Analysis of Taxi Drivers
We examine four taxis service indices that correspond with thehypotheses mentioned in Section I:1) The distributions of the number of trips served by every vehicleper day during two periods, respectively;2) The distributions of the idle time lengths of every vehicle beforeper trip during two periods, respectively;3) The distributions of the traveling distances per trip during twoperiods, respectively;4) The spatial distributions of the origins and destinations of pas-sengers during two periods, respectively.Here, we choose the distributions of service data for comparison,since these distributions can provide an overall sketch of taxi serviceobserved in practices. Moreover, these distributions are estimatedfrom thousands of taxis and are thus robust to temporary behaviorvariations of some individual taxi drivers that may be caused byvarious disturbances (including abrupt vehicle failures).Fig. 1(a) shows the (sorted) numbers of trips made by every vehiclein each day of two periods. Each curve in Fig. 1(a) stands for the sortednumbers of trips made by every vehicle in a particular day. We cansee that, on average, drivers picked up significantly made more tripsunder the money promotion. The numbers of trips made by those mostdiligent drivers (whose indices are smaller than 2500) were roughlydoubled under the money promotion. Fig. 1(b) gives the correspondinghistogram plot of the numbers of trips made by every vehicle per dayduring two periods further compares the average numbers of passengers servedper half hour during two time periods. Clearly, taxis drivers servealmost 50% more passengers under the money promotion, during theworking hours (6:00A.M.–18:00P.M.). This result strongly supportsHypothesis 1 that drivers pick up more passengers under the moneypromotion.Fig. 3 shows the idle time lengths of every vehicle before per trip ineach day of two periods. We can see that there is a notable increase ofshort (less than 5 minutes) idle time lengths in the second time period;while the long-tail shape of the idle time length distributions remainunchanged. This is mainly because the long idle times are usuallycaused by unavoidable time breaks (e.g., break for lunch) of drivers.The money promotion cannot shorten the lengths of such time breaks.However, significantly larger occurrences of short idle times supportHypothesis 2 that the money promotion reduce the average time gapbetween two trips and thus the waiting time of passengers.Fig. 4 shows the distributions of the traveling distances per tripin each day of two periods. Here, the traveling distance of each tripis approximately calculated as the Euclidean distance between theoriginal and destination points. Since the layout of Beijing city is theresult of uniform planning and has a regular chessboard pattern, the es-timation error of traveling distance can be omitted.Clearly, there exists a boom for short-distance (smaller than3 km) trips during the second time period. Meanwhile, the distributionof long-distance (larger than 8 km) trips remain almost unchanged.The only reasonable explanation to simultaneous increases in shortidle times and short-distance trips is that most trips augmented bymoney promotion were short-distance trips. These short-distance tripotspot visualizations of the pick-up locations of all the trips made(a) when the battle had not occurred and (b) when the battle is white-hot. Thecolorofeachgridindicates on average how many trips begin in this grid per day.did not consume much time and were usually finished one by one sothat their time gaps are small. Taxis drivers prefer such trips, since theycan finish more trips during the same time range and thus earn moresubsides. This explanation also accounts for the invariant patterns oflong-term trips. In summary, these findings support Hypothesis.sri siva sakthi travels
We examine four taxis service indices that correspond with thehypotheses mentioned in Section I:1) The distributions of the number of trips served by every vehicleper day during two periods, respectively;2) The distributions of the idle time lengths of every vehicle beforeper trip during two periods, respectively;3) The distributions of the traveling distances per trip during twoperiods, respectively;4) The spatial distributions of the origins and destinations of pas-sengers during two periods, respectively.Here, we choose the distributions of service data for comparison,since these distributions can provide an overall sketch of taxi serviceobserved in practices. Moreover, these distributions are estimatedfrom thousands of taxis and are thus robust to temporary behaviorvariations of some individual taxi drivers that may be caused byvarious disturbances (including abrupt vehicle failures).Fig. 1(a) shows the (sorted) numbers of trips made by every vehiclein each day of two periods. Each curve in Fig. 1(a) stands for the sortednumbers of trips made by every vehicle in a particular day. We cansee that, on average, drivers picked up significantly made more tripsunder the money promotion. The numbers of trips made by those mostdiligent drivers (whose indices are smaller than 2500) were roughlydoubled under the money promotion. Fig. 1(b) gives the correspondinghistogram plot of the numbers of trips made by every vehicle per dayduring two periods further compares the average numbers of passengers servedper half hour during two time periods. Clearly, taxis drivers servealmost 50% more passengers under the money promotion, during theworking hours (6:00A.M.–18:00P.M.). This result strongly supportsHypothesis 1 that drivers pick up more passengers under the moneypromotion.Fig. 3 shows the idle time lengths of every vehicle before per trip ineach day of two periods. We can see that there is a notable increase ofshort (less than 5 minutes) idle time lengths in the second time period;while the long-tail shape of the idle time length distributions remainunchanged. This is mainly because the long idle times are usuallycaused by unavoidable time breaks (e.g., break for lunch) of drivers.The money promotion cannot shorten the lengths of such time breaks.However, significantly larger occurrences of short idle times supportHypothesis 2 that the money promotion reduce the average time gapbetween two trips and thus the waiting time of passengers.Fig. 4 shows the distributions of the traveling distances per tripin each day of two periods. Here, the traveling distance of each tripis approximately calculated as the Euclidean distance between theoriginal and destination points. Since the layout of Beijing city is theresult of uniform planning and has a regular chessboard pattern, the es-timation error of traveling distance can be omitted.Clearly, there exists a boom for short-distance (smaller than3 km) trips during the second time period. Meanwhile, the distributionof long-distance (larger than 8 km) trips remain almost unchanged.The only reasonable explanation to simultaneous increases in shortidle times and short-distance trips is that most trips augmented bymoney promotion were short-distance trips. These short-distance tripotspot visualizations of the pick-up locations of all the trips made(a) when the battle had not occurred and (b) when the battle is white-hot. Thecolorofeachgridindicates on average how many trips begin in this grid per day.did not consume much time and were usually finished one by one sothat their time gaps are small. Taxis drivers prefer such trips, since theycan finish more trips during the same time range and thus earn moresubsides. This explanation also accounts for the invariant patterns oflong-term trips. In summary, these findings support Hypothesis 3We further compare the numbers of taxis service happened atdifferent locations with and without money promotion. We grid thecentral part of Beijing city into 1600 area grids. Each grid covers a1km×1 km square. Fig. 7 shows that the numbers of the incrementaltrips in 70% of these area grids are nearly zero. In other words, thetaxis services are approximately the same under money promotion insuch grids. Fig. 8 further shows the incremental trip numbers of thepick-ups/drop-offs made in every grid per day respectively, when thebattle is white-hot. We can see that the incremental trips mainly startedor ended in the hot locations (where more than 500 trips started inevery of these grid per day; see Fig. 6). This is mainly because driverspreferred to pick up the passengers who went to the hot locationsA battle between two Internet giants Tencent and Alipay had greatlystirred the taxis services during in early 2014. Although these twocompanies originally intended to extend their territories in mobilepayments by giving promotion fees to taxi drivers for each deal made,the money promotion had profoundly changed the pattern of taxisservices. Many people welcomed such promotion since they thoughttaxis drivers will become more diligent and available on-call. On thecontrary, many people found that they cannot find a taxi to take if theydo not use such apps.The authorities of some Chinese cities had to ban the use of taxibooking apps during rush hour periods, since they received increas-ing complaints from residents. However, no concrete data had beenreleased to explain the influence of money promotion, which makesthe ban somewhat unconvincing.In this paper, we study this problem based on the collected 40-daytrip data of over 9000 taxis in Beijing. Statistics show that the numberof taxis trips made by every vehicle per day increases under themoney promotion and the idle times become shorter, too. However,drivers preferred to pick up the passengers who travel shorter and thepassengers who went to the hot locations. In summary, the moneypromotion brings benefits to some but not all residents in the city.During the review process of this paper, Didi and Kuaidadi hadannounced their official strategic combination on Feb. 14, 2015 (SaintValentine’s Day). We will study the influence of such a “marriage” inthe near future.This study also indicates that productively and critically employingbig data can help address long-standing questions of social justice,equity, and many other concerns [4], [17]. For example, Uber [18]is an international company which operates the mobile-app-basedtransportation world widely. It is preparing to penetrate into Chinesemarket recently. The above analysis results will be useful for such newdeveloping companies which aim to better share the markets and alsolocal governments which needs to guide the responses of residents tonew challenges. We believe the coming era of big data could provideus more chance to solve such formidable problems.
