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Public transportation (PT) is an essential system for cities to achieve sustainability. However, determination of proper locations for PT stations and finding suitable routes are critical decisions. The aim of the present study is to propose a novel method to determine bus rapid transit (BRT) stations and routes in cities, using a fuzzy inference system (FIS) and binary integer programming (BIP). In the proposed method, first, a set of potential locations for BRT stations is selected based on engineering judgement. Then, by considering several variables and with the help of FIS, a weight is calculated for each location. Subsequently, the optimum corridors for BRT are identified with BIP. The objective function of the BIP model is to maximise the sum of the stations' weights (involving the most suitable locations for BRT stations), while ensuring that the maximum number of passengers can travel each route. Constraints related to technical, social and economic considerations are also taken into account. To control the model efficiency, the outputs are compared with the stations and corridor suggested by experts. Based on the results, the most important features of the developed model are its simplicity, its ability to consider various factors, a short running time and its high level of accuracy. [ABSTRACT FROM AUTHOR]

The Covid-19 outbreak changed travel behaviour in many ways. This paper evaluates these changes in Tehran, Iran, from a transportation sustainability perspective. It uses data from travel surveys before and during the pandemic to evaluate changes in travel activity and their impacts on public transportation (PT) system costs and revenues, air quality and traffic crashes. A structural equation model (SEM) is used to assess the potential impact of passengers' characteristics, details of each transportation mode and the severity of Covid-19 on travel behaviour. Hypothesis testing is used to compare the changes that occurred in air quality and traffic crashes. SEM outputs indicate that the frequency of trips previously made by sustainable modes and changes in regular trips have the highest impact on the sustainability of trips made during the Covid-19 pandemic. Air quality declined in many city districts. The pandemic caused reductions in PT ridership and an increase in private car use, which threaten the long-term sustainability of public transit services. Crash fatalities declined slightly, particularly during periods of movement restrictions. However, motorcycle fatal crashes increased in comparison with the reduction in motorcycle trips, apparently due to increased traffic speeds. The government did not use the pandemic as an opportunity to promote sustainable and low-contagion modes. This analysis infers that the Covid-19 outbreak reduced the overall transportation sustainability in Tehran. [ABSTRACT FROM AUTHOR]

Educating traffic knowledge and safe behaviours to children is an effective strategy for improving their traffic safety. However, due to the physical and cognitive limitations of children, implementing a proper and effective education and training programme can be complicated. It is thus vital to investigate how the effectiveness of such programmes can be improved. To this end, 200 children aged 6–9 years were asked to participate in this study. Different characteristics of the children and their parents were obtained using several forms and questionnaires. Structural equation modelling was then used to analyse the importance of contributing factors. The difference between the score of each child before and after completing the education programme was defined as their traffic educability. The results showed that children who do better in school, children who have older siblings and those who are more active have greater potential to learn traffic knowledge. Furthermore, with respect to parents, having a higher education level, driving frequently, trying to highlight the importance of traffic rules in front of children and being concerned about children's trip safety can increase children's ability in traffic education. [ABSTRACT FROM AUTHOR]

Parking management has always been a major concern for universities and other activity centers. Nowadays, many universities are suffering from a lack of campus parking capacity. To tackle this problem, it is necessary to take parking lots assignment into consideration, regarding intercampus users' needs. These users have different ages, physical characteristics, expectations, and administrative positions that should be considered before any parking assignment. Here, a new method is proposed to optimize parking lots management for those universities where staff (academic and administrative), in contrast to students, are allowed to park inside the campus area. For this purpose, first, the probability of using a specific parking lot by each group is determined. For staff, this is done based on their choices, revealed by the relative frequency of using parking lots. This probability for students can be calculated using a fuzzy inference system model. To develop the model, a survey is conducted to extract students' preferences, regarding parking spaces assignment inside the campus area. Afterward, an integer linear programming model with the objective function of maximizing parking probability is employed, considering several related constraints. The proposed model is applied to Shahid Bahonar University of Kerman (SBUK), Iran, as the case study. According to the results, it can be concluded that the proposed method can help to reduce wandering time of finding an appropriate parking space for both staff and students. In addition, the proposed application can help increase the satisfaction level of staff and students with regard to parking management., Author(s): Navid Nadimi (corresponding author) [1]; Sanaz Afsharipoor [2]; Amir Mohammadian Amiri [3] 1. Introduction Recently, the number of students as well as passenger car ownership has increased in an [...]

Transportation equity is vital for the fulfillment of citizens' activities. To make better decisions for transportation planning, it is appropriate to have a method to evaluate each mode and assign an equity score to it. This paper aims to propose a method to assess the level of equity using different transportation modes in each zone of a city. Here, the equity level takes into account on one side the proportion between the frequency of long and short-distance trips, the volume-to-capacity ratio of streets, the air quality, and the parking availability. On the other side, the ratio of costs associated with using passenger cars, the average income of the residents, and transit and cycling infrastructure quality is used in a zone of a city. In this regard, at first, indicators are considered to assess the condition of buses, cycling, and passenger cars. Then, the Fuzzy Inference System (FIS) is used to determine the level of equity for each mode. The method has been applied to regular buses, cycling, and passenger cars in Kerman, Iran, as a case study. The results indicate a significant difference between the equity levels perceived by citizens and the performed calculations. The citizens' perceptions about the equity of regular buses and cycling seem to be more pessimistic than what the FIS model shows. Based on the model outputs, more restrictions must be established about using passenger cars in most of the zones. Nevertheless, in this regard, citizens have stated that the current situation is fair. We believe this method can be a helpful way to quantitatively assess the equity level of transportation systems in each zone. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the impact of different parameters on promoting the role of cycling as a daily mode of transport. In the first step of the analyses, binary logistic regression was used to examine the impact of different parameters on using or not using the bicycle as a transportation mode in weekly trips. Then by text mining, the main reasons for not using a bicycle in weekly trips are outlined. Finally, for those who use bicycles for at least one utilitarian trip a week, the effect of different factors on the popularity of this mode is investigated by structural equation modeling. Tehran, as a big city in the Middle East and North Africa (MENA) region, was considered as the case study. The results suggest that it is necessary to work on social norms about cycling, especially among those with higher education levels and income. Women use bicycles less than men and it is also necessary to rethink attitudes and regulations in relation to women cycling in Islamic countries. Bicycle promotion should aim to facilitate more positive attitudes among women. Providing more facilities such as safe bicycle paths, bicycle parking, and bike-sharing facilities have significant impacts on using this mode and its popularity. In highly congested cities, alongside facilitating cycling, it is important to set restrictions on private car use. The United Nations has outlined sustainable development goals (SDGs), which among other important aspects focus on transportation systems in the cities. Attracting citizens to bicycles can be an effective step to establish a more sustainable future transport system. To contribute toward this objective, the current study aims to investigate the impact of different parameters on increasing the popularity of cycling and proposing countermeasures to attract more people to this mode. Tehran, the capital of Iran as a big Middle East and North Africa (MENA) city in a developing country with substantial sustainability challenges, was considered as the case study and 1,074 people in different districts of the city were surveyed. The practical results suggest that it is necessary to work on social norms about cycling, especially among those with higher education levels and income. Women use bicycles less than men and it is necessary to rethink attitudes and regulations in relation to women cycling in Islamic countries. Bicycle promotion should aim to facilitate more positive attitudes among females. Providing more facilities such as safe bicycle paths, bicycle parking, and bike-sharing facilities have significant impacts on using this mode and its popularity. In highly congested cities, alongside facilitating cycling, it is important to set restrictions on private car use. [ABSTRACT FROM AUTHOR]

Uncontrolled median openings contribute to more road crashes because drivers do not obey traffic laws such as driving between lanes and following speed limits. This study examined the effectiveness of the different types of signs near an uncontrolled median opening in Mahmoud Abad, Iran. There were Signs 1 and 2 with a single message, while Signs 3 and 4 contained multi-messages. An Internet of Things (IoT) test system was developed to collect and record vehicle speed data before and after each sign installation. Results showed that speeding behavior decreased almost immediately after the road signs were installed and remained almost constant until the beginning of uncontrolled median openings. Moreover, multi-message traffic signs reduce vehicle speeds twice as much as single-message signs. Additionally, only multi-message signs are effective at reducing speed limit violations. [ABSTRACT FROM AUTHOR]

Various analytic methods have been proposed to determine sections with the highest crash risk. Each method has unique specifications and tries to model the crash risk from a different viewpoint. The main objective of this article is to benefit the strengths of three methods that rely on accident data, road safety inspection, and traffic conflict technique to determine black spots for two-lane highways simultaneously. Fuzzy inference system (FIS) is considered as the method to combine the results of these methods and report one number (RMI) as the crash risk for each section. For comparative evaluations, a case study with 20 sections for two consecutive periods was considered in the roads of southeast of Iran. We have tried to select sections with various conditions from crash data, road condition, and surrogate safety measures viewpoint. First, the black spots are determined with the help of previous criteria such as crash frequency (CF), crash rate (CR), empirical Bayes (EB), and equivalent property damage only (EPDO). Then the black spots are specified by the new proposed criteria (RMI). Three tests are applied to compare the efficiency of these five methods. The results indicate that the proposed method is a powerful tool to identify black spots. RMI considers the frequency and severity of observed crashes and at the same time frequency and severity of predicted crashes based on road deficiencies and near crashes. Therefore, it has a more realistic attitude in black spot identification. [ABSTRACT FROM AUTHOR]

Until now various surrogate safety measures (SSM) have been developed and research in this area is ongoing. However, it is unclear which indicators are able to reflect danger more accurately. This paper proposes a novel method of performance comparison by taking the advantage of statistical analyses of SSMs data. This analytical approach is inspired by the dominant behavior of drivers in car-following scenarios. Afterward, those indicators that previously identified as eligible are merged using a recently developed safety indicator, named collision probability (CP). The proposed method is applied to microscopic traffic data collected from the I-80 freeway in Next Generation Simulation (NGSIM) project. The results indicated that the proposed framework helps screen SSMs and calculate the probability of rear-end collisions using a combination of the selected SSMs. The results might be helpful for improving the precision and accuracy of in-vehicle collision avoidance warning system and safety evaluations without crash data. [ABSTRACT FROM AUTHOR]

Women usually have more flexibility in time and mode choice and it might be easier to attract them to public transportation (PT) systems. In addition, in developing countries, there are limitations for women in using transportation systems. By specifying effective factors on the popularity of PT systems among women, we can pursue two objectives in developing societies. The first achievement is a decrease in traffic congestion, and the second one is reducing gender discriminations in using PT systems. This study utilized structural equation modeling (SEM) and a group of women, living in Tehran (Iran), were interviewed. Based on the results, women's characteristics have the greatest impact on the popularity of regular bus (RB). Especially, an increase in income, education, and availability of passenger cars can significantly reduce the popularity of RB systems. For Metro and BRT, women's characteristics, fare satisfaction, and waiting time have the greatest impact on their popularity. [ABSTRACT FROM AUTHOR]

The future of traffic control and management may manifest itself in the form of digital and automatic methods, which could help to decrease driver errors. One of the most important technologies in this area is the Vehicle Ad-Hoc Network (VANET), in which each vehicle is a node that can receive and transmit information from and to other vehicles. Such systems can be used for safety or nonsafety purposes. This study investigates the safety applications of this system and aims to develop a method to detect and record dangerous situations for each vehicle based on microscopic traffic data. To detect danger, VANET must transmit data between vehicles and traffic safety indicators must be applied. To increase the efficiency of VANET, artificial neural network (ANN) and fuzzy inference system (FIS) were used. This study focuses on rear-end collisions in car-following situations. For this purpose, microscopic traffic data were collected along a 400-meter long segment of the Modares highway in Tehran, Iran. Based on the analysis, applying FIS to develop a new safety index was found to help detect rear-end collisions. Results also indicate that the average mean of errors based on the motion predictions generated by ANN is negligible and that an adequate history of motion for a vehicle diminishes such errors. Therefore, applying artificial intelligence can improve the workability of VANET in detecting a dangerous car-following situation that might lead to a rear-end collision. [ABSTRACT FROM AUTHOR]

Various surrogate safety measures have been introduced, of which Time-to-Collision (TTC) is the most well-known and commonly applied indicator. TTC first was defined as the time remaining prior to a collision if the path and speed of vehicles are maintained as a constant. Various modifications have been applied to this indicator to increase the efficiency of TTC. However, there is still no specific platform that shows how all of these modifications can be applied simultaneously. Thus, this paper first reviews the modifications that have been made to TTC; subsequently, a comprehensive framework is proposed for the application of this indicator for different types of conflicts, while considering all previous modifications. The framework is then applied for microscopic traffic data collected in Tehran. Results indicate that there is a great difference between the results of different versions of TTC for collision probability prediction. However, the proposed procedure might be beneficial in increasing the precision and accuracy of detecting dangerous encounters. [ABSTRACT FROM AUTHOR]

Time to collision is an important time-based safety indicator for detecting rear-end conflicts in traffic safety evaluations. A major weakness of the time to collision notion is the assumption of constant velocities during the course of an accident. In this paper we utilise equations of motion to develop a generalised formulation for time to collision by relaxing the assumption of constant velocity, constant acceleration and so on. This paper also illustrates how this concept can be applied to real-world data, and detailed data gathered in the NGSIM project are used. Then time to collision is calculated based on the assumption of constant speed, constant acceleration and linear acceleration for leading and following vehicles. Results indicate that in the third case (linear acceleration), the average duration of exposure to critical time to collision values is greater than the others. So, applying time to collision based on the assumption of linear acceleration in collision avoidance systems would decrease driver errors more than other cases. [ABSTRACT FROM AUTHOR]

The article presents an analysis of pedestrian fatal accidents in Iran with the use of a forensic medicine binary logistic regression model. The potential risk factors influencing the probability of pedestrian mortality have been suggested by the study which includes pedestrian age, location of the accident, and involved vehicle. The author suggests measures to address pedestrian fatalities that includes traffic safety education, increase the use of surveillance cameras and media campaign.