An accurate estimation of missing data in traffic flow is crucial in urban planning, intelligent transportation, economic geography, and other fields. Thus, improving the data quality of traffic flow is a necessary step in data modeling. Most existing studies use data-driven models to determine spatiotemporal patterns in traffic flow data and fill in the missing information automatically. However, simple data-driven models have unsatisfactory results for describing complex patterns in traffic flow and filling in missing data. This study treated the complex patterns in traffic flow as integrating multiple simple patterns and proposed a hybrid missing data imputation framework called ST-PTD. We used a specific time-series analysis to mine periodic patterns and proposed a novel matrix decomposition method to describe the trend of the traffic flow data. Furthermore, we fused the periodic and trend characteristics of the missing data using a novel dendritic neural network. We applied the framework in actual traffic flow data sets collected in Wuhan, China. The results showed that the ST-PTD framework outperformed the eight existing baselines in terms of imputation accuracy.

Hierarchy is an important property of a street network, which suggests that only a small number of streets are prominent. A previous empirical study of a European city has identified four levels of scale in a street network, namely the top 1%, top 20%, bottom 80%, and bottom 20%. This paper investigates such street hierarchies in a large Asian city, Wuhan, with a complicated network of streets. Based on network analysis, we find that street hierarchies in this case study are slightly different so that the fourth scale is adjusted from the initial 20 to 25%. The detected street hierarchies are further compared to the intensity of large-scale traffic flows at different time scales. We find that distributions of both daily and hourly traffic conform well to the street hierarchies. More specifically, the 20% of top streets accommodate about 98% of traffic flow, and the 1% of top streets account for more than 60% of traffic flow. Moreover, this finding indicates that the current street network of Wuhan needs to be improved because the top 20% of streets are rather overburdened leading to traffic congestion. Our study not only provides new quantitative evidence as to the emergence of street hierarchies but also highlights the possible traffic congestion.

Some studies on the impact of traditional land use factors on traffic crashes do not take into account the limitations of spatial heterogeneity and spatial scale. To overcome these limitations this study presents a systematic method based on multi-scale geographically weighted regression (MGWR), which considers spatial heterogeneity and spatial scale differences of different influencing factors, to explore the influence of reclassified points-of-interest (POI) on traffic crashes occurring on weekdays and weekends. Experiments were conducted on 442 communities in Hankou, Wuhan, and the performance of the proposed method was compared against traditional methods based on ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). The experiments show that the proposed method yielded the best fitness of models and more accurate model results of local coefficient estimates. The highlights of the results are as follows: There are differences in the scale of the predictor variables. Residential POI, scenic POI, and transportation POI have a global effect on traffic crashes. Commercial service POI and industrial POI affects traffic crashes at the regional scale, while public service POI affects crashes at the local scale. The local coefficient estimates from residential POI and scenic POI have little impact on traffic crashes. During weekdays, more transportation POI in the entire study area leads to more traffic crashes. While on weekends, transportation POI has a significant positive effect on crashes only in some communities. The local coefficient estimates for industrial POI vary at different periods. Commercial service POI and public service POI may increase the risk of crashes in some communities, which can be observed on weekdays and weekends. Exploring the influence of POI on traffic crashes at different periods is helpful for traffic management strategies and in reducing traffic crashes.

With the rapid development of indoor localization in recent years; signals of opportunity have become a reliable and convenient source for indoor localization. The mobile device cannot only capture images of the indoor environment in real-time, but can also obtain one or more different types of signals of opportunity as well. Based on this, we design a convolutional neural network (CNN) model that concatenates features of image data and signals of opportunity for localization by using indoor scene datasets and simulating the situation of indoor location probability. Using the method of transfer learning on the Inception V3 network model feature information is added to assist in scene recognition. The experimental result shows that, for two different experiment sceneries, the accuracies of the prediction results are 97.0% and 96.6% using the proposed model, compared to 69.0% and 81.2% by the method of overlapping positioning information and the base map, and compared to 73.3% and 77.7% by using the fine-tuned Inception V3 model. The accuracy of indoor scene recognition is improved; in particular, the error rate at the spatial connection of different scenes is decreased, and the recognition rate of similar scenes is increased.

The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity.