Travel mode identification using bluetooth technology

Shu Yang, Yao-jan Wu

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Bluetooth technology has been widely used in transportation studies to collect traffic data. Bluetooth media access control (MAC) readers can be installed along roadways to collect Bluetooth-based data. This data is commonly used to measure traffic performance. One of the advantages of using Bluetooth technology to measure traffic performance is that travel time can be measured directly with a certain level of error instead of by estimation. However, travel time outliers can commonly be observed due to different travel mode on arterials. Since travel mode information cannot be directly obtained from the raw Bluetooth-based data, a mathematical methodology is in need to identify travel mode. In this study, a genetic algorithm and neural network (GANN)-based model was developed to identify travel mode. GPS-enabled devices were used to collect ground truth travel time. In order to additionally compare the model performance, K nearest neighbor (KNN) and support vector machine (SVM) were also implemented. N-fold cross validation was applied to statistically assess the models’ results. Since the model performances depend on the model inputs, seven collections of model inputs were tested in order to achieve the best travel mode identification performance. An arterial segment with four consecutive links and three intersections was selected to be the study segment. The results suggested that correctly identifying the three travel modes successfully every time was not possible, although the GANN based model had low misidentification rates. In our study, 6.12% of autos were misidentified as bikes and 10.53% of bikes were misidentified as autos using three links.

Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalJournal of Intelligent Transportation Systems: Technology, Planning, and Operations
DOIs
StateAccepted/In press - Oct 17 2017

Fingerprint

Bluetooth
Travel Time
Travel time
Traffic
Performance Model
Genetic Algorithm
Neural Networks
Model
Medium Access Control
Genetic algorithms
Cross-validation
Neural networks
Outlier
Consecutive
Nearest Neighbor
Support Vector Machine
Fold
Medium access control
Intersection
Support vector machines

Keywords

  • bluetooth
  • genetic algorithm
  • neural network
  • short corridor
  • travel mode

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

Cite this

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title = "Travel mode identification using bluetooth technology",
abstract = "Bluetooth technology has been widely used in transportation studies to collect traffic data. Bluetooth media access control (MAC) readers can be installed along roadways to collect Bluetooth-based data. This data is commonly used to measure traffic performance. One of the advantages of using Bluetooth technology to measure traffic performance is that travel time can be measured directly with a certain level of error instead of by estimation. However, travel time outliers can commonly be observed due to different travel mode on arterials. Since travel mode information cannot be directly obtained from the raw Bluetooth-based data, a mathematical methodology is in need to identify travel mode. In this study, a genetic algorithm and neural network (GANN)-based model was developed to identify travel mode. GPS-enabled devices were used to collect ground truth travel time. In order to additionally compare the model performance, K nearest neighbor (KNN) and support vector machine (SVM) were also implemented. N-fold cross validation was applied to statistically assess the models’ results. Since the model performances depend on the model inputs, seven collections of model inputs were tested in order to achieve the best travel mode identification performance. An arterial segment with four consecutive links and three intersections was selected to be the study segment. The results suggested that correctly identifying the three travel modes successfully every time was not possible, although the GANN based model had low misidentification rates. In our study, 6.12{\%} of autos were misidentified as bikes and 10.53{\%} of bikes were misidentified as autos using three links.",
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