Analysis of Travel Patterns from Cellular Network Data

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

Methods for Travel Pattern Analysis Using Large-Scale Passive Data

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.

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 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.Funding agency: The Swedish Agency for Innovation Systems (Vinnova) (grant number 2013-03077).</p

Semi-supervised Mode Classification of Inter-city Trips from Cellular Network Data

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

Travel mode classification of intercity trips using cellular network data

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

Travel demand estimation and network assignment based on cellular network data

Cellular networks signaling data provide means for analyzing the efficiency of an underlying transportation system and assisting the formulation of models to predict its future use. This paper describes how signaling data can be processed and used in order to act as means for generating input for traditional transportation analysis models. Specifically, we propose a tailored set of mobility metrics and a computational pipeline including trip extraction, travel demand estimation as well as route and link travel flow estimation based on Call Detail Records (CDR) from mobile phones. The results are based on the analysis of data from the Data for development "D4D" challenge and include data from Cote dlvoire and Senegal. (C) 2016 Elsevier B.V. All rights reserved.Funding Agencies|Swedish Governmental Agency for Innovation Systems (VINNOVA)</p

Mode Choice Latent Class Estimation on Mobile Network Data

In this paper we use a nested latent class logit specification to define and estimate a large-scale mode choice demand forecasting model. We estimate this model based on mobile phone network data translated to roughly 100 000 long-distance trips within Sweden, achieving convergence of the model and credible parameter estimates. We develop methods to address two problems stemming from the nature of this data: the difficulties of distinguishing bus trips from car trips (since they share the same infrastructure) and distinguishing business from private trips (since trip purpose is unknown). To address the first issue, we estimate a nested logit model with an artificial nest that accounts for the differences in utility between bus and car. To address the latter issue, we estimate a latent class model, identifying classes of trips interpreted as private and business trips. Addressing these two issues substantially improves model fit.

Accelerating the Renewable Energy Revolution to Get Back to the Holocene

Abstract The UN's Paris Agreement goal of keeping global warming between 1.5 and 2°C is dangerously obsolete and needs to be replaced by a commitment to restore Earth's climate. We now know that continued use of fossil fuels associated with 1.5–2°C scenarios would result in hundreds of millions of pollution deaths and likely trigger multiple tipping elements in the Earth system. Unexpected advances in renewable power production and storage have radically expanded our climate response capacity. The cost of renewable technologies has plummeted at least 30‐year faster than projected, and renewables now dominate energy investment and growth. This renewable revolution creates an opportunity and responsibility to raise our climate ambitions. Rather than aiming for climate mitigation—making things less bad—we should commit to climate restoration—a rapid return to Holocene‐like climate conditions where we know humanity and life on Earth can thrive. Based on observed and projected energy system trends, we estimate that the global economy could reach zero emissions by 2040 and potentially return atmospheric CO2 to pre‐industrial levels by 2100–2150. However, this would require an intense and sustained rollout of renewable energy and negative emissions technologies on very large scales. We describe these clean electrification scenarios and outline technical and socioeconomic strategies that would increase the likelihood of restoring a Holocene‐like climate in the next 100 years. We invite researchers, policymakers, regulators, educators, and citizens in all countries to share and promote this positive message of climate restoration for human wellbeing and planetary stability