Nonlinear pricing of taxi services

Yang, Hai; Fung, C.S.; Wong, Ka-Io; Wong, Stephen C. P.

doi:10.1016/j.tra.2010.03.004

Cited by 81 publications

(58 citation statements)

References 15 publications

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“…For instance, provider experience may suggest that a fixed number of drivers are allowed to be matched with each passenger, where each driver is selected randomly from those that satisfy the hard constraints. More sophisticated heuristics may be based on passenger features such as drop-off location as, for example, passengers that wish to travel to transport hubs (e.g., airports) are significantly more desirable than passengers that wish to travel to locations where there is unlikely to be another passenger [29]. In these cases, it may be desirable to offer more drivers the journey of passengers with undesirable drop-off locations to ensure that the chance they are served is maximized.…”

Section: The Market Formation Problemmentioning

confidence: 99%

Towards Data-Driven On-Demand Transport

Egan¹,

Drchal²,

Mrkos³

et al. 2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

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On-demand transport has been disrupted by Uber and other providers, which are challenging the traditional approach adopted by taxi services. Instead of using fixed passenger pricing and driver payments, there is now the possibility of adaptation to changes in demand and supply. Properly designed, this new approach can lead to desirable tradeoffs between passenger prices, individual driver profits and provider revenue. However, pricing and allocations-known as mechanisms-are challenging problems falling in the intersection of economics and computer science. In this paper, we develop a general framework to classify mechanisms in ondemand transport. Moreover, we show that data is key to optimizing each mechanism and analyze a dataset provided by a real-world on-demand transport provider. This analysis provides valuable new insights into efficient pricing and allocation in on-demand transport.

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Section: The Market Formation Problemmentioning

confidence: 99%

Towards Data-Driven On-Demand Transport

Egan¹,

Drchal²,

Mrkos³

et al. 2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

View full text Add to dashboard Cite

show abstract

“…Profitability and spatial equilibrium distribution of vacant taxis are discussed in (Yang et al 2010a), where some taxi drivers have private information for their decision-making and this private information is not taken into consideration in the proposed scheduling system. In these cases the effectiveness of the system will be negatively influenced.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

A novel approach to independent taxi scheduling problem based on stable matching

Bai

Atkin

et al. 2014

Journal of the Operational Research Society

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This paper describes a taxi scheduling system, which aims to improve the overall efficiency of the system, both from the perspective of the drivers and the customers. This is of particular relevance to Chinese cities, where hailing a taxi on the street is by far the most common way in which taxis are requested, since the majority of taxi drivers operate independently, rather than working for a company. The mobile phone and GPS-based taxi scheduling system, which is described in this paper, aims to provide a decision support system for taxi drivers and facilitates direct information exchange between taxi drivers and passengers, while allowing drivers to remain independent. The taxi scheduling problem is considered to be a non-cooperative game between taxi drivers and a description of this problem is given. We adopt an efficient algorithm to discover a Nash equilibrium, such that each taxi driver and passenger cannot benefit from changing their assigned partner. Two computational examples are given to illustrate the effectiveness of the approach.

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“…Phithakkitnukoon et al developed an inference engine with error based learning to predict vacant taxis [6] while Hong-Cheng et al studied travel time variability on driver route choices in Shanghai taxi service [7]. Additional research covers a variety of issues from demand versus supply to pricing issues [8][9][10][11][12]; however, these studies do not consider location profitability, which is inherent to the driver's decision.…”

Section: Related Workmentioning

confidence: 99%

Towards Reducing Taxicab Cruising Time Using Spatio-Temporal Profitability Maps

Powell

Huang

Bastani

et al. 2011

Lecture Notes in Computer Science

109

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Abstract. Taxicab service plays a vital role in public transportation by offering passengers quick personalized destination service in a semiprivate and secure manner. Taxicabs cruise the road network looking for a fare at designated taxi stands or alongside the streets. However, this service is often inefficient due to a low ratio of live miles (miles with a fare) to cruising miles (miles without a fare). The unpredictable nature of passengers and destinations make efficient systematic routing a challenge. With higher fuel costs and decreasing budgets, pressure mounts on taxicab drivers who directly derive their income from fares and spend anywhere from 35-60 percent of their time cruising the road network for these fares. Therefore, the goal of this paper is to reduce the number of cruising miles while increasing the number of live miles, thus increasing profitability, without systematic routing. This paper presents a simple yet practical method for reducing cruising miles by suggesting profitable locations to taxicab drivers. The concept uses the same principle that a taxicab driver uses: follow your experience. In our approach, historical data serves as experience and a derived Spatio-Temporal Profitability (STP) map guides cruising taxicabs. We claim that the STP map is useful in guiding for better profitability and validate this by showing a positive correlation between the cruising profitability score based on the STP map and the actual profitability of the taxicab drivers. Experiments using a large Shanghai taxi GPS data set demonstrate the effectiveness of the proposed method.

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Nonlinear pricing of taxi services

Cited by 81 publications

References 15 publications

Towards Data-Driven On-Demand Transport

Towards Data-Driven On-Demand Transport

A novel approach to independent taxi scheduling problem based on stable matching

Towards Reducing Taxicab Cruising Time Using Spatio-Temporal Profitability Maps

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