Drive tourism has become increasingly popular in the past decade; however, driving through a new city is challenging because the road and traffic environments vary significantly across cities. A driver used to driving in one city may face severe difficulty in adapting to a different driving environment, leading to road fatalities. This article develops GRIDS , an explainable model for guidelines recommendation for inter-domain driving safety, which learns the driving rules behind the changing environment and recommends the necessary personalized guidelines to a driver while driving through a new city. We develop an explainable domain adaptation model to provide customized recommendations in terms of driving guidelines, broadly categorized into four major feature categories of a driving environment. A thorough evaluation over the CARLA driving simulator shows that the recommendations generated through GRIDS can help improve driving safety.

Monitoring driving behavior is essential to ensure on-road safety. Although driving is a collective, cooperative task among the drivers of the neighboring vehicles, existing platforms for driving behavior analysis solely rely on different on-road maneuvers taken by a driver. By analyzing a large volume of publicly available data over two countries and in-house collected data, this paper argues that analyzing driving behavior needs treatment over different factors which compel a driver to take maneuvers that are otherwise recommended to be avoided. Consequently, we develop DriBe. This smartphone-based pervasive sensing system utilizes video, GPS, and inertial sensor data to investigate the causes and consequences of driving maneuvers to score a driver based on a thorough understanding of their on-road driving behavior. Considering that the causality factors are very much specific to a particular driving environment (like a country), DriBe also incorporates a domain-adaptive architecture by utilizing a transfer learning framework. Thorough evaluation of DriBe with datasets from three countries shows that a score based on such causal factors provides a more accurate representation of driving behavior compared to baselines.

Severe road accidents are reported regularly across the globe due to drivers getting distracted while using their smartphones. To prevent such fatalities, one possible approach is to make the smartphone intelligent enough to detect whether it is being used by the driver, thus providing restricted access to the applications while driving. However, this problem is challenging as the driver can behave like an adversary to fool the system; therefore, additional devices or forward communication cannot be used. This paper proposes a novel approach of smartphone localization within a car by exploiting the ambient mechanical noise within the vehicle. We utilize the periodic nature of such mechanical noises to develop a simple yet satisfactorily accurate approach, called Blah, that can utilize the acoustic properties from the ambient mechanical noise within the car to detect whether the driver or the passenger is using the smartphone while the car is on the road.

Commuter comfort in cab rides affects driver rating as well as the reputation of ride-hailing firms like Uber/Lyft. Existing research has revealed that commuter comfort not only varies at a personalized level but also is perceived differently on different trips for the same commuter. Furthermore, there are several factors, including driving behavior and driving environment, affecting the perception of comfort. Automatically extracting the perceived comfort level of a commuter due to the impact of the driving behavior is crucial for a timely feedback to the drivers, which can help them to meet the commuter’s satisfaction. In light of this, we surveyed around 200 commuters who usually take such cab rides and obtained a set of features that impact comfort during cab rides. Following this, we develop a system Ridergo which collects smartphone sensor data from a commuter, extracts the spatial time series feature from the data, and then computes the level of commuter comfort on a five-point scale with respect to the driving. Ridergo uses a Hierarchical Temporal Memory model-based approach to observe anomalies in the feature distribution and then trains a multi-task learning-based neural network model to obtain the comfort level of the commuter at a personalized level. The model also intelligently queries the commuter to add new data points to the available dataset and, in turn, improve itself over periodic training. Evaluation of Ridergo on 30 participants shows that the system could provide efficient comfort score with high accuracy when the driving impacts the perceived comfort.

Network embedding, that aims to learn low-dimensional vector representation of nodes such that the network structure is preserved, has gained significant research attention in recent years. However, most state-of-the-art network embedding methods are computationally expensive and hence unsuitable for representing nodes in billion-scale networks. In this paper, we present LouvainNE, a hierarchical clustering approach to network embedding. Precisely, we employ Louvain, an extremely fast and accurate community detection method, to build a hierarchy of successively smaller subgraphs. We obtain representations of individual nodes in the original graph at different levels of the hierarchy, then we aggregate these representations to learn the final embedding vectors. Our theoretical analysis shows that our proposed algorithm has quasi-linear run-time and memory complexity. Our extensive experimental evaluation, carried out on multiple real-world networks of different scales, demonstrates both (i) the scalability of our proposed approach that can handle graphs containing tens of billions of edges, as well as (ii) its effectiveness in performing downstream network mining tasks such as network reconstruction and node classification.

Event-based online social platforms, such as Meetup and Plancast, have experienced increased popularity and rapid growth in recent years. In EBSN setup, selecting suitable venues for hosting events, which can attract a great turnout, is a key challenge. In this paper, we present a deep learning based venue recommendation system DeepVenue which provides context driven venue recommendations for the Meetup event-hosts to host their events. The crux of the proposed model relies on the notion of similarity between multiple Meetup entities such as events, venues, groups, etc. We develop deep learning techniques to compute a compact descriptor for each entity, such that two entities (say, venues) can be compared numerically. Notably, to mitigate the scarcity of venue related information in Meetup, we leverage on the cross domain knowledge transfer from popular LBSN service Yelp to extract rich venue related content. For hosting an event, the proposed DeepVenue model computes a success score for each candidate venue and ranks those venues according to the scores and finally recommend the top k venues. Our rigorous evaluation on the Meetup data collected for the city of Chicago shows that DeepVenue significantly outperforms the baselines algorithms. Precisely, for 84 percent of events, the correct hosting venue appears in the top 5 of the DeepVenue recommended list.

Twitter is currently a popular microblogging platform for the dissemination of information by users in the form of messages such as tweets. Such tweets are shared with followers of the seed user who in turn may reshare it with their own set of followers. Long chain of such retweets form cascades. In this paper, we aim to estimate the influence tree of cascades denoting the who-influenced-whom relationship among retweeting users by leveraging on temporal pattern of its retweets. We use a principled methodology to construct the ground truth influence trees of cascades using standard diffusion models. We define diverse structural metrics to quantify the structural characteristics of different influence trees. Based on empirical observations from ground truth, we develop CasCon, an unsupervised model that leverages on temporal pattern of retweets obtained from time series of cascades and underlying follower network of Twitter to construct influence trees. Further, we provide an analytical formulation of CasCon by deriving the degree distribution of these predicted influence trees and use it to approximate the structural metrics. Our evaluation shows that CasCon exhibits superior performance compared to state-of-theart baseline algorithms in selecting influencers of high quality based on standard influence measures in Twitter as well as in correctly (re)constructing the ground truth influence trees with high accuracy. Finally, we validate the analytical formulation of CasCon on the influence tree structures of both synthetic and real cascades; experimental results demonstrate its effectiveness in closely resembling ground truth influence trees for empirical cascades with high retweet count.

With the increase in road fatalities due to various factors like aggressive driving and road rage, quantifying and monitoring the stress level of a driver is an important task for the preparation of driving rosters for the cab companies. Stress monitoring using physiological sensors is a costly and obstructive task, while stress factors impact differently for different individuals based on their personality traits. In this paper, we develop a learning-based model to predict the stress level of a driver and its effect on his driving behavior, solely based on spatio-temporal driving data collected through GPS and inertial sensors. We further establish a correlation between the stress level of a driver and his driving behavior; thus, we develop a complete system to infer stress profiling and its impact on driving behavior based on spatio-temporal driving data. The model has been tested over a publicly available dataset with 6 drivers for 500 minutes of driving data. We observe that the proposed model gives an average prediction accuracy of 79% with low false-positive rates.

Passenger comfort is a major factor influencing a commuter's decision to avail public transport. Existing studies suggest that factors like overcrowding, jerkiness, traffic congestion etc. correlate well to passenger's (dis)comfort. An online survey conducted with more than 300 participants from 12 different countries reveals that different personalized and context dependent factors influence passenger comfort during a travel by public transport. Leveraging on these findings, we identify correlations between comfort level and these dynamic parameters, and implement a smartphone based application, ComfRide, which recommends the most comfortable route based on user's preference honoring her travel time constraint. We use a 'Dynamic Input/Output Automata' based composition model to capture both the wide varieties of comfort choices from the commuters and the impact of environment on the comfort parameters. Evaluation of ComfRide, involving 50 participants over 28 routes in a state capital of India, reveals that recommended routes have on average 30% better comfort level than Google map recommended routes, when a commuter gives priority to specific comfort parameters of her choice.

Twitter has recently become one of the most popular online social networking websites where users can share news and ideas through messages in the form of tweets. As a tweet gets retweeted from user to user, large cascades of information diffusion are formed over the Twitter follower network. Existing works on cascades have mainly focused on predicting their popularity in terms of size. In this paper, we leverage on the temporal pattern of retweets to model the diffusion dynamics of a cascade. Notably, retweet cascades provide two complementary information: (a) inter-retweet time intervals of retweets, and (b) diffusion of cascade over the underlying follower network. Using datasets from Twitter, we identify two types of cascades based on presence or absence of early peaks in their sequence of inter-retweet intervals. We identify multiple diffusion localities associated with a cascade as it propagates over the network. Our studies reveal the transition of a cascade to a new locality facilitated by pivotal users that are highly cascade dependent following saturation of current locality. We propose an analytical model to show co-occurrence of first peaks and cascade migration to a new locality as well as predict locality saturation from inter-retweet intervals. Finally, we validate these claims from empirical data showing co-occurrence of first peaks and migration with good accuracy; we obtain even better accuracy for successfully classifying saturated and non-saturated diffusion localities from inter-retweet intervals.

Community detection in single layer, isolated networks has been extensively studied in the past decade. However, many real-world systems can be naturally conceptualized as multilayer networks which embed multiple types of nodes and relations. In this paper, we propose algorithm for detecting communities in multilayer networks. The crux of the algorithm is based on the multilayer modularity index Q_M, developed in this paper. The proposed algorithm is parameter-free, scalable and adaptable to complex network structures. More importantly, it can simultaneously detect communities consisting of only single type, as well as multiple types of nodes (and edges). We develop a methodology to create synthetic networks with benchmark multilayer communities. We evaluate the performance of the proposed community detection algorithm both in the controlled environment (with synthetic benchmark communities) and on the empirical datasets (Yelp and Meetup datasets); in both cases, the proposed algorithm outperforms the competing state-of-the-art algorithms.

City transit maps are one of the important resources for public navigation in today's digital world. However, the availability of transit maps for many developing countries is very limited, primarily due to the various socio-economic factors that drive the private operated and partially regulated transport services. Public transports at these cities are marred with many factors such as uncoordinated waiting time at bus stoppages, crowding in the bus, sporadic road conditions etc., which also need to be annotated so that commuters can take informed decision. Interestingly, many of these factors are spatio-temporal in nature. In this paper, we develop CityMap, a system to automatically extract transit routes along with their eccentricities from spatio-temporal crowdsensed data collected via commuters' smart-phones. We apply a learning based methodology coupled with a feature selection mechanism to filter out the necessary information from raw smart-phone sensor data with minimal user engagement and drain of battery power. A thorough evaluation of CityMap, conducted for more than two years over 11 different routes in 3 different cities in India, show that the system effectively annotates bus routes along with other route and road features with more than 90% of accuracy.

The penetration of smartphones and advancement of the location-based services have jointly contributed to the popularity of location-based social networking platforms such as Foursquare and Yelp. Business owners are important stakeholders of such location-based social network (LBSN) platforms, for whom setting up new businesses at the right location has immense importance in making a business popular. Given a wide variety of businesses, a single metric may not correctly capture the business popularity, rather, popularity measure should be specific to geographical regions, business category etc. In this paper, we systematically define popularity metrics and use them to label diverse businesses available in Yelp dataset. Notably, information diffusion plays a major role in popularizing businesses, word of mouth communication across customers facilitates information spread. This paper concentrates on two key modalities that exhibit a strong signature of business popularity: social connections across customers, showing high correlation with corresponding business popularity, and geographical proximity, which reveals two types of customers, namely, local visitor and foreign visitor observed through a comprehensive study on Yelp dataset. Depending on the business type and category, either local or foreign visitors play important role in making a business popular. Finally, we leverage on these signatures to predict business popularity and develop a recommendation model suggesting top regions to business owners for starting popular businesses. We show that both the models outperform the state-of-the-art baseline algorithms.

This paper explores the feasibility of automatically discriminating users from the activity as well as temporal information of their daily routine. We observe that everyone pursues a daily semi-regular activity pattern. Based on this observation, we have developed a system UDAT and experimented on Microsoft Geolife as well as UDAT datasets. With Geolife transportation activity log and UDAT motion-static activity log, the system achieves 73.3% and 80.68% accuracy, respectively. Although the overall system accuracy is moderate, the system achieves the highest accuracy when the users belong to the different activity buckets. This signifies the utility of two-phase classification for user discrimination.

Success of Meetup groups is of utmost importance for the members who organize them. Given a wide variety of such groups, a single metric may not be indicative of success for different groups; rather, success measure should be specific to the interest of a group. In this paper, accounting for the group diversity, we systematically define Meetup group success metrics and use them to generate labels for our machine learnt models. We crawl the Meetup dataset for three US cities namely New York, Chicago and San Francisco over a period of 8 months. The data study reveals the key players (such as core members, new members etc.) behind the success of the Meetup groups. This study leverages semantic, syntactic, temporal and location based features to discriminate between successful and unsuccessful groups. Finally, we present a model to predict success of the Meetup groups with high accuracy (0.81 with AUC = 0.86). Our approach generalizes well across groups, categories and cities. Additionally, the model performs reasonably well for new groups with little history (cold start problem), exhibiting high accuracy for the cross city validation.

In this paper, we investigate the role of mentions on tweet propagation. We propose a novel tweet propagation model SIR_MF based on a multiplex network framework, that allows to analyze the effects of mentioning on final retweet count. The basic bricks of this model are supported by a comprehensive study of multiple real datasets and simulations of the model show a nice agreement with the empirically observed tweet popularity. Studies and experiments also reveal that follower count, retweet rate & profile similarity are important factors in gaining tweet popularity and allow to better understand the impact of the mention strategies on the retweet count. Interestingly, we analytically identify a critical retweet rate regulating the role of mention on the tweet popularity. Finally, our data driven simulation demonstrates that the proposed mention recommendation heuristic "Easy-Mention" outperforms the benchmark "Whom-To-Mention" algorithm.

Public bus services in many cities in countries like India are controlled by private owners, hence, building up a database for all the bus routes is non-trivial. In this paper, we leverage smart-phone based sensing to crowdsource and populate the information repository for bus routes in a city. We have developed an intelligent data logging module for smart-phones and a server side processing mechanism to extract roads and bus routes information. From a 3 month long study involving more than 30 volunteers in 3 different cities in India, we found that the developed system, CrowdMap, can annotate bus routes with a mean error of 10m, while consuming 80% less energy compared to a continuous GPS based system.