Integrating Traffic Dynamics and Emissions Modeling: From Classical Approaches to Data-Driven Futures
A persistent disconnect between traffic modeling and environmental emissions modeling, stemming from their independent disciplinary evolution, continues to impede the accurate integration of traffic dynamics into emissions prediction. This misalignment frequently results in inconsistencies in simula...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/6/695 |
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| author | Xin Wang Xianfei Yue Jianchang Huang Shubin Li |
| author_facet | Xin Wang Xianfei Yue Jianchang Huang Shubin Li |
| author_sort | Xin Wang |
| collection | DOAJ |
| description | A persistent disconnect between traffic modeling and environmental emissions modeling, stemming from their independent disciplinary evolution, continues to impede the accurate integration of traffic dynamics into emissions prediction. This misalignment frequently results in inconsistencies in simulation outputs and limits the reliability of traffic-based environmental assessments. From a traffic engineering perspective, it is essential that emissions models more precisely reflect real-world vehicle behavior and the complexities of dynamic traffic conditions. In addressing this gap, the present study offers a comprehensive and critical review of the integration between traffic dynamics and emissions modeling across macro-, meso-, and micro-scales. Emissions models are systematically classified into four categories—driving cycle-based, speed–acceleration matrix-based, engine power-based, and vehicle-specific power-based—and assessed in terms of their responsiveness to dynamic traffic inputs. Furthermore, the review highlights the emerging challenges associated with connected and autonomous vehicles and AI-driven modeling techniques, underscoring the urgent need for modular, real-time adaptable modeling frameworks. Through a detailed examination of parameter requirements, data integration issues, and validation challenges, this study provides structured insights to guide the development of scientifically robust and operationally relevant emissions models tailored to the demands of increasingly complex and intelligent transportation systems. |
| format | Article |
| id | doaj-art-c5b01f65c84049fc97942adfe7f1feb3 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-c5b01f65c84049fc97942adfe7f1feb32025-08-20T03:26:49ZengMDPI AGAtmosphere2073-44332025-06-0116669510.3390/atmos16060695Integrating Traffic Dynamics and Emissions Modeling: From Classical Approaches to Data-Driven FuturesXin Wang0Xianfei Yue1Jianchang Huang2Shubin Li3Shandong Engineering Research Center of Intelligent Traffic Control and Guidance Technology for Public Security, Shandong Police College, Jinan 250014, ChinaShandong Engineering Research Center of Intelligent Traffic Control and Guidance Technology for Public Security, Shandong Police College, Jinan 250014, ChinaSchool of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, ChinaShandong Engineering Research Center of Intelligent Traffic Control and Guidance Technology for Public Security, Shandong Police College, Jinan 250014, ChinaA persistent disconnect between traffic modeling and environmental emissions modeling, stemming from their independent disciplinary evolution, continues to impede the accurate integration of traffic dynamics into emissions prediction. This misalignment frequently results in inconsistencies in simulation outputs and limits the reliability of traffic-based environmental assessments. From a traffic engineering perspective, it is essential that emissions models more precisely reflect real-world vehicle behavior and the complexities of dynamic traffic conditions. In addressing this gap, the present study offers a comprehensive and critical review of the integration between traffic dynamics and emissions modeling across macro-, meso-, and micro-scales. Emissions models are systematically classified into four categories—driving cycle-based, speed–acceleration matrix-based, engine power-based, and vehicle-specific power-based—and assessed in terms of their responsiveness to dynamic traffic inputs. Furthermore, the review highlights the emerging challenges associated with connected and autonomous vehicles and AI-driven modeling techniques, underscoring the urgent need for modular, real-time adaptable modeling frameworks. Through a detailed examination of parameter requirements, data integration issues, and validation challenges, this study provides structured insights to guide the development of scientifically robust and operationally relevant emissions models tailored to the demands of increasingly complex and intelligent transportation systems.https://www.mdpi.com/2073-4433/16/6/695traffic emissions modelstraffic–emission integrationdriving cyclesvehicle-specific powerconnected and autonomous vehicles |
| spellingShingle | Xin Wang Xianfei Yue Jianchang Huang Shubin Li Integrating Traffic Dynamics and Emissions Modeling: From Classical Approaches to Data-Driven Futures Atmosphere traffic emissions models traffic–emission integration driving cycles vehicle-specific power connected and autonomous vehicles |
| title | Integrating Traffic Dynamics and Emissions Modeling: From Classical Approaches to Data-Driven Futures |
| title_full | Integrating Traffic Dynamics and Emissions Modeling: From Classical Approaches to Data-Driven Futures |
| title_fullStr | Integrating Traffic Dynamics and Emissions Modeling: From Classical Approaches to Data-Driven Futures |
| title_full_unstemmed | Integrating Traffic Dynamics and Emissions Modeling: From Classical Approaches to Data-Driven Futures |
| title_short | Integrating Traffic Dynamics and Emissions Modeling: From Classical Approaches to Data-Driven Futures |
| title_sort | integrating traffic dynamics and emissions modeling from classical approaches to data driven futures |
| topic | traffic emissions models traffic–emission integration driving cycles vehicle-specific power connected and autonomous vehicles |
| url | https://www.mdpi.com/2073-4433/16/6/695 |
| work_keys_str_mv | AT xinwang integratingtrafficdynamicsandemissionsmodelingfromclassicalapproachestodatadrivenfutures AT xianfeiyue integratingtrafficdynamicsandemissionsmodelingfromclassicalapproachestodatadrivenfutures AT jianchanghuang integratingtrafficdynamicsandemissionsmodelingfromclassicalapproachestodatadrivenfutures AT shubinli integratingtrafficdynamicsandemissionsmodelingfromclassicalapproachestodatadrivenfutures |