ISPRS Int. J. Geo-Inf.2015, 4(4), 2681-2703; doi:10.3390/ijgi4042681 - published 27 November 2015 Show/Hide Abstract
Abstract: Road traffic safety is the result of a complex interaction of factors, and causes behind road vehicle crashes require different measures to reduce their impacts. This study assesses how strongly the variation in daily winter crash rates associates with weather conditions in Finland. This is done by illustrating trends and spatiotemporal variation in the crash rates, by showing how a GIS application can evidence the association between temporary rises in regional crash rates and the occurrence of bad weather, and with a regression model on crash rate sensitivity to adverse weather conditions. The analysis indicates that a base rate of crashes depending on non-weather factors exists, and some combinations of extreme weather conditions are able to substantially push up crash rates on days with bad weather. Some spatial causation factors, such as variation of geophysical characteristics causing systematic differences in the distributions of weather variables, exist. Yet, even in winter, non-spatial factors are normally more significant. GIS data can support optimal deployment of rescue services and enhance in-depth quantitative analysis by helping to identify the most appropriate spatial and temporal resolutions. However, the supportive role of GIS should not be inferred as existence of highly significant spatial causation.
ISPRS Int. J. Geo-Inf.2015, 4(4), 2660-2680; doi:10.3390/ijgi4042660 - published 26 November 2015 Show/Hide Abstract
Abstract: In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT), which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT) uses an adaptive density optimization method to remove outliers from the raw GPS trajectories based on their space-time distribution and density clustering. Second, MLIT acquires the number of lanes in two steps. The first step establishes a naïve Bayesian classifier according to the trace features of the road plane and road profiles and the real number of lanes, as found in the training samples. The second step confirms the number of lanes using test samples in reference to the naïve Bayesian classifier using the known trace features of test sample. Third, MLIT infers the turn rules of each lane through tracking GPS trajectories. Experiments were conducted using the GPS trajectories of taxis in Wuhan, China. Compared with human-interpreted results, the automatically generated lane-level road network information was demonstrated to be of higher quality in terms of displaying detailed road networks with the number of lanes and turn rules of each lane.
ISPRS Int. J. Geo-Inf.2015, 4(4), 2638-2659; doi:10.3390/ijgi4042638 - published 25 November 2015 Show/Hide Abstract
Abstract: In this paper, a scheme is presented for fusing a foot-mounted Inertial Measurement Unit (IMU) and a floor map to provide ubiquitous positioning in a number of settings, such as in a supermarket as a shopping guide, in a fire emergency service for navigation, or with a hospital patient to be tracked. First, several Zero-Velocity Detection (ZDET) algorithms are compared and discussed when used in the static detection of a pedestrian. By introducing information on the Zero Velocity of the pedestrian, fused with a magnetometer measurement, an improved Pedestrian Dead Reckoning (PDR) model is developed to constrain the accumulating errors associated with the PDR positioning. Second, a Correlation Matching Algorithm based on map projection (CMAP) is presented, and a zone division of a floor map is demonstrated for fusion of the PDR algorithm. Finally, in order to use the dynamic characteristics of a pedestrian’s trajectory, the Adaptive Unscented Kalman Filter (A-UKF) is applied to tightly integrate the IMU, magnetometers and floor map for ubiquitous positioning. The results of a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirm that the proposed scheme can reliably achieve meter-level positioning.
ISPRS Int. J. Geo-Inf.2015, 4(4), 2619-2637; doi:10.3390/ijgi4042619 - published 24 November 2015 Show/Hide Abstract
Abstract: Spectral Analysis of Surface Wave (SASW) is widely used in nondestructive subsurface profiling for geological sites. The air-coupled SASW is an extension from conventional SASW methods by replacing ground-mounted accelerometers with non-contact microphones, which acquire a leaky surface wave instead of ground vibration. The air-coupled SASW is a good candidate for fast inspection in shallow geological studies. Especially for pavement maintenance, minimum traffic interference might be induced. One issue that restrains SASW from fast inspection is the traditional slow inversion which relies on guess-and-check iteration techniques including a forward analysis. In this article, a fast inversion analysis algorithm is proposed to estimate the shear velocity profile without performing conventional forward simulation. By investigating the attenuation of particle displacement along penetrating depths, a weighted combination relationship is derived to connect the dispersion curve with the shear velocity profile directly. Using this relationship, the shear velocity profile could be estimated from a given/measured dispersion curve. The proposed procedure allows the surface wave-based method to be fully automatic and even operated in real-time for geological site and pavement assessment. The method is verified by the forward analysis with stiffness matrix method. It is also proved by comparing with other published results using various inversion methods.
ISPRS Int. J. Geo-Inf.2015, 4(4), 2604-2618; doi:10.3390/ijgi4042604 - published 24 November 2015 Show/Hide Abstract
Abstract: Irregular headways could reduce the public transit service level heavily. Finding out the exact causes of irregular headways will greatly help to develop efficient strategies aiming to improve transit service quality. This paper utilizes bus GPS data of Harbin to evaluate the headway performance and proposes a statistical method to identify the abnormal headways. Association mining is used to dig deeper and recognize six causes of bus bunching. The AHP, embedded data analysis, is applied to determine the weight of each cause in the case of that these causes are combined with each other constantly. Results show that the front bus has a greater effect on bus bunching than the following bus, and the traffic condition is the most critical factor affecting bus headway.
ISPRS Int. J. Geo-Inf.2015, 4(4), 2586-2603; doi:10.3390/ijgi4042586 - published 23 November 2015 Show/Hide Abstract
Abstract: Dengue disease incidence is related with the construction of a house roof, which is an Aedes mosquito habitat. This study was conducted to classify pitch roof (PR) and flat roof (FR) surfaces using pan-sharpened Worldview 2 to identify dengue disease patterns (DDPs) and their association with DDP. A Supervised Minimum Distance classifier was applied to 653 training data from image object segmentations: PR (81 polygons), FR (50), and non-roof (NR) class (522). Ground validation of 272 pixels (52 for PR, 51 for FR, and 169 for NR) was done using a global positioning system (GPS) tool. Getis-Ord score pattern analysis was applied to 1154 dengue disease incidence with address-approach-based data with weighted temporal value of 28 days within a 1194 m spatial radius. We used ordinary least squares (OLS) and geographically weighted regression (GWR) to assess spatial association. Our findings showed 70.59% overall accuracy with a 0.51 Kappa coefficient of the roof classification images. Results show that DDPs were found in hotspot, random, and dispersed patterns. Smaller PR size and larger FR size showed some association with increasing DDP into more clusters (OLS: PR value = −0.27; FR = 0.04; R2 = 0.076; GWR: R2 = 0.76). The associations in hotspot patterns are stronger than in other patterns (GWR: R2 in hotspot = 0.39, random = 0.37, dispersed = 0.23).