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- W4386471808 abstract "This study investigates the factors influencing the choice of online and offline shopping trips and their impacts on urban transportation, environment, and economy in Tehran, Iran. A questionnaire survey was conducted to collect data from 1,000 active e-commerce users who made successful orders in both online and offline services in the last 20 days of 2021 in areas 2 and 5 of Tehran. A deep neural network model was developed to estimate the type of shopping trip based on 10 indicators, such as age, gender, car ownership, delivery cost, product price, etc. The performance of the model was compared with three other algorithms: MLP, decision tree, and KNN. The results showed that the deep neural network model had the highest accuracy of 95.63%. The most important factors affecting the choice of shopping trips were delivery cost, delivery time, and product price. This study provides insights for transportation planners, e-commerce managers, and policymakers to design effective strategies for reducing transportation costs, pollutant emissions, urban traffic, and increasing user satisfaction and sustainable development." @default.
- W4386471808 created "2023-09-07" @default.
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- W4386471808 date "2023-09-06" @default.
- W4386471808 modified "2023-09-27" @default.
- W4386471808 title "Predicting the Choice of Online or Offline Shopping Trips Using a Deep Neural Network Model and Time Series Data: A Case Study of Tehran, Iran" @default.
- W4386471808 doi "https://doi.org/10.20944/preprints202309.0356.v1" @default.
- W4386471808 hasPublicationYear "2023" @default.
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