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Improving driving behaviour can be a fruitful intervention to improve traffic safety. This paper proposes a method to determine a driving behaviour score (DBS) for each driver based on their driving history. For this purpose, a fuzzy inference system (FIS) was used to calculate DBS for every driver between 0 and 1. The input variables for this model are the frequency and severity of previous crashes, near-crash situations, and aberrant driving behaviours. The calculated DBS can then be applied in implementing usage-based insurance schemes. The proposed method is used for a case study in Kerman (Iran). For this purpose, 40 young drivers were recruited in an experiment to record their previous history of crashes, aberrant driving behaviours, as well as surrogate safety measures while driving on a specific route. According to the results, DBQ is a useful indicator to measure a driver's level of safe driving style since it considers the history of crashes, near-crash incidents and dangerous driving behaviours. In this study, DBQ was primarily affected by the frequency of previous crashes. In Iran, drivers with dangerous driving behaviours pay the same insurance premium as those with relatively safer driving habits. Due to the disregard of a complete driving history, the insurance premiums determination process is not fair. According to this paper, usage-based insurance pricing can become fair and dependent upon a driver's behaviour by using DBQ.

The service quality of airports is a critical factor for passengers. To reach this goal, two phases are planned in this paper. First, several passengers were interviewed, focusing on unsatisfied passengers to understand what is missing and needs to be improved and be surveyed. A grounded theory method was applied to code the interview data. The results of this phase indicated that passengers’ characteristics, such as travel specifications, and details about services in different airport elements are essential factors that affect the level of travelers’ satisfaction. Second, to assure the validity of our findings, a survey was designed. To analyze the survey data, structural equation modeling (SEM) was implemented with 860 passengers. The SEM outputs pointed out that providing more facilities for passengers with disabilities or diseases, facilities for packing and wrapping passengers’ freight and information services have the highest priorities to promote the quality of service at airports.

Selecting the most suitable project is viewed as a vital decision in project-oriented organizations. In this study, we have proposed a new multi-criteria decision-making method to prioritize the executive projects of intelligent transportation systems. We use the entropy method for calculating the weights of criteria, the prioritization process is conducted with a hybrid method in which all of the TOPSIS, CODAS, and EDAS methods are applied simultaneously. The HWDP method considers both the distance from the average solution and the distance from the positive/negative solutions and the best alternative is selected according to the distance between it and the top options in different methods and the weight of importance in each method. So, the results are more reliable than the other studies. Using questionnaires filled out by experts, the present study prioritizes ITS projects based on urban transportation obstacles and needs in the metropolis of Isfahan (Iran). In the introduced HWDP method, the final priority of executive projects was determined by considering how far each project is from an optimal threshold, evaluating its proximity to this optimal threshold, and considering the weight of the method used in the hybrid method.

ABSTRACTUntil 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.

ABSTRACTWomen 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.

ABSTRACTVarious 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.

Surrogate safety measures (SSM) are suitable tools to detect dangerous situations. These indicators can be applied as a warning strategy in collision avoidance systems (CAS). Time-to-collision (TTC) and post-encroachment time (PET) are two important time-based SSM that identify the probability of a rear-end collision. TTC refers to the imminent danger, and PET implies the potential danger. However, sometimes the results from each indicator are inconsistent. An appropriate warning strategy for CAS can be developed using a new index that combines the properties of both TTC and PET. For this purpose, a new mixed index (MI) is proposed. In order to develop this MI, three main microscopic parameters, clearance, speed and the relative speed, are simultaneously applied to the leading vehicle. To calibrate MI, based on a fuzzy inference system (FIS), a value would be determined by a combination of TTC and PET at each instant and then by regression analysis the model parameters would be determined. Finally, MI, TTC and PET values for real car-following scenarios on the I-80 freeway are determined and compared. The results show that MI may be more suitable in detecting the rear-end collision risk within the proper time and with less errors.

Careful consideration of the factors that result in the traffic collision occurrence is essential from the public health point of view. This allows us to propose prevention measures based on a multidisciplinary focus, including the health, education, labor, transportation areas, and others. Accordingly, the present study pursues a twofold objective: a) investigating how socio-economic characteristics of a specific area can influence the level of traffic safety frequency in that area; b) evaluating and comparing the capabilities of different modeling approaches in pursuit of the former goal.