scholarly journals Optimized Skip-Stop Metro Line Operation Using Smart Card Data

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Peitong Zhang ◽  
Zhanbo Sun ◽  
Xiaobo Liu
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Low Cost ◽  
Optimal Solution ◽  
Cost Savings ◽  
Passenger Demand ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Transit Agencies ◽  
The Right

Skip-stop operation is a low cost approach to improving the efficiency of metro operation and passenger travel experience. This paper proposes a novel method to optimize the skip-stop scheme for bidirectional metro lines so that the average passenger travel time can be minimized. Different from the conventional “A/B” scheme, the proposed Flexible Skip-Stop Scheme (FSSS) can better accommodate spatially and temporally varied passenger demand. A genetic algorithm (GA) based approach is then developed to efficiently search for the optimal solution. A case study is conducted based on a real world bidirectional metro line in Shenzhen, China, using the time-dependent passenger demand extracted from smart card data. It is found that the optimized skip-stop operation is able to reduce the average passenger travel time and transit agencies may benefit from this scheme due to energy and operational cost savings. Analyses are made to evaluate the effects of that fact that certain number of passengers fail to board the right train (due to skip operation). Results show that FSSS always outperforms the all-stop scheme even when most passengers of the skipped OD pairs are confused and cannot get on the right train.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

Choosing the Right Test (Influenza) Versus Respiratory Viral Panel With Faster Turnaround Time and Cost Savings

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S142-S143
Author(s):  
Bo Lin ◽  
Manan Christian ◽  
Margarita Kogan ◽  
Alejandro Zuretti
Keyword(s):  
Respiratory Infections ◽  
Medical Center ◽  
Low Cost ◽  
Cost Savings ◽  
Turnaround Time ◽  
Viral Pathogens ◽  
Highly Sensitive ◽  
Hospital Patients ◽  
Syncytial Virus ◽  
The Right

Abstract Introduction Respiratory infections are very common in hospital patients. Viral pathogens including influenza (Flu) and respiratory syncytial virus (RSV) are frequent causes. Respiratory viral panels (RVPs) have been routinely ordered in our institution with a turnaround time (TAT) of 48 hours at a cost of approximately $170/test. Meanwhile, Flu and RSV PCR are offered in house with a TAT of only 40 minutes and much lower cost ($40/test) than RVP. Here, we examined the optimization of use of these tests in our medical center. Methods Results of the specimens sent for RVP testing as well as their results from Flu/RSV PCR and the negative result rate were reviewed. The TAT and costs were compared between RVP and Flu/RSV PCR. Results We reviewed 69 specimens from MICU sent for RVP during 10/1/2018 to 1/31/2019. Total negative specimen rate was 74%. The specimens identified positive for Flu or RSV by RVP were also positive for in-house Flu/RSV PCR. Therefore, we have recommended clinicians to order in-house highly sensitive and specific Flu/RSV PCR first for faster TAT and cost saving. Since the recommendation, the number of RVP orders has dropped from 660 (January 2018) to 131 (January 2019), with savings of more than $80,000 in 1 month. Conclusion In-house Flu/RSV PCR test is highly sensitive and specific for identifying the common viral pathogens in patients with respiratory infection. It is fast and relatively low cost compared to RVP and should be considered as an effective first-line test.

Low-Cost Adhesives for Temporary Substrate Support

2012 ◽  
Vol 2012 (DPC) ◽  
pp. 1-60
Author(s):  
John Moore ◽  
Jared Pettit
Keyword(s):  
Current Practice ◽  
Low Cost ◽  
Cost Savings ◽  
Specific Design ◽  
Substrate Support ◽  
Wide Range ◽  
Rapid Removal ◽  
Green Factory ◽  
The Right ◽  
Simple Materials

Temporary adhesives are a key part to 3DIC integration. Choosing the right adhesive is critical as it defines your process, tooling needs, and by virtue of its chemistry, will control throughput and yield. Although several products and tooling exist in the market, few offer a clear path to achieve HVM at an affordable cost. [1] A wide range in materials and processes are available, most which can be tailored to a specific design or tooling objective. Multiple options in adhesives allow grinding and polishing to <20um, protection during backside processing (e.g. TSVs), thermal resistance to >400C, [2] CVD acceptance (Fig. 1), and rapid removal with cleans on a film frame (Fig. 2). Using batch processing, throughput is increased by a factor of 5 while cost is reduced by 50%, suggesting a COO that is 10% relative to current practice. Instituting simple materials and processes matched to a customer's design will provide benefits beyond cost savings, including “green factory” certification. This presentation reviews several current practices in the market and contrasts these options with alternative low-cost adhesives and processes that are tuned to a customer's product design and tooling. Examples include excerpts from handling thin substrates in semiconductor, solar, and TFT/LCD fabrication lines.

Estimation of Platform Waiting Time Distribution Considering Service Reliability Based on Smart Card Data and Performance Reports

2017 ◽  
Vol 2652 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Amr M. Wahaballa ◽  
Fumitaka Kurauchi ◽  
Toshiyuki Yamamoto ◽  
Jan-Dirk Schmöcker
Keyword(s):  
Travel Time ◽  
Waiting Time ◽  
Smart Card ◽  
Time Distribution ◽  
Goodness Of Fit ◽  
Likelihood Method ◽  
Service Reliability ◽  
Waiting Time Distribution ◽  
Smart Card Data ◽  
Performance Reports

The estimation of platform waiting time has so far received little attention. This research aimed to estimate platform waiting time distributions on the London Underground, considering travel time variability by using smart card data that were supplemented by performance reports. The on-train and ticket gate to platform walking times were assumed to be normally distributed and were matched with the trip time recorded by the smart cards to estimate the platform waiting time distribution. The stochastic frontier model was used, and its parameters were estimated by the maximum likelihood method. The cost frontier function was used to represent the relation between the travel time recorded in the smart card data as an output and the on-train time and walking time between the ticket gate and the platform as inputs. All estimated parameters were statistically significant, as shown by p-values. Comparing the travel time values estimated by the proposed model with the times recorded recorded in the smart card data shows a goodness-of-fit coefficient of determination of more than 95%. The estimation proved to have quick convergence and was computationally efficient. The results could facilitate improvements in transit service reliability analysis and passenger flow assignment. Matching the obtained distributions with the observed smart card data will help with estimating route choice behavior that can validate current transit assignment models.

scholarly journals Passenger Travel Regularity Analysis Based on a Large Scale Smart Card Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Yuan Ren ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Large Scale ◽  
Spatial Clustering ◽  
Reconstruction Algorithm ◽  
Travel Patterns ◽  
Node Importance ◽  
The People ◽  
Passenger Travel ◽  
Smart Card Data

Analysis of passenger travel habits is always an important item in traffic field. However, passenger travel patterns can only be watched through a period time, and a lot of people travel by public transportation in big cities like Beijing daily, which leads to large-scale data and difficult operation. Using SPARK platform, this paper proposes a trip reconstruction algorithm and adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel patterns of each Smart Card (SC) user in Beijing. For the phenomenon that passengers swipe cards before arriving to avoid the crowd caused by the people of the same destination, the algorithm based on passenger travel frequent items is adopted to guarantee the accuracy of spatial regular patterns. At last, this paper puts forward a model based on density and node importance to gather bus stations. The transportation connection between areas formed by these bus stations can be seen with the help of SC data. We hope that this research will contribute to further studies.

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.

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