A Graph Based Analysis of User Mobility for a Smart City Project

Verma, J. P.; Mankad, Sapan H.; Garg, Sanjay

doi:10.1007/978-981-15-1718-1_12

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…The approach proposed in this work and the analysis thereafter may help in traffic planning at the city level as well as for infrastructure setup. It is more motivated compared to our previous work on user mobility detection and prediction in small premises for a smart city project [25]. As per the result computed Tables 4 and 5 the historical trajectory can be used to predict the future trajectory of moving vehicles.…”

Section: Discussionmentioning

confidence: 95%

“…By extending this work we are proposing detection of the trajectory of a taxi movement in the city based on the GPS location data collected. [25] Figure 1 shows the potential fields where the outcome of the proposed work can be exploited. One of the outcomes helps smart city users to enhance the usage of the available transport facility by sharing the location information.…”

Section: Introductionmentioning

confidence: 99%

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GeoHash tag based mobility detection and prediction for traffic management

2020

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User mobility detection and prediction can help in many ways for planning and monitoring a population distribution and displacement in a specific area or location. It also helps in planning for resource distribution and allocation. In this paper, we present a study on the mobility of the population which can be monitored by sensors, GPS devices through location detection. All the public transports like buses, taxis, etc. have GPS devices posted that can provide movement of a vehicle in city traffic. For experimental analysis, the T-Drive Taxi Trajectories dataset was selected, in which taxi location data are collected with the information of taxi ID, timestamp, latitude, and longitude. GeoHash tags were generated for the location of a taxi based on latitude and longitude. A graph was built based on vertices as GeoHash tag and edges as a direct link between the GeoHash tags. Graph-Based data analysis was applied to identify the importance of GeoHash tag based on the in-degree and PageRank of the vertices. The mobility path and movement of traffic can be predicted that can be used for disaster management and urban development for city planning.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

GeoHash tag based mobility detection and prediction for traffic management

2020

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“…The improved data mining methods are important to efficiently process and facilitate better decisions using this huge volume of data. As a consequence of churning a large volume of data, Mobility Data Analytics, or Trajectory Prediction, has become an active ongoing research avenue [5,10,32]. These research outcome will lead to improved navigation [20], route prediction [28], traffic sensing [14] and location-based recommendations [3].…”

Section: Introductionmentioning

confidence: 99%

A deep learning based approach for trajectory estimation using geographically clustered data

Shrivastava¹,

Verma²,

Jain³

et al. 2021

SN Appl. Sci.

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This study presents a novel approach to predict a complete source to destination trajectory of a vehicle using a partial trajectory query. The proposed architecture is scalable to extremely large-scale data with respect to the dense road network. A deep learning model Long Short Term Memory (LSTM) has been used for analyzing the temporal data and predicting the complete trajectory. To handle a large amount of data, clustering of similar trajectory data is used that helps in reducing the search space. The clusters based on geographical locations and temporal values are used for training different LSTM models. The proposed approach is compared with the other published work on the parameters as Average distance error and one step prediction accuracy The one-step prediction accuracy is as good as 81% and Distance error are .33 Km. Our proposed approach termed Clustered LSTM is outperforming in both the parameters when compared with other reported results. The proposed solution is a clustering-based predictive model that effectively contributes to accurately handle the large scale data. The outcome of this study leads to improvise the navigation systems, route prediction, traffic management, and location-based recommendation systems.

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