To more accurately predict short-term traffic flow, this study posits a sophisticated integrated prediction model, CNN-BiGRU-AAM, based on the additive attention mechanism of a convolutional bidirectional gated recurrent unit neural network. This model seeks to enhance the precision of traffic flow prediction by integrating both historical and prospective data. Specifically, the model achieves prediction through two steps: encoding and decoding. In the encoding phase, convolutional neural networks are used to extract spatial correlations between weather and traffic flow in the input sequence, while the BiGRU model captures temporal correlations in the time series. In the decoding phase, an additive attention mechanism is introduced to weigh and fuse the encoded features. The experimental results demonstrate that the CNN-BiGRU model, coupled with the additive attention mechanism, is capable of dynamically capturing the temporal patterns of traffic flow, and the introduction of isolation forests can effectively handle data anomalies and missing values, improving prediction accuracy. Compared to benchmark models such as GRU, the CNN-BiGRU-AAM model shows significant improvement on the test set, with a 47.49 reduction in the Root Mean Square Error (RMSE), a 30.72 decrease in the Mean Absolute Error (MAE), and a 5.27% reduction in the Mean Absolute Percentage Error (MAPE). The coefficient of determination ($R^2$) reaches 0.97, indicating the high accuracy of the CNN-BiGRU-AAM model in traffic flow prediction. It provides a good solution for short-term traffic flow with spatio-temporal features, thereby enhancing the efficiency of traffic management and planning and promoting the sustainable development of transportation.

This paper presents an electric vehicle-drone (EV–drone) collaborative-delivery routing optimization model that leverages the time-varying characteristics of electric vehicles and drones across multiple distribution centers (i.e., central depots) to address the logistics industry’s low-carbon transformation in the last-mile delivery. The model aims to minimize total delivery costs by formulating a mixed-integer programming (MIP) model that accounts for essential constraints such as nonlinear charging time, time-varying EV travel time, delivery time window, payload capacity, and maximum range. An improved adaptive large-neighborhood search (ALNS) algorithm is developed to solve the model. Experimental results validate the effectiveness of the proposed algorithm and highlight the impact of EV and drone technology parameters, along with the time-varying EV travel times, on the economic efficiency of delivery distribution and route planning.

Aiming at the uncertainty in cargo demand in the transportation process, the multimodal transportation path optimization problem is studied from the perspective of a low-carbon economy, and the robust optimization modeling method is introduced. Firstly, a robust optimization model for multimodal transportation is built using the multimodal transportation path optimization model under demand certainty, and the total transportation cost is then calculated by taking into account not just only the cost of transportation and trans-shipment but, additionally, the price of waiting because of schedule restrictions on trains and airplanes. Secondly, carbon emissions are added into the model as a constraint or cost by converting four different low-carbon policies. Then, the simulated annealing mechanism is introduced to improve the ACO algorithm. Finally, solomon calculus is used for the solution. The outcomes demonstrate that the improved annealing ant colony hybrid algorithm simulation can essentially improve the multimodal transportation path optimization problem with uncertain demand and promote multimodal transportation emission reduction. Among the four carbon emission policies, the mandatory carbon emission policy means are tough, and the greatest impact comes from reducing emissions and using less energy. Energy conservation and emission reduction have the second-best impact, while the three policy tools of carbon taxes, carbon trading and carbon payment are more modest.

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

The global pursuit of carbon neutrality requires the reduction of carbon emissions in logistics and distribution. The integration of electric vehicles (EVs) and drones in a collaborative delivery model revolutionizes last-mile delivery by significantly reducing operating costs and enhancing delivery efficiency while supporting environmental objectives. This paper presents a cost-minimization model that addresses transportation, energy, and carbon trade costs within a cap-and-trade framework. We develop a multi-depot mixed fleet, including electric and fuel vehicles, and a drone collaborative delivery routing optimization model. This model incorporates key factors such as nonlinear EV charging times, time-dependent travel conditions, and energy consumption. We propose an adaptive large neighborhood search algorithm integrating spatiotemporal distance (ALNS-STD) to solve this complex model. This algorithm introduces five domain-specific operators and an adaptive adjustment mechanism to improve solution quality and efficiency. Our computational experiments demonstrate the effectiveness of the ALNS-STD, showing its ability to optimize routes by accounting for both spatial and temporal factors. Furthermore, we analyze the influence of charging station distribution and carbon trading mechanisms on overall delivery costs and route planning, underscoring the global significance of our findings.

In response to the COVID-19 pandemic, communities utilize unmanned vehicles to minimize person-to-person contact and lower the risk of infection. This paper addresses the critical considerations of these unmanned vehicles’ maximum load capacity and service time, formulating them as constraints within a multi-traveling salesman problem (MTSP). We propose a comprehensive optimization approach that combines a genetic simulated annealing algorithm with clustering techniques and an improved Hopfield neural network (IHNN). First, the MTSP is decomposed into multiple independent TSPs using the fuzzy C-means clustering algorithm based on a genetic simulated annealing algorithm (SA-GA-FCM). Subsequently, the HNN is employed to introduce the data transformation technique and dynamic step factor to prepare more suitable inputs for the HNN training process to avoid the energy function from falling into local solutions, and the simulated annealing algorithm is introduced to solve multiple TSP separately. Finally, the effectiveness of the proposed algorithm is verified by small-scale and large-scale instances, and the results clearly demonstrate that each unmanned vehicle can meet the specified constraints and successfully complete all delivery tasks. Furthermore, to gauge the performance of our algorithm, we conducted ten simulation comparisons with other combinatorial optimization and heuristic algorithms. These comparisons indicate that IHNN outperforms the algorithms mentioned above regarding solution quality and efficiency and exhibits robustness against falling into local solutions. As presented in this paper, the solution to the unmanned vehicle traveling salesman problem facilitates contactless material distribution, reducing time and resource wastage while enhancing the efficiency of unmanned vehicle operations, which has profound implications for promoting low-carbon sustainable development, optimizing logistics efficiency, and mitigating the risk of pandemic spread.