The Issue of Subway Commuters’ Departure Time Choices under the Influence of Bike-Sharing
Bike-sharing has a significant impact on commuters’ rational planning of their travel times, which can lead to an advance or delay in the peak passenger flow of the subway system during the morning peak. To explore the impact of bike-sharing on subway commuters’ choices of departure times, we develo...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/2888275 |
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| Summary: | Bike-sharing has a significant impact on commuters’ rational planning of their travel times, which can lead to an advance or delay in the peak passenger flow of the subway system during the morning peak. To explore the impact of bike-sharing on subway commuters’ choices of departure times, we developed a departure time choice model considering the effect of bike-sharing. This model considers both constant and linear marginal-activity utility and compares it with traditional departure time choice models. Research indicates that within the timeframe that ensures on-time arrival at work, models not accounting for bike-sharing services underestimate both the departure rate and the total number of commuters compared to actual figures. Specifically, under the constant marginal-activity utility, about 6.76% of commuters actually choose to depart earlier, while under the linear marginal-activity utility, this figure is 6.91%. Conversely, during the departure timeframes that lead to late arrival at work, the traditional model overestimates both the departure rate and total number of commuters. Finally, through case analysis, we further revealed the dynamic relationship between commuter departure rates, commuting fatigue, and number of bike-sharing and calculated the actual commuting costs for different proportions of bike-sharing. The results indicate that when the number of bike-sharing reaches 30% of the commuting demand, it can maximally reduce the commuting costs for commuters by approximately 23.32%. These findings offer a crucial basis for optimizing management strategies for morning peak subway commuting. |
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| ISSN: | 2042-3195 |