scholarly journals Research on Multi-Dimensional Optimal Location Selection of Maintenance Station Based on Big Data of Vehicle Trajectory

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 495
Author(s):  
Shoujing Zhang ◽  
Fujiao Tong ◽  
Mengdan Li ◽  
Shoufeng Jin ◽  
Zhixiong Li
Keyword(s):  
Big Data ◽  
Particle Swarm Optimization Algorithm ◽  
Service Level ◽  
Two Dimensions ◽  
Trajectory Data ◽  
Location Selection ◽  
Service Stations ◽  
Maintenance Service ◽  
Vehicle Trajectory ◽  
Selection Of

In order to rationally lay out the location of automobile maintenance service stations, a method of location selection of maintenance service stations based on vehicle trajectory big data is proposed. Taking the vehicle trajectory data as the demand points, the demand points are divided according to the region by using the idea of zoning, and the location of the second-level maintenance station is selected for each region. The second-level maintenance stations selected in the whole country are set as the demand points of the first-level maintenance stations. Considering the objectives of the two dimensions of cost and service level, the location model of the first-level maintenance stations under two-dimensional programming is established, and the improved particle swarm optimization algorithm and immune algorithm, respectively, are used to solve the problem. In this way, the first-level maintenance stations in each region are obtained. The example verification shows that the location selection results for the maintenance stations using the vehicle trajectory big data are reasonable and closer to the actual needs.

scholarly journals Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections

2020 ◽  
Vol 12 (15) ◽  
pp. 5877
Author(s):  
Hyunho Chang ◽  
Dongjoo Park
Keyword(s):  
Big Data ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Explanatory Power ◽  
Real Life ◽  
Statistical Correlation ◽  
Vehicle Crashes ◽  
Trajectory Data ◽  
Accident Data ◽  
Vehicle Trajectory

Task-related fatigue, caused by prolonged driving, is a major cause of vehicle crashes. Despite noticeable academic achievements, monitoring drivers’ fatigue on road sections is still an ongoing challenge which must be met to prevent and reduce traffic accidents. Fortunately, individual instances of vehicle trajectory big data collected through advanced vehicle-GPS systems offer a strong opportunity to trace driving durations. We propose a new approach by which to monitor task-related fatigued drivers by directly using the ratio of potentially fatigued drivers (RFD) to all drivers for each road section. The method used to compute the RFD index was developed based on two inputs: the distribution of the driving duration (extracted from vehicle trajectory data), and the boundary condition of the driving duration between fatigued and non-fatigued states. We demonstrate the potentialities of the method using vehicle trajectory big data and real-life traffic accident data. Results showed that the measured RFD has a strong explanatory power with regard to the traffic accident rate, with a statistical correlation of 0.86 at least, for regional motorway sections. Therefore, it is expected that the proposed approach is a feasible means of successfully monitoring fatigued drivers in the present and near future era of smart-mobility big data.

Car-Following Model Calibration and Analysis of Intra-Driver Heterogeneity

2010 ◽  
Vol 108-111 ◽  
pp. 805-810 ◽  
Author(s):  
Hao Wang ◽  
Wei Wang ◽  
Jun Chen
Keyword(s):  
Model Calibration ◽  
Optimization Method ◽  
Acceleration Process ◽  
Trajectory Data ◽  
Car Following ◽  
Car Following Model ◽  
Vehicle Trajectory ◽  
Best Fit ◽  
Insight Into ◽  
Selection Of

This paper presents a methodology for car-following models calibration with vehicle trajectory data. A two-step optimization method is performed for searching the best-fit parameters of two popular car-following models, namely, the Helly model and the IDM model. The model calibration results verify the validity of the optimization method. Based on the results of calibrations, the intra-driver heterogeneity of driving behavior between the acceleration process and the deceleration process is studied. It is found that obvious intra-driver heterogeneities exist in driving behaviours between acceleration processes and deceleration processes of car-following. Besides, some criteria are proposed for the selection of sub-trajectories corresponding to both the acceleration and the deceleration processes of car-following. This work not only develops a general approach for car-following model calibration with vehicle trajectory data, but also provides insight into the intra-driver heterogeneity in car-following behaviours.

Path Choice Behavior Analysis Based on Vehicle Trajectory Data

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Zuyao Zhang ◽  
Li Tang ◽  
Yifeng Wang ◽  
Xuejun Zhang
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Trajectory Data ◽  
Vehicle Trajectory

Big Data in the Field of Civil Security Research

2016 ◽  
Vol 7 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Julia Gonschorek ◽  
Anja Langer ◽  
Benjamin Bernhardt ◽  
Caroline Räbiger
Keyword(s):  
Big Data ◽  
Temporal Distribution ◽  
Tactical Planning ◽  
Fire Departments ◽  
Prevention Measures ◽  
Scientific Contribution ◽  
Security Research ◽  
Geovisual Analytics ◽  
The University ◽  
Selection Of

This article gives insight in a running dissertation at the University in Potsdam. Point of discussion is the spatial and temporal distribution of emergencies of German fire brigades that have not sufficiently been scientifically examined. The challenge is seen in Big Data: enormous amounts of data that exist now (or can be collected in the future) and whose variables are linked to one another. These analyses and visualizations can form a basis for strategic, operational and tactical planning, as well as prevention measures. The user-centered (geo-) visualization of fire brigade data accessible to the general public is a scientific contribution to the research topic 'geovisual analytics and geographical profiling'. It may supplement antiquated methods such as the so-called pinmaps as well as the areas of engagement that are freehand constructions in GIS. Considering police work, there are already numerous scientific projects, publications, and software solutions designed to meet the specific requirements of Crime Analysis and Crime Mapping. By adapting and extending these methods and techniques, civil security research can be tailored to the needs of fire departments. In this paper, a selection of appropriate visualization methods will be presented and discussed.

Does Wrongdoer Reputation Matter? Impact of Auditor-Wrongdoer Performance and Likeability Reputations on Fellow Auditors' Intention to Take Action and Choice of Reporting Outlet

2011 ◽  
Vol 23 (2) ◽  
pp. 207-234 ◽  
Author(s):  
Jesse C. Robertson ◽  
Chad M. Stefaniak ◽  
Mary B. Curtis
Keyword(s):  
Two Dimensions ◽  
Data Availability ◽  
Good Performer ◽  
Reporting Decisions ◽  
Prior Literature ◽  
Reporting Channel ◽  
Channel Preferences ◽  
Main Effects ◽  
The Impact ◽  
Selection Of

ABSTRACT We investigate the effects of auditor-wrongdoer reputations for performance and likeability on fellow auditors' intentions to take action in response to a questionable audit act. We also use this context to explore auditor selection of reporting outlets, when they do choose to take action. In an experiment with 181 auditors, main effects suggest that likeability reputation is a significant determinant of intention to take action, while performance reputation is marginally significant. As expected, interaction results indicate that auditors have the greatest intention to take action against less likeable, poor performers. Contrary to expectations, intention to take action against a more likeable, good performer is no lower than the mixed conditions. Thus, the influence of the two dimensions of reputation is complex. Additionally, we find auditors are more likely to whistle-blow internally than externally, and through non-anonymous outlets than anonymous outlets. Our contributions include exploring the impact of reputation on the actions of third parties, and advancing prior literature by considering the influence of wrongdoer attributes on reporting decisions and auditors' reporting channel preferences. Data Availability: Data are available from the first author upon request.

Entrepreneurial strategies for MNCs: A typology

2020 ◽  
pp. 146575032098315
Author(s):  
Poul Houman Andersen ◽  
Linda Nhu Laursen
Keyword(s):  
Organizational Context ◽  
Research Question ◽  
Two Dimensions ◽  
Theoretical Assumption ◽  
Critical Dimensions ◽  
Organizational Resistance ◽  
Organizational Conditions ◽  
Entrepreneurial Strategies ◽  
Entrepreneurial Strategy ◽  
Selection Of

This paper, responds to the recent calls in research, to address the theoretical underpinnings of entrepreneurial strategies in MNC’s. Today, a multiplicity of entrepreneurial approaches exists, cf. skunk work, bricolage, bootlegging. However, these exists in disparate literature, that provides limited oversight to managers in, that need to select between a manifold of different entrepreneurial strategies. Moreover, these approaches typically originate from a distinctively different organizational context, namely SMEs. Through a literature review we identify two important axiomatic assumptions concerning entrepreneurial strategies within the organizational conditions of MNCs. The first fundamental assumption concerns the organizational origin of such effort. The second theoretical assumption deals with how the entrepreneurial initiative can meet either organizational resistance or support. We synthesize these two dimensions into a two-by-two matrix, that provides an answer to our research question: what are the critical dimensions for entrepreneurial strategies in an MNC context? We then employ this typology to categorize predominant entrepreneurial strategies in current literature, to create a overview that can be used both for structuring the debate in the literature; as well as a basis to discuss important implicit assumptions, that should guide the selection of entrepreneurial strategy in a MNC context in practice.

scholarly journals Three faces of the online leftists: An exploratory study based on case observations and big-data analysis

2020 ◽  
Vol 6 (1) ◽  
pp. 67-101
Author(s):  
Yong Gui ◽  
Ronggui Huang ◽  
Yi Ding
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Strong Support ◽  
Negative Attitude ◽  
Two Dimensions ◽  
Supervised Machine Learning ◽  
Interaction Patterns ◽  
Left Wing ◽  
Mixed Approach ◽  
Method Approach

Left-leaning social thoughts are not a unitary and coherent theoretical system, and leftists can be divided into divergent groups. Based on inductive qualitative observations, this article proposes a theoretical typology of two dimensions of theoretical resources and position orientations to describe left-wing social thoughts communicated in online space. Empirically, we used a mixed approach, an integration of case observations and big-data analyses of Weibo tweets, to investigate three types of left-leaning social thoughts. The identified left-leaning social thoughts include state-centered leftism, populist leftism, and liberal leftism, which are consistent with the proposed theoretical typology. State-centered leftism features strong support of the state and the current regime and a negative attitude toward the West, populist leftism is characterized by unequivocal affirmation of the revolutionary legacy and support for disadvantaged grassroots, and liberal leftism harbors a grassroots position and a decided affirmation of individual rights. In addition, we used supervised machine learning and social network analysis techniques to identify online communities that harbor the afore-mentioned left-leaning social thoughts and analyzed the interaction patterns within and across communities as well as the evolutions of community structures. We found that during the study period of 2012–2014, the liberal leftists gradually declined and the corresponding communities dissolved; the interactions between populist leftists and state-centered leftists intensified, and the ideational cleavage between these two camps increased the online confrontations. This article demonstrates that the mixed method approach of integrating traditional methods with big-data analytics has enormous potential in the sub-discipline of digital sociology.

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