Taxi-Hunting Recommendation System

Taxi-Hunting Recommendation System

 Recommender systems are constructed to search thecontent of interest from overloaded information by acquiring use-full knowledge from massive and complex data. Since the amountof information and the complexity of the data structure grow,it has become a more interesting and challenging topic to findan efficient way to process, model, and analyze the information.Due to the Global Positioning System (GPS) data recording thetaxi’s driving time and location, the GPS-equipped taxi can beregarded as the detector of an urban transport system. This pa-per proposes a Taxi-hunting Recommendation System (Taxi-RS)processing the large-scale taxi trajectory data, in order to providepassengers with a waiting time to get a taxi ride in a particularlocation. We formulated the data offline processing system basedon HotSpotScan and Preference Trajectory Scan algorithms. Wealso proposed a new data structure for frequent trajectory graph.Finally, we provided an optimized online querying subsystemto calculate the probability and the waiting time of getting ataxi. Taxi-RS is built based on the real-world trajectory data setgenerated by 12 000 taxis in one month. Under the condition ofguaranteeing the accuracy, the experimental results show that oursystem can provide more accurate waiting time in a given locationcompared with a naïve algorithm
RAJECTORY can be regarded as the trace of mobileobjects in space as times change. 
   Call Taxi in Kumbakonam

The convergence oftrajectory data allows the easy acquisition of information aboutthe trajectories of users using mobile devices [1]. Global Posi-tioning System (GPS), one of the growing technologies of ge-olocation, is widely used on taxis and makes the GPS-equippedtaxi be regarded as the detector of urban transport system. Itbecomes possible to access the location information of taxis ofa whole city at any time. Since the amount of trajectory data[3] and the complexity of data structure grow, it has become amore interesting and challenging topic to find an efficient wayto process, model, and analyze the mass data of movement.A taxi is an important tool, for people, to travel in the city,but sometimes it is common, for people, to face an awkward position [2]. Most people had such experience: you had to waitfor more than 10 min to get a taxi ride only because you were2 min late in the specific place. Another example: you had beenstanding by the street waiting for a cab for over 15 min, butyour neighbor just came out of his home, crossed the street,walked a few meters, and got a taxi ride immediately. It seemslike people have to collect more information to get a taxi rideinstead of longer hopeless waiting. Along with the increase inthe amount of taxis, it becomes urgent to solve recommendedlocation information and calculate waiting time by means oflarge-scale taxi GPS data.In the information age, it is very easy to collect in-formation. For the mentioned taxi problem, it becomes averyinterestingquestiontorecommendlocationinformationandcalculate waiting time by means of large-scale taxi GPS data.In this paper, we propose a processing method based on bigdata environment for the taxi GPS historical data, includingoffline and online processing. We implement a recommendationsystem called Taxi-hunting Recommendation System (Taxi-RS), and our system can calculate the probability and time ofgetting a taxi ride when giving information of time and point.The following are the major contents of this paper.1) We design data offline processing. First, we design analgorithm called HotSpotScan (HSS) algorithm to scanhotspots in a city. Then, the taxi preference trajectory isrecognized by a new algorithm called Preference Trajec-tory Scan (PTScan) algorithm.2) We propose an offline graph model by taxi preferencetrajectory and store offline compressed data by using amultiple adjacent table.3) Weconstructaprobabilitymodelbyusingtheofflinemodelto compute the probability and the waiting time of getting ataxi ride and analyze the complexity of online processing.The rest of this paper is organized as follows. Section IIgives a brief review on related literature. In Section III, someconception is formally introduced. The overview of the Taxi-RS is given in Section IV. In Section V, we introduce thekey algorithms of offline processing. In Section VI, we presentthe frequent trajectory graph (FTG) model. In Section VII, wepropose a calculation model to get a taxi ride. The results of theexperimental evaluation are given in Section VIII. Remarks arestated under the conclusion https://srisivasakthitravels.com/
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Development of Traveling

Development of Traveling 
A W band folded waveguide Traveling Wave Tube that adopts horseshoe-shaped attenuator and a periodic step-tapering folded waveguide slow wave structure is reported in this paper. In this Traveling Wave Tube, Reflection from severability was effectively reduced and self-oscillation was depressed, and also electronic efficiency is raised by about 1.5~2% comparing with uniform periodic folded waveguide Traveling Wave Tube.

Because of the merits of all metal structure, simple coupling structure and compatibility with micro-fabrication technology, folded waveguide slow wave structure is widely used in millimeter and Terahertz Traveling Wave Tube, but suppression of self-oscillation and improvement of interaction efficiency have been the two most important questions[1-4]. Self-oscillation in W band folded waveguide Traveling Wave Tube commonly includes reflection oscillation, backward-wave oscillation and band-edge oscillation. Reflection oscillation is mainly caused by individual frequency mismatch in its wide frequency band due to machining error and welding deviation of TWT, backward wave oscillation will occur when resonant condition is satisfied between electrical parameters and interaction length, and the band-edge oscillation is due to the mismatch of slow wave structure’s ends. Therefore, it is verynecessary to improve processing precision of the folded waveguide and optimize concentrated attenuator and periodic distribution of folded waveguide slow wave structure to suppress the oscillation and improve the interaction efficiency.     Sri Siva Sakthi Travels

This W-band folded waveguide Traveling Wave Tube adopts a horseshoe-shaped attenuator shown in Fig.1 and simulation result by HFSS is shown in Fig.2. From Fig.2, we can see that voltage standing wave ratio in wide range of 10GHz are less than 1.1. In order to improve interaction efficiency of the folded waveguide Traveling Wave Tube, a periodic step-tapering folded waveguide slow wave structure was designed in Fig.3. Compared with uniform periodic folded waveguide, interaction efficiency increased from 3% to 5%. Simulation results by CST are shown in Fig.4 and Fig.5, a stable output power and pure peak spectrum can be seen from above figures A low VSWR energy transfer system was fabricated and combined with the periodic step-tapering folded wave guide SWS for cold measurement. The test results are shown in Fig.6. From Fig.6, we can see that voltage standing wave ratio in 10GHz broadband range are less than 1.367.W-band folded wave guide periodic step-tapering Traveling Wave Tube is shown in Fig.7 and test electrical parameters are listed in table 1. Fig.8 shows that there is no oscillation power when input power is zero and Fig.9 shows that there are two peaks when input power is not zero, right peak is true power output point and left for its image produced by mixing. Fig.10 shows output power increases with input power and the maximum output power is 65W with electronic efficiency is about 4%.
A horseshoe shaped attenuator and a periodic step tapering folded waveguide slow wave structure can not only effectively suppress self-oscillation, but also raise interaction efficiency by 1.5~2%, which laid a good foundation for stable and efficient work of a W-band Traveling Wave Tube.  https://srisivasakthitravels.com/
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Optimal Multi-Taxi Dispatch forMobile Taxi-Hailing Systems

Optimal Multi-Taxi Dispatch forMobile Taxi-Hailing Systems
Traditional taxi-hailing systems through wirelessnetworks in metropolitan areas allow taxis to compete forpassengers chaotically and accidentally, which generally resultin inefficiencies, long waiting time and low satisfaction of taxi-hailing passengers. In this paper, we propose a new MobileTaxi-hailing System (called MTS) based on optimal multi-taxidispatch, which can be used by taxi service companies (TSC-s). Different from the competition modes used in traditionaltaxi-hailing systems, MTS assigns vacant taxis to taxi-hailingpassengers proactively. For the taxi dispatch problem in MTS,we define a system utility function, which involves the totalnet profits of taxis and waiting time of passengers. Moreover,in the utility function, we take into consideration the variousclasses of taxis with different resource configurations, and thecost associated with taxis’ empty travel distances. Our goalis to maximize the system utility function, restricted by theindividual net profits of taxis and the passengers’ requirementsfor specified classes of taxis. To solve this problem, we designan optimal algorithm based on the idea of Kuhn-Munkres(called KMBA), and prove the correctness and optimality of theproposed algorithm. Additionally, we demonstrate the significantperformances of our algorithm through extensive simulation https://srisivasakthitravels.com/
                    
.In recent years, taxi has been an important and indispensabletransportation mode between public and private transporta-tions, owing to its high flexibility and easy accessibility [2].Unfortunately, traditional metropolitan taxi service heavilyrelies on taxi drivers’ experience to identify and pick uppassengers on the streets, resulting in low-efficiency of taxisand long waiting time of taxi-hailing passengers. Thanks tothe rapid development of smartphones and mobile infras-tructures [23], [24], a new taxi business model based onsmartphone applications becomes prevalent rapidly [3], [13],[14]. Accordingly, many smartphone taxi-hailing systems havebeen advanced, such as Hailo, Uber, Didi and Kuaidi, makingpassengers take taxis much easier than before.A typical taxi-hailing system (e.g., Didi) generally involvesthree phases. First, passengers send their taxi-hailing requeststhrough the taxi-hailing clients installed in their smartphones,to taxi servers on the cloud. These requests, also calledorders, mainly include the current locations and destinationsof passengers. After receiving some orders, the system willpublish them to taxis through wireless networks. Second, everytaxi competes with each other for grabbing one order, as soonas it receives these orders. After winning an order, a taxi willgo to pick up and deliver the corresponding passenger to itsdestination, according to the obtained order. Third, passengerswill pay taxis after reaching their destinations. Although suchtaxi-hailing system has provided great convenience for bothpassengers and taxis, it has several non-negligible shortcom-ings. For instance, grabbing taxi-hailing orders might attracttaxi drivers’ much attention to their smartphones, resultingin the hidden danger of traffic accidents. The competitionsamong taxi drivers will also cause lots of waste of resources.Additionally, many drivers often complain that they cannotgrab any order most of time, since other drivers might cheatby using the third-party softwares.In this paper, in order to address the aforementioned prob-lems, we propose a new Mobile Taxi-hailing System, calledMTS, which can be used by Taxi Service Companies (TSCs).An MTS is composed of several Cloud Servers (CSs), lots oftaxi-hailing passengers, and many taxis with different resourceconfigurations (e.g., seat capacity, smoking permission, etc.)that have registered in a TSC. In this system, passengers firstgenerate taxi-hailing orders and send them to CSs via theirsmartphones. Meanwhile, taxis move along different streets.If a taxi becomes vacant, it will tell CSs that it can providethe taxi service. Then, CSs will collect the GPS locations ofthe vacant taxis. Based on the orders, CSs make a decisionto dispatch some vacant taxis to the passengers, and send theresults to both of them, as shown in Fig. 1. After receivingthe notifications from CSs, vacant taxis will go to pick upthe corresponding passengers. When passengers arrive at theirdestinations, they will pay for the taxi services.
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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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