سید صابر ناصرعلوی

In urban streets, severe traffic crashes mostly relate to vulnerable users such as pedestrians, and cyclists. Because of not being protected like vehicles for their occupants, the severity of such crashes is high. Now, in the world sustainable transportation (walking and bicycle) is highlighted, thus it is important to regard the safety of this group of users more. For this purpose, it is necessary to gain an insight from previous crashes of vulnerable users. Therefore it is possible to propose countermeasures to reduce the crashes and prevent future incidents. In this paper, association rules s used as a machine learning method to find the frequent patterns and rules in the crashes of pedestrians, and cyclists. Tehran (capital of Iran) as a city with high frequency of fatal crashes in the period of 2020 to 2021 is selected as the case study. The results indicated that most fatal crashes among vulnerable road users relate to pedestrians and motorcycles (43 and 42 percent). Most of those who died in pedestrian crashes were foreigners, homeless, and municipal workers (support 0.926). Most of the injuries related to the crash of vehicles with motorcycles (support 0.661). In injury crashes of motorcycles, road and environment factors did not have a great role. Nevertheless, human factor in the form of non-necessary agility and acceleration was an effective variable (support 0.661). Engineering, education, regulation and enforcement can be the most effective countermeasures to reduce the severe crashes among vulnerable road users in Tehran.

It is one of the main priorities of safety researchers to reduce the severity of crashes by predicting the severity of crash damage. Large trucks pose safety concerns and are responsible for a high percentage of fatal crashes, so studying their crashes can help determine factors that contribute to crash severity. This study examines factors affecting the severity of large truck crashes in California using the Highway Safety Information System (HSIS). Driver, road, crashes, and vehicle characteristics were categorized as predictive variables. In this study, binary logistic regression (BLR) is used to model the level of severity of accidents and evaluate the weight of various predicting variables on the severity of injuries. Typically, various classical econometric methods are used to model the severity of accidents. Following is a discussion of how the modeling can be evaluated and optimized. Based on the modeling results of this research, the first three variables that are more important than other variables include: weather in clear conditions compared to the weather in other cases such as snowy conditions (with an exponential coefficient of 1.146), AADT in two cases more than 250,000 vehicles per day (with an exponential coefficient of 1.341) and between 100,000 and 250,000 vehicles per day (with an exponential coefficient of 1.202) are less than 100,000 vehicles per day compared to AADT (all three mentioned variables were statistically significant and had positive regression coefficients). Despite the overfitting caused by binary logistic regression, this model performs well on the data set of this study (the difference between accuracy of test and training data is 0.1%).

Because of its convenience and low cost, rail transportation has become one of the most attractive modes of transportation for passengers and business owners alike. Rail transportation has been reduced in popularity due to accidents. This study investigates the factors that affect the severity of accidents for freight, passenger, and other rail vehicles in three levels of death, injury, and damage. A multinomial logit model (MNL) regression was used to model the Iranian railway accident data from 2006 to 2021. The model quantifies the effects of each variable on the severity of events by quantifying the results. The results of this study indicate that type of accidents (with pedestrians), type of accidents (with road vehicles), speed exceeding the allowable speed (equal to or exceeding 30 km/h), speed during the accident (equal to or exceeding 30 km/h), multilane rails and moderate variables are among the variables that have a greater impact on the severity of accidents than other variables, so investing in these areas improves rail safety. Because of budget constraints, this research work will be effective in attracting the attention of policy makers and transportation managers in this area.

The issue of how to make roads safer for drivers in different age groups is important due to the constant change in the age distribution of drivers. With this in mind, the purpose of this study is to investigate the different characteristics of accidents and to provide suggestions on how to improve road safety for all age groups. This study uses the Road Safety Information Database (HSIS) to determine the factors influencing the severity of motor vehicle accident injuries for young drivers (25-25 years old), middle-aged drivers (64-26 years old) and elderly drivers (over 64 years old) in California between 2015 and 2017. A multinomial logit model was used to model the accidents related to each age group and to evaluate the weight of different variables predicting the severity of the driver injury. The predictor variables were classified into four characteristics: driver, road, accident and environment. The results show that rear-end collisions significantly increase the risk of death and injury for all three age groups, while clear weather and rainy weather reduce the risk of death and injury for all three age groups. In addition, the results show that there are many similarities between the predictors of accident severity for young and middle-aged drivers, but older drivers should be more careful in environmental and road conditions compared to other age groups. Young drivers improve their driving skills over time due to less driving experience, while middle-aged drivers, especially male drivers, have more driving experience and therefore tend to engage in less risky behaviors. Another result of this study is that the driving behavior pattern of older drivers have less variation than other age groups regardless of the surrounding conditions.

Pedestrian-vehicle accidents have a higher level of severity compared to other types of accidents. Therefore, the potential contributing factors at different severity levels of this type of accident should be identified so that appropriate measures can be taken for each of these factors.

In this paper, the multinomial logit (MNL) model is used to identify the factors affecting the severity of pedestrian-vehicle accidents. The Pedestrian-vehicle accident data from Highway Safety Information System (HSIS) of California from 2015 to 2017 is used in this article. The severity of injury is defined using the KABCO scale and is classified into five levels: fatal injury (K) and 4 injury levels including: severe or disabling injury (level 1, (A)), evident injury (level 2, (B)), minor or possible injury (Level 3, (C)) and no injury (Level 4, (O)).

The results show that the factors that significantly increase the risk of death and injury are: Drivers’ age (26 to 65 and over 65 years), weekday, low and medium annual average daily traffic (AADT), morning peak and daytime off peak and daylight.

The developed model and the results of the analysis offer effective solutions to reduce the severity of pedestrian traffic accidents and improve the safety performance of the traffic system. The results of this study can be useful for identifying the potential contributing factors to pedestrian-vehicle accidents in the real world. In addition, road safety experts and officials can use the results of this study to improve pedestrian safety.

Surrogate safety measure (SSM) plays a key role in early safety evaluation. Previous research has indicated potential associations between SSM and road crashes. Several attempts have been made to develop SSM. Most studies in the field of SSM have only focused on rear-end conflicts in witch the two vehicles move in a just one lane. The principal objective of this paper was to develop a new area-based SSM (ASSM) which can take into account the two-dimensional movement of vehicles. ASSM provides a novel approach to quantifying safety. The two-dimensional hypothetical areas around the vehicle that are required for the safe movement are introduced as the influence area. The mathematical equation of the influence area is derived as a function of vehicle speed, and then ASSM could be calculated by examining the extent of overlap and intersection of the influence areas of different vehicles at any given time. In this study, techniques for ASSM were developed and applied to real-world traffic data (NGSIM). The findings indicated that there was a positive relationship between ASSM and the time of the driver's decision to change his driving position to avoid a collision. The findings can contribute to a better understanding of human driving behavior in critical situation.

This paper aims to develop a method to detect dangerous situations for each vehicle based on microscopic traffic data for intelligent vehicles. Here, surrogate safety measures (SSMs) would be applied. Each SSM has unique characteristics and if we could use the advantages of different SSM simultaneously, then the efficiency of intelligent vehicles might be increased. For this purpose, Fuzzy Inference System (FIS) is applied to develop a Combined Surrogate Safety Measure (CSSM). In order to avoid complicating the issue, only rear-end collisions are considered. Microscopic traffic data collected in Modares highway of Tehran is used to develop FIS. Finally, the CSSM results are compared by each SSM statistically. Based on the results, it can be declared that FIS can be helpful to calculate the rear-end collision probability by using different SSMs. This achievement can be useful in promoting the efficiency of autonomous vehicles.

The Driving Anger Scale (DAS) is one of the most reliable and practical tools for measuring driverschr anger. Therefore, this study intends to evaluate the validity and reliability of this scale among Iranian drivers using appropriate statistical tools. To assess the validity of this tool, two methods of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used. The results of EFA showed the 6-factor structure is suitable for this scale. Then, the 6-factor structure obtained from the EFA, was compared as three type of models. The results of this method showed that the one-factor structure of this tool is not suitable in comparison with the other two structures, especially the bi-factor one. According to the results of ANOVA test, in terms of gender and age, there are significant differences in driving anger among Iranian participants, which young and men get relatively higher scores. In addition, the results of correlation analysis indicated that there are significant relationships between DAS subscales with Depression-Anxiety-Stress Scale (DASS) and Buss-Perry Aggression (BPAQ) scores. Also, there are significant relationship between the number of major accidents with traffic jams, overall DASS and BPAQ scores. On the other hand, the relationships between the number of minor accidents with the slow driving subscale and the overall BPAQ score were statistically significant. Finally, this study showed that the DAS questionnaire is a valid and reliable criterion for assessing the anger of Iranian drivers.

The main purpose of this paper is to investigate the effect of capacity on queue parameters, in order to improve the efficiency of the road by creating a proper change in the capacity of the highway and reducing the delay caused by queuing. As a system sends a parameter out of the output at a specified time interval, in other words, the system does not have the ability to service the entire input request, the queue is created and the vehicles must wait for service. In traffic queues, in addition to reducing the quality of the trip, Undesirable effect on traffic flow safety is also pronounced. The demand for the use of the road is variable throughout the day, and unfortunately there is no possibility of storing supplies in transportation. Consequently, in many periods of time, it is not possible to use capacity. It is possible to reduce the delay in the roadway by changing the capacity at different intervals. This involves sensitivity analysis on the effect of the capacity on the delay parameters. In this study, a definite queuing model is presented that can estimate the effect of changes in freeway bottleneck capacity on user delays. The formulation of the general problem is based on the definite queue theory and is solved by numerical analysis. A sample problem is investigated using a computer program. Finally, a sensitive analysis has been made on capacity changes. The results indicate that queue performance indicators improve with increasing capacity. By analyzing each case and performing such a sensitive analysis along with applying a suitable policy that changes the capacity of the way, the delay can be reduced and the route efficiency improved. Moreover, the proposed method helps to improve understanding the failure phenomenon in the upstream of traffic jams and large queuing models, and the sensitive analysis can be used to control traffic management. Using the proposed numerical method for each log entry and exit rate, one can calculate queuing parameters in each system (even non-traffic) and calculate the appropriate capacity with expected latency parameters.

The use of Intelligent Transportation Systems (ITS) has enhanced road safety. Most of Advanced Driver Assistance Systems (ADAS) as a subgroup of ITS، can prevent accidents by facilitating driving. Vehicle Collision Avoidance Warning Systems (IVCAWS)، as an ADAS، helps drivers to detect a potential accident. The most important point in IVCAWS development is to define an appropriate warning strategy، which alert drivers when he/she is really in danger and must have a reaction. Heretofore، two time-based safety indicators named، Time-to-Collision (TTC) and Time Headway (THDW) are used to activate IVCAWS to prevent rear-end collisions. In this article، these two indicators are compared، and a critical threshold for each one is determined to be used in IVCAWS. For this purpose، microscopic traffic data of I-80 freeway is used. Results indicate that، TTC’s critical threshold is usually about 1 to 2 second less than THDW’s threshold.

Increasing population growth on the one hand and the growth of transportation industry on the other have had numerous positive and negative impacts in societies. This issue has led to an increase in the volume of traffic demand in road networks and has hence increased the number of road accidents. Research shows that a large number of accidents are caused by the human factor and the vehicle factor. By using vehicle-to-vehicle communication systems and turning static conditions (off-line) into dynamic conditions (on-line), the present study attempts to turn the human factor and the vehicle factor into the intelligent human and the intelligent vehicle, respectively, and reduce their effects on the occurrence of accidents. According to Statistics, a significant percentage of freeway accidents are rear-end crashes, in which human error has the greatest effects on such collisions. Although it is obvious that the use of such communication technologies enhances the safety level of roads, the quantitative and analytical insights of the increase of safety resulting from the use of VANETs in freeways has not been investigated yet. This study offers a new method for investigating the role of vehicular ad-hoc networks (VANETs) in reducing the number of front-to-rear accidents in freeways by reducing drivers’ reaction time. In so doing, by using the micro data of the traffic flow in the I-80 freeway of the NGSIM project, drivers’ behavior in a specific time span was analyzed; and by using indices of assessment of safety of emergency breaking (UDM), the safety of drivers at each moment was investigated. The results of data analysis reveal that the use of VANET communication networks can reduce the percentage of vehicles’ exposure to danger in the chase process from 68% to 15% by reducing drivers’ reaction time.

Annually, Millions of passengers and vehicles use country road networks which need different requirements. Constructing service complexes is a solution to provide passengers’ demands. In this paper, complexes’ construction budget, needed time of second phase timing and rate of interest for investers were modeled by ARENA software. Next, economical analyses were done by the output of COMFAR software. Statistical and traffic data are needed for real simulation. Some effective data such as arrival traffic of complex were obtained from AASHTO. Other required information were collected from transportation department of Kerman province.

Surrogate safety indices, as one of the traffic conflict techniques methods, evaluates safety condition using vehicle trajectory data and without accident data. Determining unsafe moment in car-following process is done by defined threshold of surrogate safety index; hence, determination suitable threshold for spotting unsafe situation is in important. Headway (THDW) and time to collision (TTC) indices are useful for safety evaluation utilizing dynamic equations using space between two vehicles, speed, and acceleration; whereas many other parameters such as vehicles type could be useful to find out the suitable threshold and distribution of these indices. In this paper, separates following data in order to study type of car in distribution and limitation (threshold) of two pre-mentioned safety indices. Thus, traffic data recorded by traffic detector in Arak-Salafchegan highway was divided into 4 groups of car-car, truck-car, car-truck, and truck-truck and mean quality of 85 percent was figured out for every group. Paired t-test results indicated 0.05 significance level in different following groups which is almost high. As Also, statistics analysis and highway following data distribution well-showed effect of following car pair type on distribution form and safety threshold indices.

The time headway is an important characteristic of drivers’ behavior and traffic flow, with a great effect on traffic safety, level of service and capacity of transportation systems. In this paper, different groups of the headways are analyzed based on different combination of a pair of leadingfollowing vehicles in a car following situation. Statistics deductions indicate that drivers in car following situations select proper headways based on the type of a specified vehicle and its leading vehicle type. Based on I-80 data, results indicate that there is a significant difference between the headways of different pair of vehicles. When a heavy vehicle follows another one, the headway is the most and headway for groups includes motorcycles and passenger cars which follow a passenger car is the least. In addition, groups include heavy vehicles (a heavy vehicle following a passenger car, a heavy vehicle following another one and a passenger car following a heavy vehicle) have the most headway in comparison to other pair of vehicles. So when heavy vehicles increase in traffic flow, average time headway would also increase. Results of this research can be used for improving microscopic traffic simulation models.

Traffic safety evaluation based on traffic conflict is a new method noticeable for traffic safety researchers. Traffic conflict is an observable interaction between two or more vehicles and can be calculated in various methods. In this paper, time-to-collision (TTC) as a microscopic traffic safety index is used in traffic conflict technique. Microscopic traffic data of I-80 freeway in NGSIM project has been used for analytical calculations. For different groups of traffic, parameters such as headway, spacing, leading and following vehicle velocities and leading and following vehicle types, TTCs have been calculated and then compared. Results indicate that traffic safety in lane 1, as HOV lane, is the highest. Also, vehicle following heavy vehicles have the smallest TTCs in percentage. It seems that traffic conflict technique makes new opportunities for traffic safety engineers to evaluate freeway safety without the need for freeway crash data.

Recently, using simulation means on traffic flow is one of the useful ways to manage traffic, control traffic, decision making and selecting best strategy which increases every day. So, recognizing models on computer simulation is necessary and not having correct understanding of these models may causes unsuitable usage and producing incorrect results. Here, in this article, the characteristics of traffic flow along with investigation methods and different ways to modeling traffic flow will express. Then, it will discuss about traffic simulation as the main means of investigating traffic flow and other little & large models of traffic simulation will introduce and their characteristics will mention. Finally, types and characteristics of vehicle pursuit model will explain, as one of the most important under models of little traffic simulation.

Car-Following models are integral parts of capacity analysis, safety research, traffic simulation, and developing advanced vehicle control systems. During the past six decades, various car following models have been developed. GHR is the most well-known stimulus based model, in which the stimulus is the relative velocity of vehicles. In this research, timeto- collision (TTC), as the stimulus, is proposed as a substitute for relative velocity in the GHR model. GHR model is calibrated based on the comprehensive and detailed data gathered in the NGSIM project on I-80 freeway. The Results of GHR model calibration based on the data obtained for the two stimuli indicated that coefficient of etermination (R2) increased from 0.233 in the base model to 0.638 in the proposed model. In all, the results indicated that the application of TTC as the stimulus in the GHR model would improve the model's outcome.

Majority of traffic engineering analyses are based on traffic flow micro-simulation tools. Car-following model is one of the most significant micro-simulation sub-models that control driver's behavior with respect to the preceding vehicle in the same lane. Multiplicity of the car-following models is so high that there is therefore an obvious need for recognition of these models prior to picking up for utilization. In this research, the special case of well-known General Motors (GM) car-following model, is considered and characteristics of this model are described. The considered model is GM model as well as the assumption of driver's reaction time to be zero for all drivers, namely, Quick Response Driver Model. To achieve this purpose, some usual scenarios are defined in car-following process and these scenarios are simulated with Quick Response Driver Model. Also, utilization of differential equations approach is proposed for prediction and estimation of following vehicle behavior in car-following process that preceding vehicle behavior is given. Furthermore, Quick Response Driver Model is calibrated using real car-following time series data to identify model characteristics. The analyses of model evaluation with real data show the good capability of the model to reproduce real world considerations with RMSPE=1.7%. Additionally, results demonstrate that employment of differential equations is a robust approach in analysis of quick response driver model and decrease computational costs.

Rear-end collisions is frequent type of traffic accidents, in which human errors is the most dominant factor. Along with the recent technological developments in the field of driving task automation like developing advanced driving assistance systems (ADAS), human role in vehicle controlling is changing from a controller to a supervisor, and for this reason traffic crashes are decreasing gradually. Developing such systems like, AICC, ACC and CAS needs detailed data about human driving behaviors and reactions in different situations. So it is important to define a proper warning strategy that, warns driver when he/she is really at danger and immediate reaction is required. Previously, Time-to-Collision (TTC) has proven to be an effective measure for discriminating critical from normal behavior and so could be used in ADAS as a cue for decision-making in traffic. This paper deals with applyingtwo new safety indicators derived from TTC, named TET and TIT instead of TTC.

:The purpose of this paper is identification and prioritization of main factors of delay in road construction projects in Iran. The causes of delay in road construction projects has been identified and developed by using of lessons learned from projects carried out in the world and library studies in this field. Identified delay factors categorized by the origin of the incidence (employer, contractor, consultant and external factors), then using a field survey by a questionnaire and collected elite experts opinion and while completing primary factors, factors weighted and prioritized. Based on the survey was conducted and analysis of the results of its, the most important causes of delays in road construction projects are country's economic unfavorable conditions, lack of financing and delay in payment, lack of risk identification and lack of attention to risk, unstable political conditions, contract reforms and miss estimation of project time. Finally, based on the fact that ranked factors, executive suggestions are presented in order to reduce these delays.

Accidents are multi-causal phenomena and could not be controlled without taking all the causes into account. Among the most important factors affecting the classification of the pedestrian-hazardous (accident-causing) walkways are accidents’ environmental factors– which have not yet been considered comprehensively using existing methods. Statistics and data about the current situation are complex and non-linear. Thus, the classification of these passageways cannot be done correctly using simple mathematical models and linear combinations.In this research, an observational classification method using artificial neural networks is presented, and a new model for pedestrian-oriented classification of hazardous urban walkways has been proposed.In this model, artificial neural networks and statistics data on pedestrian accidents and walkway risk level are used, while control, traffic, and geometric parameters are considered simultaneously. The factors involved in this model include length and width of the roadways, width of the walkways, parking on the sides of the roadways, median’s width, roadway’s speed limit, roadway’s lights, quality of upper asphalt layer, segregation of roadways and walkways, presence of police, proportionality of velocity and land use, and proportionality of pedestrian pathways and gradient of roadway.Being observational, this model could significantly increase the accuracy and efficiency of the process of classifying pedestrian-hazardous urban passageways.

A large number of traffic accidents are due to the human errors. Advanced driver assistance systems can reduce human errors and thereby the number of accidents. An important issue in developing such systems is the use of a proper warning strategy, which warns the driver when the situation is critical and immediate reaction is required. Although time-to-collision (TTC) has been recently suggested and used, as a cue for decision-making in ADAS, but this indicator is unable to detect potential rear-end collisions. On the other hand, short time headway (THDW) during carfollowing period indicates that there is a high potential risk of collision, as soon as the lead vehicle starts braking. In this paper, a new safety indicator, urgent deceleration index (UDI), is developed. UDI can detect critical situations a few seconds sooner than TTC giving the driver more time for reaction planning, and hence providing a better margin of safety. To compare the sufficiency of UDI, TTC and THDW, as safety indicators, percent time spent unsafe, during following period is computed per lane using the detailed data gathered in the NGSIM project for I-80 freeway. Results indicate that the percentage frequency of time in rear-end conflict is 3, 63 and 76 percent, based on TTC, UDI and THDW, respectively. Also, the average correlation coefficient of critical situations based on UDI and THDW is about 0.644. In all, results indicate that UDI is more capable than TTC in detecting rear-end conflict.

Airlines are faced with some of the biggest and hardest scheduling problems known. For example, one major US carrier periodically schedules about 2700 flights per day to over 130 cities, using more than 400 aircrafts of 14 different kinds. Flight scheduling is one of the major problems for any airline company. This problem has been investigated as an optimization problem for many years. Usually, the flight scheduling problem is divided to a number of sub-problems, and fleet assignment is one of them. In this problem, given a flight time table and aircraft specifications, the type of aircraft is determined for each flight or the fleet assignment problem is the problem of assigning the right aircraft type to the rights in the timetable, while minimizing costs and satisfying various constraints. A lot of money can be saved if the right aircraft types are used. For example, in 1994, Delta Air Lines estimated that the use of a newly developed fleet assignment system would yield savings of up to $300 million over the following three years. The trade off between different aircraft types is that with a larger aircraft, more passengers can be serviced, and thus more income is generated. On the other hand, a larger aircraft consumes more fuel and is generally more expensive to operate. The fleet assignment problem (FAP) in air transportation consists of assigning fleet types to flights of a given flying schedule while respecting feasibility constraints and maximizing expected profit. Expected operational costs can be quite accurately expressed as a linear function of the FAP decision variables. The same cannot be said of expected revenues. Future revenues depend mainly on future travel demand. A particular fleet assignment (FA) then affects revenues through capacity limitations. Hence, when one expresses the expected profit as a linear function of the FAP decision variables, it is implicit that it is based on demand forecasts and that this linear function is only an approximation, a tool whose efficiency is to be judged on the profitability of the FAs found by the FAP solver. Because of this tight economic link, the fleet assignment problem is often connected to a revenue management system, which gives information about that is the best aircraft type for an individual flight, from a purely economic point of view, considering the booking situation and other things. But if the best aircraft type for each individual flight is already known, then what is the problem, you might ask. Just assign the best type to each flight, and the problem is solved. But this is obviously a typical example of a way in which one problem in a sequential solution procedure can ruin the possibilities for the following problems. It could for example mean that an aircraft would have to make a large amount of flights without passengers, so called deadheads, to operate its flights, because they are scattered geographically. This is likely not a very good solution. In recent studies, the fleet assignment problem has been modeled as a multi-commodity network flow problem with integer and real variables, and solved by conventional methods. In this research, we modify one of the existing models for domestic airline company and use Genetic Algorithm (GA), Simulated Annealing (SA) and Ant Colony Optimization (ACO) to solve it. These algorithms were implemented for the fleet assignment problem and used to solve a sample problem. Comparison of results of solving the sample problem by algorithms and GAMS software indicates that, GA, SA and ACO have good capability for solving the fleet assignment problem.

The frequency and intensity of the traffic crashes cause great concern for traffic engineers community and they are finding accurate and realistic methods of modeling traffic flow and preventing traffic crashes. Evaluations of traffic safety in transportation systems have meant to identify hazardous locations and causal factors or have evaluated new safety programs or policies implemented in transportation systems. This paper compares methods of conventional road safety evaluation and discusses advantages / disadvantages of each method. For some decades safety evaluation methods have relied on analysis of historical crash data. Since crashes are random and rare events and, in most cases, are independent events, it is difficult to find a sufficient number of crashes on a road section in a relatively short time period (e.g., a month or even a year). Thus, multi-year collection of crash data is used in safety analysis. Another safety evaluation method that has been practiced though in small scale is traffic conflict techniques (TCT). The advantage of using TCT in safety evaluations is the ability to test or study a safety strategy or improvement applied on the roadway facility in a relatively short period of time compared with traditional methods, which are dependent on crash data. However, use of TCT is not popular; perhaps because it needs extensive resources to collect, extract, and analyze conflict information. Moreover, like crash data analysis, use of TCT also makes concerned authorities reactive to the problem by responding to the crashes that have already occurred. Lastly, necessities of alternative proactive safety evaluation techniques that can improve the quality of traffic safety evaluation are discussed.