scholarly journals Assessing cyclists’ routing preferences by analyzing extensive user setting data from a bike-routing engine

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Michael Hardinghaus ◽  
Simon Nieland
Keyword(s):  
Big Data ◽  
Choice Behavior ◽  
Focal Point ◽  
High Quality ◽  
Route Choice Behavior ◽  
Integrated Strategy ◽  
Mode Of Transport ◽  
The Many ◽  
Direct Cycle ◽  
Observation Period

Abstract Introduction Many municipalities aim to support the uptake of cycling as an environmentally friendly and healthy mode of transport. It is therefore crucial to meet the demand of cyclists when adapting road infrastructure. Previous studies researching cyclists’ route choice behavior deliver valuable insights but are constrained by laboratory conditions, limitations in the number of observations, or the observation period or relay on specific use cases. Methods The present study analyzes a dataset of over 450,000 observations of cyclists’ routing settings for the navigation of individual trips in Berlin, Germany. It therefore analyzes query data recorded in the bike-routing engine BBBike and clusters the many different user settings with regard to preferred route characteristics. Results and Conclusion Results condense the large number of routing settings into characteristic preference clusters. Compared with earlier findings, the big data approach highlights the significance of short routes, side streets and the importance of high-quality surfaces for routing choices, while cycling on dedicated facilities seems a little less important. Consequentially, providing separated cycle facilities along main roads – often the main focal point of cycle plans – should be put into the context of an integrated strategy which fulfills distinct preferences to achieve greater success. It is therefore particularly important to provide a cycle network in calm residential streets as well as catering for short, direct cycle routes.

Preserving Privacy with Federated Learning in Route Choice Behavior Modeling

2021 ◽  
pp. 036119812110111
Author(s):  
Yonghyeon Kweon ◽  
Bingrong Sun ◽  
B. Brian Park
Keyword(s):  
Big Data ◽  
Choice Behavior ◽  
Route Choice ◽  
Behavior Modeling ◽  
Model Parameters ◽  
Learning Approach ◽  
Privacy Concerns ◽  
Route Choice Behavior ◽  
One Year ◽  
Similar Accuracy

While big data helps improve decision-making and model developments, it often runs into privacy concerns. An example would be retrieving drivers’ origin and destination information from smartphone navigation apps for developing a route choice behavior model. To conserve privacy, yet to take advantage of big data in navigation applications, the authors propose to apply a federated learning approach, which has shown promising application in predicting smartphone keyboard’s next word without sending text to the server. Additional benefits of using federated learning is to save on data communications, by sending model parameters instead of entire raw data, and to distribute the computational burden to each smartphone instead of to the main server. The results from real-world route navigation usage data from about 30,000 drivers over one year showed that the proposed federated learning approach was able to achieve very similar accuracy to the traditional centralized global model and yet assures privacy.

AN ANALYSIS OF TRUCKS' ROUTE CHOICE BEHAVIOR CONSIDERING VEHICLE TYPE AND TRIP LENGTH

2018 ◽  
Vol 74 (5) ◽  
pp. I_657-I_666
Author(s):  
Hideki OKA ◽  
Makoto CHIKARAISHI ◽  
Jun TANABE ◽  
Daisuke FUKUDA ◽  
Takashi OGUCHI
Keyword(s):  
Choice Behavior ◽  
Route Choice ◽  
Trip Length ◽  
Vehicle Type ◽  
Route Choice Behavior

Big Data in the philippines: How do we actually use them?

2021 ◽  
pp. 1-30
Author(s):  
Lisa Grace S. Bersales ◽  
Josefina V. Almeda ◽  
Sabrina O. Romasoc ◽  
Marie Nadeen R. Martinez ◽  
Dannela Jann B. Galias
Keyword(s):  
Big Data ◽  
High Speed ◽  
The Philippines ◽  
Complex Data ◽  
Official Statistics ◽  
Group Discussions ◽  
The Public ◽  
The Government ◽  
The Many ◽  
Current Utilization

With the advancement of technology, digitalization, and the internet of things, large amounts of complex data are being produced daily. This vast quantity of various data produced at high speed is referred to as Big Data. The utilization of Big Data is being implemented with success in the private sector, yet the public sector seems to be falling behind despite the many potentials Big Data has already presented. In this regard, this paper explores ways in which the government can recognize the use of Big Data for official statistics. It begins by gathering and presenting Big Data-related initiatives and projects across the globe for various types and sources of Big Data implemented. Further, this paper discusses the opportunities, challenges, and risks associated with using Big Data, particularly in official statistics. This paper also aims to assess the current utilization of Big Data in the country through focus group discussions and key informant interviews. Based on desk review, discussions, and interviews, the paper then concludes with a proposed framework that provides ways in which Big Data may be utilized by the government to augment official statistics.

scholarly journals Design of an artificial simulator for analyzing route choice behavior in the presence of information system

1995 ◽  
Vol 22 (4-7) ◽  
pp. 119-147 ◽  
Author(s):  
P.D.V.G. Reddy ◽  
H. Yang ◽  
K.M. Vaughn ◽  
M.A. Abdel-Aty ◽  
R. Kitamura ◽  
...  
Keyword(s):  
Information System ◽  
Choice Behavior ◽  
Route Choice ◽  
Route Choice Behavior

Closing Remarks

2019 ◽  
Vol 15 (S367) ◽  
pp. 515-517
Author(s):  
Debra Meloy Elmegreen
Keyword(s):  
Big Data ◽  
Education Research ◽  
Strategic Plan ◽  
Astronomy Education ◽  
The Public ◽  
The Many ◽  
Collaborative Efforts ◽  
Made In

AbstractThis symposium has highlighted key first steps made in addressing many goals of the IAU Strategic Plan for 2020–2030. Presentations on initiatives regarding education, with applications to development, outreach, equity, inclusion, big data, and heritage, are briefly summarized here. The many projects underway for the public, for students, for teachers, and for astronomers doing astronomy education research provide a foundation for future collaborative efforts, both regionally and globally.

THE ROAD AHEAD TO BETTER MEDICAL CARE FOR CHILDREN

PEDIATRICS ◽  
1950 ◽  
Vol 6 (3) ◽  
pp. 553-556
Keyword(s):  
Child Health ◽  
Medical Care ◽  
Medical Center ◽  
Point Of View ◽  
Medical Services ◽  
High Quality ◽  
The Road ◽  
Provide Child Care ◽  
Ultimate Objective ◽  
The Many

THE road to better child health has been discussed in relation to the doctor and his training, health services and their distribution. We have dealt with the unavoidable question of costs. Particular attention has been given to some of the advantages and dangers of decentralization of pediatric education and services. Each of the various subjects has been discussed from the point of view of its bearing on the ultimate objective of better health for all children and the steps necessary to attain this goal. Now, we may stand back from the many details of the picture, view the whole objectively and note its most outstanding features. First is the fact that the improvement of child health depends primarily upon better training for all doctors who provide child care, general practitioners as well as specialists. This is the foundation without which the rest of the structure cannot stand. The second dominant fact is the need for extending to outlying and isolated areas the high quality medical care of the medical centers, without at the same time diluting the service or training at the center. The road to better medical care, therefore, begins at the medical center and extends outward through a network of integrated community hospitals and health centers, finally reaching the remote and heretofore isolated areas. Inherent in all medical schools is a unique potential for rendering medical services as well as actually training physicians. The very nature of medical education—whereby doctors in training work under the tutelage of able specialists in the clinic, hospital ward, and out-patient department—provides medical services of high quality to people in the neighboring communities.

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