Big data-driven public transportation network: a simulation approach

Wang, Zhaohua; Li, Xuewei; Zhu, Xin; Li, Jing; Wang, Fan; Wang, Fei

doi:10.1007/s40747-021-00462-2

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Vehicle ReID is an important application in the field of object reidentification, which aims to identify a particular vehicle across different camera views. With the continuously increasing quantity of urban vehicles, vehicle ReID has diverse applications in real-world video surveillance and intelligent transportation [1][2][3]. One challenging task of vehicle ReID is cross-camera retrieval, which means to retrieve the same vehicle in the gallery images captured across different nonoverlapping city security cameras.…”

Section: Introductionmentioning

confidence: 99%

Joint metric learning of local and global features for vehicle re-identification

Shen

Sun

Wang

et al. 2022

Complex Intell. Syst.

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Vehicle re-identification (ReID) means to identify the target vehicle in large-scale surveillance videos captured by multiple cameras, where robust and distinctive visual features of vehicles are critical to the performance. Recently, the researchers have approached the problem with attention based models. However, most of these models use strongly-supervised methods, which rely on expensive extra labels, e.g., keypoints(vehicle wheels , logo and lamps) and attributes(e.g., color and type). Therefore, we propose a joint metric learning approach to solve the problem. We present an end-to-end Partition and Fusion Multi-branch Network (PFMN), a novel approach to effectively learn discriminative features without any annotations or additional attributes. For hard samples, which means different vehicles with similar appearance or the same vehicle with different appearances, a novel variant of hard sampling triplet loss is proposed. Based on extensive experiments, we have proved the effectiveness of our proposed method. On the challenging public data sets VeRi-776 and VehicleID, our model outperforms most state-of-the-art algorithms on mAP and rank-1. Especially on mINP, which measures the cost of model retrieval hard samples, we can achieve a significant improvement.

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Section: Introductionmentioning

confidence: 99%

Joint metric learning of local and global features for vehicle re-identification

Shen

Sun

Wang

et al. 2022

Complex Intell. Syst.

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“…Nesmachnow et al [26] divided the study area of Lisbon into several grids, counted and visualized the trips in the grids, respectively, and represented the trips by the depth of color. Wang et al [27] calculated the number of taxi arrivals within a certain range of service facilities such as restaurants and shopping based on taxi trajectory data, quantified the attractiveness level of various service facilities, and analyzed the spatial distribution pattern of the attractiveness through global and local spatial autocorrelation. Xiong et al [28] extracted the pick-up and drop-off points from the taxi track data of Wuhan in a week and used the bar chart to show the daily taxi travel volume and the line chart to show the taxi travel volume in different periods of time.…”

Section: Related Workmentioning

confidence: 99%

BP Neural Network-Based Big Data Intelligent Travel Algorithm and Its Application

Huang¹

2022

Scientific Programming

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With the increase of urbanization rate, a large number of people flood into cities, increasing pressure of urban traffic, and problems accumulated in the taxi industry are gradually prominent. The phenomenon of crowded queue for taxi is frequent in peak hours, and vehicles patrol and sweep streets during peak hours. The key to solve these problems lies in mastering the rules and patterns of taxi travel and finding the factors affecting the relationship between taxi supply and demand. It is difficult to effectively understand taxi travel as a whole due to the large number of taxis and their large scale and strong mobility. Comprehensive application of trajectory data mining method can extract the spatiotemporal characteristics of massive taxi trajectory data and reveal the nature of its occurrence. This paper mainly focuses on the problems faced by urban traffic governance, such as the mismatch between data sources and demand systems, the uncoordinated operation of comprehensive transportation system, and the difficulty in sharing big data resources between government and enterprises. With massive data resources across departments, this paper designed a networked intelligent computing platform for big data of urban transportation integrated with various modes of transportation to sense the operation situation of urban comprehensive transportation system in real time, accurately grasp the space-time distribution of urban transportation supply and demand, and significantly improve the ability of coordinated operation, organization, and management of transportation in large cities. It also effectively enhances the quality of transportation information sharing and integration services and comprehensively improves the efficiency and overall carrying capacity of the urban comprehensive transportation system.

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“…This has led to the emergence of network model-based approaches as a significant research field [2]. Such approaches are particularly beneficial in visually interpreting and analyzing various elements within the transportation system and their relationships [3,4].…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of Public Transportation Systems in Seoul Based on General Multi-Layer Networks

Lee,

Kim,

Kim

2024

Preprint

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Public transportation systems play a vital role in modern cities, enhancing the quality of life and fostering sustainable economic growth. Modeling and understanding the complexities of these transportation networks are crucial for effective urban planning and management. Traditional models often fall short in capturing the intricate interactions and interdependencies in multi-modal public transportation systems. To address this challenge, recent research has embraced multi-layer network models, offering a more sophisticated representation of these networks. However, there is a need to explore and develop robustness analysis techniques tailored to these general multi-layer networks to fully assess their complexities in real-world scenarios. In this paper, we employ a general multi-layer network model to comprehensively analyze a real-world multi-modal transportation network in Seoul, South Korea. We leverage a large volume of traffic data to model, visualize, and evaluate the city's mobility patterns. Additionally, we introduce two novel methodologies for robustness analysis, one based on random walk coverage and the other on Eigenvalue, specifically designed for general multi-layer networks. Extensive experiments using the large volume of real-world data sets demonstrate the effectiveness of the proposed approaches.

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Big data-driven public transportation network: a simulation approach

Cited by 6 publications

References 17 publications

Joint metric learning of local and global features for vehicle re-identification

Joint metric learning of local and global features for vehicle re-identification

BP Neural Network-Based Big Data Intelligent Travel Algorithm and Its Application

Robustness Analysis of Public Transportation Systems in Seoul Based on General Multi-Layer Networks

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