Evaluating the Environmental Benefits of Personalized Travel Incentives in Dynamic Carpooling
Evaluating the Environmental Benefits of Personalized Travel Incentives in Dynamic Carpooling
复制标题
评估动态拼车中个性化旅行激励措施的环境效益
DOI:
10.1007/s12205-022-1568-1
复制
发表时间:
2022
影响因子:
2.2
通讯作者:
Guo, Qianwen
中科院分区:
文献类型:
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作者:
Sun, Yanshuo;Chen, Shijie;Guo, Qianwen
In a dynamic carpooling system, drivers and riders with their own intended travel plans are matched on short notice. The performance of such a system largely depends on the carpool participants’ travel flexibility (the extent to which a detour is tolerated or the willingness to accept a slightly different drop-off location). To increase travel flexibility, an incentive scheme can be introduced for carpool participants to opt for. For instance, a driver specifies how much she/he expects to be compensated (e.g., $5) if the earliest departure time is shifted to be earlier than the originally scheduled time by a certain amount (e.g., 10 minutes). Similarly, an interested passenger reports the expected incentive to willingly accept a different destination (such as a nearby transit stop or coffee shop) deviating from the request. In this dynamic carpool matching problem with incentives, the following decisions are jointly optimized from the perspective of a carpool matching coordinator: 1) incentive allocations to drivers and riders, 2) assignments of riders to drivers, and 3) vehicle routes of drivers. A case study based on data from Washington, D.C. is conducted to evaluate the potential of the personalized incentives offered to carpool participants in mitigating the environmental impact of transportation (quantified by the reduction of vehicle miles traveled). Two notable findings are reported. First, one dollar of incentive could reduce vehicle miles travelled by 2.88 in one benchmark case. Second, driver incentives are shown to be much more effective than rider incentives under reasonable cost assumptions.
DOI:
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发表时间:
2017
期刊:
影响因子:
--
作者:
Kevin Fang;J. Volker
通讯作者:
J. Volker
影响因子:
1.7
作者:
Lei Wang;Yong Jin;Ling Wang;Wanjing Ma;Ting Li
通讯作者:
Ting Li
DOI:
--
发表时间:
2019
期刊:
2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
影响因子:
--
作者:
M. P. Fanti;A. M. Mangini;M. Roccotelli;B. Silvestri;S. Digiesi
通讯作者:
S. Digiesi