Taxi Operations Research Articles

Impact of Air Taxis on Air Traffic in the Vicinity of Airports

The extension of the current urban transportation system utilising the third dimension by air taxi (AT) operations represents a potential solution for the congestion of metropolitan areas. A major asset for AT operations is the connection to existing airports enabling the access to multiple other transportation systems. This paper develops an analytical model for AT operations and their capacity impact on airports, exemplary for Hamburg airport. The model is developed, based on the results of a fast time simulation (FTS) considering multiple aspects, such as vehicle configuration and touchdown and lift-off areas (TLOF). Collectively, three integration methods were analysed, each of them impacting the conventional air traffic differently. The results show that an integration using the runway-system is not possible with five ATs per hour. Further methods allow an integration of up to 20 air taxis per hour. Additionally, an energy consumption analysis of the ATs is conducted. Finally, proposals are given for integrating ATs at an airport and further strategies to extend the analytical model. Through this work, a model to calculate and predict an AT’s influence on the airside capacity of an airport is designed. This is an important step for the practical implementation of AT operations at airports.

The COVID-19 Pandemic: Analysis of Large-Scale Social Restrictions (PSBB) Policies for the Community in Various Prevention Efforts

This study aims to determine the analysis of large-scale social restrictions (PSBB) policies for the community in various efforts to prevent the impact of Coronavirus disease transmission or known COVID-19. The wider the range of transmission of the COVID-19 pandemic, including in Indonesia, the government has taken decisions or steps to immediately break the chain of transmission of the COVID-19 pandemic by making policies, one of which is the large-scale social restriction (PSBB) policy. Government policy N0. 21 of 2020 concerning large-scale social restrictions in the context of accelerating the handling of the COVID-19. The research method is qualitative research with an empirical literature approach. This study uses the library method, namely browsing documents, reports such as journals, post office, books, articles, mass media, or news. The results of this study analyze government policies regarding large-scale social restrictions (PSBB) which have a major impact on people's lives, especially the difficulty of the economy to meet the needs of daily life, especially the lower middle class. PSBB direct impact is devastating for the people who rely on daily income, such as itinerant traders, hawkers, motorcycle taxi operators, drivers and public transportation has. This is one of the impacts of the enactment of the large-scale social restriction (PSBB) policy in Indonesia. From the results of the researchers, it can be concluded that the impact of the virus COVID-19 has resulted in an economic crisis, Learning activities previously carried out in schools today so learn at home online, difficulty to access basic health services, the implementation of the work patterns works from home.

COVID-19 related risk perception among taxi operators in Kingston and St. Andrew, Jamaica

BackgroundIn the Caribbean, all countries have confirmed COVID-19 cases. Considering the high infectivity of the virus, no preexisting immunity to the virus and an associated modest reproductive rate (R0), the high density of persons utilizing public transport is of immense public health concern. Public transport systems may facilitate and accelerate the transmission of the disease. AimThe aim of this study was to assess the COVID-19 related risk perceptions among taxi drivers by virtue of their occupation and the implications for health promotion interventions. MethodsA cross-sectional study was conducted in May 2020 among 282 taxi drivers in the Kingston and St. Andrew (KSA) metropolitan region in Jamaica. A 28-item anonymized self-administered questionnaire was used to collect data which was subsequently analyzed using SPSS version 20. A risk score was generated and the Mann-Whitney U and Kruskal Wallis tests were used to determine differences in the mean ranks for risk perception score as applicable. A 5% alpha level was utilized in determining statistical significance. ResultsRisk perception scores ranged from 10 to 21 with a median of 17 (IQR 3.25) and there was no statistically significant difference in the median risk perception score by socio-demographic variables. There was however, a statistically significant positive correlation (Spearman's rho = 0.238, p=<0.001) between risk perception and knowledge. Approximately, 86% of respondents reported that they obtained COVID-19-related information from news reports (traditional media). ConclusionTaxi drivers perceive themselves to be at occupationally related risk of COVID-19. Therefore, greater understanding of this issue is paramount as it can aid in the crafting of initiatives that may enhance personal safety of both taxi drivers and commuters.

ATEFA - A first German approach on UAM community noise and air-taxi certification

Urban air mobility (UAM) includes larger air taxis, driven by multiple distributed propellers or fans, installed in a fixed configuration or as tilted wings/engines. These novel electric air vehicles will always generate tonal and broadband noise, in some cases with components from a specific installation situation. In any case, noise will propagate to the ground, into high populated areas of large cities or into their urban environment, individually depending on the operational situation of air taxi use. Some of the populated areas, where no significant noise from air traffic has been observed so far, will be exposed to this new type of aircraft noise. ATEFA, as the first German national funded research project on UAM community noise, aims to provide first answers. Three selected air taxi concepts, strongly differing acoustically from each other, will be technically described and their noise emissions will be modeled and predicted. Air taxi operations will be simulated by generic traffic scenarios in a selected area of southern Germany, and community noise near vertiports, but also "en-route" along the flight paths, will be computed. Beside this, noise certification aspects will be assessed regarding metrics and procedures and compared to a light low-noise helicopter as reference aircraft.

Becoming a Pirate: Leaving the Gig Economy

How and why do platform workers “quit”? Using data from more than 330 ride-hail drivers, I find that platform companies downplay the costs of working a ‘gig’, such as asset depreciation and vehicle maintenance, resulting in workers underestimating the true cost of working a “gig.” Once workers realize these costs, some exit, yet others have come to rely on gig income to meet their economic needs. In response, I find these workers begin their own “pirate” (illegal) taxi operations to decrease their reliance on platform companies. This article develops both a new category of conflict and response for marketized employment relationships.

Noise assessment of taxibotted versus conventional taxiing operations using a phased microphone array

In sustainable aviation the focus is mostly applied to the greenhouse gas emissions during flight. However airports have an increasing interest in reducing emissions during ground operations such as taxiing for example to improve the local air quality. Amsterdam Airport Schiphol started a pilot for sustainable taxiing with a pilot-controlled hybrid-electric aircraft towing vehicle called TaxiBot in 2020. The COVID-19 pandemic created an opportunity for extensive operational testing on a near-empty airport. Due to the low background noise levels in this situation, also a noise assessment of taxiing with the TaxiBot versus conventional two-engine taxiing was performed. This assessment can be used to evaluate the noise levels to which ground workers or neighbouring communities are exposed due to TaxiBot operations. For the noise measurements a phased microphone array was used, which allowed not only for a noise level and directionality assessment, but also for noise source identification. This paper compares the noise emissions and noise sources between a taxibotted and conventional taxiing operation. The results show that a taxibotted taxiing operation produces significantly lower noise levels. Additionally, acoustic imaging shows that the TaxiBot engine is the main noise source for a taxibotted pass-by manoeuvre.

Self-employment through ride-hailing: Drivers’ experiences in Johannesburg, South Africa

Orientation: Ride-hailing services have gained popularity in South Africa because of their ability to provide a reliable alternative to traditional modes of transport. However, little is known about ride-hailing drivers.Research purpose: The purpose of this study was to determine the ride-hailing drivers’ job satisfaction levels and the challenges they experienced.Motivation for the study: This study was motivated by the lack of scholarly information on the experiences of drivers involved in ride-hailing in the context of South Africa.Research design, approach and method: This study adopted a pragmatic approach through a mixed-method, survey-based design. Fifty-three ride-hailing drivers were randomly interviewed in Johannesburg, South Africa, using a pre-designed and piloted questionnaire. Data were analysed using methods of descriptive interpretation and inductive thematic analysis.Main findings: A descriptive statistical analysis showed that most ride-hailing drivers provide the service to earn their primary income. Furthermore, a thematic analysis found that ride-hailing drivers felt unsafe providing these services because of the violence they experienced from minibus taxi operators in the region.Practical/managerial implications: In a country plagued by unemployment, the development of small businesses is an important income-generating opportunity for many. To further develop the employment potential of the growing ride-hailing industry, governments at various levels should provide greater support and safety measures for ride-hailing drivers.Contribution/value-add: This study contributes a unique dataset in exploring ride-hailing drivers’ self-employment, job satisfaction and challenges in Johannesburg, South Africa.

Covid-19 and Precarity in South Africa’s Minibus Taxi Industry

This article considers covid-19 and precarity in South Africa’s minibus taxi industry. Covid-19 and the resulting national lockdown interrupted the operations of the industry (like other businesses) in South Africa. During the lockdown (from level 5 to level 1), some taxi operators complained that the lockdown resulted in them losing profit. Taxi drivers also complained that they are making less money through taxi fares (noting that each day they give collected fares to taxi owners and keep some of the money for petrol). The labour inspectors of the Department of Employment and Labour (DOEL) continue to find it difficult to exercise their role of inspecting working conditions in the industry. Despite the fact that the DOEL issued a Sectoral Determination for the taxi industry (Basic Condition of Employment Act 95 of 1997, Sectoral Determination 11: Taxi Sector 2005), which specifies basic employment conditions, the industry is still predominantly informal and employees have no job protection. Taxi drivers remain exempt from job-related benefits such as the Unemployment Insurance Fund (UIF), which makes it impossible for them to benefit during difficult times such as Covid-19 and unemployment. Therefore, the virus and the lockdown revealed further the precariousness of taxi drivers and the concerns around making profit by taxi owners.

Hybrid-Data Approach for Estimating Trip Purposes

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

Research on Taxi Operation Characteristics by Improved DBSCAN Density Clustering Algorithm and K-means Clustering Algorithm

With the development of urbanization, the problem of urban traffic congestion is becoming more and more serious. An improved k-means clustering algorithm was proposed to solve the problem that the traditional k-means clustering center could easily be affected by the clustering center and fall into the local optimal solution. Based on the big data of New York City taxis, the operational characteristics are analyzed. The experimental results show that the improved K-means clustering algorithm has a better clustering analysis effect in terms of hot demand for taxis.

Capacitated Location-Allocation-Routing Problem with Time Windows for On-Demand Urban Air Taxi Operation

Urban air taxi (UAT) operation has gained traction with the advancements in distributed electric propulsion and the emergence of electric vertical take-off and landing aircraft. Start-up companies and aircraft manufacturers are pursuing the possibility of operating UAT at scale in urban and suburban areas and at an affordable price. However, considerable uncertainties remain about several strategic, tactical, and operational aspects that affect UAT adoption. We envision a mature state of UAT operation in which the UAT operator offers door-to-door, multimodal, on-demand, and per-seat service. We propose the concept of flexible meeting points for UAT operation where passengers are flexible about the location of the UAT pads for boarding and deboarding, and could therefore be pooled together to share an aircraft. Consequently, we model UAT fleet operation as a capacitated location-allocation-routing problem with time windows and present a mixed integer programming formulation. The formulation addresses decisions on request acceptance and rejection, allocation of requests to flights, and aircraft routing and scheduling. Additionally, it allows for consolidating the demand to increase the aircraft’s utilization and service rate. The numerical results indicate that the demand consolidation scheme could significantly decrease the number of rejected requests and the aerial mileage. Depending on the operator’s business model, the proposed formulation could be used offline in a static and deterministic setting when all requests are known in advance, or it could be employed online by sequentially solving the static and deterministic snapshot problems with no knowledge about future requests.

Resident air transport subsidy impact on airport ground operations: Gran Canaria airport case study

This paper seeks to analyze the impact of regional aviation subsidies for Gran Canary airport; specifically, the effect on airport capacity, air carrier economics and the environment. It was found that aircraft taxiing operations time increased producing negative effects on airport capacity management, air carrier economies and the environment in terms of decreasing airport capacity available and the increased fuel and emissions costs. However, those losses have to be balanced against the social benefit of increased resident mobility.

Supply and Demand Relationship of Taxi Battery Exchange Based on Big Data: A Case Study of Beijing, China

The power exchange mode is widely applied in the rental field as an efficient energy supply method for new energy vehicles. The power supply-demand relationship analysis swaps. In particular, the quantitative spatial analysis of sub-regions is of great significance for optimizing the spatial layout of power swapping stations, better operation of taxis, and more efficient power swapping stations. Therefore, this paper analyzes the correlation between the ten states of taxis and the corresponding power exchange. The present analysis targets the limitations in the existing methods to analyze the power exchange supply and demand and utilizing the big data pertaining to real-time taxi operation, order-taking mode, and station-swapping operation. As per the correlations, a calculation method is established to determine the power exchange demand based on the location where the orders are received and the matching method of the power exchange supply and demand. Besides verifying the scientific nature and feasibility of the method empirically, this study also ensured its great flexibility, which allows it to adapt to more complicated social scenarios. The big data analysis indicates that determining the spatial distribution of demand based on the location from where the taxi orders are received is far more rational and practical. Thus, this study has a vital role in guiding the location and layout of interchange stations.

Assessing the Access to Jobs by Shared Autonomous Vehicles in Marysville, Ohio: Modeling, Simulating and Validating

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting. To simulate the aforementioned applications, a simulation environment that has a realistic rendering of the chosen real-world environment with realistic traffic generated around the SAVs is developed first using a geo-fenced area centered at the city of Marysville in Ohio as an example. This paper, then, presents an algorithm to optimally utilize multiple autonomous vehicles for shared rides based on modeling of pickup locations corresponding to affordable housing at the periphery of the geo-fenced area connected to destination locations corresponding to jobs and other locations of opportunity. The presented work showcases SAV operation as a solution to the spatial mismatch between affordable housing and job locations in a realistic simulation environment in an urban setting.&lt;/div&gt;&lt;/div&gt;

Multi-zone prediction analysis of city-scale travel order demand.

Taxi order demand prediction is of tremendous importance for continuous upgrading of an intelligent transportation system to realise city-scale and personalised services. An accurate short-term taxi demand prediction model in both spatial and temporal relations can assist a city pre-allocate its resources and facilitate city-scale taxi operation management in a megacity. To address problems similar to the above, in this study, we proposed a multi-zone order demand prediction model to predict short-term taxi order demand in different zones at city-scale. A two-step methodology was developed, including order zone division and multi-zone order prediction. For the zone division step, the K-means++ spatial clustering algorithm was used, and its parameter k was estimated by the between–within proportion index. For the prediction step, six methods (backpropagation neural network, support vector regression, random forest, average fusion-based method, weighted fusion-based method, and k-nearest neighbour fusion-based method) were used for comparison. To demonstrate the performance, three multi-zone weighted accuracy indictors were proposed to evaluate the order prediction ability at city-scale. These models were implemented and validated on real-world taxi order demand data from a three-month consecutive collection in Shenzhen, China. Experiment on the city-scale taxi demand data demonstrated the superior prediction performance of the multi-zone order demand prediction model with the k-nearest neighbour fusion-based method based on the proposed accuracy indicator.

Future Regional Air Mobility Analysis Using Conventional, Electric, and Autonomous Vehicles

Recent activities in electric propulsion and autonomy provide opportunity to improve both regional and urban air mobility. The inclusion of electric propulsion and the promise of autonomy to simplify flight operations could provide benefits that lead to wider use of small aircraft for regional transportation. The effort here presents a computational analysis framework to evaluate three different modes of transportation (airline, automobile, and air taxi); the air taxi service options consider different conventional takeoff and landing aircraft options powered by either conventional fuels or electricity with various levels of autonomy to assess potential advantages. The framework is developed in two variants: a specific trip model and a generic trip model. The specific trip approach leverages Google Maps and Rome2Rio application programming interfaces for driving and flight information, respectively, whereas the generic approach uses curve-fit/approximate models derived from the specific trip model for rapid calculations of trip time and cost for various system-level market studies. Potential market sensitivity study reveals that the inclusion of distributed propulsion, autonomy, ride-sharing, and aircraft production rate all impact the market attractiveness of the on-demand air taxi operations. Of these, an increased level of autonomy and the ability to facilitate ride-sharing are the two most important factors that affect the market attractiveness of regional air mobility.

Development scenarios for the taxi services market in Vietnam in terms of competitiveness

The study objective was scenario forecasting of growth prospects of the service operators market on the example of Vietnam. The study is based on the results of a survey of 231 Hanoi residents who use traditional taxi service and app-based taxi service (TTS&amp;ABTS). Using an integrated model, the quality of taxi services (TS) was assessed based on the satisfaction level with the individual components of the services provided. The perception features of TTS&amp;ABTS in the context of different categories of respondents have been revealed. Using IPA-analysis, the competitiveness of the taxi operators in the country has been determined. Considering the impact of external factors that create risks and opportunities for the development of TS, economic scenarios for the development of TTS&amp;ABTS have been simulated. The results can be useful in legislative regulation of the taxi market (TM) in Vietnam and the development of effective marketing strategies by taxi operators.

Dynamic planning for simultaneous recharging and relocation of shared electric taxies: A sequential MILP approach

Short driving range, limited chargers, and long charging times challenge the profitability of electric taxi operations. In this paper, a charging algorithm is developed to accompany a taxi service with online trip requests, which uses a private charging infrastructure for both slow and fast charging. The vehicle charging curves are assumed to be piece-wise linear functions. The proposed algorithm uses historical operation data to generate a pro-active planning that avoids queuing of vehicles. The algorithm is built upon three sequential, iterative, finite-horizon Mixed Integer Linear Programs. This iterative process, in which the three MILPs are solved sequentially, allows the current time-step to be optimized, while taking future time-steps into account. This is achieved by optimizing over multiple time-steps, but only implementing the current time-step in each iteration. The sequential aspect of the algorithm allows the vast amount of information over time and space to be exploited for charging trip decisions in real time, while maintaining a tractable computation time. The first level with the longest horizon is an aggregated, daily problem, that plans the charging duration required for the fleet. The second level has a horizon of up-to three hours and is an aggregated, zone-based problem for determining charger selection and empty vehicle relocations. The third level translates the outputs of the first two problems to executable decisions for individual vehicles based on their real-time location, state of charge, and assigned passengers. The first level is the most computationally expensive and is solved using Column Generation. The performance of the first two levels is then independent of the fleet size, which makes the algorithm highly scalable. A case study with travel data for the city of Barcelona is used to test the model. Results show that the proposed method can utilize the full capacity of the charging infrastructure, and improve the number of accepted requests by 14% compared to employing a naive charging rule.

Multi-objective optimization model for airport gate assignment problem

PurposeThe purpose of this paper is to provide feasible and fast solutions for the multi-objective airport gate assignment problem (AGAP) considering both passenger-oriented and airline-oriented objectives, which is the total walking distance from gate to baggage carousels (TWD) and the total aircraft fuel consumption during taxi operations (TFC). In addition, obtaining feasible and near-optimal solutions in a short time reduces the gate planning time to be spent by air traffic controllers.Design/methodology/approachThe mixed integer linear programming (MILP) approach is implemented to solve the multi-objective AGAP. The weighted sum approach technique was applied in the model to obtain non-dominated solutions. Because of the complexity of the problem, the simulated annealing (SA) algorithm was used for the proposed model. The results were compared with baseline results, which were obtained from the algorithm using the fastest gate assignment and baggage carousel combinations without any conflict taking place at the gate assignments.FindingsThe proposed model noticeably decreased both the TWD and TFC. The improvement of the TWD and TFC changed from 22.8% to 46.9% and from 4.7% to 7.1%, respectively, according to the priorities of the objectives. Additionally, the average number of non-dominated solutions was calculated as 6.94, which presents many feasible solutions for air traffic controllers to manage ground traffic while taking the airline and passenger objectives into consideration.Practical implicationsThe proposed MILP model includes the objectives of different stakeholders: air traffic controllers, passengers and airlines. In addition, the proposed model can provide feasible gate and baggage carousel assignments together in a short time. Therefore, the model creates a flexibility for air traffic controllers to re-arrange assignments if any unexpected situations take place.Originality/valueThe proposed MILP model combines the TWD and TFC together for the AGAP problem using the SA. Moreover, the proposed model integrates passenger-oriented and airline-oriented objectives together and reveals the relationships between the objectives in only a short time.

An Integrated Reinforcement Learning and Centralized Programming Approach for Online Taxi Dispatching.

Balancing the supply and demand for ride-sourcing companies is a challenging issue, especially with real-time requests and stochastic traffic conditions of large-scale congested road networks. To tackle this challenge, this article proposes a robust and scalable approach that integrates reinforcement learning (RL) and a centralized programming (CP) structure to promote real-time taxi operations. Both real-time order matching decisions and vehicle relocation decisions at the microscopic network scale are integrated within a Markov decision process framework. The RL component learns the decomposed state-value function, which represents the taxi drivers' experience, the off-line historical demand pattern, and the traffic network congestion. The CP component plans nonmyopic decisions for drivers collectively under the prescribed system constraints to explicitly realize cooperation. Furthermore, to circumvent sparse reward and sample imbalance problems over the microscopic road network, this article proposed a temporal-difference learning algorithm with prioritized gradient descent and adaptive exploration techniques. A simulator is built and trained with the Manhattan road network and New York City yellow taxi data to simulate the real-time vehicle dispatching environment. Both centralized and decentralized taxi dispatching policies are examined with the simulator. This case study shows that the proposed approach can further improve taxi drivers' profits while reducing customers' waiting times compared to several existing vehicle dispatching algorithms.