| Title |
Enabling Green Crowdsourced Social Delivery Networks in Urban Communities |
| ID_Doc |
42985 |
| Authors |
Choi, K; Bedogni, L; Levorato, M |
| Title |
Enabling Green Crowdsourced Social Delivery Networks in Urban Communities |
| Year |
2022 |
| Published |
Sensors, 22, 4 |
| DOI |
10.3390/s22041541 |
| Abstract |
With the ever-increasing popularity of wearable devices, data on the time and location of popular walking, running, and bicycling routes is expansive and growing rapidly. These data are currently used primarily for route discovery and mobile context awareness, as it provides precise and updated information about urban dynamics. We leverage these data to build ad hoc transportation flows, and we present a novel model that creates delivery networks from these zero-emission transportation flows. We evaluate the model using data from two popular datasets, and our results indicate that such networks are indeed possible, and can help reduce traffic, emissions, and delivery times. Moreover, we demonstrate how our results can be consistently reproduced in different cities with different subsets of carriers. We then extend our work into predicting routes of vehicles, hence possible delivery flows, based on the traces history. We conclude this paper by laying the groundwork for a future real-world study. |
| Author Keywords |
mobile crowdsensing; smart city; performance evaluation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000765521600001 |
| WoS Category |
Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation |
| Research Area |
Chemistry; Engineering; Instruments & Instrumentation |
| PDF |
https://www.mdpi.com/1424-8220/22/4/1541/pdf?version=1645089232
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