Area Occupancy-Based Adaptive Density Estimation for Mixed Road Traffic
Managing congestion in mixed traffic conditions, characterized by heterogeneous and lane-less traffic, is a challenging task. Traditionally density, defined as the number of vehicles in a road stretch, is used to quantify congestion. However, direct measurement of density is difficult and hence is u...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8946555/ |
| _version_ | 1819159017891561472 |
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| author | Reenu George Lelitha Devi Vanajakshi Shankar C. Subramanian |
| author_facet | Reenu George Lelitha Devi Vanajakshi Shankar C. Subramanian |
| author_sort | Reenu George |
| collection | DOAJ |
| description | Managing congestion in mixed traffic conditions, characterized by heterogeneous and lane-less traffic, is a challenging task. Traditionally density, defined as the number of vehicles in a road stretch, is used to quantify congestion. However, direct measurement of density is difficult and hence is usually estimated from other variables. In this paper, a relationship is derived between traffic density and area occupancy, a variable that can incorporate heterogeneity and lane-less movement. Using the derived density-area occupancy relation, a non-continuum macroscopic single state linear time varying model was developed. Estimation of density was done by using the Kalman filtering technique and corroborated with simulated density. The need for dynamic estimation is motivated by evaluating the performance of two static estimation schemes in the presence of uncertainties. Performance was tested for different traffic scenarios such as congestion and non-recurrent traffic incidents. Further, to improve the estimation accuracy in scenarios involving transitions in traffic conditions, an adaptive estimator was developed. It was found that the adaptive estimator provided the best estimation accuracy. |
| first_indexed | 2024-12-22T16:33:52Z |
| format | Article |
| id | doaj.art-f2f73f2a5be342aa99e9be8fed1f0bbe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T16:33:52Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f2f73f2a5be342aa99e9be8fed1f0bbe2022-12-21T18:20:00ZengIEEEIEEE Access2169-35362020-01-0185502551410.1109/ACCESS.2019.29632738946555Area Occupancy-Based Adaptive Density Estimation for Mixed Road TrafficReenu George0https://orcid.org/0000-0003-4133-1562Lelitha Devi Vanajakshi1https://orcid.org/0000-0002-1137-9656Shankar C. Subramanian2https://orcid.org/0000-0001-6710-2202Department of Engineering Design, IIT Madras, Chennai, IndiaDepartment of Civil Engineering, IIT Madras, Chennai, IndiaDepartment of Engineering Design, IIT Madras, Chennai, IndiaManaging congestion in mixed traffic conditions, characterized by heterogeneous and lane-less traffic, is a challenging task. Traditionally density, defined as the number of vehicles in a road stretch, is used to quantify congestion. However, direct measurement of density is difficult and hence is usually estimated from other variables. In this paper, a relationship is derived between traffic density and area occupancy, a variable that can incorporate heterogeneity and lane-less movement. Using the derived density-area occupancy relation, a non-continuum macroscopic single state linear time varying model was developed. Estimation of density was done by using the Kalman filtering technique and corroborated with simulated density. The need for dynamic estimation is motivated by evaluating the performance of two static estimation schemes in the presence of uncertainties. Performance was tested for different traffic scenarios such as congestion and non-recurrent traffic incidents. Further, to improve the estimation accuracy in scenarios involving transitions in traffic conditions, an adaptive estimator was developed. It was found that the adaptive estimator provided the best estimation accuracy.https://ieeexplore.ieee.org/document/8946555/Adaptive Kalman filterarea occupancyheterogeneous trafficlane-less traffictraffic density estimation |
| spellingShingle | Reenu George Lelitha Devi Vanajakshi Shankar C. Subramanian Area Occupancy-Based Adaptive Density Estimation for Mixed Road Traffic IEEE Access Adaptive Kalman filter area occupancy heterogeneous traffic lane-less traffic traffic density estimation |
| title | Area Occupancy-Based Adaptive Density Estimation for Mixed Road Traffic |
| title_full | Area Occupancy-Based Adaptive Density Estimation for Mixed Road Traffic |
| title_fullStr | Area Occupancy-Based Adaptive Density Estimation for Mixed Road Traffic |
| title_full_unstemmed | Area Occupancy-Based Adaptive Density Estimation for Mixed Road Traffic |
| title_short | Area Occupancy-Based Adaptive Density Estimation for Mixed Road Traffic |
| title_sort | area occupancy based adaptive density estimation for mixed road traffic |
| topic | Adaptive Kalman filter area occupancy heterogeneous traffic lane-less traffic traffic density estimation |
| url | https://ieeexplore.ieee.org/document/8946555/ |
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