Towards Reducing Taxicab Cruising Time Using Spatio-Temporal Profitability Maps

Abstract

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.

Regional Co-locations of Arbitrary Shapes

“…For frequentist method, we also need to generate replicas in order to do the Monte Carlo simulation. For this purpose, for each given synthetic data set, we first apply EM algorithm to learn the overall rate of spatial features: q Real data experiment set-up For real data experiments, we use the taxi data in Shanghai, China( [12]). The data is collected with a frequency of 300 seconds from 12am, May 29, 2009 to 6pm, May 30,2009.…”

Detecting regions of disequilibrium in taxi services under uncertainty

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Exploring Spatiotemporal Patterns of Long-Distance Taxi Rides in Shanghai

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