Estimating Taxi Demand-Supply Level using TaxiTrajectory Data Stream

Estimating Taxi Demand-Supply Level using TaxiTrajectory Data Stream

Taxis provide a flexible and indispensable serviceto satisfy the urban travel demand of public commuters. Un-derstanding taxi supply and commuter demand, especially theimbalance between the supply and the demand, would directlyhelp to improve the quality of taxi service and eventually increasea city’s traffic system efficiency. In this paper, we consider thetaxi demand from a region during a period of time to includetwo parts: satisfied demand, i.e., passengers successfully receivetaxi service during this period of time, and unmet demand,i.e., passengers are still waiting for taxi service. To properlyestimate the demand-supply level (short for “the level of the taxidemand vs. supply imbalance”), we propose a novel indicator thatreflects how fast an available taxi is taken in any given region.Accordingly, we design and implement a taxi analytics systemto provide such information in near real time. Finally, we usethe passenger waiting time survey data and the taxi streamingdata to validate the proposed indicator on the built taxi analyticssystem https://srisivasakthitravels.com/

Taxi, providing personalized and efficient point-to-pointservice, is a popular form of public transport in many cities.Especially, in the densely populated Asian cities, taxi service isoften at a low cost as well, and thus many people rely on taxisfor their daily trips, to fulfil their business, social, and familyactivities. The taxi service quality not only affects the levelof satisfaction of locals to the land transportation services, butalso has an impact on the experience of visitors and touriststo the convenience of the transport facilities

 

While there are a large number of taxis on the road, ithappens that passengers have difficulties in finding availabletaxis in certain areas and hours. Fortunately, with advances intechnology in sensor and wireless communications, nowadaystaxis are commonly equipped with GPS (Global PositioningSystem) receivers and wireless communication components(such as GSM, GPRS). With these devices, taxis easily reportsthe location and status (such as FREE, HIRED) information tothe central server, for the taxi operators to explore to improvetheir quality of service, by real-time location tracking andcall dispatching to meet the needs of time-sensitive users,etc. To make taxi drivers and commuters more aware of thereal-time demand and supply, Land Transport Authority (LTA)of Singapore has also recently launched a mobile app calledTaxi-Taxi@SG [11], which displays the locations of availabletaxis to commuters in real time and also enables commutersto broadcast their current locations to drivers.How to match the supply with the demand is of criti-cal importance, for the interest of both passengers and taxidrivers, both taxi operators and land transport authorities.There are many existing research works in this direction, suchas modelling the spatio-temporal structure of taxi services[3], [5], predicting the demand at taxi stands [10], or evenrecommending the next-passenger to the drivers [6]. There arealso literatures focusing on passengers’ perspective, such asto predict where to find empty taxis [12]. However, all theabove works are understanding the number of demand as thenumber of historical pickups, while in realities the histori-cal pickups are constrained by the historical supplies. Therecould be unmet demand (i.e., unsatisfied passenger demandstill waiting for a taxi service) not captured by the pickuprecords. Moreover, the above works are either suggesting taxi’spassenger-look-up strategy based on status of other taxis andhistorical pickups, or suggesting passengers based on locationof free taxis, without focusing on the relationships of the twoparties. Therefore, in this paper, we will endeavour to infer thedemand and supply imbalance, by bearing in mind the demandas both successful pickups and unmet demand, to make a morecomplete picture for the passengers and taxis. According to ourknowledge, this is the first piece of work that deals with thedemand-supply relationship in regions

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Analysis of Taxi Drivers’ Behaviors Withina Battle Between Two Taxi

Analysis of Taxi Drivers’ Behaviors Withina Battle Between Two Taxi 
 Taxi drivers had highly welcomed this battle and had tried their bestto pick up every driver who is calling taxi through these apps. Thus,the taxi service pattern had been greatly changed during this battle.Many people began to complain that they cannot find a taxi to takeif they do not use such apps. Although these apps brings conveniencefor young high-tech people, they meanwhile make catching taxis moredifficult for senior citizens who are not familiar with mobile phone,foreign travelers who do not have a mainland Chinese bank account tolink up with the mobile payment system, and low-income citizens whocannot afford mobile phone or mobile service.Since March, 2014, the authorities of many Chinese cities had toset up new rules to ban the use of taxi booking apps during rushhour periods. For instance, the authorities of Shanghai halt the on-demand taxis booking mobile apps during morning peak (7:30A.M.to9:30A.M.) and evening peak (4:30A.M. to 6:30A.M.).However, a debate on whether such money promotion strategyshould be banned is not ended with such bans. The opinions fromdifferent people can be categorized into two groups.The first group of opinions claimed that the money promotion ben-efits the society, because the following changes optimize the suppliesof taxi service https://srisivasakthitravels.com/
 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

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.Along with the exponentially booming mobile payment market inChina, a new mechanism:money promotion, was implemented toeshape the supply and demand of taxis recently. For example, a battlebetween two Chinese taxi booking mobile apps: Didi [1] and Kuaidadi[2], had recently occurred in early 2014. These two apps, which arebacked by Internet giants Tencent and Alipay, gave promotion fees totaxi drivers for each deal made and also helped each taxi passengersave some money, when a customer had taken a taxi through the appand paid the fare through the mobile payment method.The hidden force in such battles is the players’ ambition to extendtheir territories in mobile payments, since mobile payment is one of themost important factor for any company wishing to dominate the era ofmobile Internet. Notice that money promotion for taxis booking is anefficient way to securing as many people as possible who use mobilepayments, the wrestling match between the rivals Tencent and Alibababecomes more and more intensive
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