Optimization of Urban Bus Stops Setting Based on Data Mining

2021 ◽  
pp. 2159028
Author(s):  
Ganglong Duan ◽  
Xin Ma ◽  
Jianren Wang ◽  
Zhishi Wang ◽  
Yan Wang
Keyword(s):  
Public Transportation ◽  
Real Life ◽  
Matrix Analysis ◽  
Operating Efficiency ◽  
Gps Data ◽  
Data Set ◽  
Business System ◽  
Urban Bus ◽  
Bus Station ◽  
Bus Stops

The unreasonable setting of urban bus stops is a common problem in real life, which seriously affects people’s happiness, sense of belonging and brand in the city. However, the existing related research on the above problems generally has the defects of high technical complexity and high cost. Therefore, we aim to propose a way to optimize the setting of urban public transportation stations and reduce the technical complexity and high cost of existing public transportation station optimization by using artificial intelligence algorithms. First, we extract and integrate bus GPS data and bus card swipe data in the business system and perform exploratory analysis on the pre-processed data. Second, the original k-NN algorithm is improved, and an ik-NN algorithm is proposed to determine the cardholder’s boarding point. Then, we divide the upstream and downstream lines to calculate the total number of upstream and downstream passengers. Third, we propose an algorithm for calculating the number of passengers getting off at bus stations and calculating the number of passengers getting on and off at each bus station. Finally, according to the number of passengers getting on and off at each bus station, the OD matrix is constructed, the residents’ travel rules are analyzed, and optimization suggestions for the setting of urban bus stations are proposed. This paper selects the public transit GPS data set and swipe card data set of Shenzhen, China for experiments. The experimental results show that: (1) Compared with K-means, the ik-NN algorithm we proposed can effectively determine the actual car station of each cardholder, and the algorithm is less sensitive to feature dimensions. At the same time, the ik-NN algorithm has a high operating efficiency and is less affected by the “[Formula: see text]” value. (2) The calculation algorithm for the number of passengers getting off at bus stations can effectively use the existing data of the business system to determine the number of passengers getting off at each bus station. Therefore, the calculation times of this algorithm are low, and the accuracy is high. (3) The optimization suggestions for bus stations based on the OD matrix analysis of residents’ travel rules meet the needs of urban development and have certain reference value.

Study on the Optimal Improvement of the Bus Station

2012 ◽  
Vol 253-255 ◽  
pp. 1776-1781
Author(s):  
Wen Hua Jiang ◽  
Xian Xiang Wang ◽  
Hang Fei Lin
Keyword(s):  
Site Location ◽  
Bus Stop ◽  
Site Layout ◽  
Urban Bus ◽  
Bus Station ◽  
Bus Stops

Starting from several aspects of site location, site size and site layout, this document studies the urban bus stop systematically, proposes the setting principles of urban bus stop. Take Yiwu bus stops for example, which focus on the analysis of the reasonable setting of the sites, and has provided guidance for the layout of urban bus stop.

scholarly journals Optimization of Headways and Departure Times in Urban Bus Networks: A Case Study of Çorlu, Turkey

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Huseyin Ceylan ◽  
Tayfun Ozcan
Keyword(s):  
Public Transportation ◽  
Assignment Problem ◽  
Optimization Technique ◽  
Harmony Search ◽  
Real Life ◽  
Transport Systems ◽  
Transit Assignment ◽  
Proposed Model ◽  
Urban Bus ◽  
Bus Networks

The traffic congestion, which has become one of the major problems of developed and developing countries, has led to a shift in the way public transport systems are viewed, and it has accelerated efforts to increase the efficiency of these systems. In recent studies, several approaches, in which both user and operator benefits are evaluated together in order to increase the demand for public transportation systems and to ensure the sustainability of these systems, are emphasized. In this study, a bilevel simulation/optimization model is developed to optimize service headways and departure times of first buses from the beginning of the routes in urban bus networks. At the upper level of the proposed model, a multiobjective function representing user and operator costs is evaluated using the metaheuristic harmony search (HS) optimization technique. The transit assignment problem, which represents the distribution of transit users over the routes, is handled at the lower level. In the proposed model, the transit assignment problem is solved by the timetable-based assignment approach with VISUM transport planning software. The timetable-based transit assignment is an approach in which the perception errors within the users’ route choice are taken into consideration and the transfer wait times can be precisely calculated. The proposed model is applied to a real-life urban bus network of the Çorlu district (Tekirdağ, Turkey), and the effectiveness of the model on a medium-sized urban bus system has been demonstrated. The results show that the user and operator benefits can be simultaneously increased by adding an initial departure offset parameter to the problem.

scholarly journals Facing the needs for clean bicycle data – a bicycle-specific approach of GPS data processing

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sven Lißner ◽  
Stefan Huber
Keyword(s):  
Data Processing ◽  
Gps Data ◽  
Data Set ◽  
Specific Data ◽  
Driving Mode ◽  
Mode Detection ◽  
Current State ◽  
Mode Recognition ◽  
Recorded Data ◽  
Gps Tracks

Abstract Background GPS-based cycling data are increasingly available for traffic planning these days. However, the recorded data often contain more information than simply bicycle trips. GPS tracks resulting from tracking while using other modes of transport than bike or long periods at working locations while people are still tracking are only some examples. Thus, collected bicycle GPS data need to be processed adequately to use them for transportation planning. Results The article presents a multi-level approach towards bicycle-specific data processing. The data processing model contains different steps of processing (data filtering, smoothing, trip segmentation, transport mode recognition, driving mode detection) to finally obtain a correct data set that contains bicycle trips, only. The validation reveals a sound accuracy of the model at its’ current state (82–88%).

A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

2021 ◽  
pp. 58-60
Author(s):  
Naziru Fadisanku Haruna ◽  
Ran Vijay Kumar Singh ◽  
Samsudeen Dahiru
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Real Life ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Data Set ◽  
Population Mean ◽  
The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

Long term variations of the hydrochemical composition of deep thermal ground water in the Lower Bavarian Molasse Basin – Causes and Perspectives

2021 ◽  
Author(s):  
Annette Dietmaier ◽  
Thomas Baumann
Keyword(s):  
Time Series ◽  
Real Life ◽  
Data Series ◽  
Fluid Composition ◽  
Molasse Basin ◽  
Deep Groundwater ◽  
Data Set ◽  
Groundwater Exploitation ◽  
Groundwater Aquifers

<p>The European Water Framework Directive (WFD) commits EU member states to achieve a good qualitative and quantitative status of all their water bodies.  WFD provides a list of actions to be taken to achieve the goal of good status.  However, this list disregards the specific conditions under which deep (> 400 m b.g.l.) groundwater aquifers form and exist.  In particular, deep groundwater fluid composition is influenced by interaction with the rock matrix and other geofluids, and may assume a bad status without anthropogenic influences. Thus, a new concept with directions of monitoring and modelling this specific kind of aquifers is needed. Their status evaluation must be based on the effects induced by their exploitation. Here, we analyze long-term real-life production data series to detect changes in the hydrochemical deep groundwater characteristics which might be triggered by balneological and geothermal exploitation. We aim to use these insights to design a set of criteria with which the status of deep groundwater aquifers can be quantitatively and qualitatively determined. Our analysis is based on a unique long-term hydrochemical data set, taken from 8 balneological and geothermal sites in the molasse basin of Lower Bavaria, Germany, and Upper Austria. It is focused on a predefined set of annual hydrochemical concentration values. The data range dates back to 1937. Our methods include developing threshold corridors, within which a good status can be assumed, and developing cluster analyses, correlation, and piper diagram analyses. We observed strong fluctuations in the hydrochemical characteristics of the molasse basin deep groundwater during the last decades. Special interest is put on fluctuations that seem to have a clear start and end date, and to be correlated with other exploitation activities in the region. For example, during the period between 1990 and 2020, bicarbonate and sodium values displayed a clear increase, followed by a distinct dip to below-average values and a subsequent return to average values at site F. During the same time, these values showed striking irregularities at site B. Furthermore, we observed fluctuations in several locations, which come close to disqualifying quality thresholds, commonly used in German balneology. Our preliminary results prove the importance of using long-term (multiple decades) time series analysis to better inform quality and quantity assessments for deep groundwater bodies: most fluctuations would stay undetected within a < 5 year time series window, but become a distinct irregularity when viewed in the context of multiple decades. In the next steps, a quality assessment matrix and threshold corridors will be developed, which take into account methods to identify these fluctuations. This will ultimately aid in assessing the sustainability of deep groundwater exploitation and reservoir management for balneological and geothermal uses.</p>

Fast incremental discovery of pointwise order dependencies

2020 ◽  
Vol 13 (10) ◽  
pp. 1669-1681
Author(s):  
Zijing Tan ◽  
Ai Ran ◽  
Shuai Ma ◽  
Sheng Qin
Keyword(s):  
Real Life ◽  
Effective Algorithm ◽  
Data Set ◽  
Ordering Semantics ◽  
Synthetic Datasets ◽  
Indexing Technique

Pointwise order dependencies (PODs) are dependencies that specify ordering semantics on attributes of tuples. POD discovery refers to the process of identifying the set Σ of valid and minimal PODs on a given data set D. In practice D is typically large and keeps changing, and it is prohibitively expensive to compute Σ from scratch every time. In this paper, we make a first effort to study the incremental POD discovery problem, aiming at computing changes ΔΣ to Σ such that Σ ⊕ ΔΣ is the set of valid and minimal PODs on D with a set Δ D of tuple insertion updates. (1) We first propose a novel indexing technique for inputs Σ and D. We give algorithms to build and choose indexes for Σ and D , and to update indexes in response to Δ D. We show that POD violations w.r.t. Σ incurred by Δ D can be efficiently identified by leveraging the proposed indexes, with a cost dependent on log (| D |). (2) We then present an effective algorithm for computing ΔΣ, based on Σ and identified violations caused by Δ D. The PODs in Σ that become invalid on D + Δ D are efficiently detected with the proposed indexes, and further new valid PODs on D + Δ D are identified by refining those invalid PODs in Σ on D + Δ D. (3) Finally, using both real-life and synthetic datasets, we experimentally show that our approach outperforms the batch approach that computes from scratch, up to orders of magnitude.

scholarly journals Modified Energy Statistic for Unsupervised Anomaly Detection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rupam Mukherjee
Keyword(s):  
Anomaly Detection ◽  
Real Life ◽  
Industrial Applications ◽  
Test Point ◽  
Distance Metrics ◽  
Data Set ◽  
Energy Distance ◽  
New Energy ◽  
Statistical Distance ◽  
Computational Simplicity

For prognostics in industrial applications, the degree of anomaly of a test point from a baseline cluster is estimated using a statistical distance metric. Among different statistical distance metrics, energy distance is an interesting concept based on Newton’s Law of Gravitation, promising simpler computation than classical distance metrics. In this paper, we review the state of the art formulations of energy distance and point out several reasons why they are not directly applicable to the anomaly-detection problem. Thereby, we propose a new energy-based metric called the P-statistic which addresses these issues, is applicable to anomaly detection and retains the computational simplicity of the energy distance. We also demonstrate its effectiveness on a real-life data-set.

scholarly journals Concurrent Measurement of Global Positioning System and Event-Based Physical Activity Data: A Methodological Framework for Integration

2020 ◽  
pp. 1-14
Author(s):  
Anna M.J. Iveson ◽  
Malcolm H. Granat ◽  
Brian M. Ellis ◽  
Philippa M. Dall
Keyword(s):  
Physical Activity ◽  
Outcome Measures ◽  
Global Positioning System ◽  
Gps Data ◽  
Positioning System ◽  
Activity Data ◽  
Data Set ◽  
Physical Activity Data ◽  
Global Positioning ◽  
Event Based

Objective: Global positioning system (GPS) data can add context to physical activity data and have previously been integrated with epoch-based physical activity data. The current study aimed to develop a framework for integrating GPS data and event-based physical activity data (suitable for assessing patterns of behavior). Methods: A convenience data set of concurrent GPS (AMOD) and physical activity (activPAL) data were collected from 69 adults. The GPS data were (semi)regularly sampled every 5 s. The physical activity data output was presented as walking events, which are continuous periods of walking with a time-stamped start time and duration (to nearest 0.1 s). The GPS outcome measures and the potential correspondence of their timing with walking events were identified and a framework was developed describing data integration for each combination of GPS outcome and walking event correspondence. Results: The GPS outcome measures were categorized as those deriving from a single GPS point (e.g., location) or from the difference between successive GPS points (e.g., distance), and could be categorical, scale, or rate outcomes. Walking events were categorized as having zero (13% of walking events, 3% of walking duration), or one or more (52% of walking events, 75% of walking duration) GPS points occurring during the event. Additionally, some walking events did not have GPS points suitably close to allow calculation of outcome measures (31% of walking events, 22% of walking duration). The framework required different integration approaches for each GPS outcome type, and walking events containing zero or more than one GPS points.

scholarly journals Calibration-Based Estimators using Different Distance Measures under Two Auxiliary Variables: A Comparative Study

2021 ◽  
Vol 19 (1) ◽  
pp. 2-20
Author(s):  
Piyush Kant Rai ◽  
Alka Singh ◽  
Muhammad Qasim
Keyword(s):  
Mean Squared Error ◽  
Real Life ◽  
Distance Functions ◽  
Distance Measures ◽  
Auxiliary Variables ◽  
Data Set ◽  
Life Data ◽  
Squared Error ◽  
Real Life Data ◽  
Relative Root

This article introduces calibration estimators under different distance measures based on two auxiliary variables in stratified sampling. The theory of the calibration estimator is presented. The calibrated weights based on different distance functions are also derived. A simulation study has been carried out to judge the performance of the proposed estimators based on the minimum relative root mean squared error criterion. A real-life data set is also used to confirm the supremacy of the proposed method.

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