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      Investigating Spatiotemporal Characteristics of Demand Responsive Transport (DRT) Service for the Disabled through Survival Analysis

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          Abstract

          The importance of Demand Responsive Transport (DRT) has increased as it guarantees the right to travel and improves mobility for special population groups such as disabled people and low income households. In order for DRT to become a sustainable means of transportation, it is necessary to keep the quality of DRT service above a certain level that might be expected by users, and thus DRT service needs to be provided evenly over time and space. This study aims to investigate the spatiotemporal characteristics of DRT service based on waiting time in terms of service equity. For the analysis, operating data of DRT for people with disabilities provided from the Seoul Metropolitan Government were used and a survival analysis was employed to derive median waiting time. The results showed that there exists a difference in median waiting time over time and space, indicating that DRT service for disabled is not evenly provided over time and space. The results of using a heat map and Kernel density plot with median waiting time also confirmed that the spatiotemporal imbalances of DRT service exist throughout Seoul. One of the major reasons for this phenomenon might be the uneven spatiotemporal distribution of supply as expected.

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          Statistics review 12: Survival analysis

          This review introduces methods of analyzing data arising from studies where the response variable is the length of time taken to reach a certain end-point, often death. The Kaplan–Meier methods, log rank test and Cox's proportional hazards model are described.
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            Is Open Access

            Electric Scooter Sharing and Bike Sharing User Behaviour and Characteristics

            New, shared mobility modes, including dockless e-scooters and e-bikes, were recently introduced to many cities around the world. The aim of this article is to determine the differences between the users of e-bike sharing, and e-scooter sharing systems, and the characteristics of their travel behaviour. This study is based on the survey of the citizens of Tricity in northern Poland. We find that e-bicycles are predominantly used as first and last mile transport and to commute directly to various places of interest, whereas e-scooters are more often used for leisure rides. Survey respondents that adopted shared micromobility are generally young, and e-scooter users are on average younger than e-bike users. Although all shared vehicles in Tricity are electrically assisted, this did not allow for the elimination of the gender gap, or help retired and disabled people in the adoption of shared micromobility services. We have also identified factors discouraging people from the usage of e-bike and e-scooter sharing and found them to be different for both types of services. Finally, we investigated the issue of using shared e-bikes for urban logistics.
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              Is Open Access

              Kernel density estimation and its application

              Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel technique produces smooth estimate of the pdf, uses all sample points' locations and more convincingly suggest multimodality. In its two-dimensional applications, kernel estimation is even better as the 2D histogram requires additionally to define the orientation of 2D bins. Two concepts play fundamental role in kernel estimation: kernel function shape and coefficient of smoothness, of which the latter is crucial to the method. Several real-life examples, both for univariate and bivariate applications, are shown.
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                Author and article information

                Contributors
                dokkang@uos.ac.kr
                Journal
                KSCE J Civ Eng
                KSCE Journal of Civil Engineering
                Korean Society of Civil Engineers (Seoul )
                1226-7988
                1976-3808
                7 May 2022
                : 1-8
                Affiliations
                [1 ]GRID grid.267134.5, ISNI 0000 0000 8597 6969, Dept. of Transportation Engineering, , University of Seoul, ; Seoul, 02504 Korea
                [2 ]GRID grid.267134.5, ISNI 0000 0000 8597 6969, Graduate School, Dept. of Urban Big Data Convergence, , University of Seoul, ; Seoul, 02504 Korea
                Author information
                http://orcid.org/0000-0002-5278-2054
                http://orcid.org/0000-0002-3532-5564
                Article
                807
                10.1007/s12205-022-0807-9
                9077348
                49d7da6c-6d96-4a87-999f-57a9dd765985
                © Korean Society of Civil Engineers 2022

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 7 May 2021
                : 11 November 2021
                : 14 March 2022
                Categories
                Future Urban Mobility with MaaS

                demand responsive transport (drt),survival analysis,waiting time,spatiotemporal,disabled and elderly

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