To address the limitations of traffic prediction from location-bound detectors, we present Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensive coverage of cellular traffic to capture mobility patterns. Our extensive analysis validates its potential for transportation. Focusing on vehicle-related GCT flow prediction, we propose a graph neural network that integrates multivariate, temporal, and spatial facets for improved accuracy. Experiments reveal our model's superiority over baselines, especially in long-term predictions. We also highlight the potential for GCT flow integration into transportation systems.

Traffic prediction utility often faces infrastructural limitations, which restrict its coverage. To overcome this challenge, we present Geographical Cellular Traffic (GCT) flow that leverages cellular network data as a new source for transportation evaluation. The broad coverage of cellular networks allows GCT flow to capture various mobile user activities across regions, aiding city authorities in resource management through precise predictions. Acknowledging the complexity arising from the diversity of mobile users in GCT flow, we supplement it with camera-based vehicle flow data from limited deployments and verify their spatio-temporal attributes and correlations through extensive data analysis. Our two-stage fusion approach integrates these multi-source data, addressing their coverage and magnitude discrepancies, thereby enhancing the prediction of GCT flow for accurate transportation evaluation. Overall, we propose novel uses of telecom data in transportation and verify its effectiveness in multi-source fusion with vision-based data.

Spatial-temporal prediction is a critical problem for intelligent transportation, which is helpful for tasks such as traffic control and accident prevention. Previous studies rely on large-scale traffic data collected from sensors. However, it is unlikely to deploy sensors in all regions due to the device and maintenance costs. This paper addresses the problem via outdoor cellular traffic distilled from over two billion records per day in a telecom company, because outdoor cellular traffic induced by user mobility is highly related to transportation traffic. We study road intersections in urban and aim to predict future outdoor cellular traffic of all intersections given historic outdoor cellular traffic. Furthermore, we propose a new model for multivariate spatial-temporal prediction, mainly consisting of two extending graph attention networks (GAT). First GAT is used to explore correlations among multivariate cellular traffic. Another GAT leverages the attention mechanism into graph propagation to increase the efficiency of capturing spatial dependency. Experiments show that the proposed model significantly outperforms the state-of-the-art methods on our dataset.

Based on our previous works in the design of an infrared emitter for electronic-toll-collection (ETC) applications, we use the unidirectional cosinenfunctions to approximate the irregular radiation pattern for typical infrared low-cost commercial light-emitting diodes (LEDs) with a half-intensity angle Φ1/2= 10°. With the aid of this approximation, the main characteristics of the performance of an infrared ETC system utilizing this type of LED as the emitter can be investigated based on the received signal strength of the system. For on-off keying, a simple model connecting the received signal strength and the bit error rate (BER) of the system is further established. From the calculated or the measured received signal strength of the system, it is not difficult to estimate the system performance in terms of the BER by this simple model. Roughly speaking, for a typical setting of the circuit parameters and a typical uplink and downlink data-transmission protocol, the data transmission can be very successful in terms of a very low BER if the received signal strength is 1.3 times stronger than the signal strength received at the communication boundary. The emitter presented in this paper is able to produce a relatively extended communication area in the vehicle-traveling direction, resulting in longer communication time interval for the data transmission between the onboard unit (OBU) and the roadside unit (RSU) than conventional emitters. Furthermore, the design presented in this paper is validated by experimental measurement to demonstrate its effectiveness.

In this paper, an active suspension system for the exponential decay control of the three-degree-of-freedom (3DOF) twin-shaft vehicle is proposed. A new exponential stabilization criterion of the second-order vector differential equations is derived and the particle swarm optimization (PSO) method is employed for searching the optimal controller parameters. The proposed method guarantees all of states of the vehicle in an optimally exponentially decaying manner. The simulation results show that the vibration characteristics of the vehicle by the proposed active suspension achieve high performance in the control of the vehicle body vibration and provide higher driving safety than the passive and linear quadratic (LQ) active suspensions.

Locating the radio signal coverage hole (SCH) and signal weak coverage area (SWCA) in the mobile network and taking appropriate actions to improve network quality are the major tasks for mobile network operators (MNO). A novel approach, Signal Coverage Hole and weak coverageArea DIscovering Model (SCHADIM), is proposed based on spatiotemporal data mining on the raw data collecting from location based service (LBS) to achieve this goal. It reuses the communication raw data in LBS-based applications and transforms it into mobile network communication quality monitoring information by integrating the GIS (geographical information system), road network database and mobile network base station database. By this way, the vehicles in the LBS-based applications can be regarded as the mobile network signal probing vehicles, which provides plentiful information for discovering the SCH/SWCA. Comparing to the traditionalmobile network signal probing vehicle method, which is known as radio site verification (RSV) method, the proposed SCHADIM has the spatiotemporal coverage as well as cost advantages. A mobile network monitoring system, cellular network quality safeguard (CQS), has been implemented based on the proposed model which combines with the domain expert heuristics to provide decision support information for optimizing the mobile network.

With the aid of a simple relation, which is analogous to the radar equation, the uplink signal strength received by the receiving module of a roadside unit (RSU) and emitted from the radiation module of an onboard unit (OBU) can be described. Setting the scale of this relation arbitrarily and determining the signal strength threshold from experimental measurements, and combined with the simulation of the radiation and the receiving pattern by cosinenfunction, the relative signal strength emitted from the OBU and received by the RSU can be calculated successfully. From this computed relative signal strength and the threshold, the influence of the RSU and OBU mounting parameters, such as the mounting angles and mounting height, on the available communication region is analyzed. The effect of windshield fading is also considered. With the help of the analysis results, an optimum RSU and OBU mounting configuration can be easily obtained. This method can be used conveniently and successfully for very short wavelengths. This includes visible light, infrared, and even submillimeter-wave ranges. For millimeter-wave and microwave systems, this method can, in some cases, also provide a rudimentary estimation

An architecture for simultaneously performing multitarget tracking and multidata communication suitable for millimeter-wave multilane-free-flow electronic-toll-collection (ETC) systems is presented. This architecture combines the idea of frequency multiplexing in communication systems and the technique of target tracking in the pulse-Doppler radar. For target tracking, we make use of pulse ranging by the aid of pulse compression and fine target-direction determination by amplitude comparison to obtain high resolution in the radial direction (downrange), as well as both lateral directions (cross ranges). This architecture can be utilized for both active and passive onboard-unit (OBU) systems. Another important advantage of this architecture is that it can easily identify the passing vehicles not equipped with an OBU. This will activate some subsequent enforcement activities against the violation vehicles.