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Prescriptive Analytics for Intelligent Transportation Systems with Uncertain Demand
| 题名： | Prescriptive Analytics for Intelligent Transportation Systems with Uncertain Demand |
| 正文语种： | eng |
| 作者： | Huiwen Wang;Wen Yi;Xuecheng Tian;Lu Zhen |
| 作者单位： | Dept. of Building and Real Estate Hong Kong Polytechnic Univ. Hung Horn Hong Kong SAR 999077 China;Dept. of Building and Real Estate Hong Kong Polytechnic Univ. Hung Horn Hong Kong SAR 999077 China;Dept. of Logistics and Maritime Studies Hong Kong Polytechnic Univ. Hung Horn Hong Kong SAR 999077 China;School of Management Shanghai Univ. No. 99 Shang Da Rd. Baoshan District Shanghai 200444 China |
| 关键词： | Data-driven transportation modeling; Prescriptive analytics; Large-scale optimization; Uncertain demand |
| 摘要： | Data-driven traffic modeling is revolutionizing transportation systems and provides numerous opportunities for achieving high-quality transportation services. A major challenge in optimizing transportation systems is uncertain transportation demand. With the availability of historical data on transportation demand, the uncertain transportation demand can be better modeled, and thereby practitioners can formulate well-informed transportation scheduling decisions. In this paper, we propose three effective and economical transport scheduling strategies using mathematical programming, leveraging big data to extract useful contextual information. Additionally, a perfect-foresight optimization model is proposed to evaluate our proposed data-driven strategies. Results show a negligible optimality gap (i.e., 0.47%) between the optimal solution derived by the perfect-foresight model and the scheduling plans derived by our data-driven strategies. Overall, this paper contributes to the field of transportation engineering by innovatively applying data science, mathematical modeling, and optimization techniques. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 12 |
| 页码： | 04023118.1-04023118.12 |
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