Mobility as a Service (MaaS) is a smart mobility model that integrates mobility services to deliver transportation needs through a single interface, offering users flexible and personalizd mobility. This paper presents a structural approach for developing a MaaS system architecture under Autonomous Transportation Systems (ATS), which is a new transition from the Intelligent Transportation Systems (ITS) with emerging technologies. Five primary components, including system elements, user needs, services, functions, and technologies, are defined to represent the system architecture. Based on the components, we introduce three architecture elements: functional architecture, logical architecture and physical architecture. Furthermore, this paper presents an evaluation process, links the architecture elements during the process and develops a three-layer structure for system performance evaluation. The proposed MaaS system architecture design can help the administration make services planning and implement planned services in an organized way, and support further technical deployment of mobility services.

To enhance safety and efficiency in the traffic environment, developing intelligent transportation systems (ITSs) is of paramount importance. In ITSs, roadside units (RSUs) are critical components that enable the environment awareness and connectivity via using radar sensing and communications. In this paper, we focus on RSUs with multiple radar systems. Specifically, we propose a parameter selection method of multiple radar systems to enhance the overall sensing performance. Furthermore, since different radars provide different sensing and tracking results, to benefit from multiple radars, we propose fusion algorithms to integrate the tracking results of different radars. We use two commercial frequency-modulated continuous wave (FMCW) radars to conduct experiments at Hsinchu city in Taiwan. The experimental results validate that our proposed approaches can improve the overall sensing performance.

Car navigation systems provide traffic jam information. In this study, we attempt to provide more detailed traffic jam information that considers the lane in which a traffic jam is in. This makes it possible for users to avoid long waits in queued traffic going toward an unintended destination. Lane-specific traffic jam detection utilizes image processing, which incurs long processing time and high cost. To reduce these, we propose a “suddenness index (SI)” to categorize candidate areas as sudden or periodic. Sudden traffic jams are prioritized as they may lead to accidents. This technology aggregates the number of connected cars for each mesh on a map and quantifies the degree of deviation from the ordinary state. In this paper, we evaluate the proposed method using actual global positioning system (GPS) data and found that the proposed index can cover 100% of sudden lane-specific traffic jams while excluding 82.2% of traffic jam candidates. We also demonstrate the effectiveness of time savings by integrating the proposed method into a demonstration framework. In addition, we improved the proposed method's ability to automatically determine the SI threshold to select the appropriate traffic jam candidates to avoid manual parameter settings.

Information and communication technologies have improved the quality of intelligent transportation systems (ITS). By estimating from cellular floating vehicle data (CFVD) is more cost-effective, and easier to acquire than traditional ways. This study proposes a cell probe (CP)-based method to analyse the cellular network signals (e.g., call arrival, handoff, and location update), and regression models are trained for vehicle speed estimation. In experiments, this study compares the practical traffic information of vehicle detector (VD) with the estimated traffic information by the proposed methods. The experiment results show that the accuracy of vehicle speed estimation by CP-based method is 97.63%. Therefore, the CP-based method can be used to estimate vehicle speed from CFVD for ITS.

We propose a method that detects vehicles from in-vehicle monocular camera images captured during nighttime driving. Detecting vehicles from their shape is difficult at night; however, many vehicle detection methods focusing on light have been proposed. We detect bright spots by appropriate binarization based on the characteristics of vehicle lights such as brightness and color. Also, as the detected bright spots include lights other than vehicles, we need to distinguish the vehicle lights from other bright spots. Therefore, the bright spots were distinguished using Random Forest, a multiclass classification machine-learning algorithm. The features of bright spots not associated with vehicles were effectively utilized in the vehicle detection in our proposed method. More precisely vehicle detection is performed by giving weights to the results of the Random Forest based on the features of vehicle bright spots and the features of bright spots not related to the vehicle. Our proposed method was applied to nighttime images and confirmed effectiveness.

Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.

Intelligent transportation systems (ITS) are a set of technological solutions used to improve the performance and safety of road transportation. Since one of the most important information sources on ITS are sensors, the integration and sharing the sensor data become a big challenging problem in the application of sensor networks to these systems. In order to make full use of the sensor data, is crucial to convert the sensor data into semantic data, which can be understood by computers. In this work, we propose to use the SSN ontology to manage the sensor information in an intelligent transportation architecture. The system was tested in a traffic light settings application, allowing to predict and avoid traffic accidents, and also for the routing optimization.

As the number of surveillance cameras keeps increasing, the demand for automated traffic-monitoring systems is growing. In this paper, we propose a practical vehicle detection method for such systems. In the last decade, vehicle detection mainly has been performed by employing an image scan strategy based on sliding windows whereby a pre-trained appearance model is applied to all image areas. In this approach, because the appearance models are built from vehicle sample images, the normalization of the scales and aspect ratios of samples can significantly influence the performance of vehicle detection. Thus, to successfully apply sliding window schemes to detection, it is crucial to select the normalization sizes very carefully in a wise manner. To address this, we present a novel vehicle detection technique. In contrast to conventional methods that determine the normalization sizes without considering given scene conditions, our technique first learns local region-specific size models based on scene-contextual clues, and then utilizes the obtained size models to normalize samples to construct more elaborate appearance models, namely local size-specific classifiers (LSCs). LSCs can provide advantages in terms of both accuracy and operational speed because they ignore unnecessary information on vehicles that are observable in faraway areas from each sliding window position. We conduct experiments on real highway traffic videos, and demonstrate that the proposed method achieves a 16% increased detection accuracy with at least 3 times faster operational speed compared with the state-of-the-art technique.

In order to minimize packet error rate in extremely dynamic vehicular networks, a novel vehicle to vehicle (V2V) mobile content transmission scheme that jointly employs random network coding and shuffling/scattering techniques is proposed in this paper. The proposed scheme consists of 3 steps: Step 1-The original mobile content data consisting of several packets is encoded to generate encoded blocks using random network coding for efficient error recovery. Step 2-The encoded blocks are shuffled for averaging the error rate among the encoded blocks. Step 3-The shuffled blocks are scattered at different vehicle locations to overcome the estimation error of optimum transmission location. Applying the proposed scheme in vehicular networks can yield error free transmission with high efficiency. Our simulation results corroborate that the proposed scheme significantly improves the packet error rate performance in high mobility environments. Thanks to the flexibility of network coding, the proposed scheme can be designed as a separate module in the physical layer of various wireless access technologies.

Vehicles' speed is one of the key factors in vehicle travel efficiency, as speed is related to vehicle travel time, travel safety, fuel consumption, and exhaust gas emissions (e.g., CO2 emissions). Therefore, to improve the travel efficiency, a recommended speed calculation scheme is proposed to assist driving in Vehicle Ad hoc networks (VANETs) circumstances. In the proposed scheme, vehicles' current speed and space headway are obtained by Vehicle-to-Roadside unit (V2R) communication and Vehicle-to-Vehicle (V2V) communication. Based on the vehicles' current speed and adjacent vehicles' space headway, a recommended speed is calculated by on-board units installed in the vehicles, and then this recommended speed is provided to drivers. The drivers can change their speed to the recommended speed. At the recommended speed, vehicle travel efficiency can be improved: vehicles can arrive at destinations in a shorter travel time with fewer stop times, lower fuel consumption, and less CO2 emission. In particular, when approaching intersections, vehicles can pass through the intersections with less red light waiting time and a higher non-stop passing rate.

An atmospheric visibility measurement system capable of quantifying the most common operating range of onboard exteroceptive sensors is a key parameter in the creation of driving assistance systems. This information is then utilized to adapt sensor operations and processing or to alert the driver that the onboard assistance system is momentarily inoperative. Moreover, a system capable of either detecting the presence of fog or estimating visibility distances constitutes in itself a driving aid. In this paper, we first present a review of different optical sensors likely to measure the visibility distance. We then present our stereovision based technique to estimate what we call the "mobilized visibility distance". This is the distance to the most distant object on the road surface having a contrast above 5%. In fact, this definition is very close to the definition of the meteorological visibility distance proposed by the International Commission on Illumination (CIE). The method combines the computation of both a depth map of the vehicle environment using the "v-disparity" approach and of local contrasts above 5%. Both methods are described separately. Then, their combination is detailed. A qualitative evaluation is done using different video sequences. Finally, a static quantitative evaluation is also performed thanks to reference targets installed on a dedicated test site.