scholarly journals Methodology for Extracting Potential Customized Bus Routes Based on Bus Smart Card Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2224 ◽  
Author(s):  
Jing Li ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Qi Ouyang
Keyword(s):  
Smart Card ◽  
Traffic Congestion ◽  
Spatial Clustering ◽  
Extraction Process ◽  
Methodological Framework ◽  
Transit Service ◽  
Dbscan Algorithm ◽  
Smart Card Data ◽  
Division Process

To alleviate traffic congestion and traffic-related environmental pollution caused by the increasing numbers of private cars, public transport (PT) is highly recommended to travelers. However, there is an obvious contradiction between the diversification of travel demands and simplification of PT service. Customized bus (CB), as an innovative supplementary mode of PT service, aims to provide demand-responsive and direct transit service to travelers with similar travel demands. But how to obtain accurate travel demands? It is passive and limited to conducting online surveys, additionally inefficient and costly to investigate all the origin-destinations (ODs) aimlessly. This paper proposes a methodological framework of extracting potential CB routes from bus smart card data to provide references for CB planners to conduct purposeful and effective investigations. The framework consists of three processes: trip reconstruction, OD area division and CB route extraction. In the OD area division process, a novel two-step division model is built to divide bus stops into different areas. In the CB route extraction process, two spatial-temporal clustering procedures and one length constraint are implemented to cluster similar trips together. An improved density-based spatial clustering of application with noise (DBSCAN) algorithm is used to complete these procedures. In addition, a case study in Beijing is conducted to demonstrate the effectiveness of the proposed methodological framework and the resulting analysis provides useful references to CB planners in Beijing.

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>

Impacts of Transit in National Parks and Gateway Communities

2005 ◽  
Vol 1931 (1) ◽  
pp. 129-136 ◽  
Author(s):  
Anne E. Dunning
Keyword(s):  
National Parks ◽  
Traffic Congestion ◽  
National Park ◽  
Public Lands ◽  
Transit Planning ◽  
Transit Service ◽  
Planning Decisions ◽  
Gateway Communities ◽  
Park Service

The U.S. National Park Service has worked to reduce traffic congestion in parks through a transit service. Consistently, communities around parks that are planning for transit want to know what effects they will experience from introductions or changes in service. This research developed a comprehensive understanding of the encountered effects. Seven case study investigations revealed no clear patterns in visitation changes at parks with recent transit initiatives. Circulation and economic impacts depended on local situations and followed from transit planning decisions. The case study findings provide models that can inform planning for continued expansion of transit on public lands.

Mapping multimodal random accessibility using smart card data: a case study of bus and subway stations in Beijing

2021 ◽  
pp. 1-24
Author(s):  
Wentao Yu ◽  
Huijun Sun ◽  
Jianjun Wu ◽  
Ying Lv ◽  
Xiaoting Shang ◽  
...  
Keyword(s):  
Smart Card ◽  
Smart Card Data ◽  
Subway Stations

Analysis of Passenger Transfer Patterns Based on Transit Smart Card Data in Seoul

2013 ◽  
Author(s):  
Ji-Young Song ◽  
Jin-Ki Eom
Keyword(s):  
Smart Card ◽  
Regional Level ◽  
Long Distance ◽  
Transit Service ◽  
Smart Card Data ◽  
Average Travel Time ◽  
Time Distance ◽  
Transit Connectivity ◽  
The Relationship ◽  
Transit Smart Card

This study analyzes the transfer patterns of passengers in Seoul based on transit smart card data that was observed in 2010. The smart card recorded maximum four times of transfer and reported that approximately 90% of trips were less than one transfer and the remains were more than 2 transfers. We focus on trips with more than 3 transfers to figure out the relationship between transit service and regional connectivity. The results show that the average travel time, distance, fare are 45 minutes, 18.3km, and 1,119(KW) respectively. We develop a map for investigating transfer patterns at a regional level (dong and gu). By doing this, three types of transfers are observed as: 1) trips of which origin and destination is either same or near, 2) trips with middle distance (shorter then 6km), and 3) long distance (from 6km to 12km) trip with low transit connectivity.

Multipurpose Smart Card Data: Case Study of Shizuoka, Japan

2016 ◽  
pp. 113-131
Author(s):  
T Nakamura ◽  
N Uno ◽  
J Schmöcker ◽  
T Iwamoto
Keyword(s):  
Smart Card ◽  
Smart Card Data

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.

Constructing Transit Origin–Destination Matrices with Spatial Clustering

2017 ◽  
Vol 2652 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Ding Luo ◽  
Oded Cats ◽  
Hans van Lint
Keyword(s):  
Spatial Clustering ◽  
Optimal Number ◽  
Spatial Distance ◽  
Transit Systems ◽  
Distance Information ◽  
Demand Prediction ◽  
Smart Card Data ◽  
Using Data ◽  
Cluster Flow

So-called tap-in–tap-off smart card data have become increasingly available and popular as a result of the deployment of automatic fare collection systems on transit systems in many cities and areas worldwide. An opportunity to obtain much more accurate transit demand data than before has thus been opened to both researchers and practitioners. However, given that travelers in some cases can choose different origin and destination stations, as well as different transit lines, depending on their personal acceptable walking distances, being able to aggregate the demand of spatially close stations becomes essential when transit demand matrices are constructed. With the aim of investigating such problems using data-driven approaches, this paper proposes a k-means-based station aggregation method that can quantitatively determine the partitioning by considering both flow and spatial distance information. The method was applied to a case study of Haaglanden, Netherlands, with a specified objective of maximizing the ratio of average intra-cluster flow to average inter-cluster flow while maintaining the spatial compactness of all clusters. With a range of clustering of different K performed first using the distance feature, a distance-based metric and a flow-based metric were then computed and ultimately combined to determine the optimal number of clusters. The transit demand matrices constructed by implementing this method lay a foundation for further studies on short-term transit demand prediction and demand assignment.

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