Real‐time and accurate travel time information between bus stations is critical for passengers to make suitable travel plans to reduce waiting time at the stops. By mining and analyzing bus operational data, it can be obtained that factors such as the variation of vehicle speed in adjacent sections and the proportion of bus lanes between stations have affected the travel time between bus stations. Therefore, considering the temporal feature, spatial feature, and weather feature as the prediction model’s input, travel time between bus stations prediction model based on eXtreme Gradient Boosting (XGBoost) was trained and established. The 28‐day bus operation data of a certain bus line in Guangzhou was used for training and verification, and they were compared with the prediction models based on K‐Nearest Neighbors (KNN), BP neural network, and Light Gradient Boosting Machine (LightGBM). In comparison with other models, the lowest MAPE of 11.96% was found for the XGBoost prediction model, which is 9.30% lower than other models on average. The sensitivity analysis of the proposed prediction model was further conducted: temporally, the accuracy of the prediction model was best during the flat peak hours; spatially, the MAPE of the model gradually decreased as the number of line units increased, and when the number of line units exceeded 18, the accuracy of the prediction model stabilized and was lower than 7%. The results confirm that the XGBoost model outperforms the KNN, BP, and LightGBM in terms of fitting, accuracy, and stability.

Purpose: This paper aims to study the pricing problems on the two-sided market for cases of monopoly and duopoly competition, specifically investigating the impact of platform service quality on the market. Theoretical analysis and computational studies are conducted to investigate the impact of different parameters on the system outcomes. Design/methodology/approach: Mathematical formulations are proposed for cases of monopoly and duopoly competition. For monopolistic market, the optimal pricing and service quality strategies are obtained using mathematical programming method. For duopolistic market, the equilibrium outcomes are derived by game theory. Sensitivity analysis and numerical studies are also adopted to investigate the impact of different parameters. Findings: For monopolistic market, the platform will provide a low service quality when the service cost parameter is large. However, when the cost parameter is small, the platform provides a higher service quality and higher registration prices. Furthermore, the sum of the optimal prices is proportional to the service quality and inversely proportional to the user price sensitivity. For duopolistic market, the competitive equilibrium prices exist under a certain condition. The determinants of equilibrium prices are the gap between the service qualities of two platforms and the cross-group externalities. Originality/value: For monopolistic market, this paper specifies the role of platform service quality in determining the platform’s pricing strategy. For duopolistic market, this paper presents a market sharing mechanism between two platforms and explores the equilibrium pricing strategies for platforms with different service quality level.

In densely populated and traffic-congested major cities, medical emergency rescue incidents occur frequently, making the use of drones for emergency medical supplies delivery a new emergency distribution method. However, establishing drone transportation networks in urban areas requires balancing spatiotemporal fluctuations in emergency needs, meeting hospitals’ mandatory constraints on response time, and addressing factors like airspace restrictions and weather impacts. By analyzing the spatiotemporal distribution characteristics of medical emergency logistics in large cities, this study constructs a drone base station location optimization model integrating dynamic and static factors. The model combines multi-source data including emergency needs, geographic information, and airspace limitations. It employs kernel density estimation to identify hotspot areas, uses DBSCAN clustering to detect long-term stable demand hotspots, and applies LSTM methods to predict short-term and sudden demand fluctuations. The model optimizes coverage rate, response time, and cost budget control for drone transportation networks through a multi-objective genetic algorithm. Using Guangzhou as a case study, the results demonstrate that through “dynamic-static” collaborative deployment and multi-model drone coordination, the network achieves 96.18% demand coverage with an average response time of 673.38 s, significantly outperforming traditional vehicle transportation. Sensitivity analysis and robustness testing further validate the model’s effectiveness in handling demand fluctuations, weather changes, and airspace restrictions. This research provides theoretical support and decision-making basis for scientific planning of urban medical emergency drone transportation networks, offering practical significance for enhancing urban emergency rescue capabilities.

To investigate the issue of multi-entry bus priority at intersections, an intelligent priority control method based on deep reinforcement learning was constructed in the bus network environment. Firstly, a dimension reduction method for the state vector based on the key lane was proposed, which contains characteristic parameters such as the bus states, the flow states, and the signal timing. Secondly, a control action method that can adjust phase sequence and phase green time at the same time was proposed under the constraints of maximum green and minimum green. Furthermore, a reward function, which can be uniformly converted into the number of standard cars, was established focusing on the indexes such as the busload and maximum waiting time. Finally, through building an experimental environment based on SUMO simulation, a real-time bus signal priority control method based on deep reinforcement learning was constructed. The results show that the algorithm can effectively reduce the waiting time of buses without affecting overall traffic efficiency. The findings can provide a theoretical basis for the signal control method considering bus priority and improve the operation efficiency of public transport.

At present, the optimization of public transportation networks and vehicle scheduling are carried out independently in stages. However, through analysis, it has been found that scheduling information such as route schedules is an important factor related to passenger route selection. Therefore, in order to further improve the optimization effect, this article proposes an innovative idea of simultaneously optimizing the line network and scheduling. Based on the construction of a real–virtual public transportation network, this article constructs a synchronous optimization model for the line network and scheduling by considering both passenger waiting and on-board time. To achieve the consideration of passengers for different route choices, a shortest path traversal algorithm based on Yen was proposed to analyze the number and weight of the shortest paths between the same OD, and a genetic algorithm was used to solve the model. Finally, the effectiveness of the model was verified through numerical examples, and the results showed that synchronous optimization was superior to phased optimization: the passenger time cost was reduced by 21.5%, the bus operation cost was reduced by 13.7%, and the total bus system cost was reduced by 18.0%.

The last-mile delivery crisis, exacerbated by the surge in e-commerce demands, continues to face persistent challenges. Logistics companies often overlook the possibility that recipients may not be at the designated delivery location during courier distribution, leading to interruptions in the delivery process and spatiotemporal mismatches between couriers and users. Parcel lockers (PLCs), as a contactless self-pickup solution, mitigate these mismatches but suffer from low utilization rates and user dissatisfaction caused by detour-heavy pickup paths. Existing PLC strategies prioritize operational costs over behavioral preferences, limiting their real-world applicability. To address this gap, we propose a user-centric path planning model that integrates spatiotemporal trajectory mining with kernel density estimation (KDE) to optimize PLC selection and conducted a small-scale experimental study. Our framework integrated user behavior and package characteristics elements: (1) Behavioral filtering: This extracted walking trajectories (speed of 4–5 km/h) from 1856 GPS tracks of four campus users, capturing daily mobility patterns. (2) Hotspot clustering: This identified 82% accuracy-aligned activity hotspots (50 m radius; ≥1 h stay) via spatiotemporal aggregation. (3) KDE-driven decision-making: This dynamically weighed parcel attributes (weight–volume–urgency ratio) and route regularity to minimize detour distances. Key results demonstrate the model’s effectiveness: a 68% reduction in detour distance for User A was achieved, with similar improvements across all test subjects. This study enhances last-mile logistics by integrating user behavior analytics with operational optimization, providing a scalable tool for smart cities. The KDE-based framework has proven effective in campus environments. Its future potential for expansion to various urban settings, ranging from campuses to metropolitan hubs, supports carbon-neutral goals by reducing unnecessary travel, demonstrating its potential for application.