Social behavior modeling based on Incremental Discrete Hidden Markov Models

12 pagesInternational audienceModeling multimodal face-to-face interaction is a crucial step in the process of building social robots or users-aware Embodied Conversational Agents (ECA). In this context, we present a novel approach for human behavior analysis and generation based on what we called "Incremental Discrete Hidden Markov Model" (IDHMM). Joint multimodal activities of interlocutors are first modeled by a set of DHMMs that are specific to supposed joint cognitive states of the interlocutors. Respecting a task-specific syntax, the IDHMM is then built from these DHMMs and split into i) a recognition model that will determine the most likely sequence of cognitive states given the multimodal activity of the in- terlocutor, and ii) a generative model that will compute the most likely activity of the speaker given this estimated sequence of cognitive states. Short-Term Viterbi (STV) decoding is used to incrementally recognize and generate behav- ior. The proposed model is applied to parallel speech and gaze data of interact- ing dyads

Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study

Recently, new traffic data sources have emerged raising new challenges and opportunities when applying novel methodologies. The purpose of this research is to analyse car travel time data collected from smartphones by Google Company. Geographic information system (GIS) tools and Python programming language were employed in this study to establish the initial framework as well as to automatically extract, analyse, and visualize data. The analysis resulted in the calculation of travel time fluctuation during the day, calculation of travel time variability and estimation of origin-destination (OD) skim matrices. Furthermore, we accomplished the accessibility analysis and provided recommendations for further research.</p

CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network

Mobile phone data have recently become an attractive source of information about mobility behavior. Since cell phone data can be captured in a passive way for a large user population, they can be harnessed to collect well-sampled mobility information. In this paper, we propose CT-Mapper, an unsupervised algorithm that enables the mapping of mobile phone traces over a multimodal transport network. One of the main strengths of CT-Mapper is its capability to map noisy sparse cellular multimodal trajectories over a multilayer transportation network where the layers have different physical properties and not only to map trajectories associated with a single layer. Such a network is modeled by a large multilayer graph in which the nodes correspond to metro/train stations or road intersections and edges correspond to connections between them. The mapping problem is modeled by an unsupervised HMM where the observations correspond to sparse user mobile trajectories and the hidden states to the multilayer graph nodes. The HMM is unsupervised as the transition and emission probabilities are inferred using respectively the physical transportation properties and the information on the spatial coverage of antenna base stations. To evaluate CT-Mapper we collected cellular traces with their corresponding GPS trajectories for a group of volunteer users in Paris and vicinity (France). We show that CT-Mapper is able to accurately retrieve the real cell phone user paths despite the sparsity of the observed trace trajectories. Furthermore our transition probability model is up to 20% more accurate than other naive models.Comment: Under revision in Computer Communication Journa

Speed Aware – a mobile app prototype for the promotion of responsible driving

This paper addresses the promotion and awareness of responsible driving and road safety through the development of a very simple to use mobile application prototype, Speed Aware. This application provides users with speed limit information on roads they are travelling on, together with a journey logging feature that allows off-line self-review of driving behaviour. Tracked journeys can be displayed on a map and the trace shown as a heatmap, comparing the vehicle speed to the road speed limit. Furthermore, an audible alarm is emitted whenever the vehicle is travelling at a speed higher than the legal limit. At the heart of this app is a map matching algorithm, which matches raw Global Positioning System (GPS) data to the road network. Five map matching algorithms are implemented and compared on the basis of real-time performance and accuracy. A ground truth dataset of GPS traces in dense, urban, and sub-urban environments, together with TraceView, a trace visualisation and management tool, were developed. A modified version of a weight-based topological algorithm achieved accuracy of 94.9% at a GPS sampling frequency of 1Hz. This algorithm, together with three of the reviewed map matching algorithms, were implemented on a mobile device and subjectively tested for realtime performance.peer-reviewe

Evaluating the Reliability, Coverage, and Added Value of Crowdsourced Traffic Incident Reports from Waze

Traffic managers strive to have the most accurate information on road conditions, normally by using sensors and cameras, to act effectively in response to incidents. The prevalence of crowdsourced traffic information that has become available to traffic managers brings hope and yet raises important questions about the proper strategy for allocating resources to monitoring methods. Although many researchers have indicated the potential value in crowdsourced data, it is crucial to quantitatively explore its validity and coverage as a new source of data. This research studied crowdsourced data from a smartphone navigation application called Waze to identify the characteristics of this social sensor and provide a comparison with some of the common sources of data in traffic management. Moreover, this work quantifies the potential additional coverage that Waze can provide to existing sources of the advanced traffic management system (ATMS). One year of Waze data was compared with the recorded incidents in the Iowa’s ATMS in the same timeframe. Overall, the findings indicated that the crowdsourced data stream from Waze is an invaluable source of information for traffic monitoring with broad coverage (covering 43.2% of ATMS crash and congestion reports), timely reporting (on average 9.8 minutes earlier than a probe-based alternative), and reasonable geographic accuracy. Waze reports currently make significant contributions to incident detection and were found to have potential for further complementing the ATMS coverage of traffic conditions. In addition to these findings, the crowdsourced data evaluation procedure in this work provides researchers with a flexible framework for data evaluation

Probabilistic map-matching for low-frequency GPS trajectories

The ability to infer routes taken by vehicles from sparse and noisy GPS data is of crucial importance in many traffic applications. The task, known as map-matching, can be accurately approached by a popular technique known as ST-Matching. The algorithm is computationally efficient and has been shown to outperform more traditional map-matching approaches, especially on low-frequency GPS data. The major drawback of the algorithm is a lack of confidence scores associated with its outputs, which are particularly useful when GPS data quality is low. In this paper, we propose a probabilistic adaptation of ST-Matching that equips it with the ability to express map-matching certainty using probabilities. The adaptation, called probabilistic ST-Matching (PST-Matching) is inspired by similarities between ST-Matching and probabilistic approaches to map-matching based on a Hidden Markov Model. We validate the proposed algorithm on GPS trajectories of varied quality and show that it is similar to ST-Matching in terms of accuracy and computational efficiency, yet with the added benefit of having a measure of confidence associated with its outputs