Location prediction based on a sector snapshot for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shaped cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the new Markov-based mobility prediction (NMMP) and prediction location model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression, and insufficient accuracy. In this paper, a novel cell splitting algorithm is proposed. Also, a new prediction technique is introduced. The cell splitting is universal so it can be applied to all types of cells. Meanwhile, this algorithm is implemented to the Micro cell in parallel with the new prediction technique. The prediction technique, compared with two classic prediction techniques and the experimental results, show the effectiveness and robustness of the new splitting algorithm and prediction technique

A new splitting-based displacement prediction approach for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Several location prediction models have been proposed to enhance and increase the relevance of the information retrieved by users of mobile information systems, but none of them studied the relationship between accuracy rate of prediction and the performance of the model in terms of consuming resources and constraints of mobile devices. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. One such technique is the Prediction Location Model (PLM), which deals with inner cell structure. The PLM technique suffers from memory usage and poor accuracy. The main goal of this paper is to propose a new path prediction technique for Location-Based Services. The new approach is competitive and more efficient compared to PLM regarding measurements such as accuracy rate of location prediction and memory usage

Empirical Study and Modeling of Vehicular Communications at Intersections in the 5 GHz Band

[EN] Event warnings are critical in the context of ITS, being dependent on reliable and low-delay delivery ofmessages to nearby vehicles. One of the main challenges to address in this context is intersection management. Since buildings will severely hinder signals in the 5GHz band, it becomes necessary to transmit at the exact moment a vehicle is at the center of an intersection to maximize delivery chances. However, GPS inaccuracy, among other problems, complicates the achievement of this goal. In this paper we study this problem by first analyzing different intersection types, studying the vehicular communications performance in each type of intersection through real scenario experiments. Obtained results show that intersection-related communications depend on the distances to the intersection and line-of-sight (LOS) conditions. Also, depending on the physical characteristics of intersections, the presented blockages introduce different degrees of hampering to message delivery. Based on the modeling of the different intersection types, we then study the expected success ratio when notifying events at intersections. In general, we find that effective propagation of messages at intersections is possible, even in urban canyons and despite GPS errors, as long as rooftop antennas are used to compensate for poor communication conditions.This work was partially supported by the “Ministerio de Economía y Competividad, Programa Estatal de Investigación, Desarollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014,” Spain, under Grants TEC2014-52690-R and BES-2015-075988.Hadiwardoyo, SA.; Tomás Domínguez, AE.; Hernández-Orallo, E.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2017). Empirical Study and Modeling of Vehicular Communications at Intersections in the 5 GHz Band. Mobile Information Systems. (2861827):1-15. https://doi.org/10.1155/2017/2861827S1152861827Xiong, Z., Sheng, H., Rong, W., & Cooper, D. E. (2012). Intelligent transportation systems for smart cities: a progress review. Science China Information Sciences, 55(12), 2908-2914. doi:10.1007/s11432-012-4725-1Papadimitratos, P., La Fortelle, A., Evenssen, K., Brignolo, R., & Cosenza, S. (2009). Vehicular communication systems: Enabling technologies, applications, and future outlook on intelligent transportation. IEEE Communications Magazine, 47(11), 84-95. doi:10.1109/mcom.2009.5307471Grant-Muller, S., & Usher, M. (2014). Intelligent Transport Systems: The propensity for environmental and economic benefits. Technological Forecasting and Social Change, 82, 149-166. doi:10.1016/j.techfore.2013.06.010Ma, X., Chen, X., & Refai, H. H. (2009). Performance and Reliability of DSRC Vehicular Safety Communication: A Formal Analysis. EURASIP Journal on Wireless Communications and Networking, 2009(1). doi:10.1155/2009/969164Martinez, F. J., Toh, C.-K., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2010). A Street Broadcast Reduction Scheme (SBR) to Mitigate the Broadcast Storm Problem in VANETs. Wireless Personal Communications, 56(3), 559-572. doi:10.1007/s11277-010-9989-4Sanguesa, J. A., Fogue, M., Garrido, P., Martinez, F. J., Cano, J.-C., & Calafate, C. T. (2016). A Survey and Comparative Study of Broadcast Warning Message Dissemination Schemes for VANETs. Mobile Information Systems, 2016, 1-18. doi:10.1155/2016/8714142Sommer, C., Joerer, S., Segata, M., Tonguz, O. K., Cigno, R. L., & Dressler, F. (2015). How Shadowing Hurts Vehicular Communications and How Dynamic Beaconing Can Help. IEEE Transactions on Mobile Computing, 14(7), 1411-1421. doi:10.1109/tmc.2014.2362752Lin, J.-C., Lin, C.-S., Liang, C.-N., & Chen, B.-C. (2012). Wireless communication performance based on IEEE 802.11p R2V field trials. IEEE Communications Magazine, 50(5), 184-191. doi:10.1109/mcom.2012.6194401Gozalvez, J., Sepulcre, M., & Bauza, R. (2012). IEEE 802.11p vehicle to infrastructure communications in urban environments. IEEE Communications Magazine, 50(5), 176-183. doi:10.1109/mcom.2012.6194400Tornell, S. M., Patra, S., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2015). GRCBox: Extending Smartphone Connectivity in Vehicular Networks. International Journal of Distributed Sensor Networks, 11(3), 478064. doi:10.1155/2015/478064Chou, L.-D., Yang, J.-Y., Hsieh, Y.-C., Chang, D.-C., & Tung, C.-F. (2011). Intersection-Based Routing Protocol for VANETs. Wireless Personal Communications, 60(1), 105-124. doi:10.1007/s11277-011-0257-zSaleet, H., Langar, R., Naik, K., Boutaba, R., Nayak, A., & Goel, N. (2011). Intersection-Based Geographical Routing Protocol for VANETs: A Proposal and Analysis. IEEE Transactions on Vehicular Technology, 60(9), 4560-4574. doi:10.1109/tvt.2011.2173510Guan, X., Huang, Y., Cai, Z., & Ohtsuki, T. (2015). Intersection-based forwarding protocol for vehicular ad hoc networks. Telecommunication Systems, 62(1), 67-76. doi:10.1007/s11235-015-9983-yKarney, C. F. F. (2011). Transverse Mercator with an accuracy of a few nanometers. Journal of Geodesy, 85(8), 475-485. doi:10.1007/s00190-011-0445-3Durgin, G., Rappaport, T. S., & Hao Xu. (1998). Measurements and models for radio path loss and penetration loss in and around homes and trees at 5.85 GHz. IEEE Transactions on Communications, 46(11), 1484-1496. doi:10.1109/26.729393Haklay, M., & Weber, P. (2008). OpenStreetMap: User-Generated Street Maps. IEEE Pervasive Computing, 7(4), 12-18. doi:10.1109/mprv.2008.8

Estimation and Control of Traffic Relying on Vehicular Connectivity

Vehicular traffic flow is essential, yet complicated to analyze. It describes the interplay among vehicles and with the infrastructure. A better understanding of traf-fic would benefit both individuals and the whole society in terms of improving safety, energy efficiency, and reducing environmental impacts. A large body of research ex-ists on estimation and control of vehicular traffic in which, however, vehicles were assumed not to be able to share information due to the limits of technology. With the development of wireless communication and various sensor devices, Connected Vehicles(CV) are emerging which are able to detect, access, and share information with each other and with the infrastructure in real time. Connected Vehicle Technology (CVT) has been attracting more and more attentions from different fields. The goal of this dissertation is to develop approaches to estimate and control vehicular traffic as well as individual vehicles relying on CVT. On one hand, CVT sig-nificantly enriches the data from individuals and the traffic, which contributes to the accuracy of traffic estimation algorithms. On the other hand, CVT enables commu-nication and information sharing between vehicles and infrastructure, and therefore allows vehicles to achieve better control and/or coordination among themselves and with smart infrastructure. The first part of this dissertation focused on estimation of traffic on freeways and city streets. We use data available from on road sensors and also from probe One of the most important traffic performance measures is travel time. How-ever it is affected by various factors, and freeways and arterials have different travel time characteristics. In this dissertation we first propose a stochastic model-based approach to freeway travel-time prediction. The approach uses the Link-Node Cell Transmission Model (LN-CTM) to model traffic and provides a probability distribu-tion for travel time. The probability distribution is generated using a Monte Carlo simulation and an Online Expectation Maximization clustering algorithm. Results show that the approach is able to generate a reasonable multimodal distribution for travel-time. For arterials, this dissertation presents methods for estimating statistics of travel time by utilizing sparse vehicular probe data. A public data feed from transit buses in the City of San Francisco is used. We divide each link into shorter segments, and propose iterative methods for allocating travel time statistics to each segment. Inspired by K-mean and Expectation Maximization (EM) algorithms, we iteratively update the mean and variance of travel time for each segment based on historical probe data until convergence. Based on segment travel time statistics, we then pro-pose a method to estimate the maximum likelihood trajectory (MLT) of a probe vehicle in between two data updates on arterial roads. The results are compared to high frequency ground truth data in multiple scenarios, which demonstrate the effectiveness of the proposed approach. The second part of this dissertation emphasize on control approaches enabled by vehicular connectivity. Estimation and prediction of surrounding vehicle behaviors and upcoming traffic makes it possible to improve driving performance. We first propose a Speed Advisory System for arterial roads, which utilizes upcoming traffi

ON THE INTEGRATION OF VEHICULAR AD-HOC NETWORKS AND VISION-BASED DRIVER ASSISTANCE

Vehicular ad-hoc networks (VANETs) allow for short range wireless communication to share information between vehicles. Vision-based driver assistance (VBDA) uses computer vision to obtain information about nearby objects. The goal of both systems is to create a model of the environment surrounding the vehicle in order to make decisions. With unique strengths and weaknesses the two systems complement each other well. A simulation environment for both VANETs and VBDA is created to test both systems alongside one another. They are evaluated and then combined to build the best possible model of the environment with the goal of improving vehicle safety under adverse condition

Characterizing Queue Dynamics at Signalized Intersections From Probe Vehicle Data

Probe vehicles instrumented with location-tracking technologies have become increasingly popular for collecting traffic flow data. While probe vehicle data have been used for estimating speeds and travel times, there has been limited research on predicting queuing dynamics from such data. In this research, a methodology is developed for identifying the travel lanes of the GPS-instrumented vehicles when they are standing in a queue at signalized intersections with multilane approaches. In particular, the proposed methodology exploits the unequal queue lengths across the lanes to infer the specific lanes the probe vehicles occupy. Various supervised and unsupervised clustering methods were developed and tested on data generated from a microsimulation model. The generated data included probe vehicle positions and shockwave speeds predicated on their trajectories. Among the tested methods, a Bayesian approach that employs probability density functions estimated by bivariate statistical mixture models was found to be effective in identifying the lanes. The results from lane identification were then used to predict queue lengths for each travel lane. Subsequently, the trajectories for non-probe vehicles within the queue were predicted. As a potential application, fuel consumption for all vehicles in the queue is estimated and evaluated for accuracy. The accuracies of the models for lane identification. queue length prediction, and fuel consumption estimation were evaluated at varying levels of demand and probe-vehicle market penetrations. In general, as the market penetration increases, the accuracy improves. For example. when the market penetration rate is about 40%, the queue length estimation accuracy reaches 90%. The dissertation includes various numerical experiments and the performance of the models under numerous scenarios

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation