matin shahri

Recently, the use of big data from mobile devices has received considerable attention in transportation studies. The need to do activities is the main inducement for urban trip generation. Furthermore, urban activities and their patterns vary both over space and time. Mobile phone data, as a kind of continuous spatiotemporal data, records the location of people at different times. Therefore such data is suitable for the estimation of urban activity levels and the detection of patterns. In this study, we selected Shiraz as the study area due to its cultural, religious, and tourist significance, as well as the presence of major healthcare centres in the city. The analysis of spatial and temporal patterns of urban trips using continuous spatiotemporal data, such as mobile phone records, can significantly contribute to the improvement of transportation system management, planning, and policy-making for Shiraz.

The variable under investigation in this study is the activity density within a specific time interval and a defined spatial unit. Activity is defined as the number of individuals who either enter or leave a specific area for a specific purpose. Furthermore, activity density indicates the level of activity within the area’s unit of measurement. To investigate activity density across 321 traffic analysis zones (TAZ) in Shiraz, mobile phone data was collected over a one-week period (from 2021-06-24 to 2021-06-30). Following the implementation of data cleaning and preprocessing techniques,  individuals’ stay point and home locations were identified. The population of each TAZ was estimated by utilising the location of individuals within their respective homes. The estimated population and the real population in each spatial unit were employed to calculate the expansion factor. The activity levels within one-hour time intervals on workdays, semi-workdays, and weekends were estimated using an appropriate expansion factor. To examine the spatial dependency of the variable of interest (density of activities), global and local Moran’s I indices were applied to the aggregated density of activities. The study employed exploratory analysis of urban activities time series to identify the trend of activity level, peak periods, intensity change by time, as well as other relevant temporal characteristics. Additionally, the Standardized Normal Homogeneity Test (SNHT) was employed to identify the change point of activity in time series, which indicates the commencement of the activities.

The results not only demonstrated a significant positive spatial autocorrelation of the density of activities within traffic zones (P-Value < 0.001), but also identified the hotspots in the central parts of the study areas. It is notable that the central zones of the city exhibited high activity density, which was influenced by the spatial relationships within the study area. An exploratory analysis of time series revealed variations in activity patterns. These patterns exhibited higher activity levels on workdays compared to semi-workdays, and weekends. The time series observed in the latter half of the semi-workdays exhibited a striking resemblance to that of workdays, yet subsequently exhibited a trend between workdays and non-workdays as the activity level decreased. By examining the time series of activities, it can be observed that the mid-day peak period occurs at 12:00 to 14:00, while the evening peak period occurs at 20:00 to 22:00. Additionally, the lowest level of daily activity was identified between 3 and 6 a.m. The time series uniformity test was employed to ascertain the starting times of activities on workdays and semi-workdays, which were identified as 8:00 am, and on weekends, which were identified as 9:00 am. To validate the detected population and expansion factors and thus the estimated activity level, a spatial correlation between the estimated mobile phone population and the actual population within traffic analysis zones was calculated, which yielded an approximately 82% correlation coefficient. This correlation is statistically significant and therefore acceptable.

The results of these analyses could prove beneficial for the formulation of appropriate transportation planning and policy, as well as for the management of population density at hotspots at any time of the day. Furthermore, they could inform the analysis of urban transportation environmental impacts. With the availability of accurate mobile phone data for a range of spatial units, including traffic zones and even entire countries, it is possible to extract a diverse range of urban activity patterns, including those highlighted in this research.

Analyzing traffic conditions and suggesting traffic management methods play a critical role in evaluating the effectiveness of transportation systems. Among the methods suggested for collecting traffic data, approaches based on new technologies attracted more attention due to the ability of collecting large amounts of dynamic spatio-temporal data making it easy to identify trends and patterns. In this study, Tehran, the capital of Iran with socio-economic characteristics and the variety of urban trips which lead to heterogeneous traffic state will be considered. Data obtained from digital processing of Google Maps traffic images the one-month time interval (April 7th to May 7th, 2017), has been applied for the first time to evaluate the trend and overall pattern of the changes in traffic congestion in the study area. Considering the variety of trip patterns and consequently the traffic congestion, traffic congestion index (CI) has been calculated on workdays and weekends separately and was assigned to the district center and the morning and evening peak-hours were extracted using descriptive analysis. By applying Getis-Ord hot-spot and cold-spot index, the clusters of congested areas have been recognized over the study area. Also, the temporal relationship between traffic congestion indexes in different time sections was evaluated using Kruskal-Wallis statistical test and the null hypothesis of correlation between the mean values of congestion index was confirmed. Using overlay analysis of congestion maps, clusters indicating congested areas at 90% confidence intervals were extracted during morning and evening peaks on weekdays and weekends separately. The results of this study can be effective in modifying traffic congestion zones, analyzing pollution or studies relating to road pricing, and assessing the process of traffic congestion propagation during desired time intervals.

Air pollution is now considered to be one of the most important challenges Iran faces and plays a major role in changes of its climate. Factors such as population growth and the consequent increase in the number of cars, as well as the presence of various (and often old) industries and the energy demand they satisfy have led to an increase in pollution in many Iranian metropolises. As one of the four Iranian industrial hubs, Arak has one of the worst air quality in this country. In addition to the presence of industries, having a relatively high population density (and consequently high traffic congestion level) and various climatic conditions affect the quality of air in Arak. It is essential to accurately measure air pollutants with a high spatial and temporal resolution, determine their distribution pattern and level of effectiveness, and provide provincial and national managers with applicable solutions. Unfortunately, air quality monitoring stations are not sufficiently and properly distributed in Iran.Many Iranian cities do not have even a single air monitoring station and many others have only one station. As the capital city of Markazi province and an industrial city, Arak has only four monitoring stations which are not simultaneously active in many cases.Failing to conduct proper site selection before the installation of ground-based monitoring stations results in local irregularities in the recorded concentration of pollutants. Furthermore, the stations are not usually calibrated on time and thus air quality monitoring observations are disrupted. In these cases, either this data is deleted from the final results or the station will be inactivated (for example, for a week or a month) by authorities. However, it seems that the observations made by these stations still include inaccurate data.

The present study has introduced a method based on composition and voting to validate the observations made by air quality monitoring stations using Sentinel-5 satellite images. Arak city was used as the study area. Level three images (L3) of the Sentinel-5 TROPOMI sensor received from the Google Earth Engine were used to monitor the concentration of pollutants in the present study. Sentinel-5 is a powerful atmospheric monitoring tool. Equipped with a spectrometer called TROPOMI, the satellite measures ultraviolet radiation reaching the Earth's surface in a high range. TROPOMI sensor is highly capable of imaging and monitoring a large number of pollutants. The present study has compared the concentration of NO2, SO2, CO and ozone pollutants monitored by ground-based stations in Arak city with Sentinel-5 images. Since the time resolution of ground-based observations is higher than satellite observations, a monthly average of pollutants' concentrations was calculated to increase the reliability of observations. In other words, the concentrations of pollutants were compared on a monthly basis. The proposed method has assumed that more accurate sets of ground observations show a higher linear correlation with satellite observations.
In order to select the appropriate set, the number of observations with an acceptable accuracy must be determined. To do so, a method based on a mixture of composition and voting has been used. As previously mentioned, each observation showed average pollutant concentration in a specific month of the study period. The process started with at least four monthly observations. As a result, assuming that all 19 monthly observations were available, 16 subsets were obtained with a maximum linear correlation between ground-based observations and their satellite correspondence which showed the accuracy of the observations. The second step was the proposed voting method which showed that the monthly ground-based observations (for example October 1398) were repeated several times. The high frequency of a monthly observation indicated its higher accuracy. The presence of this particular observation in different permutations has increased the linear correlation coefficient of the observations. Therefore, for an instance a frequency of 15 or 16 for the observation made by the ground-based station in October 2017 indicated high accuracy of the observation.

The present study has compared the concentration of NO2, SO2, CO and ozone pollutants Using the proposed method, some observations have been identified as outliers or errors. RMSE criterion was used to evaluate the accuracy of the proposed method. Some observations made by the ground-based station were not consistent with other ground-based and satellite observations, and removing them increased the correlation coefficient. Removing outliers from the observations, the RMSE (originally 2%) was improved and reached 47%.

Findings indicated that some observations made by ground-based monitoring stations were incorrect, or at least the stations had sometimes failed to exhibit the real general trend of environmental pollution correctly due to local irregularities caused by various reasons, such as improper location or lack of proper calibration.

Pedestrians are regarded as one of the most vulnerable road users and the source of large number of fatalities or injuries in case of road accidents. Accordingly, in recent years efforts have been carried out to present the methods in order to investigate whether there is and organized pattern among accidents. When dealing with spatio-temporal data such as traffic accidents, the effects of events locating in the neighboring of others cannot be ignored. In this research, pedestrian accidents in Mashhad, Iran over five years of study, 2012-2016 have been considered. By aggregating traffic data over 253 traffic analyses zone (TAZ), spatio-temporal relationships have been conducted using Kernel Density Estimation and radar graphs. In the next step, the meaningful factors affecting the pedestrian accident occurrence have been investigated and inattention and speeding have found to be as the most leading cause of pedestrian occurrence respectively. The results of this paper can provide the traffic engineers with an insight into regions with the clustering of pedestrian accident that need more attention and assigning budget and time from transportation authorities.

Air pollution and its negative effects on human health have become a major issue around the world, especially in developing countries and Iran. Contaminants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, and aerosols, in addition to having significant negative health consequences, cause to damage vegetation and contribute to global climate change. Therefore, the comprehensive monitoring of pollutants and, consequently, appropriate management decisions to address the issue is required. Remote sensing methods, especially Sentinel-5, the European Space Agency's most recent project (in collaboration with the Netherlands), which allows for the capture of images in various spectral bands using a TROPOspheric Monitoring Instrument (TROPOMI)sensor, are recognized as a useful tool for monitoring various types of air pollutants.-

In this analysis, the most significant air contaminants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, and aerosol were monitored using Sentinel-5 satellite images for 20 major industrial cities in Iran in 2019 and 2020. A large number of level-3 images collected from Google Earth Engine were used in this research. Ground-based stations were used to verify the monitoring process.-

The results show that monitoring values obtained using Sentinel-5 satellite images are at least 78 percent correlated with ground-based station values. As a result, it has been demonstrated that Sentinel-5 satellite images can be successfully used in management studies with the aim of reducing air pollution. Based on the results, it can also be inferred that, Tehran and Zanjan are respectively the most and the least polluted city in terms of total carbon monoxide, nitrogen dioxide, sulfur dioxide and dust in 2019. The same is true in 2020. It is also clear that air pollution levels in Karaj and Kermanshah increased significantly in 2020 compared to 2019. Another significant finding is that, in general, air pollution levels in 2020 are lower than in 2019. One significant explanation may be the effect of the Covid-19 pandemic in 2020, which resulted in a decrease in industrial activity and reduced traffic and congestion on the roads.-

In general, the results of this research showed that it is possible to systematically monitor the air pollutants using images captured by TROPOMI sensor on the Sentinel-5 satellite with acceptable accuracy. The results of this study can help researchers and urban managers for appropriate management in metropolitan areas.