afshin shariat mohaymany

Today, due to the advancement of technology, the spatial data obtained from the Global Positioning System can be used to estimate the travel time in the various components of the urban road network. In practice, spatial data from the Global Positioning System often is recorded with a low frequency on average every one or two minutes. The purpose of this paper is to provide a GIS-based tool for using low-frequency data obtained from the Global Positioning System to estimation of travel time. In order to evaluate the accuracy of the proposed tool, low frequency spatial data was used for three routes in Tehran. These data were collected using cell phones. In order to estimate the travel time of the links of each path, first, the data will be matched to the digital network at intervals of 120 seconds. Then, the path traveled between these map-matched points is identified. In the following, the average travel time of the links is estimated over the 20-minute intervals and compared with the actual travel time of the links. The results of this study indicate that the proposed method has a high ability to inference the correct paths. On average, over 91.06% of the links traveled by vehicle are identified correctly. Comparison of the estimated travel time with actual values indicates that travel time of 90 percent of the links is estimated with error of less than 20%. The fitting of points to linear regression with R2 = 0.8783 and Pearson correlation coefficient of P = 0.937 and comparison with results of some previous studies also shows the high accuracy of the estimates.

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.

In this study, we utilized mobile phone call detail records (CDR) to extract individuals’ travel patterns in Tehran, trying to demonstrate the potential and opportunities of employing CDR for studying mobility patterns compared to traditional household surveys. In this regard, the raw CDR is firstly refined and preprocessed, and the initial dataset (containing about 1.6 billion records of 12 million people) is reduced to a sample of about 400,000 users with 500 million records. Then, using a series of heuristic and learning-based clustering algorithms, we identified each individual’s activity locations and inferred their trip purposes. By identifying the origin and destination of trips for individuals, the O-D matrices of the CDR sample are extracted. The sample O-D matrices are then multiplied by expansion factors to expand the sample to the population. We observed that these matrices are satisfactorily consistent with the travel patterns of people in Tehran extracted from Tehran’s Transportation Masterplan studies. Validating the results against the previous surveys shows that the proposed methodology is applicable in transportation and urban planning.

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.

This study intends to determine the most appropriate distribution for modeling travel time variability. It also aims to explore the effects of the time of day and the length of the analysis  time interval on the type of the best-fit probability distribution function. To this end, four analysis time intervals of different lengths ranging from five minutes to three hours are considered. Subsequently, for each analysis time interval, travel time data collected at different times of day are fitted to 12 common probability distribution functions. The Akaike Information Criterion is then used to evaluate the goodness of fitting and to rank the probability distribution functions. The results of this study indicate that the Gaussian mixture distributions are superior to single distributions to represent travel time distribution. In addition, single probability distribution functions can model the distribution of travel time observations when the length of the time interval is short. Among single probability distribution functions, the burr distribution provides the best fit to the travel time data. The results of this research also show that the type of the best-fit probability distribution function does not change significantly over the time of day.

The increasing rate of traffic congestion, air pollution especially in developing countries, in addition to, the decreasing levels of physical activity have motivated walking as a non-motorized mode choice to decision makers. The study aimed to explore predictors of influencing factors on walking time duration and its choice among individuals in daily trips. For this reason, logit and duration models have been applied. The data used in this study have been gathered for updating the comprehensive transportation plan of Mashhad. The results indicate that various variables such as larger household size, younger people, and higher residential density have a positive relationship with longer walking duration and higher share of walking trips among all daily trips. Findings also indicate that men and well-educated people walk more compared to the competing groups (women and low-educated people, respectively). The results for various groups of occupations show that in general, students tend to walk more than others. Provision of required infrastructures and setting incentive policies to encourage walking in governmental organizations and private firms can promote walking especially among employees.

Car following models are considered as one of the most important driving behavior models, which have widely been applied in various fields of transportation engineering such as microscopic traffic simulation and safety. Large number of factors affect drivers’ car following behavior. In practice, due to the lack of appropriate data and latent nature of some factors, only few numbers of them have been considered in model development. Drivers’ ability to spatial anticipation of traffic flow is one of the important factors of car following behavior, which has been neglected in construction of car following models, particularly in neural network-based car following models. In order to consider this capability, a car following model based on neural network has been developed. Speed of the subject vehicle, speed of first and second leader and speed of follower as well as first and second lead gap and first lag gap were considered as input variables while the model’s output is acceleration of the subject vehicle. Calibration results indicate the better performance of the proposed model in comparison to the base model (without spatial anticipation). Additionally, in some situations both models have similar performance. Further .investigations show that this is due to the large correlation between first and second lead gap

Sustainable transport,the availability is one of the development indicators in the welfare,safety of thepopulation in developed countries,therefore,the development,optimization of public transport can beconsidered a viable routes. In this article bi-level model for optimizing the location of the feeder railway sta tions,public transport is provided. First,the multimodal feeder network designing is done then for each trafficzone the stop location has been done. Measurable indicators in the second level of the model are walking timecost,implementing cost. The mathematical model presented in order to minimize the costs of passengers,operators,society. To show,compare the results is used a real example,the public transport network inMashhad. The results show that the cost of user has more sensitive to walking time to public transit headway.It is also possible to use the index mathematical model according to the statistical distribution may be skewedlatest results

Bus network design is the first step in urban transportation planning process, and due to its influence on the consequent steps, such as timetabling, vehicle scheduling and crew scheduling, this step plays an important role in the process of transportation planning. One of the important issues in transit network design is locating transfer points. However, in the previous studies this issue was not considered, and it has been only paid attention to optimizing parameters such as travel time. This study is focused on defining the location of transfer points, as a result of transit network design, such that transfers are performed in points with higher capacities. Applying the genetic algorithm, the presented methodology is implemented on a virtual network, and the results showed that considering transfer constraint affects defining the location of transfer points.

Transportation networks are highly vulnerable to natural disasters such as earthquake. However, they are expected to perform properly in all circumstances. They are expected to connect different parts of the network and provide these connections with a proper level of service. Since transportation networks today are undividable sections of modern lives, being equipped with a sustainable and reliable network is a vital requirement of each society. Transportation authorities are always concerning about the improvement of the network influenced by the external effects as well as its the repair and maintenance due to the stochastic environment encompassing the network. However, their permanent constraint is the limitation of budget available. As a result, to get closer to the subject of improving the network’s performance, it is necessary to allocate resources to the network components in an optimized manner. During the occurrence of a disaster, the basic necessity for the injured is to receive emergency services which are directly dependent on serviceability of the network. Clearly the services are effective and savior if only they are offered in a predefined time threshold. It means that not only the network should provide at least one route between different population centers, but also it should provide a proper serviceability for each route. “Proper serviceability” here means providing complete service to demand in a pre-determined threshold of travel time. As this serviceabilityis uncertain, the reliability term is engaged which has become important parameters for planners and authorities recently. Hence, as connection and travel time are both necessary, under uncertain conditions, their respective reliability indicators are chosen as proper performance measures of the network. It is to be mentioned that the proposed reliability approximation method shows some sorts of limitations through several usage of the method in different networks. For example, increasing the dimension of network leads to a great sudden increase in required probable states to provide a proper coverage. Although the number of required states considerably less that the number of whole probable states, it is still a huge number which makes the algorithm very time consuming.The same problem will occur if the probabilities of the modes of each link are very close to each other. For example sign the algorithm for a link with the set of mode probabilities of (40, 30, 30) is inappropriate while the same algorithm is very efficient and useful for a link with probability set of (8, 7, 85). The provision of emergency services after a disaster is the most important factor to survive the wounded. This issue directly depends on serviceability of the network, which is highly vulnerable to natural disasters. In this paper, a resource allocation model was proposed for degradable transportation networks. In the proposed model, the network travel time reliability and the connectivity reliability of each OD of the network were chosen as two important performance measures under the effect of different type of disasters, e.g. earthquake. Although applying the reliability approximation method will make the challengeable procedure of reliabilityapproximation more efficient, it is does not show the same efficiency in larger or more variable probable state for links. However it can be applied in small size networks, as shown in the numerical example of this paper. For further studies, it is suggested to develop a model for networks with continuous performance function,assuming more than 1 level of investment allowed for each link, or continuous travel time functions which are affected by flow. These changes would lead to more real network studies. In addition the proposed idea can be developed for daily or slight fluctuation of capacity produced by events such as accidents or road maintenance activities.

Most of the researches in the domain of fuzzy number comparisons serve the fuzzy number ordering purpose. For making a comparison between two fuzzy numbers, beyond the determination of their order, it is needed to derive the magnitude of their order. In line with this idea, the concept of inequality is no longer crisp; however it becomes fuzzy in the sense of representing partial belonging or degree of membership. In this paper we propose a method for capturing the membership degree of fuzzy inequalities through discretizing the μ-axis into equidistant intervals. It calculates m in the fuzzy inequalities ≤ m and ≥m among two normal fuzzy numbers. In this method, the two μ-axis based discretized fuzzy numbers are compared point by point and at each point the degree of preferences is identified. To show its validity, this method is examined against the essential properties of fuzzy number ordering methods in [Wang, X. and E.E. Kerre, Reasonable properties for the ordering of fuzzy quantities (I). Fuzzy Sets and Systems, 2001. 118(3): p. 375-385.] The result provides promising outcomes that may be useful in the domain fuzzy multi criteria or multi-attribute decision making analysis and also fuzzy mathematical programming with fuzzy inequality constraints.

Iran is a country with one of the highest rates of traffic crash fatality and injury, and seventy percent of these fatalities happen on rural roads. The objective of this study is to identify the significant factors influencing injury severity among drivers involved in crashes on two kinds of major rural roads in Iran: two-lane, two-way roads and freeways. According to the dataset, 213569 drivers were involved in rural road crashes in Iran, over the 3 years from 2006 to 2008. The Classification And Regression Tree method (CART) was applied for 13 independent variables, and one target variable of injury severity with 3 classes of no-injury, injury and fatality. Some of the independent variables were cause of crash, collision type, weather conditions, road surface conditions, driver's age and gender and seat belt usage. The CART model was trained by 70% of these data, and tested with the rest. It was indicated that seat belt use is the most important safety factor for two-lane, two-way rural roads, but on freeways, the importance of this variable is less. Cause of crash, also turned out to be the next most important variable. The results showed that for two-lane, two-way rural roads, "improper overtaking" and "speeding", and for rural freeways, "inattention to traffic ahead", "vehicle defect", and "movement of pedestrians, livestock and unauthorized vehicles on freeways" are the most serious causes of increasing injury severity. The analysis results revealed seat belt use, cause of crash and collision type as the most important variables influencing the injury severity of traffic crashes. To deal with these problems, intensifying police enforcement by means of mobile patrol vehicles, constructing overtaking lanes where necessary, and prohibiting the crossing of pedestrians and livestock and the driving of unauthorized vehicles on freeways are necessary. Moreover, creating a rumble strip on the two edges of roads, and paying attention to the design consistency of roads can be a helpful factor in order to prevent events such as "overturning" and improve the overall safety of freeways.

Local bus network is the most popular transit mode and the only available transit mode in the majority of cities of the world. Increasing the utility of this mode which increases its share from urban trips is an important goal for city planners. Timetable setting as the second component of bus network design problem (network route design; timetable setting; vehicle assignment; crew assignment) have a great impact on total travel time of transit passengers. The total travel time would effect on transit utility and transit share of urban trips. One of the most important issues in timetable setting is the temporal coverage of service during the day. The coverage of demand is an objective for setting timetables which has not been well studied in the literature. In this paper a model is developed in order to maximize the temporal coverage of bus network. The model considers demand variation during the day as well as the stochastic nature of demand. A distribution function is used instead of a deterministic value for demand. The model is then implemented to an imaginary case.