Detecting taxi movements using Random Swap clustering and sequential pattern mining

Ibrahim, Rami; Shafiq, Omair

doi:10.1186/s40537-019-0203-6

Cited by 17 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is very common to use groups to classify the regions through which routes pass to analyse the traffic density. The most widespread techniques in this case are grouping by using some characteristic of the location of the data such as neighbourhoods or districts (Ibrahim and Shafiq, 2019). Another option is using clustering techniques that perform an automatic classification of the routes (Bian et al, 2018).…”

Section: State Of the Artmentioning

confidence: 99%

Software tool for analysis and visualization of GPS tracks in urban environments

Adrián¹

2021

Datasets

View full text Add to dashboard Cite

Traffic in urban environments vary significantly depending on the areas of the city and the time of day. Nowadays, thanks to the large number of GPS devices that are integrated in all kinds of devices, it is possible to make a quantitative analysis of traffic. This contribution presents an application developed to analyse this information in a simple way and with a visual representation. One of its main advantages is that it is adaptable to any city in the world, as its internal algorithms adapt to the available data are presenting and adjusting it to the traffic through the city. This is done by extracting information directly from the data provided by GPS devices moving around the city. In addition, an analysis of the execution times of all application processes is presented to determine which parts involve a higher execution cost and determine the overall scalability of the application.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Software tool for analysis and visualization of GPS tracks in urban environments

Adrián¹

2021

Datasets

View full text Add to dashboard Cite

show abstract

“…Hierarchical density-based spatial clustering (HDBSCAN), Random Swap clustering and sequential pattern mining were implemented to build a system that delivers traffic insights and recommendations to help taxi drivers with useful guidelines (Ibrahim & Shafiq, 2019). A taxi searching algorithm with an accuracy of 97.59% accuracy was built using distributed coordination and clustering to minimize the time of taxi reaching the passengers (Agrawal, Raychoudhury, Saxena, & Kshemkalyani, 2018).…”

Section: Shylaja S Kannika Nirai Vaani Mmentioning

confidence: 99%

A Machine Learning Framework for Profitability Profiling and Dynamic Price Prediction for the New York City Taxi Trips

S*,

Vaani M

2020

IJITEE

View full text Add to dashboard Cite

 Abstract: The New York City Taxi & Limousine Commission's (NYC TLC) Yellow cabs are facing increased competition from app-based car services such as Ola, Uber, Didi, Lyft and Grab which is rapidly eating away its revenue and market share. Research work: In response to this, the study proposes to do profitability profiling of the taxi trips to focus on various key aspects that generate more revenue in future, visualization to assess the departure and arrival counts of the trips in various locations based on time of the day to maintain demand and supply equilibrium and also build a dynamic price prediction model to balance both margins as well as conversion rates. Methodology/Techniques used: The NYC TLC yellow taxi trip data is analysed through a cross-industry standard process for data mining (CRISP-DM) methodology. Firstly, the taxi trips are grouped into two profitability segments according to the fare amount, trip duration and trip distance by applying K means clustering. Secondly, spatiotemporal data analysis is carried to assess the demand for taxi trips at various locations at various times of the day. Thirdly, multiple linear regression, decision tree, and random forest models are adopted for dynamic price prediction. The findings of the study are as follows, high profitable segments are characterized by airport pickup and drop trips, Count of trip arrivals to airports are more compared to departures from airports at any time of the day, and further analysis revealed that drivers making only a few numbers of airport trips can earn more revenue compared to making more number of trips in local destinations. Compared to multiple linear regression and decision tree, the random forest regression model is considered to be most reliable for dynamic pricing prediction with an accuracy of 91%. Application of research work: The practical implication of the study is the deployment of a dynamic pricing model that can increase the revenue of the NYC TLC cabs along with balancing margin and conversion rates.

show abstract

“…In [15] authors proposed a system that can be utilized in route prediction to avoid areas with high traffic. It can be also used as a recommendation system for empty taxis.…”

Section: Introductionmentioning

confidence: 99%

A GIS Approach for Revealing Urban Mobility Hotspots: A Case Study of Amman City in Jordan

Albustanji¹,

Hassan²,

Alghazawi³

et al. 2022

cea

View full text Add to dashboard Cite

Detecting taxi movements using Random Swap clustering and sequential pattern mining

Cited by 17 publications

References 29 publications

Software tool for analysis and visualization of GPS tracks in urban environments

Software tool for analysis and visualization of GPS tracks in urban environments

A Machine Learning Framework for Profitability Profiling and Dynamic Price Prediction for the New York City Taxi Trips

A GIS Approach for Revealing Urban Mobility Hotspots: A Case Study of Amman City in Jordan

Contact Info

Product

Resources

About