| 摘要: |
| 车辆共享从资源分配的角度提高了汽车资源利用率.为了激励车辆共享，有关出行需求和出行者体验的研究势在必行.通过DBSCAN算法测量了城市内的交通需求聚集度，验证了车辆共享的可行性.确定了影响用户效用的关键因素，提出了基于Logit模型的数学模型以描述出行者体验和汽车资源利用率，预测出行者选择.同时，利用真实数据和调查结果，使测量和模型更加准确、真实.通过仿真实验，观测并分析了交通模式的演化过程及结果，发现城市出行需求量和交通需求聚集度是影响交通模式演化的主要因素.出行需求量需达到一定数值，演化才能达到稳定状态.城市出行交通需求聚集度越高，车辆共享的参与者则越多，其所获效用也越高.当出行需求量大于290，且交通需求聚集度大于0.9时，所有出行者都将选择车辆共享.最后，基于神州专车的出行数据，实验并观测了北京市交通模式的演化，发现若要在不加入经济因素或政策干预的情况下实施车辆共享，其交通模式将无法达到稳定状态. |
| 关键词: 车辆共享 出行需求 聚集度 效用 演化 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金（61170292，61472212)；国家科技重大专项课题（2015ZX03003004)；国家重点基础研究发展计划（973)（2012CB315803)；国家高技术研究发展计划（863)（2013AA013302，2015AA015601)；欧盟CROWN基金（FP7-PEOPLE-2013-IRSES-610524)；清华信息科学与技术国家实验室（筹)学科交叉基金 |
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| Evolution of Urban Car Sharing Mode Based on Travel Demand |
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FU Yao, XU Ke, SU Hui
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Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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| Abstract: |
| Recently, the online car sharing is on the rise which improves the car resource utilization. In order to stimulate car sharing, the study on travel demand and user experience is essential. This paper measures the aggregation degree of travel demand in urban city through DBSCAN algorithm, which verifies the feasibility of car sharing. Mathematical models are provided based on Logit model to capture user experience and car utilization, and the user selection is predicted. Both real data and the survey result are leveraged to make the measurement and model realistic. The evolution of urban traffic mode is observed and analyzed, and it is found that the quantity of the travel demand and the traffic aggregation degree are the main factors influencing the evolution. The evolution can reach a steady state if and only if the travel demand reaches some certain value. The higher the traffic aggregation degree is, the more travelers will participate in car sharing and gain higher utilities. All the travelers will participate in the car sharing when the travel demand is larger than 290 as well as the traffic aggregation degree is larger than 0.9. Ultimately, the evolution of Beijing traffic mode is analyzed based on the real travel data from CAR Inc., which shows that the traffic mode will not reach a steady state now if car sharing is implemented without the involvement of economic factors and policies. |
| Key words: car sharing travel demand aggregation degree utility evolution |
