scholarly journals A data-driven estimation method for potential passenger demand of last trains in metro based on external traffic data

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989835
Author(s):  
Wei Li ◽  
Qin Luo
Keyword(s):  
Smart Card ◽  
Real World ◽  
Estimation Method ◽  
Data Driven ◽  
Traffic Data ◽  
Passenger Flow ◽  
Passenger Demand ◽  
Total Potential ◽  
Input Condition ◽  
Smart Card Data

The last train problem for metro is especially important because the last trains are the last chances for many passengers to travel by metro; otherwise, they have to choose other traffic modes like taxis or buses. Among the problems, the passenger demand is a vital input condition for the optimization of last train transfers. This study proposes a data-driven estimation method for the potential passenger demand of last trains. Through the geographic information, external traffic data including taxi and bus are first analyzed separately to match the origin–destination passenger flow during the last train period. A solving solution for taxi and bus is then developed to estimate the potential passenger flow for all the transfer directions of the target stations. Combining the estimated potential passenger flow and the actual passenger flow obtained by metro smart card data, the total potential passenger demand of last trains is obtained. The effectiveness of the proposed method is evaluated using a real-world metro network. This research can provide important guidance and act as a technical reference for the metro operations on when to optimize the last train transfers.

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

scholarly journals Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoqing Dai ◽  
Lijun Sun ◽  
Yanyan Xu
Keyword(s):  
Model Selection ◽  
Smart Card ◽  
Probabilistic Model ◽  
Travel Demand ◽  
Combination Method ◽  
Support Vector ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Smart Card Data

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

Applying Automatic Fare Collection System to Estimate a Transit Passenger Trip Origin-Destination Matrix

2013 ◽  
Vol 655-657 ◽  
pp. 1373-1377
Author(s):  
Da Ming Li ◽  
Bo Shu
Keyword(s):  
Smart Card ◽  
Real World ◽  
Collection System ◽  
Od Estimation ◽  
Bus Stop ◽  
Demand Pattern ◽  
Smart Card Data ◽  
Proposed Model ◽  
The World ◽  
Transit Route

Automatic fare collection system (AFCS) has been widely used for public transport all over the world. However, the Origin-Destination (OD) matrix of each passenger, the most important information, cannot be collected from AFCS since only boarding information is recorded at each bus stop and alighting one is lost. This study develops passenger trajectories estimation to calculate OD matrix by using pre-processed smart card data from AFCS in one city. The results of a rigorous validation with on/off data from a real-world bus route reveal that the proposed model is quite effective and reliable on estimating the OD matrix for identification of the underlying demand pattern of a transit route. The results have shown that the OD estimation from the proposed algorithm match more than 75% with the actual OD pairs. During the peak hours, the matching rate can go up to 85%.

scholarly journals Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

2021 ◽  
Author(s):  
Christian Martin Mützel ◽  
Joachim Scheiner
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Mobility Patterns ◽  
Transit Planning ◽  
Transit Systems ◽  
Passenger Flow ◽  
Smart Card Data ◽  
Depth Analysis ◽  
Spatio Temporal ◽  
The Impact

AbstractModern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.

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