In this research, we analyze taxi pickup data using k-means clustering to gain insights into the spatial distribution of pickups and identify areas with high demand. We apply a k-means clustering algorithm to group pickups into clusters based on their location and time, which helps us identify areas with high demand and plan our operations accordingly. To evaluate the performance of our clustering model, we use the inertia score, which measures the within-cluster sum of squares and indicates how well the data points are separated into different clusters. Our results show that our clustering model achieves a low inertia score of X, indicating that the data points are well separated into different clusters. This demonstrates the effectiveness...
Car sharing is a type of car rental service, by which consumers rent cars for short periods of time,...
Data analysis methods are important to analyze the ever-growing enormous quantity of the high dimens...
In recent years, the Japanese logistics industry has been facing an increase in freight transportati...
Taxis are one of the competitive sectors of transportation and are recognized as convenient and easy...
A clustering algorithm for urban taxi carpooling based on data field energy and point spacing is pro...
Abstract—In this paper, we introduce a variant of the density peaks clustering (DPC) approach for di...
Crowdedness spot is a crowded area with an abnormal number of objects. Detecting the crowdedness spo...
This paper proposes a novel supervised clustering algorithm to analyze large datasets. The proposed ...
Taxi trajectories reflect human mobility over the urban roads’ network. Although taxi drivers cruise...
Urban hotspot area detection is an important issue that needs to be explored for urban planning and ...
A novel method for estimating the passenger densities of minivan taxis popularly known as Trotros in...
One of the most important issues in urban planning is developing sustainable public transportation. ...
Moving objects such as people, animals, and vehicles have generated a large amount of spatiotemporal...
Massive data from different sources are becoming available in transportation field, and spurring new...
On-demand transit has become a common mode of transport with ride-sourcing companies like Uber, Lyft...
Car sharing is a type of car rental service, by which consumers rent cars for short periods of time,...
Data analysis methods are important to analyze the ever-growing enormous quantity of the high dimens...
In recent years, the Japanese logistics industry has been facing an increase in freight transportati...
Taxis are one of the competitive sectors of transportation and are recognized as convenient and easy...
A clustering algorithm for urban taxi carpooling based on data field energy and point spacing is pro...
Abstract—In this paper, we introduce a variant of the density peaks clustering (DPC) approach for di...
Crowdedness spot is a crowded area with an abnormal number of objects. Detecting the crowdedness spo...
This paper proposes a novel supervised clustering algorithm to analyze large datasets. The proposed ...
Taxi trajectories reflect human mobility over the urban roads’ network. Although taxi drivers cruise...
Urban hotspot area detection is an important issue that needs to be explored for urban planning and ...
A novel method for estimating the passenger densities of minivan taxis popularly known as Trotros in...
One of the most important issues in urban planning is developing sustainable public transportation. ...
Moving objects such as people, animals, and vehicles have generated a large amount of spatiotemporal...
Massive data from different sources are becoming available in transportation field, and spurring new...
On-demand transit has become a common mode of transport with ride-sourcing companies like Uber, Lyft...
Car sharing is a type of car rental service, by which consumers rent cars for short periods of time,...
Data analysis methods are important to analyze the ever-growing enormous quantity of the high dimens...
In recent years, the Japanese logistics industry has been facing an increase in freight transportati...