Cycling safety remains a major challenge because riders are frequently exposed to mixed traffic, which suppresses cycling adoption and limits the growth of sustainable mobility. To address this issue, we model the problem as a Stackelberg game in which the planner seeks to minimize cyclists’ exposure to mixed traffic while users respond by choosing routes that minimize their perceived travel times, and we develop a bilevel network design model that selects protected bicycle lanes under a construction-length budget to reduce cyclists’ unprotected exposure. The model is solved using a branch-and-price-and-cut framework with finite-convergence and global-optimality guarantees. Experiments on several benchmark city-scale networks demonstrate that the approach effectively lowers exposure and can solve practical urban networks to small certified optimality gaps within reasonable computation times. The results further show that key algorithmic parameters shape both the resulting network designs and the computational performance.
This paper examines how government data regulations influence advanced vehicle firms’ pricing and product quality decisions, consumers’ data-sharing behavior, and social welfare. We develop a game-theoretic framework involving the government, an advanced vehicle firm, and consumers under three scenarios: (i) consumers do not share data, (ii) consumers share data without regulation, and (iii) consumers share data with government regulation. The analysis yields several insights. First, while government regulation enhances privacy protection, it also restricts the advanced vehicle firm’s ability to utilize consumer data, relative to the unregulated data-sharing scenario, resulting in lower product quality and price. Second, when either the cost coefficient or the privacy loss is very low or very high, data sharing leads to higher profits and greater consumer surplus than no sharing. However, at intermediate levels, the increasing cost of personalization outweighs its benefits, making non-sharing more preferable. Third, moderate regulation can alleviate privacy concerns while preserving data value. Although stronger data regulation initially enhances consumer trust, excessive regulation increases technological costs, thereby reducing overall social welfare. Based on this, we further extend our model by allowing consumers to partially share data. A counterintuitive finding is that more consumer data does not necessarily increase firm profit. This indicates that, compared with mandatory full data sharing, data-sharing flexibility can improve consumers’ participation incentives by enabling them to better trade off personalization benefits against privacy loss, thereby expanding effective demand and potentially increasing firm profit despite reduced data collection.
Traffic forecasting, as a cornerstone task in intelligent transportation systems, plays a vital role in alleviating urban congestion, optimizing traffic management, and improving travel efficiency. However, in real-world traffic systems, smooth and continuous variations driven by routine commuting patterns often coexist with abrupt disturbances caused by accidents, signal transitions, and unexpected events. Although capturing such mixed dynamics is of significant practical importance, it has not been fully explored in existing studies. To address this challenge, we propose a spatial-temporal graph learning framework (ST-GINDE) incorporating an impulse-like modulation neural ordinary differential equation module. The model formulates traffic dynamics as a continuous-time evolution process on spatial-temporal graphs, enabling unified modeling of temporal evolution, spatial dependencies, and adaptive responses to significant traffic state variations. To capture abrupt disturbances, we introduce an impulse-like modulation mechanism that adaptively adjusts the continuous dynamics when significant traffic variations occur, without introducing discontinuous state jumps or explicit hybrid system assumptions. Furthermore, ST-GINDE integrates multi-scale temporal feature extraction and hybrid dynamic graph learning to jointly model short-term fluctuations, long-term periodic patterns, and time-varying spatial dependencies across traffic networks. We further analyze the mathematical properties of the constructed dynamic system to verify the rationality of the proposed modeling framework. Extensive experiments on multiple real-world traffic datasets demonstrate that ST-GINDE achieves highly competitive performance in traffic forecasting tasks, with notable improvements in prediction accuracy and robustness under complex traffic conditions and sudden disturbances.
To better satisfy the uneven spatiotemporal distribution of passenger demand on the Y-type metro line, the concept of flexible train composition is explored, where the train compositions can be changed flexibly at a joint station and terminal stations connected to depots. A mixed-integer nonlinear programming model is developed to jointly optimize train timetables, rolling stock circulation plans, and flexible train compositions on a Y-type metro line. Using standard linearization techniques, the model is reformulated as a mixed-integer linear programming (MILP) model with an objective that balances operating cost and passenger service quality. To incorporate short-term demand variations and support real-time operations, the integrated MILP is embedded into a Model Predictive Control (MPC) framework, which updates timetabling decisions dynamically based on demand forecasts. However, solving the resulting MPC-based MILP in real time is computationally challenging due to the large number of binary decision variables. To overcome this limitation, we propose a learning-based optimization framework that integrates offline learning with online mixed-integer optimization. The learning module is trained offline on historical MPC solutions and is designed to selectively predict a subset of high-impact binary decisions, including train composition and routing choices, thereby reducing the combinatorial complexity of the online optimization problem. The predicted decisions are then fixed in an online MILP to optimize the remaining variables while preserving feasibility and consistency across rolling horizons. In addition, a feasibility-efficiency balancing strategy is introduced through selective decision prediction and feasibility-aware penalized training, which reduces infeasible learning outputs. Numerical experiments based on real-world operational data from Guangzhou Metro Line 14 show that the proposed learning-based MPC framework achieves real-time timetabling, while maintaining solution quality comparable to a full MPC-based optimization benchmark, with only a marginal loss in feasibility. Computational results demonstrate the effectiveness of integrating learning and MPC for real-time timetable optimization.
Cross-border supply chain disruptions are increasingly posing significant challenges to global operations. However, their impact on firm resilience, particularly for small and medium-sized enterprises (SMEs), has been insufficiently investigated. This study examines how SMEs react to cross-border supply chain disruptions. Drawing on a panel sample of 10,501 firm-quarter observations of SMEs listed on China’s ChiNext market, we find that cross-border supply chain disruptions negatively affect SMEs’ resilience. Furthermore, this effect is significantly mitigated when SMEs effectively orchestrate three key resources: alternative suppliers (relational resources), governmental financial support (financial resources), and digital technology (technological resources). Our study advances logistics and transportation literature by uncovering the specific effects of cross-border supply chain disruptions on SMEs’ resilience. Moreover, our study extends resource orchestration theory into the context of cross-border supply chain disruption by showing that SMEs build resilience through orchestrating relational, financial, and technological resources.
