Estimation of Travel Demand for Bangkok–Chiang Mai Hyperloop Using Traveler Surveys

Agrawal, Paras; Pravinvongvuth, Surachet

doi:10.3390/su132414037

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…However, these databases do not exist for AAM. Here, potential future demand must be estimated using market research methods to determine the optimal fleet size and schedule [57]. Due to these disadvantages, regression analysis is barely able to capture the variability of demand due to seasonality and daily fluctuations, as well as fundamental uncertainty in determining demand.…”

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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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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Examining hyperloop hype on Twitter

Decaminada

2022

Journal of Mega Infrastructure & Sustainable Development

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Investigating the Potential of Nighttime Light Data to Estimate Travel Demand

Sun,

2024

Transactions in GIS

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Taking the bike‐sharing travel demand (BSTD) as an example, this study investigates the potential of Nighttime Light (NTL) data to optimize forecasting performance and replace the land use factors. Stepwise regression is trained with the travel demand in each unit as the dependent variable, and land use factors are introduced as the independent variable one by one, which finds the set of independent variables. Five machine learning algorithms driven by ensemble learning and decision trees including the GBDT, Random Forecast, Adaboost, Extratrees, and Catboost, are employed and evaluated to achieve comparative analysis of “before considering‐after considering NTL data”. The methodological verification of Beijing city shows: (1) Adaboost and GBDT are superior to all other algorithms, since they generally have the highest R2, lowest RMSE, and lowest absolute MAPE. (2) All methods by employing NTL data obviously optimize the performance of BSTD forecast with decreased RMSE, decreased MAPE, etc. In particular, GBDT performs the best in reducing MSE, with a percentage of −99.99% in the training set and −86.985% in the test set, which AdaBoost, Extratrees, and Catboost follow. (3) Land use factors no longer make sense in predicting BSTD after employing NTL data, and NTL data has covered the roles of land use factors to ensure accuracy. The conclusions presented here enrich our understanding of the relative roles of land use factors and NTL data in travel demand and boost our optimization in traffic prediction in the future.

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Estimation of Travel Demand for Bangkok–Chiang Mai Hyperloop Using Traveler Surveys

Cited by 4 publications

References 33 publications

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

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

Examining hyperloop hype on Twitter

Investigating the Potential of Nighttime Light Data to Estimate Travel Demand

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