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In this paper, a GIS-based method was developed to extract the real-time traffic information (RTTI) from the Google Maps system for city roads. The method can be used to quantify both congested and free-flow traffic conditions. The roadway length was defined as congested length (CL) and free-flow length (FFL). Chengdu, the capital of Sichuan Provin...
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... Correlations and Multifractality of Traffic Flow According to the crossover time steps shown in Fig. 4, we applied the MFDMA method to the congested road data for two different time steps, i.e. n < 24 h and n > 24 h as shown in Figs. 6 and 7, respectively. Figure 6 shows that for n < 24 h, the h(q) monotonically decreases as q varies from −10 to 10. A nonconstant h(q) indicates of multifractality in these time series. ...
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... congested roads, the h(q) spectra are shown for shuffled and surrogated data in Fig. 6. One can see that the q dependence of h(q) for the original series is higher than both the shuffled and surrogated data. The phenomenon indicates that the multifractality nature of the congested road series is mainly due to long-range correlation. For free-flow roads, as shown in Fig. 8, the q dependence h(q) of for the shuffled data ...
Citations
... For research works focused on dependency and spatiotemporal studies, traffic forecasting needs an interpretation of the spatial and temporal traffic patterns of each road segment as well as the relationship between each one. Spatial correlation studies can be simply carried out by using a Geographical Information Systembased method, as demonstrated by Baofeng et al. [7]. But the method was insufficient because the flow is a stochastic phenomenon that evolves over time and has an impact on other flows at neighboring points located in other places. ...
Due to the exponential rise in the number of vehicles and road segments in cities, traffic prediction becomes more difficult, necessitating the application of sophisticated algorithms such as deep learning (DL). The models used in the literature provide accurate predictions for specific cases when the data flow is properly prepared. However, in complex situations, these approaches fail, and thus, the prediction must be developed through a process rather than a prediction calculation method. In addition to using a pure and robust DL prediction model, an efficient approach could be built by taking into account two other factors, namely the relationships between road segments and the amount and quality of the training data. The main goal of our research is to develop a three-stage framework for road traffic prediction based on statistical and deep learning modules. First, a cross-correlation prediction with a Long Short-Term Memory model (LSTM) is implemented to predict the influential road segments; second, a deep generative model (DGM)-based data augmentation is used to improve the data of the related segments; and third, we adapt a Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) architecture, to the resulting data to implement the prediction module. The framework components are trained and validated using the 6th Beijing road traffic dataset.
... Moreover, the curves after rescaling can be well fitted by the Weibull distribution suggested as Eq. (2). The fitting parameters are found to be close for the male and female drivers, with k = 1.92, λ = 1.13 and c = 98.94 for the female drivers, and k = 1.88, λ = 1.13 and c = 99.49 ...
Accidental distance dynamics is investigated, based on the road accidental data of the Great Britain. The distance distribution of all the districts as an ensemble presents a power law tail, which is different from that of the individual district. A universal distribution is found for different districts, by rescaling the distribution functions of individual districts, which can be well fitted by the Weibull distribution. The male and female drivers behave similarly in the distance distribution. The multifractal characteristic is further studied for the individual district and all the districts as an ensemble, and different behaviors are also revealed between them. The accidental distances of the individual district show a weak multifractality, whereas of all the districts present a strong multifractality when taking them as an ensemble.
Different studies over the past 30 years have shown an increase in the rates of crash frequencies during road construction time, but this trend is not reported as valid for all cities. A question is raised whether higher rates are observed in arterial roads in Bogotá, Colombia. It is possible to apply descriptive statistics and hypothesis tests to prove them and identify the variables that affect the accident rate during road construction. Our research aims to verify the incidence of high impact construction zones, in the crash rate at the arterial road network of Bogotá. We use descriptive statistics and inferential statistical tests to analyze whether crash rates are statistically higher during construction time than during non-construction time at the same highway sections considering different crash severities (damages-only, injuries, and fatalities). Our database considered 871 road links that make up 68 artery corridors for the city of Bogota, and 5.450 road construction zones, from 2015 to 2019. An analysis by corridor was performed, in which we identified seven patterns in the behavior of accident rates influenced by the presence of a road intervention in the corridor. Within the patterns, it was evident that some corridors reported an increase in the accident rate during the time of construction, while others showed a decrease in the same comparison. We used the Wilcoxon test to establish the statistical significance of our conclusions, with a significance level of 10%. We also found that those construction interventions that do not require excavation of more than half a meter there was a decrease in the accident severity, as damages-only crashes, diminished during the construction time, while for those interventions that include excavation greater than half a meter there was an increase in overall accident rates during construction time.
To examine the overall properties of the China’s stock market affected by the COVID-19 pandemic, we provide a comprehensive study of the multifractal properties across stock market sectors applying multifractal detrended fluctuation analysis (MF-DFA). We focus on intra-day 5[Formula: see text]min transaction data of 10 sectors in the Shanghai Stock Exchange, and the time-depended MF-DFA was also used to explore the dynamical evolution of the multifractal characterize for the overall period. We found that all sectors exhibit stronger multifractality and the weak market efficiency during the period of COVID-19, MHII performed best with the lower multifractal properties and higher market efficiency, three worse performers were ITII, TBII, and EII. While ITII, TBII, MCII, and UII were the four fastest recovering sectors from the gloom of the epidemic, with the plummeted multifractal intensity and the significantly increased market efficiency for the post-phase of the pandemic. The results based on the time-varying MF-DFA confirmed that the Chinese stock market is not only affected by the COVID-19 epidemic domestic but the impact of the US stock market crash in March was also transmitted to it, causing those industries sensitive to the market to fluctuate subsequently.
In this paper, long-range temporal correlations at different scales in Moroccan family business stock returns are investigated. For comparison purpose, presence of multifractality is also investigated in Casablanca Stock Exchange (CSE) major indices: MASI which is the all shares index and MADEX which is the index of most liquid shares. It is found that return series of both family business companies and major stock market indices show strong evidence of multifractality. In particular, empirical results reveal that short (long) fluctuations in family business stock returns are less (more) persistent (anti-persistent) than short fluctuations in market indices. In addition, both serial correlation and distribution characteristics significantly influence the strength of the multifractal spectrums of CSE and family business stocks returns. Furthermore, results from multifractal spectrum analysis suggest that family business stocks are less risky. Thus, such differences in price dynamics could be exploited by investors and forecasters in active portfolio management.
In this paper, we check for existence of multifractal in volatility of Moroccan family business stock returns and in volatility of Casablanca market index returns based on multifractal detrended fluctuation analysis (MF-DFA) technique. Empirical results show strong evidence of multifractal characteristics in volatility series of both family business stocks and market index. In addition, it is found that small variations in volatility of family business stocks are persistent, whilst small variations in volatility of market index are anti-persistent. However, large variations in family business volatility and market index volatility are both anti-persistent. Furthermore, multifractal spectral analysis based results show strong evidence that volatility in Moroccan family business companies exhibits more multifractality than volatility in the main stock market. These results may provide insightful information for risk managers concerned with family business stocks.
Multi-resolution representation has wide application demands in the field of Geographic Information System (GIS). The existing fractal interpolation methods can implement the reconstruction of natural linear features from low resolution to high resolution, but they are unable to quantify the relationship between the number of fractal iterations and the resolution, and the fractal interpolation results cannot maintain the geographical characteristics of natural linear features. This study presents a multi-resolution reconstruction method for natural linear features in GIS based on the restricted fractal interpolation. First, the geographical characteristics of natural linear features are extracted to restrict the parameters of fractal interpolation function and control the process of fractal interpolation. Second, a mathematical function is deduced for identifying the relationship between the number of fractal iterations and the resolution. The experiment results demonstrate that this study can reconstruct various preset high-resolution linear features from the low-resolution linear data while the geographical characteristics and random fractal characteristics of natural linear features are kept well.
In this paper, the generalized Hurst exponent is used to investigate multifractal properties of historical volatility (CHV) in stock market price and return series before, during and after 2008 financial crisis. Empirical results from NASDAQ, S&P500, TSE, CAC40, DAX, and FTSE stock market data show that there is strong evidence of multifractal patterns in HV of both price and return series. In addition, financial crisis deeply affected the behavior and degree of multifractality in volatility of Western financial markets at price and return levels.
