Estimation of transfer walking time distribution in multimodal public transport systems based on smart card data

2021 ◽  
Vol 132 ◽  
pp. 103332
Author(s):  
Morten Eltved ◽  
Philip Lemaitre ◽  
Niklas Christoffer Petersen

The urban population in 2014 accounted for 54% of the total global population, up from 34% in 1960, and continues to grow. The global urban population is expected to grow approximately 1.84%, 1.63% and 1.44% between 2015 and 2020, 2020 and 2025, and 2025 and 2030 respectively. This growing population puts pressure on government not only to accommodate the current and potential citizens but also provide them facilities and services for a better living standard. Providing a sustainable growing environment for the citizens is the biggest challenge for the government. As the populations increase, complexity network of transportation, water and sanitation, emergency services, etc. will increase many folds. SMART CITY Mission is being implemented to resolve this issue. As the cities turn smart, so should the transportation facilities. India on June 2018 had only 20 cities with populations of over 500,000 have organized public transport systems, pointing to the large gap to be bridged in their journey to turn smart. The aim of this paper is to examine the impact of smart card data from public transport for improving the predictions and planning of public transport usage and congestions. The mobile apps like M-Indicator, Google Maps don’t interlink, do not have a real time tracking of vehicles, fare distribution, congestion-based route mapping for public transportation. These factors are addressed in the paper with its advantages and disadvantages. This paper also talks about how information from smart card is to be extracted, how Big Data is to be managed and finally come to a smart, sustainable Urban Transit System. This paper also brings into light the data security issues and measures to curb those issues. This paper proposes and emphasizes on a single smart card for all modes of public transport


2020 ◽  
Vol 12 (12) ◽  
pp. 5010
Author(s):  
Pengfei Lin ◽  
Jiancheng Weng ◽  
Dimitrios Alivanistos ◽  
Siyong Ma ◽  
Baocai Yin

Understanding commuting patterns could provide effective support for the planning and operation of public transport systems. One-month smart card data and travel behavior survey data in Beijing were integrated to complement the socioeconomic attributes of cardholders. The light gradient boosting machine (LightGBM) was introduced to identify the commuting patterns considering the spatiotemporal regularity of travel behavior. Commuters were further divided into fine-grained clusters according to their departure time using the latent Dirichlet allocation model. To enhance the interpretation of the behavior patterns in each cluster, we investigated the relationship between the socioeconomic characteristics of the residence locations and commuter cluster distributions. Approximately 3.1 million cardholders were identified as commuters, accounting for 67.39% of daily passenger volume. Their commuting routes indicated the existence of job–house imbalance and excess commuting in Beijing. We further segmented commuters into six clusters with different temporal patterns, including two-peak, staggered shifts, flexible departure time, and single-peak. The residences of commuters are mainly concentrated in the low housing price and high or medium population density areas; subway facilities will promote people to commute using public transport. This study will help stakeholders optimize the public transport networks, scheduling scheme, and policy accordingly, thus ameliorating commuting within cities.


2019 ◽  
Vol 47 (5) ◽  
pp. 2337-2365 ◽  
Author(s):  
Anne Halvorsen ◽  
Haris N. Koutsopoulos ◽  
Zhenliang Ma ◽  
Jinhua Zhao

2021 ◽  
Vol 93 ◽  
pp. 103046
Author(s):  
Shasha Liu ◽  
Toshiyuki Yamamoto ◽  
Enjian Yao ◽  
Toshiyuki Nakamura

Author(s):  
Flavio Devillaine ◽  
Marcela Munizaga ◽  
Martin Trépanier

2006 ◽  
Vol 39 (3) ◽  
pp. 399-404 ◽  
Author(s):  
Bruno Agard ◽  
Catherine Morency ◽  
Martin Trépanier

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