Crowd-sourced prediction of pedestrian congestion for bike navigation systems

Shoko Wakamiya, Yukiko Kawai, Hiroshi Kawasaki, Ryong Lee, Kazutoshi Sumiya, Toyokazu Akiyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

GPS-based navigation systems widely available on automobiles and smartphones nowadays are essential to find the best routes in the complicated urban space. However, it is still difficult for bikers to take full advantages of such navigation systems due to the lack of consideration on the different driving conditions. Generally, motorcyclists and cyclists take rides on narrow alleys and sidewalks which have a high risk of bumping against pedestrians. Therefore, it is necessary to find comfortable driving routes, also possibly avoiding areas congested by crowds. However, it is impractical to monitor crowd's existence everywhere at all times for such crowd-aware navigation. To overcome this limitation, we attempt to utilize location-based social network services where geo-tagged microblogs from massive crowd can be a good alternative source to measure pedestrian congestion in urban areas. In this paper, we introduce a route search method for bikers particularly to exploit crowd's volunteering reports being streamed via microblogs. In order to estimate human traffic from microblogs, we develop a crowd flow network which captures probable crowd movement on an urban network. We also examine the possible intersections which are expected to be highly congested based on the model. On the crowd flow network, we will find the best routes consisting of comfortable intersections and streets for the bike navigation systems.

Original languageEnglish
Title of host publicationProceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2014
EditorsAnas Basalamah, Chengyang Zhang, Abdeltawab Hendawi
PublisherAssociation for Computing Machinery, Inc
Pages25-32
Number of pages8
ISBN (Electronic)9781450331395
DOIs
Publication statusPublished - Nov 4 2014
Externally publishedYes
Event5th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2014 - Dallas, United States
Duration: Nov 4 2014 → …

Publication series

NameProceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2014

Other

Other5th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2014
CountryUnited States
CityDallas
Period11/4/14 → …

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All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design

Cite this

Wakamiya, S., Kawai, Y., Kawasaki, H., Lee, R., Sumiya, K., & Akiyama, T. (2014). Crowd-sourced prediction of pedestrian congestion for bike navigation systems. In A. Basalamah, C. Zhang, & A. Hendawi (Eds.), Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2014 (pp. 25-32). (Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS 2014). Association for Computing Machinery, Inc. https://doi.org/10.1145/2676552.2676562