| Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|
|
|---|---|---|
| Article Number | 01020 | |
| Number of page(s) | 14 | |
| Section | Big Data Analysis Application and Energy Consumption Research | |
| DOI | https://doi.org/10.1051/e3sconf/202021401020 | |
| Published online | 07 December 2020 | |
Using differential pricing to mitigate the impact of congestion on metro: a big data study base on Shanghai metro
Shanghai Pinghe High School Shanghai, China
Metro, which is one of the most popular way of public transportation, has shown inability to withstand the high intensity of congestion presented in the first two decades of the new millennia. Despite the effort made by government which includes adding multiple metro line, the situation is still grievous. Unlike other form of transportation such as train or airplane, the ridership of metro wasn’t staggered and can be manipulated. In this paper, we employ differential pricing to alleviate traffic pressure on metro during the peak hours. As people have different elasticity of demand for metro transportation in different time interval, we can reduce the number of passengers who relatively treat metro transportation as unnecessary in specific time interval and place by setting up different price. Base on the data of shanghai metro, we show different aspects in our model in which we use system clustering, optimization model of social welfare and calculate an acceptable range of price using Ramsey pricing model. We validate our solution, using the agent simulation model, to be considerably capable at easing the traffic pressure of metro, whether comparably or statistically.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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