Abstract
The collection of massive Global Positioning System (GPS) data from travel surveys has increased exponentially worldwide since the 1990s. A number of methods, which range from rule-based to advanced classification approaches, have been applied to detect travel modes from GPS positioning data collected in travel surveys based on GPS-enabled smartphones or dedicated GPS devices. Among these approaches, neural networks (NNs) are widely adopted because they can extract subtle information from training data that cannot be directly obtained by human or other analysis techniques. However, traditional NNs, which are generally trained by back-propagation algorithms, are likely to be trapped in local optimum. Therefore, particle swarm optimization (PSO) is introduced to train the NNs. The resulting PSO-NNs are employed to distinguish among four travel modes (walk, bike, bus, and car) with GPS positioning data collected through a smartphone-based travel survey. As a result, 95.81% of samples are correctly flagged for the training set, while 94.44% are correctly identified for the test set. Results from this study indicate that smartphone-based travel surveys provide an opportunity to supplement traditional travel surveys.
Highlights
With worsening traffic congestion, significant attention has been given to travel behavior research, as well as transportation demand analysis, to develop and assess transportation demand management strategies
It has been widely accepted that data collection efforts based on Global Positioning System (GPS) technology can present evident advantages over traditional travel surveys
The current study employed particle swarm optimization (PSO)-neural networks (NNs) to distinguish the four travel modes with GPS positioning data collected through a smartphone-based travel survey
Summary
Significant attention has been given to travel behavior research, as well as transportation demand analysis, to develop and assess transportation demand management strategies. Travel surveys are generally utilized to collect the required information and data for travel demand modeling and analysis. It has been widely accepted that data collection efforts based on GPS technology can present evident advantages over traditional travel surveys. Burden reduction enables researchers to collect detailed travel information for a longer period without imposing additional burdens on respondents. GPS-enabled smartphones and dedicated GPS devices are typically utilized to record positioning data in GPS-based travel surveys. There exists a low extent to which the rules obtained from one case can be generalized to another, when travel modes are detected in a city context with particular rules trained from data collected in another one. Zheng et al [14] collected the GPS positioning data of 65 respondents for approximately 10 months and employed decision trees to correctly match 75.6% of trips.
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