| Autor: |
Yinli Jin, Yiwen Gao, Ping Wang, Jun Wang, Lei Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 7, Pp 125101-125112 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2019.2933319 |
| Popis: |
In order to promote travel safety and efficiency, many management staff are engaged to provide service to avoid long queues. Those manpower demands unavoidably introduce overstaffing, which is a great waste during off-peak period. However, it is not easy to precisely forecast the traffic flow to better arrange road management manpower and eliminate queuing phenomenon. In this paper, vehicle flow is first predicted with high accuracy based on the historical multi-source traffic data. The traffic data are collected for one month to establish a forecasting model. The forecasted traffic flow is tested on the following week and shows that the hourly traffic flow can be predicted in high accuracy. Improved manpower planning strategy is proposed based on the predicted results for arranging working schedule correspondingly. The proposed method is tested on a randomly selected toll gate in a real scenario as an example. A manpower planning strategy is verified with scheduled number of operating toll lanes. The improved manpower planning shows that the proposed method effectively reduces manpower by 40% at the toll gate and saves 67.5 working hours in one week compared with the current schedule. The successful application of the manpower planning method can be extended to other toll gates and service plazas to save manpower for more efficient and intelligent operation manner. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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