Taxi-RS: Taxi-Hunting Recommendation System Based on Taxi GPS Data

Xu, Xiujuan; Zhou, Jian; Liu, Yu; Xu, Zhixiang; Zha, Xiao Wei

doi:10.1109/tits.2014.2371815

Cited by 50 publications

(21 citation statements)

References 18 publications

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“…The objective is to identify regions with high likelihood of finding potential customers by predicting the spatial distribution of taxi passengers for a short-term time horizon [17,18]. The recommendation system assigns hot spot areas to vacant taxi drivers in order to shorten the waiting time for customers [19]. In [20], the authors proposed a mutual recommendation system that assigns hot spots for both taxi and passengers based on the trajectory of taxis.…”

Section: Related Workmentioning

confidence: 99%

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

2020

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Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers' quality of experience and drivers' benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases.Electronics 2020, 9, 648 2 of 24 a means of transportation and enhance the efficiency of both services for the benefits of both customers and drivers.In regular taxi services, traditional ways for taxi drivers to find potential customers include driving around the city and waiting at some 'hot spots', e.g., taxicab stands. For the first option, taxi drivers usually follow an intuition-based trajectory hoping to find customers as soon as possible, while for the second option, most of the drivers will target the same hot spots since based on their personnel experience, they know when and where customers will be gathered. In the latter case, regular taxi drivers may be subject to an unfair competition since the number of taxis is higher than the demand or vice versa. Hence, traditional solutions for customer hunting are usually exhaustive and inaccurate. On the other hand, for the ride-hailing taxi services, although a central server is dedicated to manage the requests of customers and allocate them to drivers, similar problems that face regular taxi services still exist. Customers' requests might still be raised far away from drivers' locations and high vacant distances are accumulated, resulting in huge and redundant fuel consumption. In Portland, the average waiting times are estimated to be around six and ten minutes for regular and ride-hailing taxi services, respectively, according KGW News [2]. Therefore, it is recommended to enhance the efficiency of such transportation services by tackling the offer/demand problem in both taxi categories.Thanks to the spread of on-board and infrastructure-based sensors [3], collecting and sharing data have become very com...

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Section: Related Workmentioning

confidence: 99%

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

2020

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“…To offer efficient suggestions on the cruising routes for vacant taxis in search of customers, Hou et al [20] tries to find the optimal solution to the Taxi Cruising Guidance (TCG) problem by minimizing the global vacant rate, which is accomplished by incorporating traffic conditions based on mobile network technologies. Xu et al [21] develop a taxi-hunting recommendation system that can process large volumes of taxi trajectories and estimate the waiting time of finding a taxi at specific locations.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories

Thill

2019

IJGI

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Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affecting the predictive accuracy via a prediction accuracy analysis and prediction location evaluation. The findings of this paper can provide intelligence for the improvement of taxi services, to increase the passenger capacity of taxis and also to improve the probability of passengers finding taxis.

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“…At the second category, several specialized data structures are devised to efficiently manage taxi information [ 46 , 47 , 48 , 49 , 50 ]. Nanocube [ 46 ] is a in-memory data cube structure for easily generating visual encodes such as heatmaps, histograms, and parallel coordinate plots from spatio-temporal datasets including taxi trips.…”

Section: Related Workmentioning

confidence: 99%

“…However, it was only designed to answer queries from interactive visualization systems, thus it does not allow profitable-are queries. A frequent trajectory graph [ 47 ] was invented to handle trajectory information for finding areas of high taxi-passenger demands. The querying and extracting timeline information system [ 48 ] builds a timeline query index (TQ-index) to manage traffic information according to a timeline model.…”

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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Taxi-RS: Taxi-Hunting Recommendation System Based on Taxi GPS Data

Cited by 50 publications

References 18 publications

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories

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

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