Analysis of Taxi Drivers’ Behaviors Withina Battle Between Two Taxi Apps

Analysis of Taxi Drivers’ Behaviors With in a Battle Between Two Taxi Apps

A battle between two Chinese taxi booking mobile apps,namely, Didi and Kuaidadi, had recently occurred in early 2014. Thesetwo apps, which are backed by Internet giants Tencent and Alipay, gavepromotion fees to taxi drivers for each deal made and also allowed eachtaxi passenger to save some money, when a customer had taken a taxithrough the app and paid the fare through the mobile payment method.As expected, the taxi service pattern had been greatly changed during thisbattle. To address the debates on social justice, equity, and improvementsof taxi service, we collect 37-day trip data of over 9000 taxis in Beijing tostudy the influence of this pattern change. In the first 18 days, the battlehad not occurred and in the remaining 19 days, the battle is white-hot.We quantitatively demonstrate how several important service indices (e.g.,the traveling distances and idle time lengths) of taxi drivers had beenchanged. The spatial–temporal traveling patterns of taxis are then studied.Based on comprehensive analysis, the benefits and drawbacks broughtby money promotion are finally discussed. The obtained results indicatethat productively employing big data can help answer some importantquestions attracting the interest of the whole society. https://srisivasakthitravels.com/
Taxis service offers great benefits and convenience to our daily life.However, in many cities, the taxi service supplies fail to meet thetraveling demands especially in rush hours. To balance the supply anddemand, different measures had been executed in the last few years.On one hand, the authorities of many cities continuously estimatethe quantity of taxis and make dynamic adjustments in accordancewith the ever-increasing demand. On the other hand, some companiesprovide innovative services to connect passengers with drivers viabooking apps installed on mobile phones. These mobile booking appsbecome highly attractive around the world, since taxis are notoriouslydifficult to catch in metropolises.At first, these apps allowed users to bid for taxis by offering addi-tional bonus fees to drivers. This strategy could arouse the initiativeof drivers and make them become more active to serve passengers.The “invisible hand” would then help match the supply and demand.However, this bidding mechanism is clearly unfair for many peopleand was soon banned by the authorities.

 In this paper, we study the historical trip and fare logs of Beijingtaxis to investigate the change of taxi drivers’ behaviors under moneypromotion.Beijing is the capital of the People’s Republic of China and isone of the most populous cities around the world. Its populationhad grown over 21 000 000 in 2013. However, there are only nearly67 000 licensed taxis in Beijing, which cannot meet the ever increasingdemand of taxis service, especially in rush hours.The dataset used in this paper includes about 8.3 million taxi tripsthat made by over 9000 taxis during 40 days. Each trip record includesthe pickup and drop-off location and time, as well as anonymized taxilicense numbers. The personally identifiable information of passengershas been properly anonymized, so that we can only differ the driversbut not the passengers. Moreover, the names of the drivers are notreleased, either.The longitude and latitude location information in each taxi trip isobtained by converting the received GPS data into a planar coordinatesystem, since the latitudinal trend is not pronounced in the central partof Beijing city. The location errors caused by inaccurate GPS signalsare much smaller than the moving distances of taxi trips and are thusomitted in this paper.As pointed out in many studies, human traveling activities can beinfluenced by many factors. For example, human traveling patterns arenotably different in working-days and weekends/ holidays. Moreover,the weather also greatly affects the calling amount of taxi services. Toreduce the influences of such factors, we choose the sampling daysas Oct. 16–18, 21, 22, 24, 25, 28–31, Nov. 18–22, 25–27, 2013 andFeb. 17, 18, 20, 21, 24–28, Mar. 17–21, 24–28, 2014, respectively. Wecall the sampling days in 2013 the first time period, in which no moneypromotion was applied. The sampling days in 2014 is called the secondtime period, in which money promotion was applied. All these days areworking days and the weathers in these days are mild so that the traveldemands in these days are similar.
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