Urban development and social collaboration highlight the demand for swift and smart transportation systems as a channel to bring together people and resources for innovation and production. Most straightforward, smartness means to accommodate people’s travel convenience and preference. Various concepts and related methods have been proposed, including demand responsive transport (Ronald et al., 2017), collaborative activity based ridesharing (Wang et al., 2018), the connection to mass transit (Chen and Nie, 2017), different types of shared mobility (e.g., Furuhata et al., 2013; Hrnčíř et al., 2015; Shen et al., 2016), to name a few. Additionally, we need to consider the cost of the infrastructure. What is an optimized design of a public transit network? And what is a proper supply/distribution of shared bicycles or vehicles? The logistics of goods and products is another dimension of smart transportation. As a consequence of e-commerce, the demand for smart delivery of meals, fresh products, and online shopping items is exploding, which even inspires new modes like shared ride of goods and people.
In spite of the variety of concepts and approaches, “smart” generally means to be human-centered in an economical way, which is to satisfy what people want to a certain extent and hopefully to predict what people want for preparation with an optimized cost. The foundation of smart transportation is rooted in the understanding of human travel behaviors, its patterns, the movement context and infrastructure. Because of the complexity, new methods and views should be developed.
In parallel with remote sensing, social sensing is the term that represents a series of approaches to apply geospatial big data for socioeconomic characteristics (Liu et al., 2015). It offers a new view to understand human behaviors in terms of not only data analysis but also data source: to apply user generated data in addition to remote sensing data for socioeconomic and human-centered phenomena. This is a promising direction for smart transportation development that will benefit from the collision of minds.
We organize this session to call for papers that leverage methods under the overarching term social sensing in the field of transportation. Interested topics include but are not limited to:
- Empirical analysis with geospatial big data for smart transportation
- Methodological challenges and developments of analyzing social sensing data for transportation
- New social sensing data source or format for transportation study
- Understanding urban functions for activity scheduling and travel demand analysis
- Insights into travel behavior patterns, perceptions, and preferences
- Heuristics from social sensing data mining for scenario planning and design
- Heuristics for optimization of goods and people transportation systems with social sensing data
Zhou Huang, email@example.com, Peking University;
Yaoli Wang, firstname.lastname@example.org, Peking University;
Tao Cheng, email@example.com, University College London;
Yu Liu, firstname.lastname@example.org, Peking University.
Chen, P. (Will), Nie, Y. (Marco), 2017. Connecting e-hailing to mass transit platform: Analysis of relative spatial position. Transp. Res. Part C Emerg. Technol. 77, 444–461. https://doi.org/10.1016/j.trc.2017.02.013
Furuhata, M., Dessouky, M., Ordóñez, F., Brunet, M.-E., Wang, X., Koenig, S., 2013. Ridesharing: The state-of-the-art and future directions. Transp. Res. Part B Methodol. 57, 28–46.
Hrnčíř, J., Rovatsos, M., Jakob, M., 2015. Ridesharing on timetabled transport services: A multiagent planning approach. J. Intell. Transp. Syst. 19, 89–105. https://doi.org/10.1080/15472450.2014.941759
Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., Chi, G., Shi, L., 2015. Social sensing: A new approach to understanding our socioeconomic environments. Ann. Assoc. Am. Geogr. 105, 512–530.
Ronald, N., Navidi, Z., Wang, Y., Rigby, M., Jain, S., Kutadinata, R., Thompson, R., Winter, S., 2017. Mobility patterns in shared, autonomous, and connected urban transport, in: Disrupting Mobility. Springer International Publishing, Berlin, pp. 275–290.
Shen, B., Huang, Y., Zhao, Y., 2016. Dynamic ridesharing. SIGSPATIAL Spec. 7, 3–10. https://doi.org/10.1145/2876480.2876483
Wang, Y., Winter, S., Tomko, M., 2018. Collaborative activity-based ridesharing. J. Transp. Geogr. 72, 131–138. https://doi.org/10.1016/j.jtrangeo.2018.08.013
|Presenter||Ke Mai, Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University; The Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Wei Tu*, Shenzhen University, Yatao Zhang, Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University; The Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Yang Yue, Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University; The Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Incorporating recharging and cruising recommendation for urban taxies leveraging massive trajectories||15||12:00 AM|
|Presenter||Luyu Liu*, The Ohio State University, Harvey J Miller, The Ohio State University, Is real-time transit information helpful? Analyzing the impacts of public transit real-time information on users waiting time||15||12:00 AM|
|Presenter||Junchuan Fan*, Center for Geospatial Information Science, UMD, Kathleen Stewart, University of Maryland, College Park, Detecting collective human movement dynamics during large-scale events using big geosocial data analytics||15||12:00 AM|
|Presenter||Ran Tao*, University of South Florida, Jean-Claude Thill, University of North Carolina at Charlotte, Measuring Spatial Association for Bivariate Origin-Destination Flow Data||15||12:00 AM|
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