The emergence of Mobility as a Service (MaaS) highlights the need for personalized and exploration-oriented recommendation that account for users' subjective preferences and multisensory experiences. However, existing approaches lack real-world sensory and contextual data to support such personalization. To address this gap, we developed FreePOST, a location-based information collection system that enables users to submit photos together with multisensory and location impressions tied to specific places. In a field study spanning over three months in Kyoto, 69 participants contributed more than 8,000 posts. The collected data were visualized in real time via a web application with an interactive map interface. Because the sensory tags assigned to locations may depend not only on the characteristics of the place itself but also on the personality traits of the participants, we also collected personality and behavioral data using the Big Five Inventory and the Brief Sensation Seeking Scale. Our results demonstrate the ability to effectively capture Kyoto's sensory landscapes and context-aware experiential features, providing a valuable foundation for integrating subjective and multisensory data into future personalized search and recommendation in MaaS contexts. This demo will showcase FreePOST's core functions of multisensory data submission and interactive map visualization.

Most pedestrian navigation systems prioritize efficiency, which often directs users along the shortest route at the cost of serendipitous discovery and enjoyment. Recent efforts have explored roaming-supportive navigation, which encourages exploration by offering diverse, non-shortest routes, but its real-world impact on user experience remains underexplored. This paper presents an in-the-wild field study conducted in Asakusa, Tokyo, where participants followed either the shortest or exploratory walking routes guided in real-time by a Wizard-of-Oz prototype that enables interactive, turn-by-turn voice prompts despite urban GPS drift. We measured emotional and experiential responses using the Positive and Negative Affect Schedule (PANAS) and the Satisfaction with Travel Scale (STS). We also observed photo-taking behavior as an indicator of engagement. Our results show that participants on exploratory routes reported greater travel satisfaction, higher positive affect, and greater visual diversity in photo-taking behavior. These findings demonstrate that pedestrian navigation systems can go beyond efficiency to support emotionally enriching and cognitively meaningful mobility in everyday urban settings.

Navigation has become an indispensable technology, especially when exploring unfamiliar environments. Traditional shortest route-based navigation focuses on route effectiveness, which may deprive users of the opportunity to explore new areas. To mitigate the difficulty of exploring environments and enrich our walking activities by stimulating our natural tendency to explore, we study a system utilizing alternative non-shortest diverse routes. In this paper, we explain the concepts of our roaming navigation and show how users can travel with our system to demonstrate the effect of our new navigation service for pedestrians.

Network traffic prediction based on probe vehicle data is important for traffic management and route recommendation and has been intensively studied. Previous traffic prediction methods mainly focused on recurring traffic congestion. Predicting non-recurring traffic congestion, caused by events and accidents, is significantly more important; however, it has not been intensively studied. To predict non-recurring traffic congestion using probe data, we need to estimate the current traffic conditions based onsparseobservations forlargetraffic networks to track traffic changesonline. Conventional traffic forecasting methods have not been able to solve all of these problems. To address these problems, we propose a data assimilation method using a state space neural network (SSNN) with an incorporated topology of road networks. The SSNN model can easily model network traffic and can easily estimate its states and parameters by data assimilation using Bayesian filtering. In this study, we adopted a decoupled extended Kalman filter (DEKF) based data assimilation, which is scalable and applicable to large-scale network traffic, to estimate the states and parameters online. We evaluate the proposed method using an open dataset that includes a road network comprising over 30000 road segments. The results show that our method achieves higher prediction accuracy for predicting unknown traffic congestion and is more robust against data sparsity than conventional state estimation methods.

This paper proposes a coordination method for vehicles on merging roads to realize smooth traffic merging. The proposed method can be implemented with a low communication volume in an environment in which the automated/connected vehicles and manually operated vehicles are mixed. The method is based on a pseudo-perturbation-based broadcast controller (PBC), which has the potential to coordinate multiple vehicles with low-cost V2I communication without V2V communication. The PBC indiscriminately broadcasts an identical signal to the vehicles. The volume of such a broadcast communication is less than that of the commonly used unicast communication. This paper overcomes the following three issues to apply the PBC to the traffic merging problem. First, the time-invariant metrics are derived to design a time-invariant objective function corresponding to the traffic merging problem because the PBC is based on the minimization of such a time-invariant function. Second, a (locally) convex objective function to be globally minimized is designed. Finally, the collision avoidance between vehicles is guaranteed. The microscopic traffic simulations demonstrate the effectiveness of the proposed PBC-based coordination method in the presence of uncooperative manually operated vehicles.

Map matching is a procedure that estimates the route traveled by vehicles or people by using observed coordinates. It is an important preprocessing procedure for location services based on global positioning system (GPS) data obtained from probe vehicles. One recently proposed major map matching approach is the hidden Markov model (HMM)-based method. However, HMM-based approaches suffer from latency, because they rely on the availability of future GPS points. This latency limits the ability of real-time traffic sensing and location services. This paper presents a novel online map matching algorithm that uses a probabilistic route prediction model instead of future GPS points. The probabilistic route prediction model can be trained by using historical trajectory data. Our experimental results show that the accuracy of the untrained proposed model is competitive with a naïve online HMM-based method without any latency. Moreover, the results show that the trained model obtains even higher accuracy. The experimental results also show that the proposed method is faster than the online HMM.

Traffic anticipation enhances driving intelligence and strengthens the ability to take early vehicle control action, e.g., lane change and speed adjustment, in a dynamically varying traffic environment. This paper presents an efficient vehicle driving system, based on detailed anticipation of surrounding traffic, that aims at optimizing the driving performance of individual vehicles and smoothening traffic flows on multilane roads. More elaborately, under a connected vehicle environment, the system receives the states of all vehicles that exist within its communication range. Based on their predicted states in a look forward horizon, the system generates the optimal acceleration and makes lane change decision simultaneously in the model predictive control framework. A fast hierarchical optimization scheme is used in the framework for its onboard implementation. The proposed efficient driving system is applied to a fraction of traffic, and both the individual and overall traffic performances are evaluated using a microscopic traffic simulator. It is revealed that the vehicles under the proposed efficient driving system improve their fuel economy and travel efficiency, significantly. In the mixed traffic, by the influence of the vehicle with the proposed driving system, the other traditionally driven vehicles also improve their performance.

Anticipative control of vehicles is a potential approach for improving travel efficiency of individual vehicles, smoothing traffic flows on urban roads, alleviating impacts on the environment and elevating comforts of the users in various respects. This paper presents such a vehicle driving system in a model predictive control (MPC) framework to efficiently drive a vehicle on multi-lane roads. Anticipation enhances the driving intelligence and strengthens the vehicle's ability in taking advance action, e.g., lane change, speed adjustment, in a dynamically varying traffic environment. More elaborately, presuming a connected vehicle environment, the system receives the information form the surrounding vehicles and infrastructure instantly through V2X communication systems and, using dynamical models, predicts the future road-traffic states. Considering relevant constraints and a performance index, the system generates the optimal acceleration and executes lane change maneuver optimally if long term advantages are anticipated. Numerical simulation in realistic traffic flow conditions reveals that the vehicles with the proposed driving system improve their travel efficiency significantly.

This paper describes improvements on the excitation model of an HMM-based text-to-speech system. In our previous work, natural sounding speech can be synthesized from trained HMMs. However, it has a typical quality of “vocoded speech” since the system uses a traditional excitation model with either a periodic impulse train or white noise. In this paper, in order to reduce the synthetic quality, a mixed excitation model used in MELP is incorporated into the system. Excitation parameters used in mixed excitation are modeled by HMMs, and generated from HMMs by a parameter generation algorithm in the synthesis phase. The result of a listening test shows that the mixed excitation model significantly improves quality of synthesized speech as compared with the traditional excitation model.

This paper derives a speech parameter generation algorithm for HMM-based speech synthesis, in which the speech parameter sequence is generated from HMMs whose observation vector consists of a spectral parameter vector and its dynamic feature vectors. In the algorithm, we assume that the state sequence (state and mixture sequence for the multi-mixture case) or a part of the state sequence is unobservable (i.e., hidden or latent). As a result, the algorithm iterates the forward-backward algorithm and the parameter generation algorithm for the case where the state sequence is given. Experimental results show that by using the algorithm, we can reproduce clear formant structure from multi-mixture HMMs as compared with that produced from single-mixture HMMs.