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 lowcarbon 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 dataset of real-world taxi trips in New York City. Our methodology is to first model the computerized taxi service strategy by Markov Decision Process (MDP), and then obtain the optimized taxi service strategy based on NYC taxi trip dataset. 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.

An Optimization Model for the Temporary Locations of Mobile Charging Stations

“…Based on trip time, driving time and idle time, we can derive the energy consumed by the taxi for a trip from intersection i to intersection j. Similarly to [50,51], we estimate the energy consumption using a black-box approach using multiple linear regression. Hence, total energy consumption for a trip can be decomposed into moving energy consumption E M i (i, j) and auxiliary loading energy consumption E A t (i, j):…”

“…This value can be estimated based on historical weather data from New York City. Similarly to [50], we choose l t to be between 1 and 1.5.…”

“…As the driving is more aggressive, the EV consumes more energy. In contrast, a calm driver can save from 30% to 40% of the energy consumed in the event of aggressive driving [50]. We take η = 0.8 (calm driving), η = 1 (normal driving) and η = 1.2 (aggressive driving) [50].…”

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

“…Dalla et al [25] selected actual vehicle driving data in Europe, mined the connection and difference between driving characteristics and travel demands of EVs versus fuel vehicles, and they proposed a prediction model for future vehicle electrification charging demand. Tseng et al [26] developed an EV intelligent service system on the basis of big data for New York taxi operation, which predicted hot zones of vehicle charging load and pushed charging navigation strategy.In addition, several studies [27-29] utilized real-world traffic system monitoring data to mine residents' trip demands and road network operation rules via data-driven modeling. Arias et al [27,28] used a big data fusion model of Korean traffic flow and weather data, and they predicted the distribution of EV slow-charging demand in different seasons and different functional areas through decision-tree classification and identification methods.…”

“…Dalla et al [25] selected actual vehicle driving data in Europe, mined the connection and difference between driving characteristics and travel demands of EVs versus fuel vehicles, and they proposed a prediction model for future vehicle electrification charging demand. Tseng et al [26] developed an EV intelligent service system on the basis of big data for New York taxi operation, which predicted hot zones of vehicle charging load and pushed charging navigation strategy.…”