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"The area of transportation planning deals with transportation facilities (usually streets, roads, walkways, bike lanes, and public transit lines)." It is vital to assess the present state of traffic volume and anticipate the future state of traffic volume in this field traffic volume survey. The purpose of traffic volume studies is to determine the number, movement, and categories of highway vehicles in a given region. An attempt was made to grasp traffic patterns throughout various time periods using data collection. The characteristics of the traffic flow at that crossroads also have an impact on traffic control. As a consequence, the study's findings are valuable in controlling traffic at the intersection and giving some corrective measures to improve traffic safety in the region. Based on the findings, corrective actions such as road widening or enhanced public transit may be recommended.

Emotion-aware services are increasingly used in different applications such as gaming, mental health tracking, video conferencing, and online tutoring. The core of such services is usually a machine learning model that automatically infers its user's emotions based on different biological indicators (e.g., physiological signals and facial expressions). However, such machine learning models often require a large number of emotion annotations or ground truth labels, which are typically collected as manual self-reports by conducting long-term user studies, commonly known as Experience Sampling Method (ESM). Responding to repetitive ESM probes for self-reports is time-consuming and fatigue-inducing. The burden of repetitive self-report collection leads to users responding arbitrarily or dropping out from the studies, compromising the model performance. To counter this issue, we, in this paper, propose a Human-AI Collaborative Emotion self-report collection framework, HACE, that reduces the self-report collection effort significantly. HACE encompasses an active learner, bootstrapped with a few emotion self-reports (as seed samples), and enables the learner to query for only not-so-confident instances to retrain the learner to predict the emotion self-reports more efficiently. We evaluated the framework in a smartphone keyboard-based emotion self-report collection scenario by performing a 3-week in-the-wild study (N = 32). The evaluation of HACE on this dataset (≈11,000 typing sessions corresponding to more than 200 hours of typing data) demonstrates that it requires 46% fewer self-reports than the baselines to train the emotion self-report detection model and yet outperforms the baselines with an average self-report detection F-score of 85%. These findings demonstrate the possibility of adopting such a human-AI collaborative approach to reduce emotion self-report collection efforts.