Recommend a profitable cruising route for taxi drivers

Dong, Hao; Zhang, Xuedan; Dong, Yuhan; Chen, Chuang; Rao, Fan

doi:10.1109/itsc.2014.6957998

Cited by 38 publications

(17 citation statements)

References 22 publications

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“…Given that the profitability is usually considered as an essential evaluation metric for route recommendation [Qu et al, 2014], we define the reward S Pi as the estimated earning at pick-up area P i . Following [Dong et al, 2014], we obtain the minute-level earning for each pick-up area at a given time based on historical data. The drop-off point is predicted by capturing the distribution from historical data.…”

Section: Definition Of Rewardmentioning

confidence: 99%

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Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

Guo¹,

Xiao²,

Ye³

et al. 2021

Preprint

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Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers).

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Section: Definition Of Rewardmentioning

confidence: 99%

“…The historical data of pick-up information has an influence on the pick-up probability [Dong et al, 2014]. As mentioned in [Rong et al, 2016], the pick-up probability can be represented by the proportion of successful pick-ups.…”

Section: Definition Of Action-reward Probabilitymentioning

confidence: 99%

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

Guo¹,

Xiao²,

Ye³

et al. 2021

Preprint

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“…In the second category, several research projects built prediction models for the passenger search problem [ 10 , 11 , 15 , 29 , 30 , 31 , 32 ]. An improved auto-regressive integrated moving average (ARIMA) scheme [ 10 ] forecasts high taxi-passenger-demand spots by using GPS traces.…”

Section: Related Workmentioning

confidence: 99%

“…An incremental ARIMA model [ 15 ] predicts high passenger-demand spots by employing a learning model based on historical GPS data. Dong et al [ 31 ] proposed a recommendation system by using linear equations to compute the score of the each road segment. To find out the max-score cruising route, they also uses a skyline computation to reduce the search space.…”

Section: Related Workmentioning

confidence: 99%

DISPAQ: Distributed Profitable-Area Query from Big Taxi Trip Data

Putri

Song

Kwon

et al. 2017

Sensors

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One of the crucial problems for taxi drivers is to efficiently locate passengers in order to increase profits. The rapid advancement and ubiquitous penetration of Internet of Things (IoT) technology into transportation industries enables us to provide taxi drivers with locations that have more potential passengers (more profitable areas) by analyzing and querying taxi trip data. In this paper, we propose a query processing system, called Distributed Profitable-Area Query (DISPAQ) which efficiently identifies profitable areas by exploiting the Apache Software Foundation’s Spark framework and a MongoDB database. DISPAQ first maintains a profitable-area query index (PQ-index) by extracting area summaries and route summaries from raw taxi trip data. It then identifies candidate profitable areas by searching the PQ-index during query processing. Then, it exploits a Z-Skyline algorithm, which is an extension of skyline processing with a Z-order space filling curve, to quickly refine the candidate profitable areas. To improve the performance of distributed query processing, we also propose local Z-Skyline optimization, which reduces the number of dominant tests by distributing killer profitable areas to each cluster node. Through extensive evaluation with real datasets, we demonstrate that our DISPAQ system provides a scalable and efficient solution for processing profitable-area queries from huge amounts of big taxi trip data.

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“…Many latest e-commerce business models are actually the applications of advanced financial services in a more sophisticated structure. For example, Didi Dache and Kuaidi Dache, two of China's leading taxi-hailing applications, are based on the channel between the traditional banking system and the mobile payment services (Dong et al 2014). Even real-time bidding (RTB), the latest generation of online targeted marketing, has the key ingredients from Internet finance, such as the e-billing mechanism and the programmed real-time bidding system, which is a mimic of algorithmic trading as used in the stock market (Wang & Yuan 2015).…”

Section: Electronic Commercementioning

confidence: 99%

Harnessing Internet finance with innovative cyber credit management

Lin

Whinston

Fan

2015

Financial Innovation

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In the last two decades, Internet technologies, such as cloud computing, mobile communications, social media, and big data analytics, have brought tremendous changes to our society and reshaped the business in various industries. Specifically, the mushrooming innovations in the financial area fertilized by information and communication technologies indicates the advent of the Internet finance era. Applying the exploratory research approach, we investigate major innovative Internet-based financial services and classify them into five categories, as of e-commerce, e-payment, e-money market, online loan services, and digital currencies. Then we propose a market structure of Internet finance extended from the traditional financial market. We claim that credit management is the key issue in the marketplace of Internet finance, characterized by big data analytics, in which cyber credit appears as whole-process, multi-dimensional, and holographic. We further suggest that cyber credit be represented in the form of vector to overcome the limits of traditional single-value measure in cyber credit management. Based on this framework, we raise main research issues in Internet finance from the perspectives of theory, technology, and governance.

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Recommend a profitable cruising route for taxi drivers

Cited by 38 publications

References 22 publications

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

DISPAQ: Distributed Profitable-Area Query from Big Taxi Trip Data

Harnessing Internet finance with innovative cyber credit management

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