Dynamic taxi pricing

Cheng Zeng; Nir Oren

doi:10.3233/978-1-61499-419-0-1135

Dynamic taxi pricing

Cheng Zeng, Nir Oren

Computing Science

Research output: Contribution to conference › Unpublished paper › peer-review

13 Citations (Scopus)

55 Downloads (Pure)

Abstract

Taxi journeys are usually priced according to the distance covered and time taken for the trip. Such a fixed cost strategy is simple to understand, but does not take into account the likelihood that a taxi can pick up additional pas- sengers at the original passenger’s destination. In this paper we investigate dynamic taxi pricing strategies. By using do- main knowledge, such strategies discount trips to locations containing many potential passengers, and increase fares to those areas with few potential passengers. Identifying a closed form optimal dynamic pricing strategy is difficult, and by rep- resenting the domain as an MDP, we can identify an optimal strategy for specific domains. We empirically compare such dynamic pricing strategies with fixed cost strategies, and sug- gest future extensions to this work.

Original language	English
Pages	1135-1136
Number of pages	2
DOIs	https://doi.org/10.3233/978-1-61499-419-0-1135
Publication status	Published - Aug 2014
Event	European Conference on Artificial Intelligence (ECAI-2014) - Prague, United Kingdom Duration: 18 Aug 2014 → 22 Aug 2014

Conference

Conference	European Conference on Artificial Intelligence (ECAI-2014)
Country/Territory	United Kingdom
City	Prague
Period	18/08/14 → 22/08/14

Access to Document

10.3233/978-1-61499-419-0-1135

taxi
This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License: http://creativecommons.org/licenses/by-nc/3.0/
Final published version, 140 KB

Cite this

@conference{f9deb55858824b4494082390595d76dd,

title = "Dynamic taxi pricing",

abstract = "Taxi journeys are usually priced according to the distance covered and time taken for the trip. Such a fixed cost strategy is simple to understand, but does not take into account the likelihood that a taxi can pick up additional pas- sengers at the original passenger{\textquoteright}s destination. In this paper we investigate dynamic taxi pricing strategies. By using do- main knowledge, such strategies discount trips to locations containing many potential passengers, and increase fares to those areas with few potential passengers. Identifying a closed form optimal dynamic pricing strategy is difficult, and by rep- resenting the domain as an MDP, we can identify an optimal strategy for specific domains. We empirically compare such dynamic pricing strategies with fixed cost strategies, and sug- gest future extensions to this work.",

author = "Cheng Zeng and Nir Oren",

note = "This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License. This research is supported by the award made by the RCUK Digital Economy theme to the dot.rural Digital Economy Hub, award reference: EP/G066051/1.; European Conference on Artificial Intelligence (ECAI-2014) ; Conference date: 18-08-2014 Through 22-08-2014",

year = "2014",

month = aug,

doi = "10.3233/978-1-61499-419-0-1135",

language = "English",

pages = "1135--1136",

}

TY - CONF

T1 - Dynamic taxi pricing

AU - Zeng, Cheng

AU - Oren, Nir

N1 - This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License. This research is supported by the award made by the RCUK Digital Economy theme to the dot.rural Digital Economy Hub, award reference: EP/G066051/1.

PY - 2014/8

Y1 - 2014/8

N2 - Taxi journeys are usually priced according to the distance covered and time taken for the trip. Such a fixed cost strategy is simple to understand, but does not take into account the likelihood that a taxi can pick up additional pas- sengers at the original passenger’s destination. In this paper we investigate dynamic taxi pricing strategies. By using do- main knowledge, such strategies discount trips to locations containing many potential passengers, and increase fares to those areas with few potential passengers. Identifying a closed form optimal dynamic pricing strategy is difficult, and by rep- resenting the domain as an MDP, we can identify an optimal strategy for specific domains. We empirically compare such dynamic pricing strategies with fixed cost strategies, and sug- gest future extensions to this work.

AB - Taxi journeys are usually priced according to the distance covered and time taken for the trip. Such a fixed cost strategy is simple to understand, but does not take into account the likelihood that a taxi can pick up additional pas- sengers at the original passenger’s destination. In this paper we investigate dynamic taxi pricing strategies. By using do- main knowledge, such strategies discount trips to locations containing many potential passengers, and increase fares to those areas with few potential passengers. Identifying a closed form optimal dynamic pricing strategy is difficult, and by rep- resenting the domain as an MDP, we can identify an optimal strategy for specific domains. We empirically compare such dynamic pricing strategies with fixed cost strategies, and sug- gest future extensions to this work.

U2 - 10.3233/978-1-61499-419-0-1135

DO - 10.3233/978-1-61499-419-0-1135

M3 - Unpublished paper

SP - 1135

EP - 1136

T2 - European Conference on Artificial Intelligence (ECAI-2014)

Y2 - 18 August 2014 through 22 August 2014

ER -

Dynamic taxi pricing

Abstract

Conference

Access to Document

Fingerprint

Cite this