Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City

Phithakkitnukoon, Santi; Veloso, Marco; Bento, Carlos; Biderman, Assaf; Ratti, Carlo

doi:10.1007/978-3-642-16917-5_9

Cited by 107 publications

(65 citation statements)

References 8 publications

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“…Moreover, when compared to weekdays, the increase in trips tends to start later in the day on weekends, from 10:00 am onwards. Interestingly, the daily temporal pattern of bus ridership shares similar characteristics with other urban transportation system, i.e., taxi service (Phithakkitnukoon et al 2010). …”

Section: Daily and Weekly Distribution Of Ridershipmentioning

confidence: 75%

Regularity of Public Transport Usage: A Case Study of Bus Rides in Lisbon, Portugal

Foell¹,

Phithakkitnukoon²,

Veloso³

et al. 2016

JPT

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Section: Daily and Weekly Distribution Of Ridershipmentioning

confidence: 75%

Regularity of Public Transport Usage: A Case Study of Bus Rides in Lisbon, Portugal

Foell¹,

Phithakkitnukoon²,

Veloso³

et al. 2016

JPT

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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

110

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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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“…Currently, in most of dispatching systems, a dispatching center assigns a pick-up task to taxicab drivers according to the nearest neighbor principle in terms of distance or time. Phithakkitnukoon et al [7] employ the naive Bayesian classifier with an error-based learning approach, which can obtain the number of vacant taxicabs at a given time and location to enhance the dispatching system. Yang et al [8] propose a mode for urban taxicab services, which indicates the vacant and occupied taxicab movements as well as the relationship between passengers and taxicab waiting time.…”

Section: A Dispatching Systemsmentioning

confidence: 99%

pCruise: Reducing Cruising Miles for Taxicab Networks

Stankovic

2012

2012 IEEE 33rd Real-Time Systems Symposium

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Abstract-In taxicab industry, a long standing challenge is how to reduce taxicab's mileage spent without a fare, i.e., cruising mile. The current solution for this challenge usually requires the participation of the passengers. To solve the issue without the passengers involved, in this paper, we propose a cruising system, pCruise, for taxicab drivers to maximize theirs profits by finding the optimal route to pick up a passenger, thus reducing the cruising mile. In pCruise, base on collected GPS records about other near taxicabs, a taxicab characterizes its cruising process with a cruising graph. When a taxicab becomes vacant and tries to find a passenger, cruising graph will provide the shortest cruising route with at least one expected available passengers for this taxicab. With the shortest cruising routes, taxicabs will significantly reduce theirs cruising miles. We evaluate pCruise based on a 7 days 10 GB real world GPS dataset from a city with more than 15, 000 taxicabs. The evaluation results show that pCruise can assist taxicab drivers to reduce cruising miles by 41% on average.

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Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City

Cited by 107 publications

References 8 publications

Regularity of Public Transport Usage: A Case Study of Bus Rides in Lisbon, Portugal

Regularity of Public Transport Usage: A Case Study of Bus Rides in Lisbon, Portugal

Towards Reducing Taxicab Cruising Time Using Spatio-Temporal Profitability Maps

pCruise: Reducing Cruising Miles for Taxicab Networks

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