This document discusses improving the viability of electric taxis through optimization of taxi service strategies using big data from New York City taxi trips. It proposes using a Markov decision process to model an optimized computerized taxi service strategy. The profitability of electric taxis is empirically studied under different battery capacities and charging conditions. The optimal strategy from the Markov decision process can be implemented in an intelligent recommender system to help electric taxi drivers earn comparable revenues to those using internal combustion engine taxis.

1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
Improving Viability of Electric Taxis by Taxi Service Strategy Optimization: A Big
Data Study of New York City
Abstract:
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.
Existing System:
In this approach, a comparison is required for every two values existing in the dataset. The
observed dominant value in each comparison can be used to find the final top-k dominant
values later. Indicating the top-k dominant values can be challenging when facing big data;
processing time can become astronomical even if the computational cost can be ignored.
Therefore, this approach may not be the best way of solving this problem.
Proposed System:
Several algorithms have been proposed to apply the TKD queries with performance more
acceptable and efficient than the naive approach. Based on [2], [3], applying the top-k
dominance query is possible in the incomplete dataset using Skyline query processing. The
Skyline algorithm separates the uniform values into different buckets. The buckets group the
dataset by dividing it into chunks that have same missing-value dimension. The buckets are
easier and faster to process. By having separate top-k dominant values for each bucket and
combining them together, determining the top-k values of the whole dataset becomes

2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
possible. The Upper Bound Based algorithm is another method which works by finding top
scores using the bit-wise comparison between values, covered later in the paper [4].
Conclusion:
In this paper, we employ Markov Decision Process to model computerized taxi service
strategy and optimize the strategy for taxi drivers considering electric taxi operational
constraints. We evaluate the effectiveness of the optimal policy of Markov Decision Process
using a big data study of realworld taxi trips in New York City. The optimal policy can be
implemented in an intelligent recommender system for taxi drivers. This becomes more
viable especially due to the advent of autonomous vehicles. Our evaluation shows that
computerized service strategy optimization allows electric taxi drivers to earn comparable net
revenues as ICE drivers, who also employ computerized service strategy optimization, with at
least 50 kWh battery capacity. Hence, this sheds light on the viability of electric taxis.
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3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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