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 (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.
Highlights
In order to meet an increasing travel demand and the need to reduce environmental impacts, today’s traffic system needs to become more efficient
The simple STOP algorithm performed in some ways better than the more complex MOVEMENT algorithm for our validation dataset from Google location history. e biggest difference can be observed for shorter trips ( ≤ 2 km), while the difference becomes small for longer trips. e recall achieved is more than 80% (STOP) for trips made with public transit, while it is poor with only 25%–50% for walking or cycling trips
We find a reasonable correlation between the inferred travel demand from cellular network data and the existing travel demand model of Norrkoping municipality after aggregating the zoning to 24 zones. e difference between the travel demand inferred using the two trip extraction methods is marginal when it comes to the correlation to the model with R2 values of 0.82 for MOVEMENT and 0.81 for STOP
Summary
In order to meet an increasing travel demand and the need to reduce environmental impacts, today’s traffic system needs to become more efficient. We propose a process to obtain travel patterns from cellular network data, consisting of two alternative algorithms to extract trips and a method to infer time-sliced travel demand. Research studies investigating the potential uses of cellular network data as a new data source for traffic analysis have been ongoing for at least a decade. To judge the quality of the travel demand inference from cellular network data, different approaches and metrics are used in the literature to validate and compare results to other data sources. Pollard et al [24] discuss new similarity measures to overcome this problem Another approach is to use a traffic model to estimate link flows from the inferred travel demand as done by Iqbal et al [25], which allows validating against actual traffic counts
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