Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

Abstract

Understanding travel behavior is critical for an effective urban planning as well as for enabling various context-aware service provisions to support mobility as a service (MaaS). Both applications rely on the sensor traces generated by travellers’ smartphones. These traces can be used to interpret travel modes, both for generating automated travel diaries as well as for real-time travel mode detection. Current approaches segment a trajectory by certain criteria, e.g., drop in speed. However, these criteria are heuristic, and, thus, existing approaches are subjective and involve significant vagueness and uncertainty in activity transitions in space and time. Also, segmentation approaches are not suited for real time interpretation of open-ended segments, and cannot cope with the frequent gaps in the location traces. In order to address all these challenges a novel, state based bottom-up approach is proposed. This approach assumes a fixed atomic segment of a homogeneous state, instead of an event-based segment, and a progressive iteration until a new state is found. The research investigates how an atomic state-based approach can be developed in such a way that can work in real time, near-real time and offline mode and in different environmental conditions with their varying quality of sensor traces. The results show the proposed bottom-up model outperforms the existing event-based segmentation models in terms of adaptivity, flexibility, accuracy and richness in information delivery pertinent to automated travel behavior interpretation.

Segmenting human trajectory data by movement states while addressing signal loss and signal noise

“…Wan and Lin (2016) estimated probabilities that belong to three states (stop, slow move and fast move) for each individual GPS point using fuzzy inference, and aggregated GPS points into segments based on the smoothed probabilities within a kernel. Das and Winter (2016) estimated states over a kernel and merged consecutive data points using a set of heuristic rules. In this approach, gaps are typically used to split a trajectory into segments and states during signal loss are estimated based on similarity among data points adjacent to gaps.…”

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems

“…It is necessary to find a way to keep the details to a certain extent. (2) Only considering the distances between the pairs of points, the mentioned algorithms cannot take shape factors into account [16]. However, shape factor is an important feature of a natural trajectory.…”

“…With the development of the Internet of Things, urban computing, and other research fields, the analysis of spatio-temporal data-based transportation systems have become a hot topic in the fields of machine learning. The trajectory data analysis can be a great driving force for all of the fields, for example, through applying the trajectory similarity measure algorithm, the distance matrix can be computed, which can be used to cluster the trajectory of peoples' activities for finding the popular routes and hot spots and visualizing in OpenStreetMap [1,2]. In the intelligent transportation systems, it is of great practical value to measure the similarity of the trajectories of moving objects in a real-time, accurate, and reliable way.…”

A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

“…In the era of big data, however, mobile-phone Signaling Data (MSD) has provided a good aid for the dynamic detection of traffic flows in the entire multimodal transport system. We particularly focus on travel mode identification, mainly due to the following two reasons: (1) Understanding the travel modes people take is the key to travel behavior studies [1]; (2) The process of travel mode detection often involves data cleaning, segmentation, and inference, which are common to many motilities and urban planning applications. Some recent studies on the analysis of MSD only focus on the broad spectrum of their applications [2] (including social network analysis, mobility analysis, event detection, and urban planning), and the topic addressed in this paper is not thoroughly discussed.…”