Taxi Hotspot

cab hotspot

Hotspot prediction with automatic ARIMA model. Prediction of context sensitive taxi demand hotspots. Boarding and alighting in a taxi: Space-time hotspot analysis for floating taxi data in Shanghai.

Hotspot forecast for taxi passengers with ARIMA automated modell

Summary: As a means of transport, at one point in history taxi drivers have difficulties with low load factors and at another point in history they have difficulties with excess use. The problem is due to the disequilibrium between taxi services' offer and request. A number of surveys have shown that the disequilibrium is due to inadequate taxiing. Recently, the use of satellite-based technologies has made it possible to obtain spatial and chronological information from various source materials.

These spatiotemporal information are invaluable for the development of an smart taxi system. Many earlier researches related to the smart taxi system have been carried out in several research areas, one of them in the field of analytical hotspots. Using the automatic ARIMA model, this paper provides a timeline model for predicting the passenger's hotspot area using the spatio-temporal information of the Bandung taxi company.

This research is challenging in using the right methodology in the preparation stage so that the automated ARIMA model can handle the spatio-temporal information. The results of research show that with the ARIMA system an analyse of the spatial-temporal spatial information can be carried out. Even if the real need is quite large, the cross-validation results were satisfying.

If, however, the real need is near zero, the outcome of the analyses becomes less accurate. It can be seen as a deficiency in terms of information accuracy, not in the predictive mode. A taxi fleets with low load factor at certain times and excess peak traffic.

The Taipei Ministry of Transport reports that 60-73% of taxi operation times are without passengers[1]. In Taipei Citys the utilization of taxi capacity is below 40%. While there are still unattended peak passengers[2]. Purba said that the utilization of taxi cabs in the town of Medan averaged 48.41%[3].

This means that if a taxi drives for 10 hrs, it is empty for more than 5 hrs without people. 1905/KP.801/DrJD/2010 the minimal utilisation of proper urban traffic should be at least 60%[3]. As Rus et al. have argued, this problem is due to an unbalanced offer and request in the taxi service[4].

A mismatch between offer and request resulted in a decrease in corporate earnings and thus a decrease in consumer satisfaction[5].

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