Analysis and Visualization for Hot Spot Based Route Recommendation Using Short-Dated Taxi GPS Traces

Shen, Ying; Zhao, Ligang; Fan, Jing

doi:10.3390/info6020134

Cited by 43 publications

(26 citation statements)

References 19 publications

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“…Li et al [13] defined hotspots as areas where the pick-up and drop-off points are clustered and discovered the spatiotemporal dynamic patterns of dwellers in these areas. Shen et al [14] presented a grid-adaptive DBSCAN algorithm to help drivers find passengers' loading and unloading hotspots using short-dated taxi GPS traces. Inspired by the 2D Fourier transform, Pei et al [15] proposed a density-based method for identifying two-component clusters.…”

Section: Related Workmentioning

confidence: 99%

Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Xia

Chen

et al. 2019

IJGI

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Pick-up and drop-off events of taxi trajectory data contain rich information about residents’ travel activities and road traffic. Such data have been widely applied in urban hotspot detection in recent years. However, few studies have attempted to delimitate the urban hotspot scope using taxi trajectory data. On this basis, the current study firstly introduces a network-based spatiotemporal field (NSF) clustering approach to discover and identify hotspots. Our proposed method expands the notion from spatial to space–time dimension and from Euclidean to network space by comparing with traditional spatial clustering analyses. In addition, a concentration index of hotspot areas is presented to refine the surface of centredness to delimitate the hotspot scope further. This index supports the quantitative depiction of hotspot areas by generating two standard deviation isolines. In the case study, we analyze the spatiotemporal dynamic patterns of hotspots at different days and times of day using the NSF method. Meanwhile, we also validate the effectiveness of the proposed method in identifying hotspots to evaluate the delimitating results. Experimental results reveal that the proposed approach can not only help detect detailed microscale characteristics of urban hotspots but also identify high-concentration patterns of pick-up incidents in specific places.

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Section: Related Workmentioning

confidence: 99%

Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Xia

Chen

et al. 2019

IJGI

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“…The hot spots for loading and unloading passenger(s) are extracted using an improved DBSCAN algorithm after data preprocessing, including cleaning and filtering. Then, the start-end point-based similar trajectory method is adopted to identify coarse-level trajectory clusters and then obtaining the recommended routes [16]. Chang et al proposed an approach for recommending pick-up locations for taxi drivers.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China

Naji

Zhang

2017

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Taxi trajectories reflect human mobility over the urban roads' network. Although taxi drivers cruise the same city streets, there is an observed variation in their daily profit. To reveal the reasons behind this issue, this study introduces a novel approach for investigating and understanding the impact of human mobility patterns (taxi drivers' behavior) on daily drivers' profit. Firstly, a K-means clustering method is adopted to group taxi drivers into three profitability groups according to their driving duration, driving distance and income. Secondly, the cruising trips and stopping spots for each profitability group are extracted. Thirdly, a comparison among the profitability groups in terms of spatial and temporal patterns on cruising trips and stopping spots is carried out. The comparison applied various methods including the mash map matching method and DBSCAN clustering method. Finally, an overall analysis of the results is discussed in detail. The results show that there is a significant relationship between human mobility patterns and taxi drivers' profitability. High profitability drivers based on their experience earn more compared to other driver groups, as they know which places are more active to cruise and to stop and at what times. This study provides suggestions and insights for taxi companies and taxi drivers in order to increase their daily income and to enhance the efficiency of the taxi industry.

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“…Literature [4] proposed a Hausdorff distance similarity measure based on line segments to detect anomalies in moving objects. The literature [5,6,7,8] detects and analyzes hotspots of on-road and off-road passengers in road network space. On the one hand, it improves people's perception of urban spatial dynamics, and on the other hand, it provides guidance for the vehicle to find passengers as soon as possible.…”

Section: Related Workmentioning

confidence: 99%

DBSCAN-Based No-Load Road Detection Algorithm

2018

2018 3rd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2018)

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Abstract:Taxi plays an important role in the urban passenger transport system. Good taxi service quality will help increase the happiness of urban residents. However, the issue of no-load taxis has always been one of the most concerned issues for urban managers. This paper uses the improved DBSCAN method to carry on the cluster analysis to the taxi trajectory, prompts the taxi driver to avoid the no-load area, and achieves the purpose that to raise the taxi utilization factor. The improved DBSCAN method introduces two distance metrics, segment feature distance and dynamic space-time distortion distance. The feature distances of the segment include the vertical distance, horizontal distance, and angular distance, which can effectively measure the distance between trajectory segments. The dynamic space-time distortion distance introduces spatial factors on the basis of the dynamic distortion distance, and successfully overcomes the similarity error problem caused by different trajectory lengths. At the same time, using the GPS data set of Nanjing taxi to carry out experiments, the results show that the algorithm has higher accuracy and stability than the algorithms in the literature.

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Analysis and Visualization for Hot Spot Based Route Recommendation Using Short-Dated Taxi GPS Traces

Cited by 43 publications

References 19 publications

Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China

DBSCAN-Based No-Load Road Detection Algorithm

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