With the trend toward the use of large-scale vehicle probe data, an urban-scale analysis can now provide useful information for taxi drivers and passengers. Unfortunately, traffic congestion has become a critical problem in urban cities. Road traffic congestion reduces productivity in transportation services, and the daily profit earned is consequently reduced. This is opposite to the cost of living, which is increasing rapidly. Therefore, these issues are causing difficulties in all occupations in terms of managing daily expenses, particularly for taxi drivers. The taxi driving is classified as low income compared to other occupations. Such facts are a symbol of economic inefficiency. To this end, this study aims to assist taxi agencies and the government in improving taxi driver profits in Bangkok using large-scale data. To deal with these large-scale data, we propose a big data-driven model. With this model, we first calculate costs using a cost–distance algorithm and trip reconstruction. The data are then modeled to understand distance-based profits with respect to the departure time and traffic conditions. Finally, several cost predictive models using machine learning are evaluated using the ground truth from 50 taxis for a 1-month period. The experiment results show that more frequent trips over a short distance yield higher profits than long-distance trips. Finally, a solution to improve taxi driver profits is determined. We also compare the advantages and disadvantages of a unified solution.
Currently, the number of deliveries handled by transportation logistics is rapidly increasing because of the significant growth of the e-commerce industry, resulting in the need for improved functional vehicle routing measures for logistic companies. The effective management of vehicle routing helps companies reduce operational costs and increases its competitiveness. The vehicle routing problem (VRP) seeks to identify optimal routes for a fleet of vehicles to deliver goods to customers while simultaneously considering changing requirements and uncertainties in the transportation environment. Due to its combinatorial nature and complexity, conventional optimization approaches may not be practical to solve VRP. In this paper, a new optimization model based on reinforcement learning (RL) and a complementary tree-based regression method is proposed. In our proposed model, when the RL agent performs vehicle routing optimization, its state and action are fed into the tree-based regression model to assess whether the current route is feasible according to the given environment, and the response received is used by the RL agent to adjust actions for optimizing the vehicle routing task. The procedure repeats iteratively until the maximum iteration is reached, then the optimal vehicle route is returned and can be utilized to assist in decision making. Multiple logistics agency case studies are conducted to demonstrate the application and practicality of the proposed model. The experimental results indicate that the proposed technique significantly improves profit gains up to 37.63% for logistics agencies compared with the conventional approaches.
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