Mahdi Hashemi

A critical review of real-time map-matching algorithms: Current issues and future directions

Abstract Current navigation systems/services allow drivers to keep track of their precise whereabouts and provide optimal routes to reach specified locations. A reliable map-matching algorithm is an indispensable and integral part of any land-based navigation system/service. This paper reviews existing map-matching algorithms with the aim of highlighting their qualities as well as unfolding their unresolved issues as a means to provide directions for future studies in this field. Existing map-matching algorithms are compared and contrasted with respect to positioning sensors, map qualities, assumptions and accuracies. The results of these comparisons provide interesting insights into the workings of existing algorithms and the issues they must address for improving their performance. Example findings are: (a) not all map-matching algorithms pay sufficient attention to topology of networks, directionality of roads or turn-restrictions; (b) most map-matching algorithms make an unbalanced trade-off between performance and accuracy; and (c) weight-based map-matching algorithms balance simplicity and accuracy and advanced map-matching algorithms provide high accuracy but with low performance. Based on the findings, suggestions are made to improve existing algorithms.

Indoor Spatial Model and Accessibility Index for Emergency Evacuation of People with Disabilities

AbstractAlthough indoor emergency evacuation drills, simulations, and plans are addressed and well covered in literature, little attention is currently paid to the evacuation of people with disabilities (PWD). Routing is a major process in all indoor emergency evacuations, simulations, and plans. Current indoor emergency evacuation routing algorithms find the shortest path or attempt to balance the traffic on exit doors. However, accessibility is more important than travel distance during the emergency evacuation of PWD. A spatial model, an accessibility index assigned to appropriate elements in the spatial model, and a way-finding technique are the major requirements of such routing. Existing spatial models of buildings are general purpose, fall short of efficiently and effectively handling emergency evacuations, and do not take into account the requirements of PWD. This paper proposes a spatial model for indoors which is specifically developed for the routing requirements of PWD during emergency evacuat...

A weight-based map-matching algorithm for vehicle navigation in complex urban networks

ABSTRACT A map-matching algorithm is an integral part of every navigation system and reconciles raw and inaccurate positional data (usually from a global positioning system [GPS]) with digital road network data. Since both performance (speed) and accuracy are equally important in real-time map-matching, an accurate and efficient map-matching algorithm is presented in this article. The proposed algorithm has three steps: initialization, same-segment, and next-segment. Distance between the GPS point and road segments, difference between the heading of the GPS point and direction of road segments, and difference between the direction of consecutive GPS points and direction of road segments are used to identify the best segment among candidates near intersections. In contrast to constant weights applied in existing algorithms, the weight of each criterion in this algorithm is dynamic. The weights of criteria are calculated for each GPS point based on its: (a) positional accuracy, (b) speed, and (c) traveled distance from previous GPS point. The algorithm considers a confidence level on the assigned segment to each GPS point, which is calculated based on the density and complexity of roads around the GPS point. The evaluation results indicate 95.34% correct segment identification and 92.19% correct segment assignment. The most important feature of our algorithm is that the high correct segment identification percentage achieved in urban areas is through a simple and efficient weight-based method that does not depend on any additional data or positioning sensors other than digital road network and GPS.

A Machine Learning Approach to Improve the Accuracy of GPS-Based Map-Matching Algorithms (Invited Paper)

Advanced map-matching algorithms use location and heading of GPS points along with geometrical and topological features of digital road networks to find the road segment on which the vehicle is moving. However, GPS errors sometimes impede map-matching algorithms in finding the correct segment, especially in dense and complicated parts of the network, such as near intersections with acute angles or on close parallel roads. In this paper an artificial neural network (ANN) approach is explored to improve the segment identification accuracy of map-matching algorithms. The proposed ANN is continuously trained by using the horizontal shift imposed on GPS points and once it is trained, it will be used to correct raw GPS points before inputting them into the map-matching algorithm. Integrating the proposed ANN enabled an existing map-matching algorithm to find the correct segments for some of the GPS points where the original map-matching algorithm had failed to do so.

Reusability of the Output of Map-Matching Algorithms Across Space and Time Through Machine Learning

A map-matching algorithm outputs a vector per GPS point, projecting the moving object on one of the segments of the transportation network. Although developing more sophisticated map-matching algorithms for vehicle and pedestrian navigation systems have been the focus of research in this field, reusability of the historical information already provided by map-matching algorithms has not been addressed yet. In other words, although researchers have been attempting to improve the accuracy of the aforementioned vector to correctly project GPS points on the transportation network, no research has exploited the spatial-temporal pattern in the arrangement of these projection vectors. This pattern, if properly detected, can be used as a rough surrogate for map-matching algorithms, in addition to other applications that require better positional accuracy for moving objects in smart cities. This paper detects and validates the spatial-temporal pattern in projection vectors produced by map-matching algorithms via machine learning. Projection vectors showed a strong spatial-temporal pattern in Chicago, IL, USA, which was captured best via a local nonlinear regressor, K-nearest neighbors, and helped double the positional accuracy of unseen GPS points. While a global nonlinear regressor, multilayer Perceptron was able to slightly improve the positional accuracy of GPS points, the linear least squares had an exacerbating effect on the positional accuracy.

A Theoretical Framework for Ubiquitous Computing

You may forget where you left your keys when you need them. In ubiquitous computing space your keys will find you and inform you where they are. Ubiquitous computing, the third generation of computing spaces, following mainframes and personal computers, is in its incipient evolution steps. In ubiquitous computing space, sensors and computing nodes are invisibly, inconspicuously, and overwhelmingly embedded in all real-world objects and are all connected to each other through omnipresent wireless networks. The goal is to make real-world objects seem intelligent and autonomous in providing users with electronic and Internet services with users not even noticing how they are provided with these services. The real world, cyberspace, modeling, and mathematics are identified as the main constituents of ubiquitous computing in this study. These four areas are investigated one-by-one and in combination to show how they create a solid foundation for ubiquitous computing. An application of ubiquitous computing in car navigation systems is used to indicate the reliability of the proposed framework.

Seismic Source Modeling by Clustering Earthquakes and Predicting Earthquake Magnitudes

Seismic sources are currently generated manually by experts, a process which is not efficient as the size of historical earthquake databases is growing. However, large historical earthquake databases provide an opportunity to generate seismic sources through data mining techniques. In this paper, we propose hierarchical clustering of historical earthquakes for generating seismic sources automatically. To evaluate the effectiveness of clustering in producing homogenous seismic sources, we compare the accuracy of earthquake magnitude prediction models before and after clustering. Three prediction models are experimented: decision tree, SVM, and kNN. The results show that: (1) the clustering approach leads to improved accuracy of prediction models; (2) the most accurate prediction model and the most homogenous seismic sources are achieved when earthquakes are clustered based on their non-spatial attributes; and (3) among the three prediction models experimented in this work, decision tree is the most accurate one.

A testbed for evaluating network construction algorithms from GPS traces

Abstract Developing algorithms which construct street/pedestrian networks from crowd-sourced GPS traces has been an ongoing research since the outbreak of inexpensive GPS receivers on mobile devices. Although, the proposed algorithms are evaluated by their developers, the evaluation results cannot be used to compare their accuracy because: (a) different algorithms target different types of networks, some designed for complicated networks while others for simple ones, (b) GPS traces, used in different studies, are not the same, some of them are more accurate and denser than others, and (c) the constructed networks are evaluated either qualitatively or with different quantitative metrics. Lack of a comprehensive testbed for evaluating network construction algorithms has made it difficult for authors, reviewers, and readers to monitor the effectiveness of such algorithms. This study establishes a testbed for evaluating network construction algorithms containing three components: (a) street and pedestrian networks with different densities and complexities as the baseline, (b) collections of GPS traces with different accuracies and sampling rates to be used by algorithms to construct those networks, and (c) three quantitative metrics to indicate the completeness, precision, and topology correctness of the constructed network, in addition to the algorithms time complexity, conventionally used to indicate its time performance. This testbed not only paves the way for comparing network construction algorithms but also allows researchers to focus on their algorithms rather than collecting data for testing it or looking for ways to describe its accuracy.

Intelligent GPS trace management for human mobility pattern detection

Abstract Large volumes of volunteered GPS traces in the last decade have provided location-based services with an opportunity to become more intelligent and personalized. Individual and group mobility patterns, detected from GPS traces, can be used for this purpose. In this paper, we show the potential of GPS traces, if managed properly in the database, for detecting points of interest for individual users and even recognizing individual users from their walking patterns. However, when it comes to GPS traces, databases can be very complicated and cumbersome to populate. Databases provided by OSM and GeoLife do not effectively pave the path for data mining and machine learning techniques which require a much more detailed and organized database. A GPS trace database must provide statistics and detailed information about GPS traces not only for visualization purposes at the front-end, but also for cross checking purposes to eliminate erroneous records and to be applied in mobility pattern detection applications. This study provides the design of an interactive database management system for GPS traces whose applications in detecting points of interest and user identification are tested with GPS traces from the GeoLife project. The results show that while the accuracy of detected points of interest depends mostly on the size of data, the accuracy of user identification relies more upon the appropriate choice of input features to machine learning techniques.

Emergency evacuation of people with disabilities: A survey of drills, simulations, and accessibility

Abstract A natural or man-made disaster may destabilize the structure of a building and endanger the lives of its occupants. Evacuating occupants in the shortest possible time is the first reaction in such situations, often referred to as indoor emergency evacuation. Indoor emergency evacuations pay little attention to people with disabilities (PWD) who face additional challenges in emergency situations than people without disabilities. This work highlights the major findings in literature with regard to emergency evacuation of PWD and underscores the related shortcomings and gaps for future research. Current studies can be categorized in: evacuation drills, computer evacuation models, and indoor accessibility measures for PWD. Evacuation drills are focused on assessing the ability of PWD to negotiate different surface types and bottlenecks, but none on understanding their behavior and decisions during an emergency evacuation. Computer simulations are focused on developing evacuation plans by minimizing the overall evacuation time, but fail to capture the dynamics, uncertainties, and complexities in a real-world evacuation scenario. Only few studies are devoted to measuring the accessibility of indoor environments to PWD, most of which are not suitable for wayfinding purposes. Finally, we discuss research gaps in developing indoor spatial models, accessible, personalized, and collaborative wayfinding, and real-time dynamic evacuation systems with accessible user-interfaces.