Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

Abstract

Transport mode information is essential for understanding people's movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other's limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection.

Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data

“…To consider the uncertainty of anomalies, many studies have applied an uncertainty reasoning method to GNSS trajectory data [30][31][32][33]. The method includes, for example, fuzzy inference, logical reasoning, evidence theory, and Bayesian networks.…”

Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach

“…In addition, some specific features were proposed for the purposes of improving accuracy of classification, such as average rail location closeness [19], estimated horizontal accuracy uncertainty [20] and average proximity to bus, tram, or train network [28]. The design of a specific features extracting method should be based on characteristics of transportation options.…”

“…Thus it is arranged in row 3 column 1. Indicators in this evaluation system were widely used in existing studies to represent performance [19,21,25,27,28]. …”

“…As to classifiers, many studies have introduced different classifiers into transportation mode detection, such as Decision Tree [14,19,24,26,34], K-Nearest-Neighbor (KNN) [21,34,35], Support Vector Machines (SVM) [12,14,21,34], Artificial Neural Network (ANN) [20,28,33], Fuzzy Logic [13,28], Hidden Markov Models [25,27], and Bayesian Network [19,36,37]. In addition, ensemble classifiers have become a hotspot in the field of transportation mode detections since ensemble classifiers are a combination set of weak learners, which are superior to a single stronger learner [21,38].…”

Parallel Spatiotemporal Spectral Clustering With Massive Trajectory Data

“…Massive taxi trajectory data contains much regular knowledge (Wang et al, 2012). The behavior patterns (Das et al, 2016) and travel hotspots can be explored from these data, they provide important supports for urban management and traffic management (Han et al, 2016).…”