The bus passenger trip flow is the base data for transit route design and optimization, and the characteristic of urban land use is the important factor for transit trip. However, the standard land use data are difficult to reflect the intensity of transit trip. This research proposed a method based on each zone building, land use situation, and bus accessibility to forecast the bus passenger trip flow in future period. Traffic zone is divided into three categories in accordance with the purpose of the residents travel: residential, commercial, and industrial. Then, by artificial neural network model, the three categories of the traffic zone bus passenger trip flow are forecasted. The method is assessed with the data of Dalian developing zone in China and results show its feasibility and reliability. Finally, the future research direction is discussed.
With the rapid growth of car ownership, traffic congestion has become one of the most serious social problems. For us, accurate real-time travel time predictions are especially important for easing traffic congestion, enabling traffic control and management, and traffic guidance. In this paper, we propose a method to predict urban road travel time by combining XGBoost and LightGBM machine learning models. In order to obtain a relatively complete data set, we mine the GPS data of Beijing and combine them with the weather feature to consider the obtained 14 features as candidate features. By processing and analyzing the data set, we discussed in detail the correlation between each feature and the travel time and the importance of each feature in the model prediction results. Finally, the 10 important features screened by the LightGBM and XGBoost models were used as key features. We use the full feature set and the key feature set as input to the model to explore the effect of different feature combinations on the prediction accuracy of the model and then compare the prediction results of the proposed fusion model with a single model. The results show that the proposed fusion model has great advantages to urban travel time prediction.
This study has been motivated from a real western-style food delivery problem in Dalian city, China, which can be described as a vehicle routing problem with time windows. An integer linear model for the problem is developed, and an improved artificial bee colony algorithm, which possesses a new strategy called an adaptive strategy, a crossover operation, and a mutation operation, is proposed to solve the problem. Then, the effectiveness of the proposed improved artificial bee colony is first validated by some benchmark instances. Furthermore, results obtained on a real-case instance for western-style food delivery problem in Dalian city are also discussed. In this case, the results indicate that the improved artificial bee colony algorithm is a feasible method to solve the real vehicle routing problem with time windows such as western-style food delivery.
Tramp shipping transport is an important part of ocean transportation. However, facing the spot market with many uncertain conditions, it is not easy for fleet operators to plan vessel's routes and schedule in the later period time, especially considering the situation that loading time window for a lot of cargoes has strong randomness. This article designed a linear programming model with chance constraints for the time window of loading cargo. Before the optimization, a survey for the waiting time of ships for berths is carried out in some of the ports with large export volume. Combined with the degree of acceptance how long ship owners can wait for the berth, the uncertain time window constraints can be transformed into deterministic constraints. The model is solved by column generation optimization technique. The model and algorithm are verified by a case of Panamax bulker fleet planning in real market. The results show that the model and the algorithm proposed in the article can well work on large-scale problem and can achieve good precision. Also, via sensitivity analysis, we provide decision makers good reference to balance profit and risks coming from randomness.
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