A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning Techniques
The proliferation of e-commerce in recent years has been driven in part by the increasing ease of making purchases online and having them delivered directly to the consumer. However, these last-mile delivery logistics have become complex due to external factors (traffic, weather, etc.) affecting the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/10/5888 |
| _version_ | 1797601286600785920 |
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| author | Valeria Laynes-Fiascunari Edgar Gutierrez-Franco Luis Rabelo Alfonso T. Sarmiento Gene Lee |
| author_facet | Valeria Laynes-Fiascunari Edgar Gutierrez-Franco Luis Rabelo Alfonso T. Sarmiento Gene Lee |
| author_sort | Valeria Laynes-Fiascunari |
| collection | DOAJ |
| description | The proliferation of e-commerce in recent years has been driven in part by the increasing ease of making purchases online and having them delivered directly to the consumer. However, these last-mile delivery logistics have become complex due to external factors (traffic, weather, etc.) affecting the delivery routes’ optimization. Intelligent Transportation Systems (ITS) also have a challenge that contributes to the need of delivery companies for traffic sensors in urban areas. The main purpose of this paper is to propose a framework that closes the gap on accurate traffic prediction tailored for last-mile delivery logistics, leveraging social media analysis along with traditional methods. This work can be divided into two stages: (1) traffic prediction, which utilizes advanced deep learning techniques such as Graph Convolutional and Long-Short Term Memory Neural Networks, as well as data from sources such as social media check-ins and Collaborative Innovation Networks (COINs); and (2) experimentation in both short- and long-term settings, examining the interactions of traffic, social media, weather, and other factors within the model. The proposed framework allows for the integration of additional analytical techniques to further enhance vehicle routing, including the use of simulation tools such as agent-based simulation, discrete-event simulation, and system dynamics. |
| first_indexed | 2024-03-11T03:59:01Z |
| format | Article |
| id | doaj.art-cc23d8302c0f403ab2846bdc4e951c4d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:59:01Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cc23d8302c0f403ab2846bdc4e951c4d2023-11-18T00:17:15ZengMDPI AGApplied Sciences2076-34172023-05-011310588810.3390/app13105888A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning TechniquesValeria Laynes-Fiascunari0Edgar Gutierrez-Franco1Luis Rabelo2Alfonso T. Sarmiento3Gene Lee4Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32826, USAMassachusetts Institute of Technology, Center for Transportation and Logistics, Cambridge, MA 02142, USADepartment of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32826, USAGrupo de Investigación en Sistemas Logísticos, Facultad de Ingeniería, Campus del Puente del Común, Universidad de La Sabana, Km. 7, Autopista Norte, Chía 250001, ColombiaDepartment of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32826, USAThe proliferation of e-commerce in recent years has been driven in part by the increasing ease of making purchases online and having them delivered directly to the consumer. However, these last-mile delivery logistics have become complex due to external factors (traffic, weather, etc.) affecting the delivery routes’ optimization. Intelligent Transportation Systems (ITS) also have a challenge that contributes to the need of delivery companies for traffic sensors in urban areas. The main purpose of this paper is to propose a framework that closes the gap on accurate traffic prediction tailored for last-mile delivery logistics, leveraging social media analysis along with traditional methods. This work can be divided into two stages: (1) traffic prediction, which utilizes advanced deep learning techniques such as Graph Convolutional and Long-Short Term Memory Neural Networks, as well as data from sources such as social media check-ins and Collaborative Innovation Networks (COINs); and (2) experimentation in both short- and long-term settings, examining the interactions of traffic, social media, weather, and other factors within the model. The proposed framework allows for the integration of additional analytical techniques to further enhance vehicle routing, including the use of simulation tools such as agent-based simulation, discrete-event simulation, and system dynamics.https://www.mdpi.com/2076-3417/13/10/5888traffic predictionintelligent transportation systemsocial media analyticslast-mile delivery |
| spellingShingle | Valeria Laynes-Fiascunari Edgar Gutierrez-Franco Luis Rabelo Alfonso T. Sarmiento Gene Lee A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning Techniques Applied Sciences traffic prediction intelligent transportation system social media analytics last-mile delivery |
| title | A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning Techniques |
| title_full | A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning Techniques |
| title_fullStr | A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning Techniques |
| title_full_unstemmed | A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning Techniques |
| title_short | A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning Techniques |
| title_sort | framework for urban last mile delivery traffic forecasting an in depth review of social media analytics and deep learning techniques |
| topic | traffic prediction intelligent transportation system social media analytics last-mile delivery |
| url | https://www.mdpi.com/2076-3417/13/10/5888 |
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