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Improving Viability of Electric Taxis by Taxi Service Strategy Optimization: A Big Data Study of New York City

机译:通过出租车服务策略优化提高电动出租车的生存能力:纽约市的大数据研究

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Electrification of transportation is critical for a low-carbon society. In particular, public vehicles (e.g., taxis) provide a crucial opportunity for electrification. Despite the benefits of eco-friendliness and energy efficiency, adoption of electric taxis faces several obstacles, including constrained driving range, long recharging duration, limited charging stations, and low gas price, all of which impede taxi drivers' decisions to switch to electric taxis. On the other hand, the popularity of ride-hailing mobile apps facilitates the computerization and optimization of taxi service strategies, which can provide computer-assisted decisions of navigation and roaming for taxi drivers to locate potential customers. This paper examines the viability of electric taxis with the assistance of taxi service strategy optimization, in comparison with conventional taxis with internal combustion engines. A big data study is provided using a large data set of real-world taxi trips in New York City (NYC). Our methodology is to first model the computerized taxi service strategy by Markov decision process, and then obtain the optimized taxi service strategy based on NYC taxi trip data set. The profitability of electric taxi drivers is studied empirically under various battery capacity and charging conditions. Consequently, we shed light on the solutions that can improve viability of electric taxis.
机译:运输电气化对于低碳社会至关重要。尤其是,公共交通工具(例如出租车)为电气化提供了关键的机会。尽管具有生态友好性和能源效率的好处,但采用电动出租车仍然面临一些障碍,包括行驶距离受限,充电时间长,充电站数量有限以及汽油价格低廉,所有这些都阻碍了出租车司机改用电动出租车的决定。 。另一方面,乘车移动应用程序的普及促进了出租车服务策略的计算机化和优化,从而可以为出租车司机提供计算机辅助的导航和漫游决策,以找到潜在的客户。与传统的带有内燃机的出租车相比,本文借助出租车服务策略优化研究了电动出租车的可行性。使用纽约市(NYC)的真实出租车行程的大数据集来提供大数据研究。我们的方法是首先通过马尔可夫决策过程对计算机化的出租车服务策略进行建模,然后根据纽约市出租车出行数据集获得优化的出租车服务策略。在各种电池容量和充电条件下,对电动出租车驾驶员的盈利能力进行了经验研究。因此,我们阐明了可以提高电动出租车的生存能力的解决方案。

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