Do Androids Dream of Electric Cars? Public Transit in the Age of Google, Uber, and Elon Musk by James Wilt

2021 ◽  
Vol 87 (1) ◽  
pp. 203-205
Author(s):  
Joël Laforest
Keyword(s):  
2020 ◽  
Vol 41 (2) ◽  
pp. 134-159
Author(s):  
Jan Ploeger ◽  
Ruth Oldenziel

The search for “smart” or Information and Communication Technology (ICT) based mobility solutions goes back to at least the 1960s. The Provo anarchist Luud Schimmelpennink is well-known for designing mobility solutions and for being the driving force behind the 1965 “white-bike” experience. Less known is his 1968 project for shared electric cars (“Witkar”), which laid the foundations for the ICT-based bicycle sharing systems as we know today. By combining his talent for innovation with activism, he created a socially embedded design that could be part of the public transit system. Based on primary sources, we argue that these sociotechnical experiences paved the way for today’s mainstream bicycle sharing projects worldwide. We then show how since the 1990s, the Dutch railroad’s public transit bicycle (OV-fiets) has transformed Schimmelpennink’s original anarchist idea of bike sharing into a sustainable public transit system – a feat that has eluded other programmes worldwide: the integration of the bicycle’s share in a door-to-door experience.


CICTP 2020 ◽  
2020 ◽  
Author(s):  
Shenghui Zhao ◽  
Lishan Sun ◽  
Dewen Kong ◽  
Jinghan Cao ◽  
Yan Wang

Author(s):  
Jung-Hoon Cho ◽  
Seung Woo Ham ◽  
Dong-Kyu Kim

With the growth of the bike-sharing system, the problem of demand forecasting has become important to the bike-sharing system. This study aims to develop a novel prediction model that enhances the accuracy of the peak hourly demand. A spatiotemporal graph convolutional network (STGCN) is constructed to consider both the spatial and temporal features. One of the model’s essential steps is determining the main component of the adjacency matrix and the node feature matrix. To achieve this, 131 days of data from the bike-sharing system in Seoul are used and experiments conducted on the models with various adjacency matrices and node feature matrices, including public transit usage. The results indicate that the STGCN models reflecting the previous demand pattern to the adjacency matrix show outstanding performance in predicting demand compared with the other models. The results also show that the model that includes bus boarding and alighting records is more accurate than the model that contains subway records, inferring that buses have a greater connection to bike-sharing than the subway. The proposed STGCN with public transit data contributes to the alleviation of unmet demand by enhancing the accuracy in predicting peak demand.


2021 ◽  
Vol 286 ◽  
pp. 112166
Author(s):  
Mohammad Ali Sahraei ◽  
Emre Kuşkapan ◽  
Muhammed Yasin Çodur

2020 ◽  
Vol 54 (24) ◽  
pp. 15613-15621
Author(s):  
Derek V. Mallia ◽  
Logan E. Mitchell ◽  
Lewis Kunik ◽  
Ben Fasoli ◽  
Ryan Bares ◽  
...  

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