Spatio-temporal prediction of freeway congestion patterns using discrete choice methods
Predicting freeway traffic states is, so far, based on predicting speeds or traffic volumes with various methodological approaches ranging from statistical modeling to deep learning. Traffic on freeways, however, follows specific patterns in space–time, such as stop-and-go waves or mega jams. These...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | EURO Journal on Transportation and Logistics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2192437624000190 |
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| author | Barbara Metzger Allister Loder Lisa Kessler Klaus Bogenberger |
| author_facet | Barbara Metzger Allister Loder Lisa Kessler Klaus Bogenberger |
| author_sort | Barbara Metzger |
| collection | DOAJ |
| description | Predicting freeway traffic states is, so far, based on predicting speeds or traffic volumes with various methodological approaches ranging from statistical modeling to deep learning. Traffic on freeways, however, follows specific patterns in space–time, such as stop-and-go waves or mega jams. These patterns are informative because they propagate in space–time in different ways, e.g., stop and go waves exhibit a typical propagation that can range far ahead in time. If these patterns and their propagation become predictable, this information can improve and enrich traffic state prediction. In this paper, we use a rich data set of congestion patterns on the A9 freeway in Germany near Munich to develop a mixed logit model to predict the probability and then spatio-temporally map the congestion patterns by analyzing the results. As explanatory variables, we use variables characterizing the layout of the freeway and variables describing the presence of previous congestion patterns. We find that a mixed logit model significantly improves the prediction of congestion patterns compared to the prediction of congestion with the average presence of the patterns at a given location or time. |
| format | Article |
| id | doaj-art-097df00a226b48cdbb5a95740e77bb01 |
| institution | OA Journals |
| issn | 2192-4384 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EURO Journal on Transportation and Logistics |
| spelling | doaj-art-097df00a226b48cdbb5a95740e77bb012025-08-20T02:37:42ZengElsevierEURO Journal on Transportation and Logistics2192-43842024-01-011310014410.1016/j.ejtl.2024.100144Spatio-temporal prediction of freeway congestion patterns using discrete choice methodsBarbara Metzger0Allister Loder1Lisa Kessler2Klaus Bogenberger3Chair of Traffic Engineering and Control, Technical University of Munich (TUM), Arcisstrasse 21, 80333 Munich, Germany; Corresponding author.Professorship of Mobility Policy, TUM School of Social Sciences and Technology, Technical University of Munich (TUM), Arcisstrasse 21, 80333 Munich, GermanyChair of Traffic Engineering and Control, Technical University of Munich (TUM), Arcisstrasse 21, 80333 Munich, GermanyChair of Traffic Engineering and Control, Technical University of Munich (TUM), Arcisstrasse 21, 80333 Munich, GermanyPredicting freeway traffic states is, so far, based on predicting speeds or traffic volumes with various methodological approaches ranging from statistical modeling to deep learning. Traffic on freeways, however, follows specific patterns in space–time, such as stop-and-go waves or mega jams. These patterns are informative because they propagate in space–time in different ways, e.g., stop and go waves exhibit a typical propagation that can range far ahead in time. If these patterns and their propagation become predictable, this information can improve and enrich traffic state prediction. In this paper, we use a rich data set of congestion patterns on the A9 freeway in Germany near Munich to develop a mixed logit model to predict the probability and then spatio-temporally map the congestion patterns by analyzing the results. As explanatory variables, we use variables characterizing the layout of the freeway and variables describing the presence of previous congestion patterns. We find that a mixed logit model significantly improves the prediction of congestion patterns compared to the prediction of congestion with the average presence of the patterns at a given location or time.http://www.sciencedirect.com/science/article/pii/S2192437624000190Traffic state predictionMixed logitCongestion patternsFreeway traffic |
| spellingShingle | Barbara Metzger Allister Loder Lisa Kessler Klaus Bogenberger Spatio-temporal prediction of freeway congestion patterns using discrete choice methods EURO Journal on Transportation and Logistics Traffic state prediction Mixed logit Congestion patterns Freeway traffic |
| title | Spatio-temporal prediction of freeway congestion patterns using discrete choice methods |
| title_full | Spatio-temporal prediction of freeway congestion patterns using discrete choice methods |
| title_fullStr | Spatio-temporal prediction of freeway congestion patterns using discrete choice methods |
| title_full_unstemmed | Spatio-temporal prediction of freeway congestion patterns using discrete choice methods |
| title_short | Spatio-temporal prediction of freeway congestion patterns using discrete choice methods |
| title_sort | spatio temporal prediction of freeway congestion patterns using discrete choice methods |
| topic | Traffic state prediction Mixed logit Congestion patterns Freeway traffic |
| url | http://www.sciencedirect.com/science/article/pii/S2192437624000190 |
| work_keys_str_mv | AT barbarametzger spatiotemporalpredictionoffreewaycongestionpatternsusingdiscretechoicemethods AT allisterloder spatiotemporalpredictionoffreewaycongestionpatternsusingdiscretechoicemethods AT lisakessler spatiotemporalpredictionoffreewaycongestionpatternsusingdiscretechoicemethods AT klausbogenberger spatiotemporalpredictionoffreewaycongestionpatternsusingdiscretechoicemethods |