan Komitmen Afektif pada Ksryawan Koperasi Taksi Bandar Udara (KOPSIDARA) Sultan Hasanuddin

This study aims to determine the effect of appreciation on affective commitment with perceived organizational support as intervening variable at the Airport Taxi Cooperative (KOPSIDARA) Sultan Hasanuddin. So the purpose of this study are: 1) To determine the effect of respect for affective commitment. 2) To determine the effect of rewards on perceived organizational support. 3) To determine the effect of perceived organizational support on affective commitment. 4) To determine the effect of respect for affective commitment through perceived organizational support. This research was conducted at Airport Taxi Cooperative (KOPSIDARA) Sultan Hasanuddin. The sampling technique used is proportional random sampling technique. Data analysis using model test technique (path analysis). The results of this study indicate that: 1) Awards (X) have a positive and significant effect on Affective Commitment (Y). 2) Appreciation (X) has a positive and significant influence on Perceived Organizational Support (Z). 3) Perceived Organizational Support (Z) has a positive and significant influence on Affective Commitment (Y). 4) Perceived Organizational Support (Z) mediates Award (X) has a positive and significant impact on Affective Commitment (Y).

Airport Taxi Situation Awareness with a Macroscopic Distribution Network Analysis

This paper proposes a framework for airport taxi situation awareness to enhance the assessment of aircraft ground movements in complex airport surfaces. Through a macroscopic distribution network (MDN) of arrival and departure taxi processes in a spatial-temporal domain, we establish two sets of taxi situation indices (TSIs) from the perspectives of single aircraft and the whole network. These TSIs are characterized into five categories: aircraft taxi time indices (ATTIs), surface instantaneous flow indices (SIFIs), surface cumulative flow indices (SCFIs), aircraft queue length indices (AQLIs), and slot resource demand indices (SRDIs). The coverage of the TSIs system is discussed in detail based on the departure and arrival reference aircraft. A real-world case study of Shanghai Pudong airport demonstrates significant correlations among some of the proposed TSIs such as the ATTIs, SCFIs and AQLIs. We identify the most crucial influencing factors of the taxi process and propose two new metrics to assess the taxi situation at the aircraft and network levels, by establishing taxi situation assessment models instead of using two systems of multiple TSIs. The findings can provide significant references to decision makers regarding airport ground movements for the purposes of air traffic scheduling and congestion control in complex airports.

Wait Time Prediction for Airport Taxis Using Weighted Nearest Neighbor Regression

In this paper, we address the neighborhood identification problem in the presence of a large number of heterogeneous contextual features. We formulate our research as a problem of queue wait time prediction for taxi drivers at airports and investigate heterogeneous factors related to time, weather, flight arrivals, and taxi trips. The neighborhood-based methods have been applied to this type of problem previously. However, the failure to capture the relevant heterogeneous contextual factors and their weights during the calculation of neighborhoods can make existing methods ineffective. Specifically, a driver intelligence-biased weighting scheme is introduced to estimate the importance of each contextual factor that utilizes taxi drivers’ intelligent moves. We argue that the quality of the identified neighborhood is significantly improved by considering the relevant heterogeneous contextual factors, thus boosting the prediction performance (i.e., mean prediction error < 0.09 and median prediction error < 0.06). To support our claim, we generated an airport taxi wait time dataset for the John F. Kennedy International Airport by fusing three real-world contextual datasets, including taxi trip logs, passenger wait times, and weather conditions. Our experimental results demonstrate that the presence of heterogeneous contextual features and the drivers’ intelligence-biased weighting scheme significantly outperform the baseline approaches for predicting taxi driver queue wait times.