| 1. |
- Breyer, Nils, 1988-
(author)
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Analysis of Travel Patterns from Cellular Network Data
- 2019
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Licentiate thesis (other academic/artistic)abstract
- Traffic planners are facing a big challenge with an increasing demand for mobility and a need to drastically reduce the environmental impacts of the transportation system at the same time. The transportation system therefore needs to become more efficient, which requires a good understanding about the actual travel patterns. Data from travel surveys and traffic counts is expensive to collect and gives only limited insights on travel patterns. Cellular network data collected in the mobile operators infrastructure is a promising data source which can provide new ways of obtaining information relevant for traffic analysis. It can provide large-scale observations of travel patterns independent of the travel mode used and can be updated easier than other data sources. In order to use cellular network data for traffic analysis it needs to be filtered and processed in a way that preserves privacy of individuals and takes the low resolution of the data in space and time into account. The research of finding appropriate algorithms is ongoing and while substantial progress has been achieved, there is a still a large potential for better algorithms and ways to evaluate them.The aim of this thesis is to analyse the potential and limitations of using cellular network data for traffic analysis. In the three papers included in the thesis, contributions are made to the trip extraction, travel demand and route inference steps part of a data-driven traffic analysis processing chain. To analyse the performance of the proposed algorithms, a number of datasets from different cellular network operators are used. The results obtained using different algorithms are compared to each other as well as to other available data sources.A main finding presented in this thesis is that large-scale cellular network data can be used in particular to infer travel demand. In a study of data for the municipality of Norrköping, the results from cellular network data resemble the travel demand model currently used by the municipality, while adding more details such as time profiles which are currently not available to traffic planners. However, it is found that all later traffic analysis results from cellular network data can differ to a large extend based on the choice of algorithm used for the first steps of data filtering and trip extraction. Particular difficulties occur with the detection of short trips (less than 2km) with a possible under-representation of these trips affecting the subsequent traffic analysis.
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| 2. |
- Breyer, Nils, 1988-, et al.
(author)
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Cellpath Routing and Route Traffic Flow Estimation Based on Cellular Network Data
- 2018
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In: The Journal of urban technology. - : Taylor & Francis. - 1063-0732 .- 1466-1853. ; :2, s. 85-104
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Journal article (peer-reviewed)abstract
- The signaling data in cellular networks provide means for analyzing the use of transportation systems. We propose methods that aim to reconstruct the used route through a transportation network from call detail records (CDRs) which are spatially and temporally sparse. The route estimation methods are compared based on the individual routes estimated. We also investigate the effect of different route estimation methods when employed in a complete network assignment for a larger city. Using an available CDR dataset for Dakar, Senegal, we show that the choice of the route estimation method can have a significant impact on resulting link flows.
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| 3. |
- Breyer, Nils, 1988-, et al.
(author)
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Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model
- 2020
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In: Journal of Advanced Transportation. - : John Wiley & Sons. - 0197-6729 .- 2042-3195.
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Journal article (peer-reviewed)abstract
- 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 () using the original zoning used in the travel demand model with 189 zones, while it is significant with 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 that the choice of the trip extraction method is crucial for the travel demandestimation as we find systematic differences in the resulting travel demand matrices using two different methods.
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| 4. |
- Breyer, Nils, 1988-
(author)
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Methods for Travel Pattern Analysis Using Large-Scale Passive Data
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Comprehensive knowledge of travel patterns is crucial to enable planning for a more efficient traffic system that accommodates human mobility demand. Currently, this knowledge is mainly based on traffic models based on relatively small samples of observations collected from travel surveys and traffic counts. The data is expensive to collect and provides only partial observations of travel patterns. With the rise of new technology, new largescale passive data sources can be used to analyse travel patterns. This thesis aims to expand the knowledge about how to use cellular network data collected by cellular network operators and smart-card data from public transit systems to analyse travel patterns. The focus is particularly on the data processing methods needed to extract travel patterns. The thesis’s contributions include new methods for extracting trips, estimating travel demand, route inference and travel mode choice from cellular network data and a method to extract travel behaviour changes from smart-card data. Different approaches are proposed to evaluate the methods: the validation using experimental data, validation using other available data sources, and comparison of results obtained using different methods. The findings include that methods for extracting travel patterns from largescale passive data need to account for the data’s characteristics. Paper II illustrates that route inference from Call Detail Records by strictly following the used cell towers’ locations is problematic due to the noise and low resolution of the data. Both rule-based and machine learning methods can be used to extract travel patterns. Paper I shows that a rule-based stop detection algorithm can be used to extract longer trips from cellular network data reliably. On the other hand, Paper III shows that for travel mode classification of trips extracted from cellular network data, supervised classification can outperform rule-based methods. Unsupervised machine learning can be used to find patterns without prior specification. Paper V shows how clustering of smart-card data could be used to group public transit users by travel behaviour to understand the effects of a disruption. Supervised machine learning requires training data. When no or little training data is available, using semi-supervised learning is a promising approach as demonstrated in Paper IV. In the studies of this thesis, real-world, large-scale passive datasets have been used to demonstrate how the extraction of travel patterns works under realistic circumstances. This has exposed limitations due to the data source’s characteristics and limitations due to possible sample bias. At the same time, the studies of this thesis show the potential of using large-scale passive data. Changes in travel patterns can be identified quickly as new data can be collected continuously. Due to the large sample size, the data allows understanding travel patterns based on observations instead of relying on traffic models’ underlying assumptions.
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| 5. |
- Breyer, Nils, 1988-, et al.
(author)
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Semi-supervised Mode Classification of Inter-city Trips from Cellular Network Data
- 2022
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In: Journal of Big Data Analytics in Transportation. - : Springer. - 2523-3564 .- 2523-3556. ; :1, s. 23-39
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Journal article (peer-reviewed)abstract
- Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.
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| 6. |
- Breyer, Nils, 1988-, et al.
(author)
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Travel mode classification of intercity trips using cellular network data
- 2021
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In: Transportation Research Procedia. - Paphos, Cyprus : Elsevier. ; , s. 211-218
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Conference paper (peer-reviewed)abstract
- Many applications in transport planning require an understanding of travel patterns separated by travel mode. To use cellular network data as observations of human mobility in these applications, classification by travel mode is needed. Existing classification methods for GPS-trajectories are often inefficient for cellular network data, which has lower resolution in space and time than GPS data.In this study, we compare three geometry-based mode classification methods and three supervised methods to classify trips extracted from cellular network data in intercity origin-destination pairs as either road or train. To understand the difficulty of the problem, we use a labeled dataset of 255 trips in two OD-pairs to train the supervised classification methods and to evaluate the classification performance. For an OD-pair where the road and train routes are not separated by more than four kilometers, the geometry-based methods classify 4.5% - 7.1% of the trips wrong, while two of the supervised methods can classify all trips correctly. Using a large-scale dataset of 29037 trips, we find that separation between classes is less evident than in the labeled dataset and show that the choice of classification methods impacts the aggregated modal split estimate.
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| 7. |
- Breyer, Nils, 1988-, et al.
(author)
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Trip extraction for traffic analysis using cellular network data
- 2017
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In: 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). - Naples : IEEE Press. - 9781509064847 ; , s. 321-326
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Conference paper (peer-reviewed)abstract
- To get a better understanding of people’s mobility, cellular network signalling data including location information, is a promising large-scale data source. In order to estimate travel demand and infrastructure usage from the data, it is necessary to identify the trips users make. We present two trip extraction methods and compare their performance using a small dataset collected in Sweden. The trips extracted are compared with GPS tracks collected on the same mobiles. Despite the much lower location sampling rate in the cellular network signalling data, we are able to detect most of the trips found from GPS data. This is promising, given the relative simplicity of the algorithms. However, further investigation is necessary using a larger dataset and more types of algorithms. By applying the same methods to a second dataset for Senegal with much lower sampling rate than the Sweden dataset, we show that the choice of the trip extraction method tends to be even more important when the sampling rate is low.
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| 8. |
- Eltved, Morten, et al.
(author)
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Impacts of long-term service disruptions on passenger travel behaviour: A smart card analysis from the Greater Copenhagen area
- 2021
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In: Transportation Research Part C. - : Elsevier. - 0968-090X .- 1879-2359. ; 131
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Journal article (peer-reviewed)abstract
- Disruptions in public transport are a major source of frustration for passengers and result in lower public transport usage. Previous studies on the effect of disruptions on passenger travel behaviour have mainly focused on shorter disruptions, while the few studies on impacts of long-term disruptions have had limited focus on individual passenger behaviour. This paper fills the gap in research by proposing a novel methodology based on smart card data for analysing the impacts of long-term planned disruptions on passenger travel behaviour. We use k-means clustering to group passengers based on their travel behaviour before and after the closure. We can thus observe how different passenger groups changed travel behaviour after the disruption. We compare these observations to a group of reference lines without disruption to account for general trends. Using hierarchical clustering of daily travel patterns, we are able to in-depth analyse the reactions of certain passenger groups to the disruption. We apply the method on a 3-month closure of a rail line in the Greater Copenhagen area. The results suggest that, in particular, passengers with an everyday commuting behaviour have decreased after the disruption. The proposed methodology enables explicit analysis of the impact of disruptions on diverse passengers segments, while the specific results are useful for public transport agencies when planning long-term maintenance projects.
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