Transportation Science最新文献

{"title":"An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance","authors":"Can Chen, N. Geroliminis, Renxin Zhong","doi":"10.1287/trsc.2023.0091","DOIUrl":"https://doi.org/10.1287/trsc.2023.0091","url":null,"abstract":"Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce delays in large-scale MFD-based urban networks. For MFD-based PCRG, one needs to distinguish between the dynamics of (a) the plant that represents reality and is used as the simulation tool and (b) the model that contains easier-to-measure states than the plant and is used for devising controllers, that is, the model-plant mismatch should be considered. Traditional model-based methods (e.g., model predictive control (MPC)) require an accurate representation of the plant dynamics as the prediction model. However, because of the inherent network uncertainties, such as uncertain dynamics of heterogeneity and demand disturbance, MFD parameters could be time-varying and uncertain. Conversely, existing data-driven methods (e.g., reinforcement learning) do not consider the model-plant mismatch and the limited access to plant-generated data, for example, subregional OD-specific accumulations. Therefore, we develop an iterative adaptive dynamic programming (IADP)-based method to address the limited data source induced by the model-plant mismatch. An actor-critic neural network structure is developed to circumvent the requirement of complete information on plant dynamics. Performance comparisons with other PCRG schemes under various scenarios are carried out. The numerical results indicate that the IADP controller trained with a limited data source can achieve comparable performance with the “benchmark” MPC approach using perfect measurements from the plant. The results also validate the IADP’s robustness against various uncertainties (e.g., demand noise, MFD error, and trip distance heterogeneity) when minimizing the total time spent in the urban network. These results demonstrate the great potential of the proposed scheme in improving the efficiency of multiregion MFD systems. Funding: The work was jointly funded by the National Natural Science Foundation of China under [Grant 72071214] (R. Zhong), the Dit4Tram project from the European Union’s Horizon 2020 Research and Innovation Programme under [Grant 953783] (N. Geroliminis), and the Research Student Attachment Programme of The Hong Kong Polytechnic University (C. Chen). Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0091 .","PeriodicalId":510068,"journal":{"name":"Transportation Science","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-Based Robust and Adaptive Optimization Method for Heteroscedastic Transportation Problems","authors":"Ziyuan Gu, Yifan Li, M. Saberi, Zhiyuan Liu","doi":"10.1287/trsc.2023.0485","DOIUrl":"https://doi.org/10.1287/trsc.2023.0485","url":null,"abstract":"Simulation-based optimization is an effective solution to complex transportation problems relying on stochastic simulations. However, existing studies generally perform a fixed number of evaluations for each decision vector across the design space, overlooking simulation heteroscedasticity and its effects on solution efficiency and robustness. In this paper, we treat the number of evaluations as an adaptive variable depending upon the simulation heteroscedasticity and the potential optimality of each decision vector. A statistical method, which automatically determines the variable number of evaluations, is presented for a range of derivative-free optimization methods. By fusing Bayesian inference with the probability of correct selection, it permits adaptive allocation of budgeted computational resources to achieve improved solution efficiency and robustness. The method is integrated with the deterministic global optimizer DIviding RECTangles (DIRECT) to yield NoisyDIRECT as a continuous simulation-based robust optimization method (which is open sourced). The key properties of the method are proved and discussed. Numerical experiments on difficult test functions are first conducted to verify the improvement of NoisyDIRECT compared with DIRECT and Bayesian optimization. Given the same computational budget, NoisyDIRECT can better locate the global optimum than the other two alternatives. Applications to representative simulation-based transportation problems, including an M/M/1 queueing problem and a parking pricing problem, are then presented. The results demonstrate the ability of NoisyDIRECT to pinpoint the optimal solution via adaptive computational resources allocation, achieving the desired level of robustness. Funding: This work was supported by the Youth Program [Grant 52102375] and the Key Project [Grant 52131203] of the National Natural Science Foundation of China, the Youth Program [Grant BK20210247] of the Natural Science Foundation of Jiangsu Province, and the High-Level Personnel Project of Jiangsu Province [Grant JSSCBS20220099]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0485 .","PeriodicalId":510068,"journal":{"name":"Transportation Science","volume":"31 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalized Riskiness Index in Vehicle Routing Under Uncertain Travel Times: Formulations, Properties, and Exact Solution Framework","authors":"Zhenzhen Zhang, Yu Zhang, Roberto Baldacci","doi":"10.1287/trsc.2023.0345","DOIUrl":"https://doi.org/10.1287/trsc.2023.0345","url":null,"abstract":"We consider a vehicle routing problem with time windows under uncertain travel times where the goal is to determine routes for a fleet of homogeneous vehicles to arrive at the locations of customers within their stipulated time windows to the maximum extent while ensuring that the total travel cost does not exceed a prescribed budget. Specifically, a novel performance measure that accounts for the riskiness associated with late arrivals at the customers, called the generalized riskiness index (GRI), is optimized. The GRI covers several existing riskiness indices as special cases and generates new ones. We demonstrate its salient managerial and computational properties to motivate it better. We propose alternative set partitioning-based models of the problem. To obtain the optimal solution, we develop an exact solution framework combining route enumeration and branch-price-and-cut algorithms, in which the GRI is dealt with in route enumeration and column generation subproblems. We mainly reduce the solution space by exploiting the GRI and budget constraints’ properties without losing optimality. The proposed method is tested on a collection of instances derived from the literature. The results show that a new instance of the GRI outperforms several existing riskiness indices in mitigating lateness. The exact method can solve instances with up to 100 nodes to optimality. It can consistently solve instances involving up to 50 nodes, outperforming state-of-the-art methods by more than doubling the manageable instance size. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101187, 72371204, 72021002, and 71901180], the Qatar National Research Fund [Grant ARG01-0430-230029], Natural Science Foundation of Sichuan Province [24NSFSC6232], and Guanghua Talent Project of the Southwestern University of Finance and Economics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0345 .","PeriodicalId":510068,"journal":{"name":"Transportation Science","volume":"32 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140982789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}