scholarly journals TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data

2021 ◽  
Vol 2 (3) ◽  
pp. 1-21
Author(s):  
Xiancai Tian ◽  
Baihua Zheng ◽  
Yazhe Wang ◽  
Hsiao-Ting Huang ◽  
Chih-Chieh Hung
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Total Duration ◽  
The Other ◽  
Smart Card Data ◽  
Additional Input ◽  
Metro Systems ◽  
The City ◽  
Metro System ◽  
Inference Task

In this article, we target at recovering the exact routes taken by commuters inside a metro system that are not captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategically propose two inference tasks to handle the recovering, one to infer the travel time of each travel link that contributes to the total duration of any trip inside a metro network and the other to infer the route preferences based on historical trip records and the travel time of each travel link inferred in the previous inference task. As these two inference tasks have interrelationship, most of existing works perform these two tasks simultaneously. However, our solution TripDecoder adopts a totally different approach. TripDecoder fully utilizes the fact that there are some trips inside a metro system with only one practical route available. It strategically decouples these two inference tasks by only taking those trip records with only one practical route as the input for the first inference task of travel time and feeding the inferred travel time to the second inference task as an additional input, which not only improves the accuracy but also effectively reduces the complexity of both inference tasks. Two case studies have been performed based on the city-scale real trip records captured by the AFC systems in Singapore and Taipei to compare the accuracy and efficiency of TripDecoder and its competitors. As expected, TripDecoder has achieved the best accuracy in both datasets, and it also demonstrates its superior efficiency and scalability.

scholarly journals Understanding the integration of buses and metro systems using smart card data

2019 ◽  
Vol 2 ◽  
pp. 1-6
Author(s):  
Diao Lin ◽  
Ruoxin Zhu
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Pearson Correlation ◽  
Metro Station ◽  
Smart Card Data ◽  
Pearson Correlation Test ◽  
Metro Systems ◽  
The City ◽  
Afternoon Peak

<p><strong>Abstract.</strong> Buses are considered as an important type of feeder model for urban metro systems. It is important to understand the integration of buses and metro systems for promoting public transportation. Using smart card data generated by automatic fare collection systems, we aim at exploring the characteristics of bus-and-metro integration. Taking Shanghai as a case study, we first introduced a rule-based method to extract metro trips and bus-and-metro trips from the raw smart card records. Based on the identified trips, we conducted three analyses to explore the characteristics of bus-and-metro integration. The first analysis showed that 46% users have at least two times of using buses to access metro stations during five weekdays. By combining the ridership of metro and bus-and-metro, the second analysis examined how the share of buses as the feeder mode change across space and time. Results showed that the share of buses as the feeder mode in morning peak hours is much larger than in afternoon peak hours, and metro stations away from the city center tend to have a larger share. Pearson correlation test was employed in the third analysis to explore the factors associated with the ratios of bus-and-metro trips. The metro station density and access metro duration are positively associated with the ratios. The number of bus lines around 100&amp;thinsp;m to 400&amp;thinsp;m of metro stations all showed a negative association, and the coefficient for 200&amp;thinsp;m is the largest. In addition, the temporal differences of the coefficients also suggest the importance of a factor might change with respect to different times. These results enhanced our understanding of the integration of buses and metro systems.</p>

scholarly journals Using Smart Card Data of Metro Passengers to Unveil the Urban Spatial Structure: A Case Study of Xi’an, China

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guohong Cheng ◽  
Shichao Sun ◽  
Linlin Zhou ◽  
Guanzhong Wu
Keyword(s):  
Smart Card ◽  
Policy Implications ◽  
Transport Sector ◽  
Urban Structure ◽  
Urban Spatial Structure ◽  
Smart Card Data ◽  
Western Area ◽  
Metro Systems ◽  
The City

This study adopted smart card data collected from metro systems to identify city centers and illustrate how city centers interacted with other regions. A case study of Xi’an, China, was given. Specifically, inflow and outflow patterns of metro passengers were characterized to measure the degree of population agglomeration of an area, i.e., the centricity of an area. On this basis, in order to overcome the problem of determining the boundaries of the city centers, Moran’s I was adopted to examine the spatial correlation between the inflow and outflow of ridership of adjacent areas. Three residential centers and two employee centers were identified, which demonstrated the polycentricity of urban structure of Xi’an. With the identified polycenters, the dominant spatial connections with each city center were investigated through a multiple linkage analysis method. The results indicated that there were significant connections between residential centers and employee centers. Moreover, metro passengers (commuters mostly) flowing into the identified employee centers during morning peak-hours mainly came from the northern and western area of Xi’an. This was consistent with the interpretation of current urban planning, which validated the effectiveness of the proposed methods. Policy implications were provided for the transport sector and public transport operators.

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.

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.

Evaluating the Dynamic Accessibility of Metro Systems in Overcrowded Conditions

2020 ◽  
Vol 2674 (11) ◽  
pp. 42-52
Author(s):  
Wei Li ◽  
Qin Luo ◽  
Samwel Zephaniah ◽  
Zhaoran Liu
Keyword(s):  
Travel Time ◽  
Evaluation Model ◽  
Transportation Systems ◽  
Dynamic Nature ◽  
Transportation Service ◽  
Static Approach ◽  
Metro Systems ◽  
Metro System ◽  
Metro Network

Metro accessibility is a concept used to describe the dynamic accessibility index of metro transportation systems. Most studies on accessibility apply a static approach, which overlooks the index fluctuations throughout the day. While it is possible to represent the accessibility using a single index, the ability to capture the dynamic nature of the index is important in understanding the quality of a transportation service and identify actionable aspects for improvement. For example, when the metro system is overcrowded and passengers fail to board the next arriving train, they instead wait for the subsequent train. As a result, the accessibility of the system is deemed to be lower than when its condition is uncrowded. This paper analyzes and integrates two aspects of metro accessibility: the performance of metro networks; and the dynamics of train schedules. An evaluation model in the form of a gravity model is proposed to measure the accessibility between each pair of stations with the dynamic travel time. This allows us to obtain profiles that highlight the daily variations in accessibility in the metro network and to identify the influence of congestion among stations. The effectiveness of the proposed evaluation model is validated using the metro network of Shenzhen in China. Results show that the proposed methodology provides useful information for passengers and travel guides as well as technical reference for the metro operation guidelines that are used to compile train schedules.

scholarly journals Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

2017 ◽  
Vol 18 (4) ◽  
pp. 790-801 ◽  
Author(s):  
Juanjuan Zhao ◽  
Fan Zhang ◽  
Lai Tu ◽  
Chengzhong Xu ◽  
Dayong Shen ◽  
...  
Keyword(s):  
Smart Card ◽  
Route Choice ◽  
Smart Card Data ◽  
Choice Pattern ◽  
Metro Systems

scholarly journals Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Li ◽  
Qin Luo ◽  
Qing Cai ◽  
Xiongfei Zhang
Keyword(s):  
Smart Card ◽  
Route Choice ◽  
Time Constraints ◽  
Peak Period ◽  
Smart Card Data ◽  
Noise Data ◽  
Metro System ◽  
Walking Time ◽  
Operation And Management

The metro passenger route choice, influenced by both train schedule and time constraints, is important to metro operation and management. Smart card data (Automatic Fare Collection (AFC) data in metro system) including inbound and outbound swiping time are useful for analysis of the characteristics of passengers’ route choices in metro while they could not reflect the property of train schedule directly. Train schedule is used in this paper to trim smart card data through removing inbound and outbound walking time to/from platforms and waiting time. Thus, passengers’ pure travel time in accord with trains’ arrival and departure can be obtained. Synchronous clustering (SynC) algorithm is then applied to analyze these processed data to calculate passenger route choice probability. Finally, a case study was conducted to illustrate the effectiveness of the proposed algorithm. Results showed the proposed algorithm works well to analyze metro passenger route choice. It was shown that passenger route choice during both peak period and flat period could be clustered automatically, and noise data are isolated. The probability of route choice calculated through SynC algorithm can be used to revise traditional model results.

scholarly journals Measurement of Opportunity Cost of Travel Time for Predicting Future Residential Mobility Based on the Smart Card Data of Public Transportation

2018 ◽  
Author(s):  
Takashi Nicholas Maeda ◽  
Junichiro Mori ◽  
Masanao Ochi ◽  
Tetsuo Sakimoto ◽  
Ichiro Sakata
Keyword(s):  
Urban Development ◽  
Travel Time ◽  
Residential Mobility ◽  
Smart Card ◽  
Public Transportation ◽  
Opportunity Cost ◽  
Travel Cost Method ◽  
Causal Influence ◽  
Mobility Data ◽  
Smart Card Data

This study attempts to investigate a method for creating an index from mobility data that not only correlates with the number of people who relocate to a place but also has causal influence on the number of such individuals. By creating an index based on human mobility data, it becomes possible to predict the influence of urban development on future residential movements. In this paper, we propose a method called the travel cost method for multiple places (TCM4MP) by extending the conventional travel cost method (TCM). We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of a neighborhood. However, conventional TCM does not assume that the opportunity cost of travel time varies according to the departure place. In this paper, TCM4MP is proposed to estimate the opportunity cost of travel time with respect to the departure place. We consider such estimation to be possible due to the use of massive mobility data. We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of the neighborhood. Therefore, we consider that the opportunity cost of travel time has a causal influence on future residential mobility. In this paper, the validity of the proposed method is tested using the smart card data of public transportation in Western Japan. Our proposed method is beneficial for urban planners in estimating the effects of urban development and detecting the shrinkage and growth of a population.

Development of Estimating Methodology for Transit Accessibility Using Smart Card Data

2021 ◽  
pp. 036119812110275
Author(s):  
Hyunsoo Yun ◽  
Eun Hak Lee ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Spatial Distribution ◽  
Travel Time ◽  
Smart Card ◽  
Explanatory Variable ◽  
Travel Information ◽  
Transit Network ◽  
Important Index ◽  
Transit Accessibility ◽  
Smart Card Data ◽  
Two Factors

Transit accessibility is an explanatory variable evaluating the mobility of a region in consideration of the connectivity and demand among the regions, which has been used for an important index to determine transport policy on the transit network. This study aims to develop an accessibility index considering the two factors with a demand-weighted approach, that is, impedance and attraction level. Two variables, travel time and the ratio of trips, are employed to calculate the accessibility index, and comparative assessments between zones are conducted. The application of smart card data makes it possible to analyze travel information and reflect them empirically in the model. This study identifies zones with vulnerable accessibility and suggests criteria for transit investment plans with two aspects, that is, intensive transit area and spatial distribution of the accessibility index. These aspects contribute to transit planners by suggesting transit investment criteria and comprehensible statistics to evaluate accessibility. Since zones with low accessibility indexes are identified as being vulnerable to access from other zones, policymakers should focus on those zones to improve the overall transit network.

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