A Dynamic Adjustment Model of Cruising Taxicab Fleet Size Combined the Operating and Flied Survey Data

Ye, Xiaofei; Li, Min; Yang, Zhongzhen; Yan, Xingchen; Chen, Jun

doi:10.3390/su12072776

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…Investigating path decisions possesses superior appliance value (Yu et al 2019;Tang et al 2020). Also, in the fleet adjust experiment, it appears that dynamic fleet size delivers lower cost and higher utilization, while dynamic fleet size adjustment models such like determining when, where and which taxis should join and exit the operation (Ye et al 2020) are missed in this work. Including such appealing models in the future would be more beneficial.…”

Section: Discussionmentioning

confidence: 85%

Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island

Yang,

Bao,

Ramezani

et al. 2020

Preprint

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Nowadays, autonomous taxis become a highly promising transportation mode, which helps relieve traffic congestion and avoid road accidents. However, it hinders the wide implementation of this service that traditional models fail to efficiently allocate the available fleet to deal with the imbalance of supply (autonomous taxis) and demand (trips), the poor cooperation of taxis, hardly satisfied resource constraints, and on-line platform's requirements. To figure out such urgent problems from a global and more farsighted view, we employ a Constrained Multi-agent Markov Decision Processes (CMMDP) to model fleet allocation decisions, which can be easily split into sub-problems formulated as a Dynamic assignment problem combining both immediate rewards and future gains. We also leverage a Column Generation algorithm to guarantee the efficiency and optimality in a large scale. Through extensive experiments, the proposed approach not only achieves remarkable improvements over the state-of-theart benchmarks in terms of the individuals efficiency (arriving at 12.40%, 6.54% rise of income and utilization, respectively) and the platforms profit (reaching 4.59% promotion) but also reveals a time-varying fleet adjustment policy to minimize the operation cost of the platform.

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Section: Discussionmentioning

confidence: 85%

Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island

Yang,

Bao,

Ramezani

et al. 2020

Preprint

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show abstract

“…Ehlers et al [45] also demonstrated a further possibility for evaluating a mixed fleet based on an economic efficiency compared to other fleet compositions via a Fleet Efficiency Factor. Recent works based on regression methods are focusing on fleet sizing summarized under the term Mobility on Demand for shared bikes [46][47][48][49], cars and taxis [50][51][52]. Similarities can be found also for the optimum mix and size of air taxi fleets in the aviation industry [53][54][55][56].…”

Section: Vehicle Fleet Sizing Problemsmentioning

confidence: 99%

The Optimal Size of a Heterogeneous Air Taxi Fleet in Advanced Air Mobility: A Traffic Demand and Flight Scheduling Approach

Lindner,

Brühl,

Berger

et al. 2024

Future Transportation

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Introducing Advanced Air Mobility (AAM) as a novel transportation mode poses unique challenges due to limited practical and empirical data. One of these challenges involves accurately estimating future passenger demand and the required number of air taxis, given uncertainties in modal shift dynamics, induced traffic patterns, and long-term price elasticity. In our study, we use mobility data obtained from a Dresden traffic survey and modal shift rates to estimate the demand for AAM air taxi operations for this regional use case. We organize these operations into an air taxi rotation schedule using a Mixed Integer Linear Programming (MILP) optimization model and set a tolerance for slight deviations from the requested arrival times for higher productivity. The resulting schedule aids in determining the AAM fleet size while accounting for flight performance, energy consumption, and battery charging requirements tailored to three distinct types of air taxi fleets. According to our case study, the methodology produces feasible and high-quality air taxi flight rotations within an efficient computational time of 1.5 h. The approach provides extensive insights into air taxi utilization, charging durations at various locations, and assists in fleet planning that adapts to varying, potentially uncertain, traffic demands. Our findings reveal an average productivity of 12 trips per day per air taxi, covering distances from 13 to 99 km. These outcomes contribute to a sustainable, business-focused implementation of AAM while highlighting the interaction between operational parameters and overall system performance and contributing to vertiport capacity considerations.

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“…However, due to the regulation of the scale of taxi service, passenger demand is far from being met and usually exceeds taxi supply at peak periods. At the same time, the large number of empty vehicles and high operating cost of taxis seriously restrict the development of the taxi market [1]. In 2015, online car-hailing began to gain in popularity in China.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the mixed method combining LSTM and an attention mechanism was applied to forecast the short-term demand of online car-hailing. The main contributions of this paper are as follows: (1) The importance of temporal and spatial sequences for online car-hailing demand are revealed, which provides a basis for extraction scheduling and dynamic pricing of online car-hailing. (2) Based on the historical data of large-scale online car-hailing, a short-term demand forecasting model of online car-hailing is developed, namely, LSTM + Attention.…”

Section: Introductionmentioning

confidence: 99%

Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches

Yan

et al. 2021

Electronics

Self Cite

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The accurate prediction of online car-hailing demand plays an increasingly important role in real-time scheduling and dynamic pricing. Most studies have found that the demand of online car-hailing is highly correlated with both temporal and spatial distributions of journeys. However, the importance of temporal and spatial sequences is not distinguished in the context of seeking to improve prediction, when in actual fact different time series and space sequences have different impacts on the distribution of demand and supply for online car-hailing. In order to accurately predict the short-term demand of online car-hailing in different regions of a city, a combined attention-based LSTM (LSTM + Attention) model for forecasting was constructed by extracting temporal features, spatial features, and weather features. Significantly, an attention mechanism is used to distinguish the time series and space sequences of order data. The order data in Haikou city was collected as the training and testing datasets. Compared with other forecasting models (GBDT, BPNN, RNN, and single LSTM), the results show that the short-term demand forecasting model LSTM + Attention outperforms other models. The results verify that the proposed model can support advanced scheduling and dynamic pricing for online car-hailing.

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A Dynamic Adjustment Model of Cruising Taxicab Fleet Size Combined the Operating and Flied Survey Data

Cited by 8 publications

References 19 publications

Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island

Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island

The Optimal Size of a Heterogeneous Air Taxi Fleet in Advanced Air Mobility: A Traffic Demand and Flight Scheduling Approach

Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches

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