Autistic adults often use different communication styles than neurotypical individuals (NTs). While prior research has documented how such gaps disadvantage autistic job seekers, no study has systematically examined when these differences arise in language use and why autistic adults encounter interpretive gaps. This work seeks to datafy and characterize these communication challenges. We built an annotation interface and recruited 20 autistic adults to analyze 10 job postings each that they had selected as cases where they felt “lost in translation.” Participants annotated text spans using six categories informed by speech and language literature: unclear, ambiguous, incomplete, inappropriate, negative, and other. Follow-up interviews showed that lexical difficulties were rarely barriers; rather, challenges stemmed from interpreting implicit social arrangements or unstated expectations. We release the anonymized annotation data as the first-of-its-kind dataset documenting autistic–NT communication style differences. We conclude with implications for designing supports that foster clearer autistic–NT communication.

Successful job search results from job seekers' well-shaped social communication. While well-known diferences in communication exist between people with autism and neurotypicals, little is known about how people with autism collaborate with their social surroundings to strive in the job market. To better understand the practices and challenges of collaborative job seeking for people with autism, we interviewed 20 participants including applicants with autism, their social surroundings, and career experts. Through the interviews, we identified social challenges that people with autism face during their job seeking; the social support they leverage to be successful; and the technological limitations that hinder their collaboration. We designed four probes that represent major collaborative features found from the interviews-executive planning, communication, stage-wise preparation, and neurodivergent community formation-and discussed their potential usefulness and impact through three focus groups. We provide implications regarding how our findings can enhance collaborative job seeking experiences for people with autism through new designs.

Data annotation interfaces predominantly leverage ground truth labels to guide annotators toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in domain-specific professional tasks, it has become increasingly important to help beginning annotators identify how their early-stage knowledge can lead to inaccurate answers, which in turn, helps to ensure quality annotations at scale. To investigate this issue, we conducted a formative study involving eight individuals from the field of disaster management, each possessing varying levels of expertise. The goal was to understand the prevalent factors contributing to disagreements among annotators when classifying Twitter messages related to disasters and to analyze their respective responses. Our analysis identified two primary causes of disagreement between expert and beginner annotators: 1) a lack of contextual knowledge or uncertainty about the situation, and 2) the absence of visual or supplementary cues. Based on these findings, we designed a Context interface, which generates aids that help beginners identify potential mistakes and provide the hidden context of the presented tweet. The summative study compares Context design with two widely used designs in data annotation UI, Highlight and Reasoning-based interfaces. We found significant differences between these designs in terms of attitudinal and behavioral data. We conclude with implications for designing future interfaces aiming at closing the knowledge gap among annotators.

Ride hailing services, such as Uber, Lyft, and Grab, have become a major transportation mode in the last decade. The number of current passenger requests is one of the important factors for such services routing and pricing algorithms. Therefore, predicting future passenger request for ride hailing services can boost the efficiency of the service for both drivers and riders by pre-planning the allocation of vehicles and avoiding traffic congestions. Demand forecasting for ride hailing services relies upon the spatial and temporal correlations of its features. The existing literatures mostly divide the target area into rectangular grids (based on the longitude and latitude), consider only adjacent grids for spatial correlation, and calculate demand for each grid independently. An individual grid can contain different regions with high and low demand or have a major part of it outside the land area, which obscures the granularity and precision of estimations and predictions. This paper attempts to mitigate the limitations of grid-based methods by estimating and predicting ride hailing service demand between geographic regions as pickup and destination zones. For predicting demand, a convolutional neural network is integrated with a recurrent autoencoder network to best capture the spatial–temporal correlations of features, including time of the day, month, year, weekend, holiday, pickup zone, destination, and demand. In our experiments, we forecast the demand for each pickup–destination pair for the next day at a certain hour by observing the demands over the past 2 weeks during the same hour in the New York City hire vehicle data set. Using the same model (CNN-biLSTM-AE) to predict demand for geographical regions, it achieved an [Formula: see text] of 0.984, while predicting demand for cells in the grid achieved an [Formula: see text] 0.545. While using the geographical regions instead of grids for partitioning the space, we compared our deep learning model with LSTM, CNN, CNN-LSTM, and LSTM-AE models and observed an improvement in [Formula: see text] from 0.632 to 0.767 and an improvement in RMSE from 20.53 to 16.33 against CNN.

Accurate traffic flow prediction is required in traffic management and optimal route selection. Traffic data patterns are influenced by factors such as road characteristics, time of the day, day of the week, weather conditions, and special events. The accuracy of traffic flow prediction depends on the spatial and temporal extent and diversity of the available data, along with the prediction algorithm. This paper presents a long short-term memory (LSTM) network for traffic prediction which is boosted by optimizing the spatial and temporal extent of the features that are used as input. A grid search method is employed to choose the spatial and temporal window sizes that result in the highest generalization accuracy. The optimal temporal window size is the number of time steps for input features and the spatial window size refers to the size of the geographical neighborhood, where features are used as input. Our experiments with traffic prediction in the State of California showed that optimizing the spatial and temporal window sizes in an LSTM network could improve the RMSE and R2by up to 40.02% and 25.21%.

Ride hailing services, such as Uber, Lyft, and Grab have become a major transportation mode in the last decade.Current ride demand is one of the major factors in such services' pricing algorithm.Therefore, forecasting future travel demand for such services is essential to both drivers and riders.This study constructs a deep learning based model for ride hailing demand forecast aiming to achieve high accuracies in solving similar problems.This study attempts to address a limitation in existing ride hailing demand prediction models, where the area is divided into a rectangular grid and all travel demand forecasts are made between rectangular cells, rather than city neighborhood zones.The proposed model forecasts travel demand between city neighborhood zones.The forecast model integrates convolutional and recurrent neural networks and forecasts the demand for each pickup-destination pair for a particular hour, during the next day, by observing the demand over the past two weeks for that particular hour.Our experiments with a real-world hire vehicle dataset in New York City showed that the proposed model outperforms the CNN and LSTM models up to 18.41 % in RMSE and 22.65% in R 2 values.