Embed Size (px)
Citation preview
1
Investigating the Safety and Operational Benefits of Mixed Traffic Environments in the Proximity of a Driveway on an Urban Arterial
Seyedeh Maryam Mousavi a, b, *, Osama A. Osman c, Dominique Lord a, Karen K. Dixon b, Bahar Dadashova b
a Zachry Department of Civil and Environmental Engineering, Texas A&M Transportation Institute (TTI), Texas A&M University, College Station, TX, USA, 77840 b Texas A&M Transportation Institute (TTI), Texas A&M University, Bryan, TX, USA, 77807 c Department of Civil and Chemical Engineering, University of Tennessee, Chattanooga, TN, USA, 37403
ABSTRACT Traffic congestion is monotonically increasing, especially in large cities, due to rapid urbanization. Traffic congestion not only deteriorates traffic operation and degrades traffic safety, but also imposes costs to the road users. The concerns associated with traffic congestion increase when considering more complicated situations such as unsignalized intersections and driveways at which maneuvers are entirely dependent upon drivers’ judgment. Urban arterials are characterized by closely spaced signalized and unsignalized intersections and high traffic volumes, which make them a priority while analyzing traffic safety and operation. Autonomous Vehicles (AV) provide ample opportunities to overcome the aforementioned challenges. In essence, this study evaluates the impact of various AV Market Penetration Rates (MPR) on the safety and operation of urban arterials in proximity of a driveway under different traffic levels of service (LOS). Twenty-four separate scenarios were developed using VISSIM, considering six AV MPRs of 0%, 10%, 25%, 50%, 75%, and 100%, and four LOS including A, B, C, and D. Various operational and safety measures were analyzed including traffic density, traffic speed, traffic conflict (rear-end and lane-changing), and driving volatility. The trajectory and lane-based analysis of the traffic density indicates that MPR significantly improves the overall traffic density for all the scenarios, especially under high traffic LOS. Additionally, by increasing the MPR and decreasing the traffic volume of the network, the mean speed increases significantly by up to 6%. Exploring the safety of the scenarios indicates that by increasing the MPR from 0% to 100% for all the LOS, the number of rear-end conflicts and lane-changing conflicts decreases 84%-100% and 42%-100%, respectively. Moreover, assessing the longitudinal driving volatility measures, which represent risky driving behaviors, showed that higher MPRs significantly reduce some of the driving volatility measures and enhance safety.
Keywords: Autonomous vehicles; mixed traffic; traffic safety, traffic operation; driveway; access management 1. Introduction
Urban arterials, as the backbone of urban traffic networks, connect major activity centers, and carry high traffic volumes; therefore, urban arterials experience traffic congestion daily. In general, urban arterials are associated with closely spaced signalized and unsignalized intersections and high traffic volumes. Signalized and unsignalized intersections along urban
* Corresponding author’s E-mail address: [email protected]
2
arterials facilitate accessibility to adjacent land-uses but simultaneously lead to more traffic conflicts, traffic congestion, and safety and operational concerns. Therefore, access points need special consideration when designing a roadway (American Association of State Highway and Transportation Officials (AASHTO), 2011; Federal Highway Administration (FHWA), 2010; Khan et al., 2017).
The increase in traffic congestion derives from the rapid growth in cities, population, and vehicle industry. Traffic congestion can originate from either recurrent or nonrecurrent sources, including a sudden increase in traffic demand, limited road capacity, traffic incidents, and adverse weather conditions (Al-Kadi et al., 2014). Also, traffic congestion contributes to roadway crashes, injuries, and fatalities, in addition to a significant increase in fuel consumption, economic loss, travel time, air pollution, stop delays, and users' fatigue and frustration (Al-Kadi et al., 2014; Transportation Research Board (TRB), 2014; Tyagi et al., 2012). Poorly designed roadways and drivers’ distraction are also among the causes of traffic congestion (Barrachina et al., 2015). Drivers’ errors not only worsen the traffic operation but are also among the leading causes of roadway crashes (Gora Pawełand Rüb, 2016; Morando et al., 2018a; Waymo, 2018).
Based on the World Health Organization (WHO) (2018), approximately 1.35 million people died in roadway crashes in 2018, and crash injury is the 8th leading cause of death for the entire world. These statistics make the roadway safety a major concern. Also, the United States reported 37,133 and 40,000 fatalities due to the roadway crashes in 2017 and 2018, respectively (Highway Traffic Safety Administration and Department of Transportation, 2017; U.S. Department of Transportation-NHTSA, 2018). There is a rich literature describing and analyzing all aspects of roadway crashes and recommending countermeasures to mitigate traffic safety (Al-Kadi et al., 2014; Ariannezhad et al., 2020; Azimi et al., 2019; Bagdadi and Várhelyi, 2011; Mousavi et al., 2019b; Rahimi et al., 2020).
Due to the adverse safety and operational impacts of traffic congestion that urban arterials are also experiencing, public transportation agencies have been continuously attempting to reduce traffic pressure (Barrachina et al., 2015). Capacity expansion is one of the traditional solutions that has been implemented to mitigate traffic congestion; however, due to the lack of space, the associated high cost, and latent and induced demand, capacity expansion is no longer a feasible solution (Ajitha et al., 2015). Instead, the focus has been moved towards operational tools to improve the performance of the existing infrastructure. Active Traffic Management (ATM) and Intelligent Transportation System (ITS) are acknowledged as efficient tools for providing real-time data and managing the transportation to help road users through route choice, departure time, and speed choice (Ajitha et al., 2015). Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications are ITS solutions that transmit information about traffic conditions to the road users to enhance traffic safety and operation (Azad et al., 2019; Liu et al., 2007; Ma et al., 2012, 2009). Even though V2V and V2I contribute to traffic safety and operation, realizing their benefits rely on drivers’ compliance. Moreover, there will be a mixed traffic environment before all vehicles are able to utilize V2V and V2I technology (Sezer et al., 2015). On the other hand, Autonomous Vehicles (AVs) have the capability of overcoming the drivers’ compliance issues in addition to eliminating drivers’ judgment and distraction from the equation, even under the mixed traffic environment.
The impact of various AV Market Penetration Rates (MPR) is not entirely clear for all roadway categories, especially urban junctions. However, according to the previous studies, it is hypothesized that, in general, increasing the AV MPR improves both traffic safety and operation, but it is not clear how various levels of traffic demand influence the scenarios. In addition, over
3
the past few decades, researchers consistently reported that an increase in the density of junctions/intersections/ driveways results in a higher crash rate (Eisele and Frawley, 2005; Federal Highway Administration (FHWA), 2010; Huffman and Poplin, 2002). The AV related literature only examined the performance of mixed environments for signalized intersections, roadway segments and roundabouts (Arvin et al., 2019; Kockelman et al., 2016); however, the influence of a driveway on the safety and operational performance of mixed traffic environment has not been evaluated. Thus, this study aims to assess the operational and safety performance of AVs under various traffic demand levels in the proximity of a driveway on an urban arterial. Moreover, since there will be a transition period to reach a fully AV environment, this study assesses the impact of various AV Market Penetration Rates (MPR) starting from 0% (a fully conventional vehicle environment) through 100% (representing a fully AV environment). The operational and safety benefits of AVs at various MPRs at various traffic demand levels will be evaluated thoroughly in this study. 2. Literature Review The following sections summarize the previous studies that evaluated the operational and safety aspects of autonomous vehicles.
2.1. Operation of Autonomous Vehicles Various studies have evaluated the advantage of ATM strategies and access management for different demand levels. The results of assessing access management strategies proved that their efficiency is location-specific and depend on traffic conditions (Liu et al., 2007). For instance, comparing direct left turn (DLT) and right-turn followed by U-turn (RTUT) indicated that DLT is advantageous over RTUT on multilane divided arterials, especially for traffic volumes less than 650 pc/hr/lane. However, RTUR performs better when the through traffic volume is higher than 1000-6000 pc/hr and the left-turn volume from driveways is higher than 50-150 pc/hr (Chowdhury et al., 2005; Liu et al., 2007).
Several research studies have examined the impact of AVs on the network-level operation. Aria et al. (2016) compared AVs and conventional vehicles and revealed that AVs improve traffic operation, capacity, speed, and travel time of the network, especially when the network is congested. Hoogendoorn et al. (2014) also confirmed that AVs could improve traffic flow efficiency, but the effect of human-driven vehicles and human factors should not be neglected, e.g., behavioral adaption and user acceptance. Nonetheless, Ebnali et al. (2019) indicated that training human-driven vehicles could improve their interaction with AVs and in general traffic operation. VanderWerf et al. (2004) also stated that there is a considerable improvement in capacity when implementing partially automated vehicles equipped with Cooperative Adaptive Cruise Control (CACC). Other studies evaluated the impact of AVs on the capacity and operation of freeways and merging points (Karaaslan et al., 1990; Letter and Elefteriadou, 2017).
2.2. Safety of Autonomous Vehicles
While the implementation of AVs is progressing substantially, there is still a lack of real-world data to evaluate their safety. Moreover, due to the random and rare nature of the crashes, it takes a long time to observe an adequate number of crashes to do a reliable statistical safety analysis (Mousavi et al., 2019c). Hence, researchers have been using simulations to evaluate the safety of AVs. Morando et al. (2018) assessed the safety impacts of various AV penetration rates at
4
signalized using micro-simulation software. The results indicated that the AVs are capable of improving traffic safety significantly under higher MPRs. The number of conflicts decreased by 20% to 65%, with the AV penetration rates of 50% to 100% for the signalized intersection. Papadoulis et al. (2019) confirmed the benefit of implementing CAVs on the safety of the motorways, even at a low MPR. Jeong et al. (2017) implemented a micro-simulation model to evaluate the effect of an optimization method that minimizes the overall crash risk by focusing on vehicle maneuvering control parameters under different MPRs of automated driving systems (ADS). The results suggested significant reductions in the rear-end (RE) crash risk for both vehicle safety-based maneuvering (VSM) and traffic safety-based maneuvering (TSM). Fagnant and Kockelman (2015) estimated that at 90% AV MPR, there is a saving of 21,700 dollars due to the fatality reductions and 4.2 million dollars due to the overall crash reduction per year.
In general, even though providing access points on an arterial promotes accessibility, it reduces network efficiency and safety at the same time. Gluck et al. showed a positive relationship between the number of crashes and the frequency of driveways and intersections (American Association of State Highways and Transportation Officials (AASHTO), 2010). Sezer et al. (2015) indicated that in a mixed traffic environment and at unsignalized intersections, it is challenging for driverless cars to identify the intention of conventional vehicles. In addition, T-junctions are associated with the highest crash rates comparing to the other intersection configurations, even among human-driven vehicles (Sezer et al., 2015).
As mentioned earlier, the safety of roadway facilities is most often evaluated through analyzing the historical crash data. However, due to the infrequency and random nature of the crashes, it takes a long time to obtain an adequate number of crashes and subsequently find black spots (Federal Highway Administration (FHWA), 2008; Mousavi et al., 2019b). Alternatively, various surrogate safety measures (SSM) could be used to detect potential traffic conflict locations before getting numerous crashes, fatalities, and injuries (Bagdadi and Várhelyi, 2011; Guo et al., 2010; Williamson et al., 2015). Time-to-collision (TTC), jerk, gap time (GT), deceleration rate (DR), the proportion of stopping distance (PSD), and post-encroachment time (PET) are example variables used as safety surrogates (Kim et al., 2019; Kockelman et al., 2016; Morando et al., 2018a; Papadoulis et al., 2019; Ye and Yamamoto, 2019). More recently, a new term has been introduced as the drivers’ volatility that captures the aggressiveness of the drivers (Kamrani et al., 2018; Khattak and Wali, 2017; Khoda Bakhshi and Ahmed, n.d.; Wali et al., 2019, 2018). The driving volatility measures include the coefficient of variation, percent of acceleration/deceleration over a threshold, percent of the jerk over a threshold, etc. The volatility could be used for both longitudinal and lateral movements (Kamrani et al., 2018; Khattak and Wali, 2017; Wali et al., 2019, 2018); however, most of the studies have more focused on the longitudinal movements of the vehicles, rather than lateral volatilities.
In summary, the literature review conducted herein shows that the studies only focused on signalized intersections, roadway segments, and roundabouts, and minimal or no work has been conducted to comprehensively evaluate how AVs influence the operation and safety of an arterial nearby an unsignalized intersection/ driveway. Therefore, in an attempt to explore the extent of the abovementioned challenges for AVs, this paper evaluates the effects of various combinations of AV MPRs and traffic demands on the safety and operation of an urban arterial nearby a driveway, which works as a T-junction. The following sections describe the methodology and data analyses.
5
3. Methodology and Data Connectivity between roadways and surrounding land-uses and the reliability of travel time are both important factors. By increasing the number of access points along a highway, the connectivity increases; however, at the same time, the efficiency decreases. Also, the connectivity that defines the spacing of unsignalized intersections impacts both traffic safety and operation. Therefore, it is crucial to evaluate how vehicles can operate in the proximity of an unsignalized intersection in a mixed traffic environment, which also specifies how unsignalized intersections density should be increased/decreased accordingly. To this aim, this study evaluates the impact of various traffic levels of service (LOS) and AV MPRs on the operation and safety of an urban arterial in the proximity of a driveway. For the operational analyses, both traffic speed and density were analyzed thoroughly by conducting a trajectory analysis. Moreover, a comprehensive safety analysis was conducted through Surrogate Safety Assessment Model (SSAM) and driving volatility. In the SSAM analysis, each conflict type (RE, LC, and crossing) extracted from SSAM was assessed separately to determine how they change over different MPRs and traffic demands. In addition, in this study, various TTC values were implemented to determine the AV conflicts and conventional vehicle conflicts. for the volatility section, this study evaluates the effect of MPR and traffic LOS on various longitudinal and lateral driving volatility measures and their influence. Further information on the methodology will be provided in the following sections.
3.1. Developing the Simulation Environment Among the various available micro-simulation packages, the widely used PTV VISSIM 10.0 was utilized to develop a model and examine the purpose of this research. Figure 1 depicts the simulation scenario consisting of a 2,000 ft stretch of a three-lane urban arterial (lanes are numbered as shown in the figure), representing only one direction of the traffic, and a two-lane two-way driveway perpendicular to the arterial. The driveway ingress and egress points are located approximately in the middle of the arterial, which is at the distance of 1,000 ft from the arterial’s begin point.
Figure 1. Simulated Roadway Network and Lane Numbers.
The speed limit of the arterial was set to 45 mph, commonly practiced speed limit on urban arterials, and the driveway to 20 mph, representing common operating speed for driveway vehicles (Fitzpatrick and Das, 2019). To account for the stochastic nature of simulation models, each scenario should be run for more than once. For this study, to achieve a 95% confidence interval and an acceptable error rate of 10%, each scenario was run for ten times using Equation 1.
μEquation 1
6
Where, minimumsamplesize ; ;
, 1.95 95% ; μ ; .
In addition, according to the network size and characteristics (arterial length= 2,000 ft and speed limit= 45 mph), each simulation run was run for ten simulation minutes with a warm-up period to avoid having any outliers.
VISSIM offers two car following models: Wiedemann-74 and Wiedemann-99. Wiedemann-74 is designed for urban environments and merging areas while Wiedemann-99 is implemented for freeway segments with no merging area (Chowdhury et al., 2005; PTV VISSIM, 2018). Therefore, the default Wiedmann 74 driving behavior was implemented to represent conventional vehicle driving behavior. To replicate lateral and longitudinal controls of AVs, Wiedemann-74 associated driving parameters, including average standstill distance, additive part of safety distance, and multiplicative part of safety distance were modified using the VISSIM protocols and manual, presented in Table 1 (PTV VISSIM, 2018).
Table 1. Wiedemann 74 Driving Behavior Parameters.
Driving Behavior AV Value Average Standstill Distance 1.0 Additive Part of Safety Distance 1.5 Multiplicative Part of Safety Distance 0.0
Eventually, the simulation results were stored for every 50 ft long segment at the
frequency of 10 Hz, i.e., data were stored for each 0.1 second time. The 50-ft long segments and 0.1-second time intervals were selected to ensure that the details of the driving behaviors and movements are captured. The 50-ft segment length was chosen for the purpose of density analysis since it represents the required storage to accommodate two passenger cars, considering an average length of 19 ft for passenger cars, and the spacing between the vehicles (Sezer et al., 2015). In addition, 0.1 second time interval, i.e., 10 Hz, was considered for the purpose of safety analysis to capture good quality data since erratic safety maneuvers generally take place over a short time.
3.1.1. Traffic LOS
The LOS defined by the Highway Capacity Manual (HCM) is used as a measure to set the boundaries of different levels of traffic congestion (Transportation Research Board (TRB), 2016). The impact of four traffic LOS (A, B, C, and D), were evaluated in this research to account for various demand levels. Table 2 presents traffic flow for each LOS for an urban arterial, and the adjusted 10-minute traffic volume, for the entire 10-minute simulation run, for the arterial and the driveway during the simulation period (City/County Association of Governments of San Mateo County, 2005; Margiotta and Washburn, 2017).
Table 2. Traffic volume for multilane highways for different LOS.
LOS Flow Rate (pcphpl)
Input Volume for the arterial for 10 Minutes (pcpl)
Input Volume for Driveway for 10 Minutes (pcpl)
A 600 100 40 B 1,000 167 40
7
C 1,400 233 40 D 1,670 278 40
To run the models for each traffic LOS, the traffic volume of the arterial in the simulation
model was altered according to the defined volumes for each LOS (City/County Association of Governments of San Mateo County, 2005; Margiotta and Washburn, 2017), as given in Error! Reference source not found.. However, the traffic volume of the driveway remained constant at 240 pc/hr/ln across all the scenarios, which represents the minimum of the maximum number of vehicles that could merge to the arterial from all the scenarios (considering all LOS).
3.1.2. AV Market Penetration Rate
Each LOS was simulated under six AV market penetration rates of 0%, 10%, 25%, 50%, 75%, and 100% by altering the vehicle composition setting in VISSIM. The 0% AV MPR represents the base scenario that replicates a fully conventional vehicle environment, and MPR of 100% represents a fully AV environment.
Ultimately, with defining all the simulation considerations, a total of 24 scenarios (considering four LOS: A, B, C, and D; six AV MPRs: 0%, 10%, 25%, 50%, 75%, and 100%) were developed and run for further analyses. The simulation outputs for all the scenarios were extracted and investigated in the following sections. From this point on, each scenario has been entitled as a combination of its LOS and MPR.
3.2. Analyses of Traffic Operation Traffic operational analyses have two methodological approaches, including 1) traffic density analyses and b) traffic speed evaluation. The followings explain each approach separately.
3.2.1. Traffic Density After running the simulation models for four traffic LOS, A through D, and six AV MPRs, starting from 0% through 100%, the outputs were extracted to analyze the lane-based operational performance of the scenarios. Traffic density was selected as the performance measure variable to compare the operation of the scenarios for each lane and under different traffic LOS and MPRs. Equation 2 represents how traffic density is calculated:
Equation 2
Where,
; ; At each LOS, the second-by-second traffic density on each lane, refer to Figure 1 for the lanes’ number, over time and space were compared for various AV MPRs to determine how traffic operation, i.e., traffic density, changes. For each traffic LOS, analysis of variance (ANOVA) was conducted to compare the mean value of traffic density among scenarios with various AV MPRs. Moreover, the percentage of the times that each simulation scenario experienced traffic density of more than 34 veh/ln/mi, which implies LOS E and F, were extracted and compared (City/County Association of Governments of San Mateo County, 2005).
8
3.2.2. Traffic Speed One of the variables that is often used to define congestion is speed (Dias et al., 2009). Therefore, in this study, speed was also used as an operational performance measure of the scenarios. Since the arterial operational performance is the objective of the study, only the vehicles drove on the arterial were included in the analysis. It is worth mentioning that if the origin of a vehicle was the driveway, the vehicle was still included in the speed analysis when it was driving on the arterial. For the analysis, VISSIM provides the speed of the vehicles for each 0.1 second. The data were then extracted to determine the average speed of the vehicles over each simulation scenario. The results were then compared using the multiple pairwise comparison to determine if the traffic LOS and AV MPR impacts the travel speed of the network. The results are presented in section 4.1.2.
3.3.Analyses of Traffic Safety
As mentioned previously, the safety evaluation consists of two parts, a) analyzing safety using SSAM, and b) analyzing safety through driving volatility measures. The following sections cover the implemented methodologies that were used for the safety analyses.
3.3.1. Surrogate Safety Measures and Surrogate Safety Assessment Model Time to Collision (TTC) is an indicator of the crash risk and is defined as the required time to collide if two vehicles continue moving along the same path with the same speed, as indicated in Figure 2 (Dijkstra et al., 2010; Hayward, 1972; Morando et al., 2018). In other words, lower TTC is associated with higher crash risk, and high TTC represents a lower crash risk. It is notable that a vehicle on a section of a roadway only has one TTC value. However, the TTC on a junction is calculated based on one or more vehicles coming from the legs of the intersection (Dijkstra et al., 2010). Equation 3 demonstrates how to calculate TTC mathematically based on Figure 2 (Saunier and Sayed, 2010).
Figure 2. Time-To-Collision Definition and Variables.
TTC =
2
2
d
v if 1 2 1 1 2
1 2 1
d d d l w
v v v
(side)
Equation 3
1
1
d
v if 2 1 2 2 1
2 1 2
d d d l w
v v v
(side)
1 2 1
2 1
X X l
v v
if 2 1v v (rear-end)
9
1 2
2 1
X X
v v
(head-on)
Where: 1v and 2v : vehicles speeds; 1l and 2l : vehicles lengths; 1w and 2w : vehicles widths; 1X
and 2X : vehicle positions; 1d and 2d : distance to conflict areas.
Several studies defined the critical TTC value to detect conflicts and unsafe situations
when the actual TTC of the vehicles lessens the critical value (Dijkstra et al., 2010). Archer (2005) determined a TTC of 1.5 seconds as the critical value for urban areas, however Horst (1990) identified this critical value as 2.5 seconds. It is notable that the TTC threshold for the AVs is smaller due to the ability of the AVs to react to different situations abruptly compared to the human-driven vehicles, Morando et al. (Morando et al., 2018a) defined 1.0 second as the critical TTC for AVs.
Surrogate Safety Assessment Model is a package for analyzing surrogate safety measures obtained from micro-simulation models to identify the number of potential conflicts (Federal Highway Administration (FHWA), 2008; Pu and Joshi, 2008). SSAM uses micro-simulation trajectory files as input with the user-defined values for time-to-collision, post-encroachment time, RE angle, and crossing angle to determine the number and type of potential conflicts. Figure 3 shows the conflict angle diagram presented by SSAM (Pu and Joshi, 2008). In this study, SSAM will be used to determine the number of conflicts using VISSIM outputs.
Figure 3. SSAM Conflict Angle Diagram.
Due to the infeasibility of defining one single TTC value for mixed traffic environments,
the SSAM outputs should be implemented cautiously to find the correct number of conflicts. In this paper, the TTC value of 1.5 seconds was used to determine the number of conflicts for conventional vehicles using SSAM. TTC value of 1.5 seconds is the calibrated TTC value for conventional vehicles recommended by the U.S. Federal Highway Administration (Federal Highway Administration (FHWA), 2008). Other studies also implemented 1.5 seconds as the TTC value for conventional vehicles (Archer, 2005; Bahram et al., 2014; Katrakazas et al., 2019; Morando et al., 2018a).
10
As mentioned earlier, the TTC values are different for AVs and conventional vehicles and is smaller for AVs due to their smaller standstill distance (Morando et al., 2018a). Therefore, the TTC value for AVs was determined as a percentage of the TTC for conventional vehicles based on the VISSIM settings for AVs, indicated in Table 1. Since the standstill distance and following distance values for AVs were reduced to two-third of the values for conventional vehicles (PTV VISSIM, 2018), the TTC of AVs was also reduced to two-third of that of conventional vehicles. The TTC value of 1.0 second for AVs was used in previous studies as well (Morando et al., 2018a).
For mixed traffic environments, as 1.5 seconds is higher than the TTC for AVs, the number of AV conflicts were overestimating in the mixed traffic environments, where AVs existed. Therefore, a few adjustments were applied to ascertain the conflicts in which the AVs are at fault are not overestimated. In other words, the type of the vehicle at fault, AV or conventional vehicle, should be determined in each conflict to enable using the corresponding TTC value. According to the conflict angle diagram obtained from SSAM, the vehicle that hits from the back is the determining as the vehicle at fault, and the correct TTC value is used accordingly.
The SSAM is capable of estimating the number of RE, lane-changing (LC), and crossing conflicts. In this study, only RE and LC conflicts will be evaluated since the faulty vehicle in these two types of conflicts is the second vehicle, which hits from the back. This enables determining the number of conflicts accurately by using the associated TTC for the type of faulty vehicle. It is worth noting that the studied scenarios were free of crossing conflicts. Eventually, the average number of conflicts for ten runs of each scenario was calculated, and since conflicts are infrequent events, the analyses were conducted for each scenario by combining the conflicts for all lanes.
3.3.2. Driving Volatility
Various measures of volatility, including speed, acceleration, and jerk were calculated to quantify the overall safety of each simulation run. As mentioned earlier, all the indicated driving volatility measures were calculated for both lateral and longitudinal movements of the vehicles to further assess the overall safety for the network-level analysis. To this aim, the equations represented in Table 3, (Wali et al., 2018), were used to calculate the measures of volatility for each vehicle to further determine the network level aggregated values for each simulation run, regardless of the driving behavior of every single vehicle.
Table 3. Measures of Driving Volatility (Wali et al., 2018).
Driving Volatility Measure
Formula
Performance Measure Variables for Longitudinal and Lateral Movements
Sp Acc Dec Acc and Dec
Pos Jrk
Neg Jrk
Pos and Neg Jrk
Average ∑
× × × × × × ×
Standard Deviation
∑ × × × × × × ×
Coefficient of Variation
100 × × × × × × ×
11
Percent of Outliers
% 100
( 2 )
× ×
Sp: Speed; Acc: Acceleration; Dec: Deceleration; Pos Jrk: Positive Jerk; Neg Jerk: Negative Jerk
In other words, the driving volatility for every single vehicle at a rate of 10 Hz was calculated, and subsequently, the aggregated volatility measures were calculated by merging the measures over each simulation run. Table 3 indicates which measures of driving volatility were implemented for each performance measure variables.
The outputs of the driving volatility measures were used to assess how AV MPR and traffic LOS affect each measure. 4. Discussion and Results Using the abovementioned methodology, the following sections expand on traffic operation and safety analyses, respectively. Section 4.1 thoroughly describes the analyses of traffic operation of the network using traffic density and speed. In section 4.2, traffic safety is assessed using SSAM for each conflict type, separately, including RE and LC conflicts. In addition, to the best of the authors’ knowledge, for the very first time, all the lateral and longitudinal driving volatility measures will be evaluated simultaneously to develop a conflict prediction model.
4.1.Traffic Operation For analyzing the operational performance of the network for each scenario, two performance measures were implemented, traffic density and speed. The following sections provide details on each performance measure, separately.
4.1.1. Traffic Density Analysis Figure 4 Figure 4 depicts the distribution of hourly traffic volumes for each simulation scenario. The x-axis represents each scenario by providing both traffic LOS and AV MPR, represented as LOS:MPR. The y-axis represents the traffic flow in vehicles per hour. The figure also confirms that all levels of service were achieved. As the figure shows, traffic flow values have minor fluctuations across the various MPRs, but with a stable average flow value. The following explain and compare traffic density for different traffic LOS and AV MPRs.
12
Figure 4. Traffic Volume Pattern for Each Simulation Scenario.
As mentioned above, traffic density was used as the performance measure variable for evaluating traffic operation. At LOS A, due to the homogeneity of the heat-maps under various MPRs and travel lanes, only the heat-map of the MPR of 50% is presented in Figure 5. As depicted in this figure, the traffic is evenly distributed among the lanes without causing any high-density point/area. Table 4 also summarizes the results of an ANOVA test and confirms that under LOS A, there is no statistical difference between the operational performance of the vehicles under various AV MPRs.
Lane 1 Lane 2 Lane 3
Tim
e In
terv
al (
sec)
MP
R 5
0%
Segment (ft)
Figure 5. Traffic Density Heat-Map for LOS A.
Table 4. ANOVA for Density of Various MPRs at LOS A. Df Sum Sq Mean Sq F-Value Pr(>F) MPR 5 364 72.79 1.102 0.357 Residuals 14 3,494,388 66.04
13
Similarly, at LOS B, vehicles can operate smoothly without creating any high-density
spots, as shown in Figure 6 for AV MPR of 50%. Table 5 also conveys the same results by statistically comparing the mean density values for different MPRs. In fact, the traffic volume on the arterial is low enough and allows the driveway vehicles to find adequate and large enough gaps to merge to the arterial without interrupting the flow of the arterial.
Lane 1 Lane 2 Lane 3
Tim
e In
terv
al (
sec)
MP
R 5
0%
Segment (ft)
Figure 6. Traffic Density Heat-Map for LOS B.
Table 5. ANOVA for Density of Various MPRs at LOS B. Df Sum Sq Mean Sq F-Value Pr(>F) MPR 5 508 101.7 0.692 0.629 Residuals 52,914 7,770,834 146.9
14
When reaching LOS C, the difference in the traffic operation under various AV MPRs begins to be observable. Figure 7 depicts the traffic density of each lane for all the AV MPRs under LOS C. The blue shades indicate lower density areas, and the traffic density increases by moving towards the red shades. For the fully conventional vehicle environment and AV MPR of 10%, both lane 1 and lane 2, as indicated in Figure 1, experience high traffic density in the proximity of the driveway, where the driveway vehicles merge to the arterial. At higher AV MPRs, 25%, 50%, and 75%, the traffic operation begins to improve, and higher densities are only seen on lane 2. The reason for the high traffic density on lane 2 might be due to the vehicles that are on lane 1 and trying to avoid the driveway vehicles that merge to the arterial by changing lane to lane 2, as also indicated in Mousavi et al. (2019a).
As indicated in the depiction, the AV environment only consists of blue shades that represent low traffic densities. Therefore, by providing a fully AV environment, the vehicles can operate smoothly without causing any congestion.
Lane 1 Lane 2 Lane 3
Tim
e In
terv
al (
sec)
Con
vent
iona
l Veh
icle
s M
PR
10%
15
MP
R 2
5%
MP
R 5
0%
MP
R 7
5%
AV
Segment (ft)
Figure 7. Traffic Density Heat-Map for LOS C.
Table 6 also statistically confirms that the means of traffic density are statistically different under each AV MPR. By increasing the AV MPR up to 50%, the density on the arterial increases due to the increase in interactions between the conventional vehicles and AVs. But as indicated in the literature, training drivers could improve their interaction with AVs and enhance traffic operation (Ebnali et al., 2019). Eventually, by moving towards a fully AV environment, the traffic density decreases.
Table 6. ANOVA for Density of Various MPRs at LOS C.
Df Sum Sq Mean Sq F-Value Pr(>F) MPR 5 70,836 14,167 27.74 <0.0001 Residuals 52,914 27,023,267 511
16
At LOS D, the input traffic volume is 1,670 veh/ln/hr for the arterial and 240 veh/ln/hr
for the driveway. As depicted in Figure 8, the fully conventional vehicle environment is associated with multiple high-density spots over all the lanes and time intervals. As also indicated in Table 7, by increasing the AV MPR, although the mean traffic density over time and lanes increases, the traffic operation becomes smoother without inducing any high traffic density point. At a MPR of 50%, the scenario experiences the highest average traffic density compared to the other MPRs because of the increase of the interaction between the conventional vehicles and AVs. Sezer et al. (2015) also pointed out that under mixed traffic environments and at unsignalized intersections, AVs and conventional vehicles encounter interactions since AVs cannot identify conventional vehicles intentions before undertaking a maneuver.
Figure 9 depicts the percentage of the times that each LOS and MPR experienced traffic density equal and greater than 34 veh/ln/mi, which implied to LOS E and LOS F. Even though, according to Table 7, the AV MPR 50% has the highest average density, it experiences the traffic density ≥ 34 veh/ln/mi less frequently compared to the MPR 0%, as indicated in Figure 9. As in LOS C, the mean traffic density started decreasing by increasing the MPR beyond 50% until reaching a fully AV environment with the minimum average density. There is a high-density spot for the AV scenario right before the driveway that might be due to the lack of connectivity between AVs that prevents them from communicating efficiently.
Lane 1 Lane 2 Lane 3
Tim
e In
terv
al (
sec)
Con
vent
iona
l Veh
icle
s
17
MP
R 1
0%
MP
R 2
5%
MP
R 5
0%
MP
R 7
5%
AV
Segment (ft)
Figure 8. Traffic Density Heat-Map for LOS D.
In fact, the arterial vehicles cannot get notified of the driveway vehicles that intend to merge to the arterial; therefore, they cannot provide a gap for the vehicles to merge to the arterial, and once they merge, the density increases. Also, it is notable that since the standstill distance and headway (therefore, spacing) in the AV environment are smaller than the conventional vehicles, in case of traffic congestion, AVs may experience a higher traffic density compared to the conventional vehicles. In other words, at LOS D, the demand is higher, vehicles drive closer, and might experience slow speed traffic, especially near the driveway when a vehicle joins the arterial. Therefore, the spacing between the vehicles decreases and the traffic
18
density increases. This increase might be higher for AVs due to the smaller headway and standstill distance associated with them.
Table 7 summarizes the results of the statistical analysis and indicates that the mean traffic density of the arterial for different AV MPRs is statistically different, and the AV environment experiences the lowest mean traffic density, 36.041 veh/ln/mi.
Table 7. ANOVA for Density of Various MPRs at LOS D. Df Sum Sq Mean Sq F-Value Pr(>F) MPR 5 138,391 27.678 17.62 <0.0001 Residuals 52,914 83,131,420 1,571
Figure 9. Percent of the Times with Density ≥ 34 veh/ln/mi for LOS A, B, C, and D.
19
4.1.2. Traffic Speed Analysis Another performance measure that was implemented to evaluate the operation of each scenario was speed. Since the speed varied for the arterial and driveway, only speed of the vehicles on the arterial was analyzed. To compare the average speed of the vehicles on the arterial between the scenarios, a multiple pairwise comparison between groups was conducted.
Table 8 presents the results of the test, along with a graphical depiction of the speed distribution for each scenario. As indicated, the mean values of the arterial speed are statistically different between the majority of the scenarios, except for LOS A: 25 and LOS A: 50, LOS A: 00 and LOS A: 100, and LOS B: 10 and LOS B: 25 (indicated in bold and italic). As depicted by the figure, by increasing the traffic LOS, the median and mean speed of the network decreases. On the other hand, by increasing the AV MPR, the median and mean speed increases almost steadily for most of the cases. Therefore, both the traffic LOS and MPR influence the operation of the arterial in the proximity of an unsignalized intersection, and mixed traffic environments with AVs as the dominant vehicle type are capable of operating at a higher speed.
Table 8. Multiple Pairwise Comparison Test for Arterial Speed. Scenario A:00 A:10 A:25 A:50 A:75 A:100 B:00 B:10 B:25 B:50 B:75 B:100 C:00 C:10 C:25 C:50 C:75 C:100 D:00 D:10 D:25 D:50 D:75 Mean 43.303 43.263 43.276 43.274 43.296 43.325 42.854 42.88 42.879 42.87 42.914 42.901 42.034 41.961 42.15 42.2 42.281 42.08 40.656 39.897 40.68 41.254 41.137 A:10 43.263 <.0001 A:25 43.276 <.0001 0.05 A:50 43.274 <.0001 0.002 0.261 A:75 43.296 <.0001 <.0001 <.0001 <.0001 A:100 43.325 0.87 <.0001 <.0001 <.0001 <.0001 B:00 42.854 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 B:10 42.88 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 B:25 42.879 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.61 B:50 42.87 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 B:75 42.914 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 B:100 42.901 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.041 <.0001 C:00 42.034 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 C:10 41.961 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 C:25 42.15 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 C:50 42.2 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 C:75 42.281 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 C:100 42.08 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 D:00 40.656 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 D:10 39.897 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 D:25 40.68 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 D:50 41.254 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 D:75 41.137 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 D:100 40.738 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001
20
4.2. Safety Abovementioned, the safety analysis was conducted in three different parts of RE conflicts, LC conflicts, and driving volatility analysis. The followings describe each section separately.
4.2.1. SSAM Analysis The initial analysis indicated that all the conflicts that occurred in the simulation environments were either RE or LC conflicts. Therefore, the following two sections cover each conflict type, separately.
4.2.1.1.Rear-End Conflicts As mentioned earlier, as conflicts are rare events, the safety analyses were conducted for different AV MPRs and LOS, regardless of the lane distribution. However, the results of a previous study on the lane-based analysis of all types of conflicts indicated that AVs are capable of improving the overall safety significantly (Mousavi et al., 2020). In addition, although AVs result in a significantly fewer number of conflicts, the majority of the conflicts occurred on the arterial with a higher speed that might result in more severe crashes (Hauer, 2009; Mousavi et al., 2020). Table 9 summarizes the average number of RE conflicts for all the conducted simulation runs by the type of faulty vehicle for each LOS and AV MPR.
Table 9. Distribution of the Average Number of RE Conflicts by LOS, MPR, and Vehicle
Type. LOS MPR AV at Fault RV at Fault Total LOS MPR AV at Fault RV at Fault Total
A
0 0 2.8 2.8
C
0 0 66.8 66.8
10 0 1.3 1.3 10 0.8 57.8 58.6
25 0 0.9 0.9 25 1.7 51.1 52.8
50 0 0.6 0.6 50 3.5 39.3 42.8
75 0 0.3 0.3 75 6.5 19 25.5
21
100 0 0 0 100 5.5 0 5.5
B
0 0 53.2 53.2
D
0 0 31.8 31.8
10 0.3 31.9 32.2 10 1 33.8 34.8
25 0.5 24.4 24.9 25 1.2 22.6 23.8
50 0.7 17.1 17.8 50 2.6 15.9 18.5
75 0.3 6.2 6.5 75 6.4 7.1 13.5
100 0.6 0 0.6 100 5 0 5
Table 10 and the associated depiction represent the number of RE conflicts by MPR per
simulation. As indicated in this table, by increasing the AV MPR, the overall number of RE conflicts decreases, and the means of conflicts are significantly different. By moving from AV MPR of 0% to 100%, the average number of RE conflicts for all the simulation runs decreases significantly.
Table 10. ANOVA for the Number of Conflicts per Simulation Run for Various MPRs.
Df Sum Sq Mean Sq F-Value Pr(>F) MPR 5 26,534 5,307 14.58 <0.0001 Residuals 192 69,878 364
On the other hand, as depicted in Figure 10, the impact of LOS on the number of RE conflicts varies. By increasing the traffic volume until reaching to LOS C, the average number of conflicts increases, which is expected due to the increase in traffic exposure. But, LOS D results in a fewer number of conflicts. The reason for the fewer number of conflicts at LOS D is due to the fact that the vehicles are less free to perform their maneuvers. In fact, it is harder for the driveway vehicles to find an appropriate gap on the arterial to merge, which results in a fewer number of maneuvers and, consequently, conflicts.
22
Figure 10. Average Number of RE Conflicts by LOS.
Since traffic volume plays an important role in both traffic safety and operation (American Association of State Highways and Transportation Officials (AASHTO), 2010), the number of conflicts was normalized by the traffic exposure to make the scenarios comparable by having the number of conflicts per vehicle. It is worth noting that the conflicts just represent near-miss events, not crashes. Figure 11 shows the percent of average number of RE conflicts normalized by the traffic volume for each traffic LOS and MPR per run. As indicated above, by increasing the AV MPR in all the traffic LOS, the number of RE conflicts decreases as AVs are capable of reacting to the situations abruptly. However, in general, by increasing the traffic volume, the number of conflicts increases since the traffic exposure increases. But, similar to Figure 10, LOS D is associated with a lower number of conflicts as the vehicles cannot perform their maneuvers freely due to the unavailability of adequate gaps.
Figure 11. Normalized Number of RE Conflicts by Volume per Simulation Run (%).
As the last step, to predict the number of RE conflicts, as a discrete and skewed variable, Negative Binomial (NB) regression was used to develop a safety performance function (SPF)
0
50
100
150
200
250
A B C D
Total AV Conflict Total RV Conflict
23
based on AV MPR and LOS using R statistical package. Table 11 summarizes the results of the NB model.
Table 11. Results of NB Regression Model to Predict the Number of RE Conflicts. Variable Estimate Std Error t value Pr(>|t|)
(Intercept) -10.736 1.356 -9.453 < 0.0001
Log (Traffic Flow) 2.650 0.208 12.730 < 0.0001
MPR -0.0208 0.0016 -13.124 < 0.0001
Dispersion parameter = 2.607, AIC = 1,540.5
As indicated in Equation 4 by increasing the AV MPR, the number of RE conflicts decreases. However, the traffic volume has a positive relationship with the number of conflicts. In other words, by increasing the traffic volume, the number of conflicts increases.
2.17 10 . . Equation 4 Where,
% ;
;
4.2.1.2.Lane-Changing Conflicts
Figure 12 provides the average number of LC maneuvers, both total and normalized frequencies. As depicted and expected in the figure, the average number of LC maneuvers increases by increasing traffic LOS. But the normalized number of LC maneuvers (normalized by the traffic volume) indicates that the average number of LC maneuvers per vehicle decreases by increasing the traffic volume. This reduction in the average number of LC maneuvers per vehicle is due to the increase in the traffic volume and decrease in the number of available gaps.
a) Average Number of LC Maneuvers
b) Average Number of Normalized LC
Maneuvers Figure 12. Frequency of LC Maneuvers.
The same strategy as for the RE conflicts was implemented to explore the LC near-miss
events. According to the SSAM manual (Pu and Joshi, 2008), any conflict that occurs on the same link but different lanes with 30 ≤ θ ≤ 85 (please refer to Figure 3 for θ), is considered an
0
20
40
60
80
100
LOS A LOS B LOS C LOS D0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
LOS A LOS B LOS C LOS D
24
LC conflict. In our analysis, the second vehicle that approaches the first vehicle was considered as the determining vehicle. If the determining vehicle was an AV, TTC of 1.0 sec was implemented; otherwise, TTC of 1.5 seconds was used. Figure 13 depicts the normalized number of LC conflicts by traffic volume for each vehicle type per LOS and AV MPR. As depicted, for every traffic LOS, for low AV MPR of up to 10%, the number of LC conflicts increases. However, by increasing the MPR, the number of LC conflicts decreases almost steadily until reaching the MPR of 100% with the lowest number of LC conflicts for all the LOS.
By increasing the traffic volume, the number of LC conflicts declined until reaching to LOS C. This change is due to the reduction in the number LC maneuvers per vehicle, as shown in Figure 12, as well as the reduction in the number of available gaps, which restrains vehicles from executing their LC maneuvers. At LOS D, even though the average number of LC maneuvers per vehicle decreases, the normalized number of LC conflicts escalated because the driveway vehicles had to merge to the arterial when finding any gaps. These mandatory LC maneuvers reduce the spacing between vehicles, which consequently result in a TTC value below the critical TTC, and lead to a conflict.
Figure 13. Normalized Number of LC Conflicts by Volume per Simulation Run (%).
Additionally, the results indicate a uniform pattern along each LOS. In other words, the total number of LC near-miss events raised up by increasing the MPR to 10%, but by providing mixed traffic environments with higher MPRs, the number of LC conflicts started decreasing. For all the evaluated LOS, the fully AV environment experienced the lowest number of LC conflicts. Table 12 presents the results of a regression model for estimating the total number of LC conflicts. As indicated, by increasing the AV MPR and decreasing the traffic flow, the number of LC conflicts decreases. Equation 5 could be implemented to determine the number of LC conflicts.
Table 12. Results of NB Regression Model to Predict the Number of LC Conflicts. Variable Estimate Std Error t value Pr(>|t|)
25
(Intercept) -0.874 0.968 -0.903 0.3677
Log (Traffic Flow) 0.452 0.180 2.511 < 0.0001
MPR -0.008 0.002 -4.063 0.0129
Dispersion parameter = 3.94, AIC= 825.49
0.417 . . Equation 5 Where,
%
4.2.2. Driving Volatility As indicated, the network-level measures of volatilities were calculated for each simulation run using Table 3. Table 13 summarizes the descriptive statistics for the simulation runs. The outputs show that, on average, 24.5 conflicts occurred during each simulation run with 21.7 RE conflicts and 2.8 LC near-miss events. Some scenarios experienced zero near-crash events, while the maximum total number of conflicts was 89.
Table 13. Descriptive Statistics for the Measures of Volatilities per Simulation Run. Variable Mean Sd Min Max Variable Mean Sd Min Max
Arterial Volume 194.5 67.5 100 278 Lateral Maneuvers
MPR 43.3 35.6 0 100 Average Acceleration 0.00 0.00 0.00 0.01 Longitudinal Maneuvers Acceleration Sd 0.08 0.02 0.04 0.12
Average Speed 62.1 1.8 51.9 64.05 Acceleration CV 2290.3 481.4 1409.7 3887.2Speed Sd 4.0 2.5 2.2 18.33 Average Deceleration -0.94 0.03 -1.01 -0.87 Speed CV 6.5 4.6 3.5 35.29 Deceleration Sd 0.86 0.02 0.80 0.91 Average Acceleration
0.7 0.1 0.7 1.19 Deceleration CV -91.2 1.73 -95.1 -86.7
Acceleration Sd 0.97 0.21 0.74 1.93 Average Positive Jerk 0.07 0.03 0.02 0.17 Acceleration CV 128.4 13.3 111.3 179.79 Positive Jerk Sd 1.3 0.27 0.71 2.03 Average Deceleration
-0.80 0.07 -1.29 -0.72 Positive Jerk CV 2001.5 423.07 1231.7 3433.2
Deceleration Sd 0.80 0.18 0.64 1.87 Average Negative Jerk -15.4 0.55 -16.9 -13.9 Deceleration CV -99.9 12.9 -
145.0 -84.74 Negative Jerk Sd
12.6 0.68 10.9 13.9 Average Positive Jerk
9.8 0.1 8.7 9.90 Negative Jerk CV -82.2 3.96 -91.6 -70.1
Positive Jerk Sd 8.3 0.1 8.2 8.61 Percent of Erratic Maneuvers
Positive Jerk CV
85.1 1.4 83.9 99.3 Acceleration/Deceleration over Threshold (%) 1.5 1.0 0.53 5.7
Average Negative Jerk
-10.4 0.05 -10.6 -10.3 Jerk over Threshold (%) 4.5 0.28 3.9 5.2
Negative Jerk Sd 8.2 0.04 8.1 8.4 Lateral Acceleration/Deceleration over Threshold (%) 0.33 0.14 0.10 0.75
Negative Jerk CV
-78.9 0.36 -80.2 -78.0 Lateral Jerk over Threshold (%) 0.40 0.16 0.12 0.91
Lateral Maneuvers Conflicts
Average Speed 62.1 1.8 51.9 64.1 LC Conflicts 2.8 3.1 0 19
26
Speed Sd 4.0 2.5 2.2 18.3 RE Conflicts 21.7 22.4 0 87 Speed CV 6.5 4.6 3.5 35.3 Total Conflicts 24.5 22.7 0 89
Speed unit: ; Acceleration/deceleration unit: ; Jerk unit:
To analyze the effect of various MPRs and traffic LOS on the driving volatility measures, various generalized linear regression models (GLM) were developed considering each driving volatility measure as the dependent variable in each model. Table 14 provides the results of the regression models for each variable, the missing measures of volatility from the table could not be predicted significantly using MPR and traffic LOS. The table presents the results in two different categories of longitudinal movements and lateral movements. Moreover, it is notable that the dispersion parameter for some of the variables is not within an acceptable range, but the models are included to roughly demonstrate the impact of MPR and LOS on the dependent variable.
Table 14. Regression Models for the Driving Volatility Measures. Estimate Std. Error t value Pr(>|t|) Estimate Std. Error t value Pr(>|t|)
Longitudinal Movements
Speed Sd Speed Cv (Intercept) 0.784 0.469 1.670 0.096 0.208 0.801 0.260 0.795 MPR -0.007 0.004 -1.714 0.088 Traffic Volume 0.018 0.002 8.413 <0.0001 0.032 0.004 8.280 <0.0001 AIC= 1,066.7 AIC= 1,357.9 Acceleration Sd Acceleration Cv (Intercept) 0.688 0.037 18.606 <0.0001 105.573 2.109 50.059 <0.0001 MPR Traffic Volume 0.001 0.0002 8.005 <0.0001 0.117 0.010 11.432 <0.0001 AIC= -118.34 AIC= 1,822.3 Deceleration Sd Deceleration Cv (Intercept) 0.598 0.034 17.646 <0.0001 -81.086 2.276 -35.632 <0.0001 MPR -0.0005 0.0003 -1.846 0.066 0.039 0.019 2.000 0.047 Traffic Volume 0.001 0.0002 7.756 <0.0001 -0.106 0.010 -10.282 <0.0001 AIC= -195.28 AIC= 1,824.4 Positive Jerk Sd Positive Jerk Cv (Intercept) 8.239 0.009 914.152 <0.0001 84.025 0.270 311.413 <0.0001 MPR Traffic Volume 0.0004 0.00004 9.242 <0.0001 0.006 0.001 4.322 <0.0001 AIC= -796.19 AIC= 85.36
Negative Jerk Sd Percent of Acceleration/Deceleration over
Threshold (Intercept) 8.160 0.008 1059.324 <0.0001 0.644 0.016 40.977 <0.0001 MPR Traffic Volume 0.00021 0.00004 5.556 <0.0001 -0.002 0.0001 -21.375 <0.0001 AIC= -871.6 AIC= -529.26 Percent of Jerk over Threshold (Intercept) 5.166 0.026 201.864 <0.0001 MPR Traffic Volume -0.004 0.000 -29.047 <0.0001 AIC= -295.23
Lateral Movements
27
Acceleration Sd Acceleration Cv (Intercept) 0.115 0.002 55.878 <0.0001 1214.47 60.081 20.214 <0.0001 MPR 0.00004 0.00002 2.292 0.023 Traffic Volume -0.0002 0.00001 -20.435 <0.0001 5.531 0.292 18.950 <0.0001 AIC= -1,539.2 AIC= 3,430.1 Deceleration Sd Deceleration Cv (Intercept) 0.8350 0.0015 558.637 <0.0001 -92.054 0.161 -571.00 <0.0001 MPR 0.0005 0.00003 18.1361 <0.0001 0.020 0.003 6.956 <0.0001 Traffic Volume AIC= -1,340.9 AIC= 905.89 Positive Jerk Sd Positive Jerk Cv (Intercept) 1.914 0.034 56.257 <0.0001 1050.77 52.37 20.06 <0.0001 MPR 0.001 0.000 2.111 0.036 Traffic Volume -0.003 0.000 -20.946 <0.0001 4.887 0.254 19.21 <0.0001 AIC= -192.9 AIC= 3,3364.2 Negative Jerk Sd Negative Jerk Cv (Intercept) 12.068 0.076 158.81 <0.0001 -83.319 0.710 -117.39 <0.0001 MPR 0.016 0.0006 24.852 <0.0001 -0.050 0.006 -8.322 <0.0001 Traffic Volume -0.0008 0.0003 -2.314 0.022 0.017 0.003 5.368 <0.0001 AIC= 192.68 AIC= 1,265.2
Percent of Acceleration/Deceleration over
Threshold Percent of Jerk over Threshold
(Intercept) 0.0017 0.180 0.0097 0.992 0.780 0.019 40.024 <0.0001 MPR -0.004 0.002 -2.824 0.005 Traffic Volume 0.009 0.0008 10.559 <0.0001 -0.002 0.0001 -20.466 <0.0001 AIC= 607.8 AIC= -425.83
For the longitudinal movements, as indicated in Table 14, the MPR significantly influences speed Sd, deceleration Sd, and deceleration Cv. The MPR has a negative effect on the former ones, while the increase in the MPR increases the deceleration Cv. In other words, the increase in the AV MPR reduces the speed Sd and deceleration Sd, which results in a more uniform traffic flow and subsequently safer environment (Duncan et al., 1998). Moreover, the results indicate that for the majority of the longitudinal measures of volatility, any increment in the traffic LOS leads to an increase in the volatility measures. In fact, by increasing the traffic volume, the safety deteriorates due to the higher likelihood of vehicles interactions.
Considering the lateral volatility measures, the MPR is a significant variable for most of the volatility measures except for the acceleration Cv, positive jerk Cv, and percent of jerk over threshold. A higher MPR results in a smaller negative jerk Cv and lower percent of acceleration/deceleration maneuvers over a threshold, while higher MPRs increase the deceleration Sd, deceleration Cv, positive jerk Sd, and negative jerk Sd. In addition, the influence of the traffic LOS on the lateral measures of volatility does not follow a determined pattern and presents different effects on each volatility measure.
In summary, although both lateral and longitudinal driving volatility measures were evaluated, longitudinal volatility measures are more critical since the majority of the conflicts were RE, as indicated in Table 13. Additionally, Kamrani et al. (2018) indicated that the lateral acceleration/deceleration, and consequently jerks, are critical variables where there is a noticeable amount of curvature on a roadways, which is not the case for this research scenario. In conclusion, by increasing the AV MPR and decreasing the traffic LOS (where the predictors are
28
significant), the majority of the longitudinal volatility measures decreases, which leads to an improvement in traffic safety.
5. Conclusions Traffic congestion, as a serious problem, has been increasing steadily, especially in large cities. Traffic congestion not only diminishes traffic operation and roadway safety but also imposes costs to roadway users by increasing travel time, fuel consumption, fatigue, and frustration. Urban arterials, as a major component of urban traffic networks, are characterized by a high density of signalized and unsignalized intersections and serve a high traffic volume each day; Therefore, they experience traffic congestion regularly.
To address traffic congestion, implementing conventional solutions, such as increasing roadway capacity, is not any more efficient and effective due to the cost and space limitations. Nevertheless, AV as innovative technology is developing and will be carried out extensively in the near future. Thus, it is crucial to comprehensively evaluate and determine the impacts of AVs on traffic safety and operation. Since there will be a transition period to reach a fully AV environment, it is also essential to identify the impacts of various AV MPRs.
Considering the abovementioned, this study investigated the impacts of different AV MPRs under various traffic LOS on the operational and safety performance of an urban arterial in the proximity of a driveway. To this aim, a simulation model was developed to run twenty-four combinations of six MPRs (0%, 10%, 25%, 50%, 75%, and 100%) and four traffic LOS (A, B, C, and D). Traffic operation evaluation focused on trajectory analysis of traffic density and speed, separately, while traffic safety divided up into two sections of SSAM and driving volatility.
The results of the operational analyses indicated that the AVs are capable of improving traffic operation by mitigating the overall traffic density, especially under high LOS, e.g., LOS C and LOS D. For the high-density points in LOS D in the AV environment, since AVs have no means of communication with the driveway vehicles, it is recommended to add an acceleration lane for the driveway vehicles, even for mixed traffic environments, to be able to join to the arterial smoothly. Facilitating the arterial with an acceleration lane will enhance traffic operation and safety, but it needs further investigations for AVs. Additionally, evaluating the average network speed indicated that for each traffic LOS, by increasing the AV MPR from 0% to 100%, the average speed of the arterial increased significantly between 5% to 20%.
The results of the safety assessment expressed that for all the traffic LOS, by increasing the AV MPR, the number of RE and LC conflicts decreased significantly by 84%-100% and 42%-100%, respectively. Moreover, the influence of AV MPR and traffic LOS on various longitudinal driving volatility measures indicated that the higher MPRs result in smaller longitudinal speed Sd and deceleration Sd, which leads to a safer traffic environment (Duncan et al., 1998). Also, higher AV MPRs improves lateral safety by decreasing the acceleration Cv, positive jerk Cv, and percent of jerk over threshold.
In conclusion, this paper can contribute to an important issue in access management that is the spacing and density of the driveways. The spacing between the access points is critical to both traffic safety and operation and is determined by separating the operational and conflict areas of the access points according to the performance of the vehicles. Since AVs are capable of improving the overall traffic operation and safety in the proximity of a driveway, the driveways spacing could be decreased to provide more frequent access points to the adjacent land-uses and address the accessibility issues.
29
Formatting of Funding Sources This research was partly funded by the A.P. and Florence Wiley Faculty Fellow at Texas A&M University.
References Ajitha, T., Vanajakshi, L., Subramanian, S.C., 2015. Real-time traffic density estimation without
reliable side road data. J. Comput. Civ. Eng. 29 2 , 1–8. doi:10.1061/(ASCE)CP.1943-5487.0000310
Al-Kadi, Omar, Al-Kadi, Osama, Al-Sayyed, R., Alqatawna, J., 2014. Road scene analysis for determination of road traffic density. Front. Comput. Sci. 8 4 , 619–628. doi:10.1007/s11704-014-3156-0
American Association of State Highway and Transportation Officials (AASHTO), 2011. A Policy on Geometric Design of Highways and Streets.
American Association of State Highways and Transportation Officials (AASHTO), 2010. Highway Safety Manual.
Archer, J., 2005. Indicators for Traffic Safety Assessment and Prediction and Their Application in Micro-Simulation Modelling: A Study of Urban and Suburban Intersections.
Aria, E., Olstam, J., Schwietering, C., 2016. Investigation of Automated Vehicle Effects on Driver’s Behavior and Traffic Performance. Transp. Res. Procedia 15, 761–770. doi:10.1016/j.trpro.2016.06.063
Ariannezhad, A., Karimpour, A., Wu, Y.-J., 2020. Incorporating Mode Choices into Safety Analysis at the Macroscopic Level. J. Transp. Eng. Part A Syst. 146 4 . doi:10.1061/JTEPBS.0000337
Arvin, R., Khattak, A., Rios Torres, J., 2019. Evaluating Safety with Automated Vehicles at Signalized Intersections: Application of Adaptive Cruise Control in Mixed Traffic, in: Transportation Research Board.
Azad, M., Hoseinzadeh, N., Brakewood, C., Cherry, C.R., Han, L.D., Chien, S.I., 2019. Fully Autonomous Buses: A Literature Review and Future Research Directions. J. Adv. Transp. doi:10.1155/2019/4603548
Azimi, G., Rahimi, A., Asgari, H., Jin, X., 2019. Severity analysis for large truck rollover crashes using a random parameter ordered logit model. Accid. Anal. Prev. 135.
Bagdadi, O., Várhelyi, A., 2011. Jerky driving - An indicator of accident proneness? Accid. Anal. Prev. 43 4 , 1359–1363. doi:10.1016/j.aap.2011.02.009
Bahram, M., Ghandeharioun, Z., Zahn, P., Baur, M., Huber, W., Busch, F., 2014. Microscopic traffic simulation based evaluation of highly automated driving on highways, in: 2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014. Institute of Electrical and Electronics Engineers Inc., pp. 1752–1757. doi:10.1109/ITSC.2014.6957946
Barrachina, J., Garrido, P., Fogue, M., Martinez, F.J., Cano, J.C., Calafate, C.T., Manzoni, P., 2015. A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios. Wirel. Pers. Commun. 83 1 , 259–280. doi:10.1007/s11277-015-2392-4
Chowdhury, M., Derov, N., Tan, P., Sadek, A., 2005. Prohibiting left-turn movements at mid-block unsignalized driveways: Simulation analysis. J. Transp. Eng. 131 4 , 279–285. doi:10.1061/(ASCE)0733-947X(2005)131:4(279)
City/County Association of Governments of San Mateo County, 2005. Congestion Management Program (CMP).
30
Dias, C., Miska, M., Kuwahara, M., Warita, H., 2009. Relationship Between Congestion and Traffic Accidents on Expressways an Investigation with Bayesian Belief Networks.
Dijkstra, A., Marchesini, P., Bijleveld, F., Kars, V., Drolenga, H., Maarseveen, M. Van, 2010. Do calculated conflicts in microsimulation model predict number of crashes? Transp. Res. Rec. 2147 , 105–112. doi:10.3141/2147-13
Duncan, C.S., Khattak, A.J., Council, F.M., 1998. Applying the Ordered Probit Model to Injury Severity in Truck-Passenger Car Rear-End Collisions. Transp. Res. Rec. J. Transp. Res. Board 1635 1 , 63–71. doi:10.3141/1635-09
Ebnali, M., Hulme, K., Mazloumi, A., 2019. How does training affect users’ attitudes and skills needed for highly automated driving? Transp. Res. Part F Traffic Psychol. Behav. 66, 184–195.
Eisele, W.L., Frawley, W.E., 2005. Estimating the Safety and Operational Impact of Raised Medians and Driveway Density. Transp. Res. Rec. J. Transp. Res. Board 1931 1 , 108–116. doi:10.1177/0361198105193100114
Fagnant, D.J., Kockelman, K., 2015. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 77, 167–181. doi:10.1016/j.tra.2015.04.003
Federal Highway Administration (FHWA), 2010. Access Management in the Vicinity of Intersections. Department of Transportation.
Federal Highway Administration (FHWA), 2008. Surrogate Safety Assessment Model and Validation.
Fitzpatrick, K., Das, S., 2019. Vehicle Operating Speed on Urban Arterial Roadways. College Station.
Gora Pawełand Rüb, I., 2016. Traffic Models for Self-driving Connected Cars. Transp. Res. Procedia 14, 2207–2216. doi:10.1016/j.trpro.2016.05.236
Guo, F., Klauer, S.G., McGill, M.T., Dingus, T.A., 2010. Evaluating the Relationship Between Near-Crashes and Crashes: Can Near-Crashes Serve as a Surrogate Safety Metric for Crashes?
Hauer, E., 2009. Speed and Safety. Transp. Res. Rec. J. Transp. Res. Board 2103(2103) 10–17. Hayward, J.C., 1972. Near-miss determination through use of a scale of danger. Highw. Res. R
384, 22–34. Highway Traffic Safety Administration, N., Department of Transportation, U., 2017. Research
Note: 2017 Fatal Motor Vehicle Crashes: Overview October . Hoogendoorn, R., Van Arem, B., Hoogendoorn, S., 2014. Automated driving, traffic flow
efficiency, and human factors. Transp. Res. Rec. 2422 2422 , 113–120. doi:10.3141/2422-13
Horst, V. der, 1990. A time-based analysis of road user behaviour in normal and critical encounters.
Huffman, C., Poplin, J., 2002. The Relationship between Intersection Density and Vehicular Crash Rate on the Kansas State Highway System, in: TRansportation Research Board.
Jeong, E., Oh, C., Lee, S., 2017. Is vehicle automation enough to prevent crashes? Role of traffic operations in automated driving environments for traffic safety. Accid. Anal. Prev. 104 February , 115–124. doi:10.1016/j.aap.2017.05.002
Kamrani, M., Arvin, R., Khattak, A.J., 2018. Extracting Useful Information from Basic Safety Message Data: An Empirical Study of Driving Volatility Measures and Crash Frequency at Intersections. Transp. Res. Rec. J. Transp. Res. Board 2672 38 , 290–301.
31
doi:10.1177/0361198118773869 Karaaslan, U., Varaiya, P., Walrand, J., 1990. UC Berkeley Research Reports Title Two
Proposals To Improve Freeway Traffic Flow Publication Date. Katrakazas, C., Quddus, M., Chen, W.H., 2019. A new integrated collision risk assessment
methodology for autonomous vehicles. Accid. Anal. Prev. 127, 61–79. doi:10.1016/j.aap.2019.01.029
Khan, S.M., Mitchell, J., Chowdhury, M., Dey, K., Huynh, N., 2017. Operational analysis of a connected vehicle-supported access control on urban arterials. IET Intell. Transp. Syst. 12.
Khattak, A.J., Wali, B., 2017. Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles. Transp. Res. Part C Emerg. Technol. 84, 48–73. doi:10.1016/j.trc.2017.08.004
Khoda Bakhshi, A.., Ahmed, M.M., n.d. Real-Time Crash Prediction for a Long Low-Traffic Volume Corridor Using Corrected-Impurity Importance and Semi-Parametric Generalized Additive Model. J. Transp. Saf. Secur. (Unpublished results).
Kim, I., Pakdamanian, E., Hiremath, V., 2019. A Case Study of Trust on Autonomous Driving, in: IEEE Intelligent Transportation Systems(ITSC).
Kockelman, K., Avery, P., Bansal, P., Boyles, S.D., Bujanovic, P., Choudhary, T., Clements, L., Domnenko, G., Fagnant, D., Helsel, J., Hutchinson, R., Levin, M., Li, J., Li, T., LoftusOtway, L., Nichols, A., Simoni, M., Stewart, D., 2016. Implications of Connected and Automated Vehicles on the Safety and Operations of Roadway Networks: A Final Report Implications of Connected and Automated Vehicles on the Safety and Operations of Roadway Networks: A Final Report, Fhwa/Tx-16/0-6849-1. doi:FHWA/TX-16/0-6849-1
Letter, C., Elefteriadou, L., 2017. Efficient control of fully automated connected vehicles at freeway merge segments. Transp. Res. Part C Emerg. Technol. 80, 190–205. doi:10.1016/j.trc.2017.04.015
Liu, P., Lu, J.J., Zhou, H., Sokolow, G., 2007. Operational effects of U-turns as alternatives to direct left-turns. J. Transp. Eng. 133 5 , 327–334. doi:10.1061/(ASCE)0733-947X(2007)133:5(327)
Ma, Y., Chowdhury, M., Sadek, A., Jeihani, M., 2012. Integrated Traffic and Communication Performance Evaluation of an Intelligent Vehicle Infrastructure Integration (VII) System for Online Travel-Time Prediction. IEEE Trans. Intell. Transp. Syst. 13 3 , 1369–1382. doi:10.1109/tits.2012.2198644
Ma, Y., Chowdhury, M., Sadek, A., Jeihani, M., 2009. Real-time highway traffic condition assessment framework using vehicleInfrastructure integration (VII) with artificial intelligence (AI). IEEE Trans. Intell. Transp. Syst. 10 4 , 615–627. doi:10.1109/TITS.2009.2026673
Margiotta, R., Washburn, S., 2017. Simplified Highway Capacity Calculation Method for the Highway Performance Monitoring System.
Morando, M.M., Tian, Q., Truong, L.T., Vu, H.L., 2018a. Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures. J. Adv. Transp. 2018. doi:10.1155/2018/6135183
Morando, M.M., Tian, Q., Truong, L.T., Vu, H.L., 2018b. Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures. J. Adv. Transp. 2018. doi:10.1155/2018/6135183
Mousavi, S.M., Dadashova, B., Lord, D., Mousavi, S.R., 2020. Can Autonomous Vehicles Enhance Traffic Safety at Unsignalized Intersections?, in: International Conference on
32
Transportation and Development. Mousavi, S.M., Osman, O.A., Lord, D., 2019a. Impact of Urban Arterial Traffic LOS on the
Vehicle Density of Different Lanes of the Arterial in Proximity of an Unsignalized Intersection for Autonomous Vehicle vs. Conv, in: International Conference on Transportation and Development 2019. pp. 303–314.
Mousavi, S.M., Zhang, Z., Parr, S.A., Pande, A., Wolshon, B., 2019b. Identifying High Crash Risk Highway Segments Using Jerk-Cluster Analysis, in: International Conference on Transportation and Development. pp. 112–123.
Mousavi, S.M., Zhang, Z., Parr, S.A., Pande, A., Wolshon, B., 2019c. Identifying high crash risk highway segments using jerk-cluster analysis, in: International Conference on Transportation and Development 2019. American Society of Civil Engineers (ASCE), pp. 112–123. doi:10.1061/9780784482575.012
Papadoulis, A., Quddus, M., Imprialou, M., 2019. Evaluating the safety impact of connected and autonomous vehicles on motorways. Accid. Anal. Prev. 124 December 2018 , 12–22. doi:10.1016/j.aap.2018.12.019
PTV VISSIM, 2018. PTV VISSIM Manual. Pu, L., Joshi, R., 2008. Surrogate Safety Assessment Model (SSAM): Software User Manual
May , 96. Rahimi, E., Shamshiripour, A., Samimi, A., Kouros, A., 2020. Investigating the injury severity of
single-vehicle truck crashes in a developing country. Accid. Anal. Prev. 137 January , 105444. doi:10.1016/j.aap.2020.105444
Saunier, N., Sayed, T., 2010. Surrogate Safety Analysis. Polytech. Montréal. Sezer, V., Bandyopadhyay, T., Rus, D., Frazzoli, E., Hsu, D., 2015. Towards autonomous
navigation of unsignalized intersections under uncertainty of human driver intent. IEEE Int. Conf. Intell. Robot. Syst. 3578–3585. doi:10.1109/IROS.2015.7353877
Transportation Research Board (TRB), 2016. Highway Capacity Manual 6th Edition: A Guide for Multimodal Mobility Analysis.
Transportation Research Board (TRB), 2014. Access Management Manual. Washington, D.C. Tyagi, V., Kalyanaraman, S., Krishnapuram, R., 2012. Vehicular Traffic Density State
Estimation Based on Cumulative Road Acoustics. IEEE Trans. Intell. Transp. Syst. 13 3 , 1156–1166. doi:10.1109/tits.2012.2190509
U.S. Department of Transportation-NHTSA, 2018. Fatal Motor Vehicle Crashes 2017: Overview. Dot Hs 812603 October , 1–9.
VanderWerf, J., Shladover, S., Miller, M.A., 2004. Conceptual Development and Performance Assessment for the Deployment Staging of Advanced Vehicle Control and Safety Systems. Calif. PATH Res. Rep. 147.
Wali, B., Khattak, A.J., Bozdogan, H., Kamrani, M., 2018. How is driving volatility related to intersection safety? A Bayesian heterogeneity-based analysis of instrumented vehicles data. Transp. Res. Part C Emerg. Technol. 92, 504–524. doi:10.1016/j.trc.2018.05.017
Wali, B., Khattak, A.J., Karnowski, T., 2019. Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility. Accid. Anal. Prev. 132, 105277. doi:10.1016/j.aap.2019.105277
Waymo, 2018. On the Road to Fully Self-Driving. Williamson, A., Grzebieta, R., Eusebio, J., Zheng, W.Y., Wall, J., Charlton, J.L., Lenné, M.,
Haley, J., Barnes, B., Rakotonirainy, A., 2015. The Australian naturalistic driving study:
33
From beginnings to launch. Proc. 2015 Australas. Road Saf. Conf. World Health Organization (WHO), 2018. Global Status Report on Road. World Heal. Organ.
20. Ye, L., Yamamoto, T., 2019. Evaluating the impact of connected and autonomous vehicles on
traffic safety. Phys. A Stat. Mech. its Appl. 526, 121009. doi:10.1016/j.physa.2019.04.245
