Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model
Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they ar...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/3267474 |
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| _version_ | 1832566307610951680 |
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| author | Nils Breyer Clas Rydergren David Gundlegård |
| author_facet | Nils Breyer Clas Rydergren David Gundlegård |
| author_sort | Nils Breyer |
| collection | DOAJ |
| description | Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak (R2<0.2) using the original zoning used in the travel demand model with 189 zones, while it is significant with R2=0.82 when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find systematic differences in the resulting travel demand matrices using two different methods. |
| format | Article |
| id | doaj-art-e7bcfc1a766049e1907d9d940d984f18 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e7bcfc1a766049e1907d9d940d984f182025-02-03T01:04:39ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/32674743267474Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand ModelNils Breyer0Clas Rydergren1David Gundlegård2Department of Science and Technology, Linköping University, Linköping, SwedenDepartment of Science and Technology, Linköping University, Linköping, SwedenDepartment of Science and Technology, Linköping University, Linköping, SwedenData on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak (R2<0.2) using the original zoning used in the travel demand model with 189 zones, while it is significant with R2=0.82 when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find systematic differences in the resulting travel demand matrices using two different methods.http://dx.doi.org/10.1155/2020/3267474 |
| spellingShingle | Nils Breyer Clas Rydergren David Gundlegård Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model Journal of Advanced Transportation |
| title | Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model |
| title_full | Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model |
| title_fullStr | Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model |
| title_full_unstemmed | Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model |
| title_short | Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model |
| title_sort | comparative analysis of travel patterns from cellular network data and an urban travel demand model |
| url | http://dx.doi.org/10.1155/2020/3267474 |
| work_keys_str_mv | AT nilsbreyer comparativeanalysisoftravelpatternsfromcellularnetworkdataandanurbantraveldemandmodel AT clasrydergren comparativeanalysisoftravelpatternsfromcellularnetworkdataandanurbantraveldemandmodel AT davidgundlegard comparativeanalysisoftravelpatternsfromcellularnetworkdataandanurbantraveldemandmodel |