Context-aware taxi demand hotspots prediction
WebDec 14, 2009 · The four-step process consists of data filtering, clustering, semantic annotation, and hotness calculation. The results of three clustering algorithms are compared and demonstrated in a web mash-up application to show that context-aware demand prediction can help improve the management of taxi fleets. http://ir.kdu.ac.lk/bitstream/handle/345/2493/Untitled(11).pdf?sequence=1
Context-aware taxi demand hotspots prediction
Did you know?
WebApr 11, 2024 · Combined with the pre-set charging mode, electric vehicle type, and other data, it is possible to simulate the journey of an electric taxi for a day. Finally, the spatiotemporal distribution of the charging demand of electric taxis for one day is obtained. The electric taxi charging demand forecasting process is shown in Figure 9. WebDec 15, 2009 · The four-step process consists of data filtering, clustering, semantic annotation, and hotness calculation. The results of three clustering algorithms are compared and demonstrated in a web mash-up application to show that context-aware demand prediction can help improve the management of taxi fleets. Keywords
WebDec 14, 2009 · The four-step process consists of data filtering, clustering, semantic annotation, and hotness calculation. The results of three clustering algorithms are … WebJul 21, 2024 · Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance. However, most taxi demand studies are based on …
WebFinally, the predicting system then predicts potential hotspots of taxi requests and provides hotspots information for drivers to reduce vacant time of the taxi. Keywords: Data Mining,... WebAug 13, 2024 · The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online …
WebSep 1, 2015 · Context-aware taxi demand hotspots prediction. IJBIDM (2010) Wei Chu et al. ... Short-term taxi demand forecasting is of great importance to incentivize vacant cars moving from over-supply regions to over-demand regions, which can minimize the wait time for passengers and drivers. With the consideration of spatiotemporal dependences, …
Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance. However, most taxi demand studies are based on historical taxi trajectory data. In this study, we detected hotspots and proposed three methods to predict the taxi demand in hotspots. Next, we … See more Taxi is an essential part of urban public transportation, and taxi demand is different from others because of its stochastic trajectory and dependence of spatial location [ 1. H. Yang, K. I. Wong, and S. C. Wong, “Modeling … See more GPS data are from the Xi’an Taxi Management Office and consist of vehicle location data that are recorded every 5 s for 30 days. The dataset consists of 40 million track points. The GPS data have undergone extensive … See more The “STAT” attribute in taxi GPS data is the record of the taxi driving state, in which “4” represents the passenger and “5” represents empty driving. A change from “4” to “5” indicates … See more RFM is an ensemble learning algorithm and an extension of bagging [ 1. M. Ristin, M. Guillaumin, J. Gall, and L. Van Gool, “Incremental learning of random forests for large-scale image … See more d\u0027agostino grocery familyWebFeb 1, 2012 · This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a … d\u0027ages photography meeker co locationWebDec 1, 2010 · This paper proposes mining historical data to predict demand distributions with respect to contexts of time, weather, and taxi location. The four-step process … common ear piercingsWebJul 8, 2024 · Context-aware taxi demand hotspots prediction. IJBIDM 5 (01 2010), 3--18. Google Scholar Digital Library; Leon Yang Chu, Zhixi Wan, and Dongyuan Zhan. 2024. Harnessing the Double-Edged Sword via Routing: Information Provision on Ride-Hailing Platforms. Technical Report. Social Science Research Network, Rochester, NY. d\u0027agee florist liberty moWebThis paper proposes mining historical data to predict demand distributions with respect to contexts of time, weather, and taxi location. The four-step process consists of data … common earthworm nameWebTo achieve these objectives, firstly we preprocess the large scale taxi GPS traces data set to generate the Map Grid Based (MGB) index. Secondly, with the MGB index, we apply the nonhomogeneous Poisson process corrected by the conditions of road and weather (NPPCRW) method to perform estimation and recommendation. common ear infection medicationWebIn the research a context aware taxi demand hotspots prediction is done using data mining techniques they only consider about finding clusters not the optimum clusters, clusters which are more profitable to taxi drivers. And they have included in their future works to consider the events in that area (social event) that a taxi driver can find d\\u0027agnese\\u0027s white pond