The rapid growth of shared micromobility, such as e-scooters and e-bikes, has transformed urban transportation, bridging the gap between public transit and first/last-mile mobility. As users and cities need information on the status and usage of such micromobility vehicles, operators publish the data using General Bikeshare Feed Specification (GBFS)-compliant APIs. These feeds are extremely useful for operational transparency and enabling third-party integration into navigation apps. However, it has raised significant privacy concerns, particularly around the fine-grained spatiotemporal data sharing, which can reveal potentially sensitive information about travel patterns, even without explicit personal identifiers. For instance, by leveraging high-precision GPS coordinates, battery levels, and timestamps, malicious actors can infer trip origins and destinations, posing a risk of membership inference attacks. Despite efforts to mitigate such risks through dynamic vehicle IDs and GBFS guidelines, the potential for privacy leakage remains. In this paper, we investigate these privacy risks in the context of micromobility data, addressing four key research questions: (1) identifying vulnerable fields in GBFS data that can leak trip trajectories; (2) validating trip origin-destination inference attacks without access to ground truth data from operators; (3) assessing the generalizability of such attacks across different cities, operators and GBFS version; and (4) proposing effective mitigation strategies. We propose a heuristic method for reconstructing trip origins and destinations using only publicly available GBFS data, without relying on vehicle identifiers or auxiliary quasi-identifiers. Our empirical analysis, conducted on data from two cities with varying sizes, shows that a significant proportion of trips can be accurately recalled, with over 80% of trip source and destination pairs identified across both cities. Furthermore, our proposed anonymization techniques, such as data generalization and removal of quasi-identifiers, can prevent up to 97% of successful attacks, ensuring privacy without sacrificing data utility.

With the growing adoption of location-based services, ensuring user privacy without compromising data utility has become a critical research focus. In this paper, we investigate the impact of different statistical noise distributions-Laplace, Gaussian, Exponential, Cauchy, and Uniform, on trajectory anonymization across different transportation modes using Differential Privacy. We propose a modality-wise anonymization approach and assess its effectiveness through two down-stream tasks utility metric: (i) counting the number of unique users within a predefined area, and (ii) predicting modes of transport. Experimental results on the two public datasets Geolife and Roma taxi reveal that our modality based anonymization retains high utility, achieving up to 5% improvement in micro-F1 scores compared to single-noise based anonymization and preserves user counts (deviates by 4 users) closely as of original data. These findings emphasize that downstream tasks play a pivotal role in designing privacy-preserving mechanisms while maintaining the practical usability of mobility data.

Road traffic congestion affects not only the commute delay but also a city's overall social, economic, and environmental growth. Existing approaches for road congestion mitigation primarily adopt a reactive approach by detecting congestion after it occurs and recommending alternate routes to the vehicles, which fails to prevent congestion cascading. In contrast, we propose a pervasive platform called ProCon that proactively infers the driving micro-behaviors that can contribute to congestion formation and assist the drivers in avoiding such maneuvers in real-time during the navigation. Thorough evaluations over multiple real-life and simulated datasets indicate that ProCon can reduce congestion for more than 60% of the scenarios on average while significantly reducing the travel time of the vehicles.

Road safety remains a critical global concern, with millions of crashes reported annually. Understanding the safety of individual road junctions is vital, especially in areas prone to road rage and reckless driving. However, current navigation systems lack detailed safety information, increasing risk for drivers and pedestrians. Recognizing this need, this paper introduces øurmethod that automatically annotates the road segments with a driving safety level to aid cautious maneuvering and safe driving practices. By leveraging onboard sensors, øurmethod identifies causal chains behind poor driving maneuvers, enabling the modeling of safety levels for various road segments. We perform a thorough evaluation of øurmethod over publicly available and collected datasets from multiple countries and observe >80% accuracy (in terms of F1-score) in correctly annotating the safety concerns. In addition, a thorough user study indicates the generalizability and usability of the proposed approach for its practical deployment considerations.

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.

Driving is a complex task carried out under the influence of diverse spatial objects and their temporal inter-actions. Therefore, a sudden fluctuation in driving behavior can be due to either a lack of driving skill or the effect of various on-road spatial factors such as pedestrian movements, peer vehicles' actions, etc. Therefore, understanding the context behind a degraded driving behavior just-in-time is necessary to ensure on-road safety. In this paper, we develop a system called DriCon that exploits the information acquired from a dashboard-mounted edge-device to understand the context in terms of micro-events from a diverse set of on-road spatial factors and in-vehicle driving maneuvers taken. DriCon uses the live in-house testbed and the largest publicly available driving dataset to generate human interpretable explanations against the unexpected driving events. Also, it provides a better insight with an improved similarity of 80% over 50 hours of driving data than the existing driving behavior characterization techniques.

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.

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.