Hanieh Zhendeh Khatibi; Afshin Shariat Mohaymany; Matin Shahri
Abstract
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. In addition, urban activities and their patterns vary over space and time. Mobile phone data, as a kind of continuous ...
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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. In addition, urban activities and their patterns vary 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 appropriate for urban activity level estimation, and its pattern detection. In the present study, mobile phone data was applied to estimate the density of activities (standardized by area) in Shiraz metropolitan area. To examine the spatial dependency of the variable of interest (density of activities), global and local Moran’s I indices were applied on density of activities aggregated over 321 traffic analyses zone in Shiraz on workdays, semi-workdays, and weekends. The results not only confirmed significant positive spatial autocorrelation of density of activities (P_Value<0.001), but also detected the hotspots in the central parts of study areas. Using exploratory analysis of time series and time-series heterogeneity tests, the study identified the trend of activity level, intensity change by time, and change-point of activity in time series. The study also extracted the start time of activities (8 a.m. for workdays and semi-workdays and 9 a.m. for weekends), mid-day peak (12-14), evening peak of trips (20-22), and the minimum activity time (3-6 a.m.). Results of these analyses could be beneficial for appropriate transportation planning, policy-making, demand management, management of population density at hotspots at any time of the day, as well as urban transportation environmental impacts analysis.
Matin Shahri; Afshin Shariat Mohaymany
Abstract
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 ...
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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.
