PRACE: A Taxi Recommender for Finding Passengers with Deep Learning Approaches

Huang, Zhenhua; Zhao, Zhenqi; Shijia, E; Yu, Chang; Shan, Guangxu; Li, Tienan; Cheng, Jiujun; Sun, Jian; Xiang, Yang

doi:10.1007/978-3-319-63315-2_66

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Accurate demand prediction can lead to an efficient disposition of supplies. As in the case of crowd flow, the entity type of which is humans, there is no spatial restriction; thus, in most studies [86]- [90], [90]- [95], the regions are defined first, and then grid representation is used for the spatial dependency.…”

Section: B Crowd Demand Prediction (Taxi Bike Ridesharing)mentioning

confidence: 99%

Short-Term Traffic Prediction With Deep Neural Networks: A Survey

Lee

Jung

et al. 2021

IEEE Access

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In modern transportation systems, an enormous amount of traffic data is generated every day. This has led to rapid progress in short-term traffic prediction (STTP), in which deep learning methods have recently been applied. In traffic networks with complex spatiotemporal relationships, deep neural networks (DNNs) often perform well because they are capable of automatically extracting the most important features and patterns. In this study, we survey recent STTP studies applying deep networks from four perspectives. 1) We summarize input data representation methods according to the number and type of spatial and temporal dependencies involved. 2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and meta-learning networks. 3) We summarize previous STTP studies in terms of the type of DNN techniques, application area, dataset and code availability, and the type of the represented spatiotemporal dependencies. 4) We compile public traffic datasets that are popular and can be used as the standard benchmarks. Finally, we suggest challenging issues and possible future research directions in STTP.INDEX TERMS Artificial intelligence, deep neural network (DNN), intelligent transportation systems (ITS), neural networks, prediction algorithms, short-term traffic prediction (STTP), traffic forecasting

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Section: B Crowd Demand Prediction (Taxi Bike Ridesharing)mentioning

confidence: 99%

Short-Term Traffic Prediction With Deep Neural Networks: A Survey

Lee

Jung

et al. 2021

IEEE Access

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show abstract

“…Accurate demand prediction can lead to an efficient disposition of supplies. As in the case of crowd flow, the entity type of which is humans, there is no spatial restriction; thus, in most studies [73][74][75][76][77][77][78][79][80][81][82], the regions are defined first, and then grid representation is used for the spatial dependency.…”

Section: Crowd Demand Prediction (Taxi Bike Ridesharing)mentioning

confidence: 99%

Short-term Traffic Prediction with Deep Neural Networks: A Survey

Lee¹,

Eo²,

Jung³

et al. 2020

Preprint

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In modern transportation systems, an enormous amount of traffic data is generated every day. This has led to rapid progress in short-term traffic prediction (STTP), in which deep learning methods have recently been applied. In traffic networks with complex spatiotemporal relationships, deep neural networks (DNNs) often perform well because they are capable of automatically extracting the most important features and patterns. In this study, we survey recent STTP studies applying deep networks from four perspectives. 1) We summarize input data representation methods according to the number and type of spatial and temporal dependencies involved. 2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and metalearning networks. 3) We summarize previous STTP studies in terms of the type of DNN techniques, application area, dataset and code availability, and the type of the represented spatiotemporal dependencies. 4) We compile public traffic datasets that are popular and can be used as the standard benchmarks. Finally, we suggest challenging issues and possible future research directions in STTP.

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“…In the fourth category, several machine learning based approaches have been studied [ 33 , 34 , 35 , 36 ]. Time series analysis techniques based on non-homogeneous Poisson processes are utilized to predict short-term approximate local probability density functions of taxi stands [ 33 ].…”

Section: Related Workmentioning

confidence: 99%

“…A reinforcement learning based system [ 36 ] is developed to learn from real trajectory logs of taxi drivers and to recommend the profitable locations to the drivers. PRACE [ 34 ] is a deep learning based taxi recommender system for finding passengers. It executes a prediction task as a multi-classification problem rather than a regression problem.…”

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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PRACE: A Taxi Recommender for Finding Passengers with Deep Learning Approaches

Cited by 8 publications

References 10 publications

Short-Term Traffic Prediction With Deep Neural Networks: A Survey

Short-Term Traffic Prediction With Deep Neural Networks: A Survey

Short-term Traffic Prediction with Deep Neural Networks: A Survey

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

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