Advances in Civil Engineering

Volume 2018, Article ID 8702854, 17 pages

https://doi.org/10.1155/2018/8702854

## Proposing New Methods to Estimate the Safety Level in Different Parts of Freeway Interchanges

Department of Civil Engineering, Iran University of Science and Technology, P.O. Box 13114-16846, Tehran, Iran

Correspondence should be addressed to Hamid Behbahani; ri.ca.tsui@inahabheb

Received 18 August 2017; Revised 7 December 2017; Accepted 27 December 2017; Published 29 March 2018

Academic Editor: Alessandro Palmeri

Copyright © 2018 Hamid Behbahani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Since attention to the safety of traffic facilities including freeway interchanges has been increased during recent years, accident prediction models are being developed. Simulation-based surrogate safety measures (SSMs) have been used in the absence of real collision data. But, obtaining different outputs from different SSMs as safety indicators had led to a complexity of using them as the collision avoidance system basis. Additionally, applying SSM requires trajectory data which can be hardly obtained from video processing or calibrated microsimulations. Estimating safety level in different parts of freeway interchanges through a new proposed method was considered in this paper. Fuzzy logic was applied to combine the outputs of different SSMs, and an index called no-collision potential index (NCPI) was defined. 13608 calibrated simulations were conducted on different ramps, weaving, merge, and diverge areas with different geometrical and traffic characteristics, and NCPI was determined for every case. The geometrical and traffic characteristics formed input data of two safety estimator models developed by Artificial Neural Network and Particle Swarm Optimization. Ten freeway interchanges were investigated to calibrate the simulations and to ensure the validity of the fuzzy method and accuracy of the models. Results showed an appropriate and accurate development of the models.

#### 1. Introduction

Providing an acceptable safety level of traffic facilities is logically vital due to its consequent effects on prevention of fatality and property damage. On the other hand, the low level of safety shall lead to the low performance of these facilities, as well. Since the freeways have always played a significant role in road transportation, determination of the safety level in different segments of the freeways has been one of the main concerns of researchers. Previous investigations are divided into two categories. These two categories include the real-accident data analyzing and the simulation-based safety study. In the real-accident studies, the effect of some factors (such as mainline speed at the beginning of the weaving segments, the speed difference between the beginning and the end of the weaving segment, the logarithm of the volume, the maximum length of the weaving area [1], the heavy vehicle rate, the hourly traffic volume, the speed differential between cars and heavy vehicles, the number of accesses [2], the number of vehicles that enter and exit the freeway at a specific segment, the length of the speed change lane, the speed of off-ramps [3], parallel-type or taper-type exit ramps [4], left-side or right-side merging and diverging areas [5, 6], the number of lanes on freeways, the number of lanes on ramps, and the speeding-related crashes [7]) on the number and/or severity of accidents was separately investigated and some models were developed. When there are no registered data about the accidents in a specific facility *or* when someone is trying to design that facility *or* when the facility has not yet been built, simulation-based safety studies/estimations such as conflict analysis by microscopic simulation and surrogate safety measures (SSMs) including time-to-collision (TTC), postencroachment time (PET), proportion of stopping distance (PSD), crash potential index (CPI), unsafe density (UD), max speed (Max *S*), relative speed (Δ*V*), kinetic energy (KE), and deceleration rate to avoid collision (DRAC) are often used to estimate the danger or risk of possible collisions. Integrating the rate of TTC variation and the level of hazard associated with TTC as an approximate safety indicator [8] and calculating the risk of sideswipe collisions [9] are some kind of using these measures. An FHWA-sponsored research project has also studied the potential to derive SSMs from existing traffic simulation models. In that research, TTC, PET, and DRAC were used to measure the severity of the conflict, and by use of additional information about the mass of the vehicles, Max *S* and Δ*V* were used to measure the severity of the potential collision [10]. These are some examples of using simulation-based SSMs. These measures are so useful because they occur more frequently than accidents and therefore need shorter periods of the study compared with real-accident investigations [11]. It is important to note that the results of using SSMs mostly showed a good relationship between the proposed SSM and actual accident data [12]. So, using the SSM will help to stay far from long-term real-accident data analysis. But, there are some shortcomings in this way. Different surrogate measures for safety result in different outcomes, and unfortunately, an acceptable method to choose the best outcome especially to use as the basis of collision avoidance systems has not yet been presented. On the other hand, using these measures requires some prerequisites such as providing microsimulation software, simulation experts, and trajectory data. In addition, when it is intended to investigate the safety aspects of interchanges, there is not enough literature which focused well on these traffic facilities to review. Here, the questions are that by focusing on freeway interchanges:(1)Is it possible to combine the outcomes of different SSMs and have an exclusive outcome which could be a safety indicator of the interchange or not?(2)Could the level of safety be indicated just by having traffic and geometrical characteristics of the interchange instead of simulation attempts, trajectory data, or accident data?

The first purpose of the paper is to present a method to show that it is possible to give a positive answer to the first question. To do this, the fuzzy inference system (FIS) was used to combine the outcomes of the SSM, and fuzzy logic was applied to define an index called no-collision potential index (NCPI) as a safety indicator of the interchange (i.e., the exclusive outcome). Fuzzy logic could be a suitable technique where qualitative definitions cannot be directly quantified. The next purpose is to develop a model to estimate the safety level in the ramps, weaving, merge, and diverge areas of interchanges based on their traffic and geometrical properties. One of the approaches in developing the model is using a powerful tool dealing with the prediction and classification problems, and that is Artificial Neural Network (ANN) which has been a consistent alternative method to analyze the freeway accident frequency and does not require any predefined underlying relationship between dependent and independent variables [13]. Another approach is using an evolutionary computation algorithm motivated by the behavior of organisms and gathered social psychology principles in sociocognition human agents and evolutionary computations. The algorithm is particle swarm optimization (PSO) which has a simple concept and efficiency in computations and is implemented easily [14–17].

#### 2. Methods

##### 2.1. Description of the Proposed Fuzzy Method

###### 2.1.1. NCPI Definition and Determination

In this paper, a fuzzy-based method was proposed to estimate the safety level in different parts of freeway interchanges. In this method, different outputs of SSMs were combined, and the outcome was NCPI which is defined as a safety indicator. The value of NCPI falls within the range of zero to 100. The higher the value of NCPI, the higher the safety level. Since it was intended to indicate the level of safety despite many other types of research that usually determine the level of risk, NCPI was defined to show the likelihood of no collision in the interchange instead of indicating the probability of collisions to occur. To determine the NCPI, the SSM were categorized into two groups: *Number estimation measures*: the measures consist of TTC, PET, PSD, CPI, UD, and DRAC which are usually used to measure the severity of the conflicts. These measures can be used to estimate the number of near-crash events. *Severity estimation measures*: the measures include Max *S*, Δ*V*, and KE which are usually used to measure the severity of the potential collisions.

Due to the complexity and time-consuming of using all measures, in this research study, the measures of TTC and DRAC were selected to estimate the number of possible collisions, and the severity of these potential collisions was estimated by the measures KE and Δ*V*.

###### 2.1.2. Definition of the Used SSM

Here, the definitions of the measures TTC, DRAC, KE, and Δ*V* are briefly described.

TTC: this was first defined by Hayward as the remaining collision occurrence time between two vehicles if the collision course and speed difference were maintained constant [18]. When TTC is low, there is an imminent danger of collision [19]. TTC for rear-end conflicts can be calculated by [20]where TTC is the time-to-collision, *X* is the vehicle position (L: leading and F: following), *V* is the vehicle speed (L: leading and F: following), and *l* is the vehicle length.

DRAC: deceleration rate is a good measure to detect dangerous maneuvers. DRAC is the rate at which a vehicle must decelerate to avoid a probable collision. For vehicles traveling in the same path, DRAC is defined as [11]where *l*_{veh L} is the length of the leading vehicle and other parameters were described previously. For angled conflicts, the equation changes aswhere *ΔV*_{ij(t)} is the relative speed of two vehicles engaged in the conflict and *D*_{i(t)} is the distance between the current position of the vehicle *i* and point of the intersection ahead of two vehicles.

KE: from Newtonian physics, we know that a moving vehicle has a kinetic energy as [21]where *K* is the kinetic energy, *m* is the mass, and is the speed of the vehicle. The kinetic energy transferred to the target vehicle can be calculated bywhere KE_{s} is the kinetic energy transferred to the target vehicle, *m*_{s} is the mass of the target vehicle, and is the change of the target vehicle speed before and after the collision [21].

: is the relative speed of vehicles involved in the conflict as a collision severity reflector [22].

###### 2.1.3. Trajectory Data Analysis

Finding the variables’ values of (1–5) requires analyzing the data of trajectory. It was assumed that the vehicles had a linear movement with a constant acceleration or deceleration rate between every two consecutive time intervals in the analysis. So, the coordinates of the intersection ahead of two vehicles *i* and *j* could be computed. The acceleration or deceleration rate and speed of vehicles could also be determined using these assumptions. It should be noted that any collision in the study areas could either be a rear-end collision or occurs at an angle of *β*. Thus, the analysis should be done with respect to angled collisions. In a special case in which the angle of collision is zero, it will be a rear-end collision. The collision of two vehicles at an angle of *β* is depicted in Figure 1.