Drive Smartly as a Taxi Driver

Zheng, Yu; Yuan, Jing; Xie, Wenlei; Xie, Xing; Sun, Guoqiang

doi:10.1109/uic-atc.2010.19

Cited by 25 publications

(8 citation statements)

References 7 publications

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“…Meanwhile, each taxi driver's activity space is quite different from the other drivers, and the analysis of taxi driver's travel activity space is a more interesting research field to explore. When the taxi is with a passenger, the driver will complete the trip in accordance with the passenger's travel will by adopting the shortest path from the origin to the destination [11]; this destination set can be called the taxi driver's drop-off locations; when the taxi is without passenger, the driver always wants to search the next potential passenger during the shortest possible time near or around his last drop-off passenger location [10,12]; this set can be regarded as the taxi driver's pick-up locations. Although the taxi driver will not pick up his passengers at the same location each day, each taxi driver's pick-up passenger's location and drop-off passenger's location are close to each other; the relationship between taxi driver's pick-up locations and drop-off locations can be explored to describe the spatial distribution of taxi drivers.…”

Section: Introductionmentioning

confidence: 99%

Exploring Urban Taxi Drivers’ Activity Distribution Based on GPS Data

Wang

2014

Mathematical Problems in Engineering

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With the rapid development of information communication technology and data mining technology, we can obtain taxi vehicle’s real time operation status through the large-scale taxi GPS trajectories data and explore the drivers’ activity distribution characteristics. Based on the 204 continuous hours of 3198 taxi vehicles’ operation data of Shenzhen, China, this paper analyzed the urban taxi driver’s activity distribution characteristics from different temporal and spatial levels. In the time level, we identified the difference with taxi daily operation pattern (weekday versus weekends), continuous time in one day, passengers in vehicle time, and taxi drivers’ operation frequency; in the space level, we explored the taxi driver’s searching pattern, including searching activity space distribution and the relationship between the pick-up locations and the drop-off locations. This research can be helpful for urban taxi drivers’ operation and behavior pattern identification, as well as the contribution to the geographical activity space analysis.

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Section: Introductionmentioning

confidence: 99%

Exploring Urban Taxi Drivers’ Activity Distribution Based on GPS Data

Wang

2014

Mathematical Problems in Engineering

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“…Taxi-GSP traces have been used in a number of studies to develop better solutions and services in urban areas such as estimating optimal driving paths [15,18,19], predicting next taxi pick-up locations [3, 6 ,10, 14], modeling driving strategies to improve taxi's profit [3,5], identifying flaws and possible improvements in urban planning [17], and developing models for urban mobility, social functions, and dynamics between the different city's areas [11,13].…”

Section: Related Workmentioning

confidence: 99%

“…Zheng et al [18] describe a three-layer architecture using the landmark graph to model knowledge of taxi drivers. Ziebart et al [19] present a decision-modeling framework for probabilistic reasoning from observed context-sensitive actions.…”

Section: Related Workmentioning

confidence: 99%

Exploring relationship between taxi volume and flue gases' concentrations

Veloso

Phithakkitnukoon

Bento

2013

Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication

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With the rapid increase in size and population of urban areas, it becomes important to understand urban environmental influencers so that better informed decisions can be made for more sustainable urban environments. Taxis represent one of the urban dynamics from which city planners can gain a better understanding of urban mobility as well as its relationship with other environmental elements. In this work, an analysis of the relationship between flue gases' concentrations (represented by nitrogen dioxide) and taxi volume in Lisbon, Portugal was carried out from which a strong correlation between the two was observed. Based on four months of data, we found that the flue gases' concentrations varied with taxi volume and in particular, taxi volume can be used to estimate the change in flue gases' concentrations of the next hour.

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“…Previous works in opportunistic and participatory sensing have tried to exploit the users, by using their devices like sensors for specific tasks [2,5] or path-finding [11]; while other works exploit user activity on online social networks to real-time detect events or situations [4]. However, we could identify several use cases (e.g.…”

Section: Case Study Scenariomentioning

confidence: 99%

Towards an Infrastructure for Urban Superorganisms: Challenges and Architecture

Fontana

Zambonelli

2012

2012 IEEE International Conference on Green Computing and Communications

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Future urban computing scenarios will be characterized by the close integration of ICT devices and humans. Citizens, by using their own capabilities augmented and integrated with ICT technologies, could thus collaboratively realize a sort of socio-technical superorganism to support highlevels of collective "urban" intelligence and various forms of collective actions. This position paper, also with the help of a representative case study, identifies the key engineering challenges for future urban superorganisms and proposes a twotier architecture for the infrastructure to support the activities of novel urban superorganisms.

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Drive Smartly as a Taxi Driver

Cited by 25 publications

References 7 publications

Exploring Urban Taxi Drivers’ Activity Distribution Based on GPS Data

Exploring Urban Taxi Drivers’ Activity Distribution Based on GPS Data

Exploring relationship between taxi volume and flue gases' concentrations

Towards an Infrastructure for Urban Superorganisms: Challenges and Architecture

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