为缓解大型客运枢纽夜间打车难问题, 提高枢纽客流疏解效率, 该文研究了定制公交与出租车接力接驳的复合服务模式。首先, 由定制公交将乘客从枢纽送到目的地周边且容易打车的公交站点; 然后, 由停靠在公交站点的出租车将乘客送到目的地。为有效缩短乘客的总体出行时间和降低整个系统的运营成本, 该文构建了混合整数非线性优化模型, 并以定制公交运营、出租车运营和乘客出行时间的加权综合成本最小化为目标, 优化了定制公交的线路和发车频率。该文首先采用Logit模型预估枢纽乘客选择复合服务模式和直接乘坐出租车的实际出行需求; 其次, 在线路走向、发车频率和乘客选择的约束条件下, 设计梯度搜索算法求解模型, 获得定制公交线路和发车频率的最优方案; 最后, 以上海虹桥枢纽为例, 分析并验证了所提方法的有效性。研究结果表明：共开通4条不同方向的定制公交线路用于疏解枢纽客流, 最大发车间隔不超过15.0min, 开通复合服务模式可以使枢纽乘客直接乘坐出租车的平均排队时间降低71%, 系统综合成本降低47%。该文所提方法可为大型客运枢纽夜间客流疏解提供参考。

Objective: Transportation hubs, such as airports and high-speed rail stations, frequently experience taxi shortages, especially at night, when conventional public transit is unavailable. This challenge diminishes passenger satisfaction and reduces the efficiency of passenger dispersal from these hubs. To address this issue, this study proposes an optimization framework for static customized shuttle bus routes that incorporates collaborative taxi services. Unlike traditional door-to-door customized bus services, the proposed approach utilizes customized shuttle buses to transport passengers from the hub to strategic stops closer to their destinations, where taxis are more accessible to complete the final leg of their journeys. Methods: A mixed-integer nonlinear programming model was developed to optimize shuttle bus routes and service frequencies. The objective function minimizes the weighted total costs, encompassing the operational costs for companies and the travel time costs for passengers. The model accounts for the possibility that passengers may depart within a 60.0 min window after making a reservation caused by delays, such as baggage claim. Route design is based on estimated passenger demand patterns. Passengers select services according to the performance level of the collaborative mode, ultimately achieving an equilibrium state. This study integrates passenger choice behavior through a mode choice model that estimates the proportion of travelers opting for either collaborative service or direct taxi service from the hub. To solve this complex model, this study developed a customized algorithm that initially relaxes the problem through fixed proportions for passenger mode choices. The algorithm then designs the customized shuttle bus system, calculates the actual passenger choice probabilities, and iteratively updates the choice proportions until convergence is reached. This study evaluated the proposed model using data from Shanghai Hongqiao Hub and analyzed the system performance under various scenarios, including different service modes, pricing strategies, and vehicle types. Results: A case study of Shanghai Hongqiao Hub revealed the following findings: (1) The optimization yielded four customized shuttle bus routes that efficiently dispersed passenger flow from the hub. The implementation of the collaborative service reduced the system's hourly comprehensive cost by 47% compared with a taxi-only service. The average taxi waiting time at the hub decreased dramatically by 71%, from 30.0 min to 8.7 min. (2) The collaborative approach demonstrated significant advantages over traditional door-to-door customized shuttle bus services, offering substantial advantages in both operational cost savings and passenger appeal. (3) Sensitivity analysis revealed an optimal price point of 1.50 yuan/km that balances operator profitability with service attractiveness to passengers. Additionally, by selecting differentiated vehicle types based on demand density, the profitability of customized shuttle bus services can be significantly increased while also improving service quality. Conclusions: The optimized collaborative service model effectively resolves taxi shortage problems at transportation hubs by integrating strategically designed customized shuttle bus routes with taxi services. This integration ensures an optimal balance between operational efficiency and passenger convenience. The optimization framework and solution algorithm developed in this study provide a practical approach for planning static customized shuttle bus routes and schedules while incorporating cooperation with taxi services. These findings offer valuable guidance for transportation planners and hub managers seeking to increase passenger dispersal efficiency and the overall travel experience through innovative intermodal solutions.