重大公共卫生事件下通勤出行合乘车辆路径优化

Route optimization of commuter vehicles under major public health events

【目的】降低无车员工通勤出行风险,提高通勤效率。【方法】首先,以参与合乘人数最多为第一优化目标、通勤合乘时间最短为第二优化目标,并考虑车辆额定载客量、员工上班时间限制及绕行时间等因素,构建单位通勤合乘车辆路径优化模型;然后,基于遗传算法,进行最佳合乘路径求解;最后,进行算例分析、参数灵敏度分析及员工居住地分散程度的影响分析。【结果】与非合乘相比,单位组织员工内部合乘后,员工的平均通勤时间减少了24.77 min,合乘匹配成功率达到了97.14%;合乘小组人数和员工拥车率在一定范围内对合乘效果影响较大;员工居住地集中时,合乘效果更好。【结论】合乘可有效地为无车员工提供通勤帮助,减少员工通勤时间。

[Purposes]The paper aims to reduce the commuting risk of car-free employees and improve commuting efficiency.[Methods]First, taking the largest number of people participating in carpooling as the first optimization goal, and the minimum commuting carpooling time as the second optimization goal, and considering factors such as the rated passenger capacity of the vehicle, staff working time restrictions and detour time, constructing a unit commuter carpooling vehicle path optimization model. Then, based on the genetic algorithm, the optimal ride-sharing path is solved. Finally, the case analysis, parameter sensitivity analysis and impact analysis of employees’ residence dispersion degree are carried out.[Findings]The average commuting time of employees in carpooling is reduced by 24.77 minutes compared with non-carpooling employees, and the matching success rate of carpooling has reached 97.14%. The number of carpooling groups and the car ownership rate of employees have a great impact on the carpooling effect within a certain range. And the carpooling efficiency is better when employees live in concentrated locations.[Conclusions]Carpooling can effectively provide commuting assistance for car-free employees and reduce employees’ commuting time.