Traffic predictions play a crucial role in intelligent transportation systems. The rapid development of IoT devices allows us to collect different kinds of data with high correlations to traffic predictions, fostering the development of efficient multi-modal traffic prediction models. Until now, there are few studies focusing on utilizing advantages of multi-modal data for traffic predictions. In this paper, we introduce a novel temporal attentive cross-modality transformer model for long-term traffic predictions, namely xMTrans, with capability of exploring the temporal correlations between the data of two modalities: one target modality (for prediction, e.g., traffic congestion) and one support modality (e.g., people flow). We conducted extensive experiments to evaluate our proposed model on traffic congestion and taxi demand predictions using real-world datasets. The results showed the superiority of xMTrans against recent state-of-the-art methods on long-term traffic predictions. In addition, we also conducted a comprehensive ablation study to further analyze the effectiveness of each module in xMTrans.

The paper aims to present a collaboration between the industry and government sectors, focusing on creating a benchmarking platform for predicting urban risk transportation through the utilization of multimodal data. In this collaboration, the industry partner contributes datasets and customer preference surveys obtained from its business operations. On the other hand, government partners curate open datasets sourced from non-profit organizations in both private and public domains. Furthermore, the government provides an accessible platform that allows individuals to conveniently access and leverage resources for the purpose of advancing application development and engaging in research endeavors. Throughout the collaborative effort, a variety of techniques have been under development for forecasting urban risk transportation through the analysis of weather patterns, congestion levels, and people flow data. The core objective of this partnership is to formulate two foundational prediction methods. These methods are intended to serve as benchmarks, offering future users a dependable means to assess the performance of their own approaches in terms of both time-series and datapoints analytics methodologies.

A major issue with real-time monitoring is to collect complete data. Hardware or software failures, network issues or, more frequently, time delays can disrupt such a collection. This results in having two versions of the same information: one in real-time but with potentially missing data, and the another, albeit complete, is delayed. Many works have studied how to handle missing data for classification and prediction. However, to the best of our knowledge, they do not consider how to leverage the delayed complete data to assist in learning the representation of real-time available data with missing values. This is despite the fact that the delayed complete data contain all the information (e.g., periodicities and trends). In this paper, we propose a framework to enhance the representation learning of the real-time available data by aligning the representation of past real-time but with missing data to that of past delayed but complete data. We test both a distance metric and contrastive learning to achieve this alignment. We implement our framework on a Transformer-based model and experiment it on three datasets. The efficiency of our solution is evaluated against seven baselines and considering four distinct patterns of missing data. Our experiments show that this proposal has a significant improvement in prediction accuracy (5.21% on average) over the baselines.

Understanding human mobility is mostly based on destination prediction. The reality that training and test data frequently differ makes it challenging to broaden the application of destination prediction, and when a prediction model that was trained on certain areas is then applied to the area of interest but with a different data distribution, the accuracy is suboptimal at best. The objective of the IEEE Big Data Cup 2022 is to solicit a robust and generalizable model that can effectively forecast a person’s daily destination based on facts and qualities obtained from four Japanese urban areas, as well as to predict the destination for a new metropolitan area using the models that were trained with the prior data. To address the issue and face the challenge of this Cup, our KDDI Research team has developed a prediction method based on Exploratory Data Analysis (EDA) that does not employ geographical zone information that varies from area to area. Instead, we employ the mobility characteristics of human groups, where each group is categorized according to demographics, which according to our EDA are relatively universal across areas. Our experimental findings show that our method achieves a relatively good prediction accuracy, which is attested by this Cup’s leaderboard. Albeit our method is simple and straightforward, we can argue that our approach can be used as good baseline for human mobility destination prediction.

In a world where the growing concern about climate change becomes more apparent, electricity industry has its role to play in slowing down such an evolution. In fact, tools and services could be provided to consumers in order to better manage their ever-increasing consumption. But, while waiting for such a complex solution to be deployed, a first step would be to better forecast current consumption. Such an improvement would enable to generate electricity accordingly, while renewable solutions are not presently optimal – i.e., intermittent, long-term storage issue, etc.Electricity Load Demand (ELD) fluctuations do not solely depend on temporal factors, they also have a spatial contribution that is often underestimated. Indeed, it is possible to map different ELD profiles with their Land Use (LU) information. For example, ELD in residential areas fits with residents’ commuting style, while in industrial areas, it mostly correlates with working hours. This paper aims to investigate such a relation. Considering ELD data on a 500m square grid and LU data on a 100m square grid, different 500m cell labeling has been defined. In order to assess if cells with similar labels have similar ELD, they have been grouped under the same forecast model. Several types of models (Multilayer Perceptron, Recurrent Neural Network) have been used to compare their performance and efficiency. This study confirms that, all models considered, some labels are more difficult to forecast than others. Such associations can reduce by over 30% the prediction error compare to a per cell scenario. Additional investigations would be needed to further reduce prediction error and to help models better seize the land specificity of each grid-cell. External data also affects ELD, and pairing them with an optimal LU labeling could be a promising solution.

The need for electricity has never been more important these days. In order to achieve balance between generation and distribution - as well as schedule operations accordingly-high-accuracy load demand predictions are mandatory. But our society is currently undergoing modifications in electricity consumption allocation. We are witnessing a fast shift from office-to home- based working style. As a consequence, Electricity load demand prediction models are in need for additional data in order to quickly adapt to these modifications and maintain efficient predictions accuracy. The rising popularity of "tracking" devices and alike-applications opens up to a new type of multi-modal investigations. The availability of associated location data enables researcher to study mobility routine and patterns in order to cross it with other data. Electricity consumption is one domain impacted by people's mobility behavior (commuting, telework, etc.) as people are not "plugged" onto the power grid while moving. This paper presents a study on population staying patterns and how it can relate to electricity load demand. Time-series data providing the number of people staying in a given area has been used within a Deep Learning model in order to enhance electricity load demand predictions at the provider level. It unveils the potential usage of such dynamics data, while setting the foundations for more complex studies.