# Illegal Maneuver Effect on Traffic Operations at Signalized Intersections: An Observational Simulation-Based Before-After Study Using Response Surface Methodology

Authors: Mahdi Jahangard; Mahyar Madarshahian; Hadi Sabur; Reza Rezvani; Abolfazl Mohammadzadeh Moghaddam; Nathan Huynh
Journal: Journal of Advanced Transportation
 (2023)

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## Abstract

This study investigates three illegal maneuvers at signalized intersections: pedestrians jaywalking at signalized crosswalks (JSC), vehicles stopping near the intersections (SNI), and vehicles occupying the through lane instead of the left-turn lane to make a left turn (OTL). Traffic microsimulation models of four intersections were developed using Aimsun, and data were collected by a drone over a 3-hour period. The car-following model (Gibbs model) implemented in Aimsun was calibrated for each of the intersections and validated at the 95% confidence level. The validated Aimsun models were used to perform 13 experiments designed to investigate the interaction effects of decreasing two or more illegal maneuvers on travel time and fuel consumption. These 13 experiments were identified using the design of experimentsD-optimality criterion. To investigate the main and interaction effects of decreasing two or more illegal maneuvers on travel time and fuel consumption, the response surface methodology (RSM) was used. Using RSM, the statistical model that was found to best fit the simulation results was a quadratic form. The results showed that the dependent variables “travel time ratio” and “fuel consumption ratio” are affected not only by a decrease in violations ratio but also by the volume of traffic as the exogenous variable. It was found that decreasing two of the violations, namely, JSC and SNI, improves travel time and fuel consumption but decreasing OTL has the opposite effect, resulting from the inadequate design of left-turning lane length/capacity and/or inadequate signal timing to accommodate left-turning volume.


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## Body

## 1. Introduction

In underdeveloped countries or developing countries like Iran, traffic violations such as double parking, picking up/dropping off passengers or cargo in unauthorized places, and jaywalking occur frequently. These violations not only disrupt traffic flow and exacerbate congestion but also increase safety risks, fuel consumption, and air emissions. In the city of Mashhad in Iran, there were more than 3.5 million traffic violations in 2019, 25% of which were related to parking in unauthorized places [1]. The results of a 2012 study in Mashhad [2] showed that 55.7% of drivers in this city do not respect lane marking/keeping, 63.2% do not respect the right-of-way signs, 73.2% do not respect the speed limit signs, and 35.6% do not respect the no-park signs. Furthermore, according to the reports published by the police and traffic management authorities of Mashhad, illegal maneuvers such as jaywalking or stopping on the through lanes instead of the left-turn lanes to make a left turn are very common in this city. Currently, these illegal maneuvers are not being enforced by the authorities. One possible reason is the lack of understanding of their consequences on traffic operations and environment, which this study seeks to determine.Intersections are critical elements of an urban traffic network, and they account for a large portion of the network’s emissions [3], traffic incidents, and traffic violations [4]. Previous studies have examined the effect of speeding, red-light running [5], pedestrian-vehicle conflict [6], or interventions such as the installation of traffic cameras [7] to improve safety [8]. Illegal maneuvers negatively affect the discharge rate of approaches at intersections and thereby increase delay and fuel consumption. A few studies have investigated the effect of an illegal maneuver on traffic operations at signalized intersections. This study differs from those studies in that it examines three different illegal maneuvers simultaneously. Since the effect of the illegal maneuvers is not additive, it is necessary to study the main and interaction effects of the three considered maneuvers.The objective of this study is to investigate the main and interaction effects of three common illegal maneuvers on traffic indicators at signalized intersections. The maneuvers considered in this study are pedestrians jaywalking at signalized crosswalks (JSC), vehicles stopping at near intersections (SNI), and vehicles occupying the through lane instead of the left-turn lane to make a left turn (OTL). The analysis uses a number of illegal maneuvers and traffic flow data collected using a drone at four intersections over a 3-hour period. These data, along with intersection geometric data, were used to develop a traffic microsimulation model using Aimsun. The validated model was used to perform 13 experiments (scenarios) designed to investigate the main and interaction effects of decreasing the illegal maneuvers on travel time and fuel consumption. The contribution of this paper, with respect to the existing literature, is that it is the first study to consider multiple violation types as opposed to only one as done in previous studies. The other contribution is the use of response surface methodology (RSM) to investigate the interaction effects of reducing multiple violations (i.e., JSC, SNI, and OTL) on travel time and fuel consumption.The rest of this paper is organized as follows. Section2 provides a review of related papers. Section 3 describes the research framework, and Section 4 presents the simulation results. Lastly, Section 5 provides a summary of the findings and discusses direction for future work.


## 2. Literature Review

Researchers in both developed and developing countries have conducted studies on traffic violations from different perspectives, such as how they affect safety [9, 10], how they affect traffic performance [11], how to reduce the number of violations [12, 13], and how they are related to bicyclist and pedestrian behavior [14–18]. Traffic violations can be classified into one of four categories: (1) driving violations (e.g., counterflow driving, speeding, or running a red light), (2) parking violations (stopping in unauthorized locations), (3) documentation violations (not having a license or insurance card), and (4) vehicle violations (lack of working tail light) [19]. The first two categories of violations (driving and parking violations) often cause safety and operational problems when they occur at signalized intersections [14, 20–22]. Pedestrian-vehicle conflicts are among the most common type of incidents at urban intersections. About 60% of pedestrian accidents in Tokyo [23] and over 50% of pedestrian accidents in China occur at signalized intersections [24]. Recently, several studies have examined the effect of crashes on factors such as travel time, fuel consumption, and air emissions [25, 26]. Sheu et al. [25] have shown that traffic congestion due to crashes and parking violations can significantly destabilize the traffic system at any given time and space by causing traffic control and management strategies to lose their effectiveness. Furthermore, traffic congestion increases fuel consumption and air emissions significantly [26]. In a study conducted by Chevallier and Leclercq [27], the authors studied the effect of parking violation near intersections on the traffic flow upstream of the intersections. Their results showed that these illegal maneuvers not only severely slow down traffic but also create bottlenecks which further delay the passing vehicles.One of the challenges in examining the effect of parking violations such as double parking is the lack of reliable analytical approaches [28]. In a study by Baykal-Gursoy [29], the authors applied an M/M/∞ queuing model to estimate the average travel delay based on the number and duration of double-parking incidents. In their stochastic queuing model, the road is subject to random interruptions with exponentially distributed durations and the vehicles arrive following a Poisson process. Gao and Ozbay [28] used a queuing model to estimate the effect of the double parking by delivery vehicles on the average travel time. The simulation results of their study showed that travel time is affected by factors such as location, frequency, and duration of double parking [30]. In another study, Kladeftiras and Antoniou [11] used simulation to estimate the effect of double parking on average speed, delay, and stopped time over a one-hour period for a small transportation network. The results of their study showed that reducing the number of double parking will increase the average speed by about 10–15% and reduce the delay and stopped time by about 15–20%. Also, they estimated that eliminating double-parking violations completely will increase the average speed by about 44% and decrease the delay and stopped time by 33% and 47%, respectively [11].In a study by Li et al. [31], a mathematical model was used to investigate the effect of conflict between pedestrians and left-turning vehicles at intersections on traffic operations. The aim of their study is to improve intersections’ design and increase their capacities. In [32], the author investigated the effect of adding a left-turn lane to urban intersections on traffic safety. The author estimated that adding a left-turn lane will reduce the number of traffic incidents by 27% at unsignalized four-leg intersections with the major and minor approaches, 33% at unsignalized three-leg intersections with the major and minor approaches, and 10% at signalized intersections [32]. The findings suggest that it is beneficial to have a separate lane for left-turning vehicles at both signalized and non-signalized intersections. Table 1 provides a summary of previous studies that examined the effect of traffic violations on traffic safety and traffic operations at intersections or along a route.Table 1
Previous studies on traffic violations.
Violation categoryViolation typeResponse variable (s)Analysis scopeMethodologyReference (s)Driving violationsRed-light runningAccident frequencySignalized intersectionData analysis[7, 33, 34]Driving speedAccident rateExpresswayGeneralized additive model[34]Driving speedAccident rateSignalized intersectionGeneralized linear regression model[33]Parking violationsIncident-induced lane changingDelayStreetDiscrete-time non-linear stochastic model[25]Double parkingDelay, stopped time, speedStreetMicrosimulation[11]Double parkingTravel timeStreetM/M/∞ queuing model and microsimulation model[11, 28]On-street parkingCapacity, delayStreet near intersectionAnalytical model[36]Pedestrian violationsPedestrian crossing violationCrash rateSignalized intersectionObservational study[37–39]Left turning and pedestrian conflictsSafety, capacitySignalized intersectionCross-sectional study[31, 40].As shown in Table1, most of the previous studies analyzed risk factors associated with traffic violations and accident severity, whereas this study analyzed the impact of traffic violations on traffic operations. Among those studies that investigated the impact of traffic violations on traffic operations, they considered only one violation, whereas this study considered three and their interactions on travel time and fuel consumption using observational simulation-based before-after study and response surface methodology.


## 3. Research Procedure

Figure1 shows an overview of the procedure utilized in this study. The steps carried out in this study are as follows: (1) data collection, (2) data preparation, (3) model construction, (4) model calibration and validation, (5) design of experiments, (6) simulation of scenarios, (7) analysis of results, and (8) discussion of findings. The details of these steps are presented in subsequent sections.Figure 1
Research procedure.


### 3.1. Data Collection

With an area of 352.3 km2 and a population of over three million people, Mashhad is the second largest city in Iran. In 2019, vehicles in this city consumed 2,353,237 m3 of petrol [41]. According to a 2019 traffic violation report published by the Mashhad police department, the three most common violations at signalized intersections are those considered in this study. Figure 2 illustrates these three violations. Figure 2(a) shows the case of a vehicle occupying the through lane (OTL). In this situation, the vehicle intends to turn left at the intersection but stops on the through lane (reserved for vehicles going straight through the intersection) while waiting for the left-turn light to turn green. In doing so, the vehicle blocks many vehicles from going through the intersection. Figure 2(b) shows the case of a vehicle stopping near an intersection (SNI). In this situation, the vehicles in the blocked lane need to merge left. Similar to OTL, SNI violations reduce the intersection capacity. Figure 2(c) shows the case of pedestrians jaywalking at signalized crosswalk (JSC) while vehicular traffic has the right of way.Figure 2
Traffic violations at intersections: (a) OTL, (b) SNI, and (c) JSC.To minimize heterogeneity, the four intersections selected for this study all have three-lane four-legged signalized intersections operating at level of serviceC. Other similarities between these intersections are their traffic volumes and the presence of all three considered traffic violations. Figure 3 shows the locations of these four intersections: (i) Hashemieh-Fakuri, (ii) Moallem-Daneshamooz, (iii) Kooshesh-Fadaiyan, and (iv) Gaz-Ebadi.Figure 3
Schematic location of intersections under study. (a) Hashemieh-Fakuri. (b) Moallem-Daneshamooz. (c) Kooshesh-Fadaiyan. (d) Gaz-Ebadi.According to the reports published by the Mashhad transportation agency and police department, the peak hours of traffic violations in this city are from 10:30 to 13:30. Therefore, data collection, which was done in 2019, was limited to these hours. The data were extracted from videos taken by a drone; it should be noted that traffic flow and violations could be extracted automatically (see [17]). For the JSC violations, the number and headway of legal crossings (during green pedestrian light) and illegal crossings (during red pedestrian light) in all of the chosen intersections were measured at 5-minute intervals [42]; note that both legal and illegal crossing maneuvers need to be modeled in Aimsun. For SNI violations, the length of the incident zone and the start and end time of each incident were measured. For the OTL violations, the number of left-turning vehicles making the illegal maneuvers (stopped on a straight-through lane) and those that made the maneuvers properly (stopped on the left-turn lane) were measured; as in the case with JSC, both legal and illegal OTL maneuvers need to be modeled in Aimsun. Traffic volume by vehicle types on each approach of the chosen intersections was also collected at 5-minute intervals. The smaller interval reduced the temporal effect on the response variables. A summary of intersection information collected for the hours of interest (10:30 to 13:30) is given in Table 2.Table 2
Summary of the data observed at the four study intersections.
IntersectionTraffic volume∗Left-turning vehicles∗∗SNI∗∗Pedestrian crossing∗∗LegalIllegal (OTL)LegalIllegal (JSC)Hashemieh-Fakuri11564798406127254169Moallem-Daneshamooz1116010011554314671111Kooshesh-Fadaiyan11609724144364878674Gaz-Ebadi1218737213450115511105∗Total number of vehicles traveled through the intersection in three hours. ∗∗Number of violations over a three-hour period.To model the four study intersections in Aimsun, data related to signal timing, such as cycle length, number of phases, green interval, yellow interval, and all-red interval, are also collected. All four of the study intersections utilize adaptive signal control systems (ASCSs). Unlike conventional signal control systems with predefined timing parameters, ASCSs change the signal timings (i.e., phase splits, phase sequence, offsets, and cycle length) in real time to accommodate fluctuating traffic demand at the intersections. Thus, at these intersections, different cycles may have different lengths, phase patterns, and green time distributions. Table3 shows the signal timing information of the study intersections, collected from the Mashhad traffic control center. Figure 4 shows the phase diagrams of the study intersections during the data collection period (10:30 to 13:30).Table 3
Timing plans in the studied signalized intersections (second).
10:30–11:3011:30–12:3012:30–13:30IntersectionMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeHashemieh-FakuriCBACBACBA21810∗∗∗2201022210∗∗∗2231022210∗∗∗22310GEDGEDGED223102261021810228102311022010228102311021910Moallem-DaneshamoozCBACBACBA229122331421413226122331421413229122321421413Kooshesh-FadaiyanCBACBACBA223823010238122248231102381222382311023812FEDFEDFED∗∗∗2281222412∗∗∗2281222412∗∗∗2281222412Gaz-EbadiCBACBACBA∗∗∗227823212∗∗∗227823012∗∗∗227822712FEDFEDFED237102361222610237102351222710239102341222910∗Time was not allocated to these phases during the data collection period.Figure 4
Phase diagrams of the study intersections. (a) Hashemieh-Fakuri. (b) Kooshesh-Fadaiyan. (c) Moallem-Daneshamooz. (d) Gaz-Ebadi.
(a)(b)(c)(d)


### 3.2. Data Preparation

Prior to using the extracted data for the traffic microsimulation model, the Kolmogorov–Smirnov goodness of fit tests were performed at the 5% significance level with the hypothesis that the number of observed violations is equal to the expected number of incidents according to the assumed probability distribution functions. For these tests, the headways for vehicles and pedestrians in each approach of the intersection were assumed to follow the exponential distributions. For SNI, the headway distribution function of violations and the stopped time of different vehicles were fitted using the maximum likelihood method and their accuracy was confirmed by the Kolmogorov–Smirnov test (p value >0.05). Table 4 shows the mean and variance of the estimated probability distribution functions for SNI violations at the four studied intersections. In Table 4, “Interval” denotes the time between two SNI incidents, whereas “Duration” denotes the elapsed time of the SNI incident.Table 4
Probability distribution functions for the intervals and duration of SNI violation.
IntersectionWest approachEast approachSouth approachNorth approachIntervalDurationIntervalDurationIntervalDurationIntervalDurationS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionHashemieh-Fakuri16301208Normal4540Normal∗∗∗∗∗∗∗∗∗∗∗∗14451414Normal10347NormalMoallem-Daneshamooz∗∗∗∗∗∗∗∗∗∗∗179201Normal453247Normal927504Normal27NormalKooshesh-Fadaiyan1116880Normal1613Normal417481Normal15944Normal119139Normal361205Normal174147Normal3920NormalGaz-Ebadi∗∗∗∗∗∗126122Normal472218Normal174160Normal766121Normal141150Normal16897Normal∗There is no violation on that approach of the intersection.


### 3.3. Model Construction

In this study, the traffic microsimulation model was constructed using the Aimsun software. To have a valid simulation model that can be used for evaluating the performance of the intersection, care must be taken in specifying input data into the simulation model for the intersection approaches, signal timing, traffic volume, turning movements, illegal maneuvers, posted speed limits, number of lanes, lane configurations, queue lengths, lane changing, and car-following parameters. Figure5 shows how traffic and violations were coded in the Aimsun environment. In this figure, large red rectangles indicate the parking spaces occupied by vehicles during the data collection period. Small red rectangles indicate SNI violations involving vehicles picking up or dropping off passengers or cargo. Yellow vehicle in the east approach indicates those making OTL violations. Figure 6 shows side-by-side comparisons between the actual intersections and the modeled intersections.Figure 5
Encoding traffic violations in Aimsun simulation environment.Figure 6
Exemplification of the studied signalized intersections in Aimsun environment and the real world. (a) Kooshesh-Fadaiyan. (b) Gaz-Ebadi. (c) Moallem-Daneshamooz. (d) Hashemieh-Fakuri.
(a)(b)(c)(d)


### 3.4. Model Calibration and Validation

Model calibration involves tweaking model parameters so that the simulation model works as intended. Model validation, on the other hand, is the process of establishing that the constructed model provides an appropriate representation of the studied system [43]. In this study, validation was performed by comparing the model’s output variables (traffic volume, queue length, and travel time) against the actual data and adjusting the parameters of the Gibbs model within the allowed ranges. The model calibration process was performed for all four intersections independently as described in the work of Guo et al. [44]. One of the important determinants of the model accuracy is the number of times the experiments are repeated, which usually varies depending on the type of problem [45]. In this study, the simulation results obtained from 10 runs were compared against the observed travel time, queue length, and traffic volume at the 95% confidence level. The results of these tests are presented in Tables 5–7. The t-test was used to assess the equality of the means of simulated and observed samples, and Levene’s test was used to assess the equality of variances. In Table 5, the number of observed samples is the number of travel time records obtained using the floating car method. Travel time is measured from the back of the queue on any ingress approach to 50 meters into an egress approach.Table 5
Validation of simulation model’s vehicle travel times through the study intersections.
IntersectionSample size (number)Travel time (second)Variation test (Levene)Mean test (t-test)ObservationSimulationObservationSimulation95% confidence intervalp value95% confidence intervalp valueMeanS.D.MeanS.D.Hashemieh-Fakuri122360103.117.497.113.5(0.476; 5.318)0.096(−2.30; 14.20)0.156Moallem-Daneshamooz19536059.922.462.617.3(0.246; 3.194)0.261(−6.49; 1.04)0.155Kooshesh-Fadaiyan250360208.656.5202.843(0.964; 4.931)0.073(−3.35; 14.83)0.215Gaz-Ebadi210360198.275.4194.372(0.178; 1.418)0.607(−12.43; 4.82)0.387Table 6
Validation of simulation model’s traffic volume at the study intersections.
IntersectionApproachSample size (number)Traffic volume (second)Variation test (Levene)Mean test (t-test)ObservationSimulationObservationSimulation95% confidence intervalp value95% confidence intervalp valueMeanS.D.MeanS.D.Hashemieh-FakuriNorth3636076.413.276.511.8(0.758; 2.065)0.485(−4.64; 4.48)0.971South3636056.91657.17.85(1.03; 3.360)0.091(−5.60; 5.32)0.959East3636092.418.892.711.4(1.061; 3.893)0.078(−6.64; 6.17)0.941West3636091.418.891.59.9(0.975; 4.783)0.110(−6.57; 6.24)0.960Moallem-DaneshamoozNorth3636060.29.3160.48.3(0.770; 2.080)0.449(−3.40; 3.01)0.904South3636089.622.789.312.5(0.901; 5.202)0.24(−7.49; 7.99)0.948East3636085.918.485.913.1(0.213; 3.061)0.172(−6.30; 6.29)0.999West3636094.937 895.123(0.988; 3.245)0.201(−3.19; 12.62)0.964Kooshesh-FadaiyanNorth36360105.713 6108.711.7(0.845; 2.425)0.228(−7.78; 1.71)0.203South3636078.713 47810.8(0.950; 2.621)0.085(−3.92; 5.29)0.765East363608215.58214.4(0.689; 1.788)0.817(−5.46; 5.22)0.964West3636091.613.691.69.9(1.097; 3.611)0.061(−4.75; 4.57)0.969Gaz-EbadiNorth3636093.511.689.710.3(0.89; 1.93)0.191(−1.73; 3.56)0.32South36360119.112.4117.313.2(0.239; 4.341)0.873(−2.54; 6.04)0.413East3636041.913 341.838.13(1.024; 3.234)0.083(−4.47; 4.59)0.979West3636081.218.480.213.7(0.728; 5.396)0.210(−5.27; 7.32)0.743Table 7
Validation of simulation model’s queue length at the study intersections.
IntersectionApproachSample size (number)Queue length (number)Variation test (Levene)Mean test (t-test)ObservationSimulationObservationSimulation95% confidence intervalp value95% confidence intervalp valueMeanS.D.MeanS.D.Hashemieh-FakuriNorth363607.62.258.62.38(0.538; 2.410)0.996(−1.811; 0.253)0.093South3636014.925.816.67.29(0.394; 1.412)0.262(−3.68; 0.38)0.108East3636010.972.510.92.84(0.537; 1.486)0.515(−0.852; 0.888)0.967West363608.832.659.622.17(0.915; 3.447)0.106(−1.692; 0.125)0.089Moallem-DaneshamoozNorth363604.081.795.571.92(0.663; 4.256)0.441(−2.112; 0.868)0.097South363607.755.288.44.36(0.668; 3.164)0.499(−2.459; 1.165)0.474East363606.281.978.815.22(0.103; 1.408)0.089(−3.291; 1.768)0.23West3636011.588.0510.17.5(0.698; 4.592)0.354(−1.30; 4.25)0.289Kooshesh-FadaiyanNorth3636016.196.1917.93.53(0.604; 5.690)0.073(−3.84; 0.37)0.104South3636024.410.519.611(0.527; 3.041)0.887(−1.12; 8.40)0.112East3636026.61327.218.9(0.294; 1.090)0.067(−5.18; 4.01)0.798West36360247.7122.29.82(0.446; 2.069)0.646(−0.96; 4.43)0.201Gaz-EbadiNorth3636036.259.8233.618(0.149; 1.405)0.180(−0.87; 6.25)0.135South3636044.57.0939.915.8(0.088; 1.300)0.211(−1.91; 7.21)0.081East3636025.612.72318.6(0.323; 1.098)0.146(−1.92; 7.09)0.254West3636021.510.319.513.6(0.441; 2.543)0.775(−0.323; 0.988)0.261In Tables6 and 7, the number of observed samples is 36 because samples were collected at 5-minute intervals over a three-hour period. However, the number of simulated samples is 360 because the simulation was repeated 10 times. The tests of equality of means and variances show that the results of the model are consistent with the observed data at the 95% confidence level. That is, all p values are greater than 0.05; thus, the null hypothesis (actual mean = simulated mean or actual variance = simulated variance) cannot be rejected.


### 3.5. Design of Experiments

The design of experiments (DOE) method was used to investigate the main and interaction effects of the three violations on the response variables through a set of experiments. Four levels are used for each of the three violations: (i) current situation (observed) or 0% decrease in violations, (ii) 30% decrease in violations, (iii) 60% decrease in violations, and (iv) 100% decrease in violations. The optimal number of required experiments is 13 based on the DOE D-optimality criterion. These experiments are presented in Table8.Table 8
Fractional factorial design of experiments.
Randomized experiment (scenario)JSC (%)SNI (%)OTL (%)10002030030030430005303030606007006086000960606010010001100100121000013100100100%: percent of reduction in traffic violations.


### 3.6. Simulation of Scenarios

To model the different scenarios (experiments) in Aimsun software, it is essential to have control variables. As such, the total number of vehicles and the total number of pedestrians were kept constant across scenarios, with only the fraction of vehicles and pedestrians that commit violations being varied. The number of violations was calculated by the formulay = (1 − α) x, where y is the number of violations at the target level of the experiment, x is the current number of violations (observed), and α is the target percentage of decrease. The parameter α was given values of 0, 0.3, 0.6, and 1. For each experiment, the number of vehicles/pedestrians that do not commit the violations was increased by α times x so that the total number of vehicles/pedestrians remains constant.Given the use of distribution functions for stopped headway (i.e., time between incidents) and duration in the simulation model for SNI violations, the mean and variance of headway and duration were estimated by bootstrap simulation with 200 replications. The formula for estimating the bootstrap mean and variance of sizen (number of violations) of the random variable x for B replications is(1)x˜i=∑j=1nxjn,i=1,2,…,B,x˜=∑i=1B∑j=1nxijB∗n,σ2˜i=∑j=1nxij−x˜i2n−1,σ2˜=∑i=1B∑j=1nxij−x˜i2B∗n−1.


## 3.1. Data Collection

With an area of 352.3 km2 and a population of over three million people, Mashhad is the second largest city in Iran. In 2019, vehicles in this city consumed 2,353,237 m3 of petrol [41]. According to a 2019 traffic violation report published by the Mashhad police department, the three most common violations at signalized intersections are those considered in this study. Figure 2 illustrates these three violations. Figure 2(a) shows the case of a vehicle occupying the through lane (OTL). In this situation, the vehicle intends to turn left at the intersection but stops on the through lane (reserved for vehicles going straight through the intersection) while waiting for the left-turn light to turn green. In doing so, the vehicle blocks many vehicles from going through the intersection. Figure 2(b) shows the case of a vehicle stopping near an intersection (SNI). In this situation, the vehicles in the blocked lane need to merge left. Similar to OTL, SNI violations reduce the intersection capacity. Figure 2(c) shows the case of pedestrians jaywalking at signalized crosswalk (JSC) while vehicular traffic has the right of way.Figure 2
Traffic violations at intersections: (a) OTL, (b) SNI, and (c) JSC.To minimize heterogeneity, the four intersections selected for this study all have three-lane four-legged signalized intersections operating at level of serviceC. Other similarities between these intersections are their traffic volumes and the presence of all three considered traffic violations. Figure 3 shows the locations of these four intersections: (i) Hashemieh-Fakuri, (ii) Moallem-Daneshamooz, (iii) Kooshesh-Fadaiyan, and (iv) Gaz-Ebadi.Figure 3
Schematic location of intersections under study. (a) Hashemieh-Fakuri. (b) Moallem-Daneshamooz. (c) Kooshesh-Fadaiyan. (d) Gaz-Ebadi.According to the reports published by the Mashhad transportation agency and police department, the peak hours of traffic violations in this city are from 10:30 to 13:30. Therefore, data collection, which was done in 2019, was limited to these hours. The data were extracted from videos taken by a drone; it should be noted that traffic flow and violations could be extracted automatically (see [17]). For the JSC violations, the number and headway of legal crossings (during green pedestrian light) and illegal crossings (during red pedestrian light) in all of the chosen intersections were measured at 5-minute intervals [42]; note that both legal and illegal crossing maneuvers need to be modeled in Aimsun. For SNI violations, the length of the incident zone and the start and end time of each incident were measured. For the OTL violations, the number of left-turning vehicles making the illegal maneuvers (stopped on a straight-through lane) and those that made the maneuvers properly (stopped on the left-turn lane) were measured; as in the case with JSC, both legal and illegal OTL maneuvers need to be modeled in Aimsun. Traffic volume by vehicle types on each approach of the chosen intersections was also collected at 5-minute intervals. The smaller interval reduced the temporal effect on the response variables. A summary of intersection information collected for the hours of interest (10:30 to 13:30) is given in Table 2.Table 2
Summary of the data observed at the four study intersections.
IntersectionTraffic volume∗Left-turning vehicles∗∗SNI∗∗Pedestrian crossing∗∗LegalIllegal (OTL)LegalIllegal (JSC)Hashemieh-Fakuri11564798406127254169Moallem-Daneshamooz1116010011554314671111Kooshesh-Fadaiyan11609724144364878674Gaz-Ebadi1218737213450115511105∗Total number of vehicles traveled through the intersection in three hours. ∗∗Number of violations over a three-hour period.To model the four study intersections in Aimsun, data related to signal timing, such as cycle length, number of phases, green interval, yellow interval, and all-red interval, are also collected. All four of the study intersections utilize adaptive signal control systems (ASCSs). Unlike conventional signal control systems with predefined timing parameters, ASCSs change the signal timings (i.e., phase splits, phase sequence, offsets, and cycle length) in real time to accommodate fluctuating traffic demand at the intersections. Thus, at these intersections, different cycles may have different lengths, phase patterns, and green time distributions. Table3 shows the signal timing information of the study intersections, collected from the Mashhad traffic control center. Figure 4 shows the phase diagrams of the study intersections during the data collection period (10:30 to 13:30).Table 3
Timing plans in the studied signalized intersections (second).
10:30–11:3011:30–12:3012:30–13:30IntersectionMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeMax gapMax green timeMin green timeHashemieh-FakuriCBACBACBA21810∗∗∗2201022210∗∗∗2231022210∗∗∗22310GEDGEDGED223102261021810228102311022010228102311021910Moallem-DaneshamoozCBACBACBA229122331421413226122331421413229122321421413Kooshesh-FadaiyanCBACBACBA223823010238122248231102381222382311023812FEDFEDFED∗∗∗2281222412∗∗∗2281222412∗∗∗2281222412Gaz-EbadiCBACBACBA∗∗∗227823212∗∗∗227823012∗∗∗227822712FEDFEDFED237102361222610237102351222710239102341222910∗Time was not allocated to these phases during the data collection period.Figure 4
Phase diagrams of the study intersections. (a) Hashemieh-Fakuri. (b) Kooshesh-Fadaiyan. (c) Moallem-Daneshamooz. (d) Gaz-Ebadi.
(a)(b)(c)(d)


## 3.2. Data Preparation

Prior to using the extracted data for the traffic microsimulation model, the Kolmogorov–Smirnov goodness of fit tests were performed at the 5% significance level with the hypothesis that the number of observed violations is equal to the expected number of incidents according to the assumed probability distribution functions. For these tests, the headways for vehicles and pedestrians in each approach of the intersection were assumed to follow the exponential distributions. For SNI, the headway distribution function of violations and the stopped time of different vehicles were fitted using the maximum likelihood method and their accuracy was confirmed by the Kolmogorov–Smirnov test (p value >0.05). Table 4 shows the mean and variance of the estimated probability distribution functions for SNI violations at the four studied intersections. In Table 4, “Interval” denotes the time between two SNI incidents, whereas “Duration” denotes the elapsed time of the SNI incident.Table 4
Probability distribution functions for the intervals and duration of SNI violation.
IntersectionWest approachEast approachSouth approachNorth approachIntervalDurationIntervalDurationIntervalDurationIntervalDurationS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionS.D. (sec)Mean (sec)Probability distributionHashemieh-Fakuri16301208Normal4540Normal∗∗∗∗∗∗∗∗∗∗∗∗14451414Normal10347NormalMoallem-Daneshamooz∗∗∗∗∗∗∗∗∗∗∗179201Normal453247Normal927504Normal27NormalKooshesh-Fadaiyan1116880Normal1613Normal417481Normal15944Normal119139Normal361205Normal174147Normal3920NormalGaz-Ebadi∗∗∗∗∗∗126122Normal472218Normal174160Normal766121Normal141150Normal16897Normal∗There is no violation on that approach of the intersection.


## 3.3. Model Construction

In this study, the traffic microsimulation model was constructed using the Aimsun software. To have a valid simulation model that can be used for evaluating the performance of the intersection, care must be taken in specifying input data into the simulation model for the intersection approaches, signal timing, traffic volume, turning movements, illegal maneuvers, posted speed limits, number of lanes, lane configurations, queue lengths, lane changing, and car-following parameters. Figure5 shows how traffic and violations were coded in the Aimsun environment. In this figure, large red rectangles indicate the parking spaces occupied by vehicles during the data collection period. Small red rectangles indicate SNI violations involving vehicles picking up or dropping off passengers or cargo. Yellow vehicle in the east approach indicates those making OTL violations. Figure 6 shows side-by-side comparisons between the actual intersections and the modeled intersections.Figure 5
Encoding traffic violations in Aimsun simulation environment.Figure 6
Exemplification of the studied signalized intersections in Aimsun environment and the real world. (a) Kooshesh-Fadaiyan. (b) Gaz-Ebadi. (c) Moallem-Daneshamooz. (d) Hashemieh-Fakuri.
(a)(b)(c)(d)


## 3.4. Model Calibration and Validation

Model calibration involves tweaking model parameters so that the simulation model works as intended. Model validation, on the other hand, is the process of establishing that the constructed model provides an appropriate representation of the studied system [43]. In this study, validation was performed by comparing the model’s output variables (traffic volume, queue length, and travel time) against the actual data and adjusting the parameters of the Gibbs model within the allowed ranges. The model calibration process was performed for all four intersections independently as described in the work of Guo et al. [44]. One of the important determinants of the model accuracy is the number of times the experiments are repeated, which usually varies depending on the type of problem [45]. In this study, the simulation results obtained from 10 runs were compared against the observed travel time, queue length, and traffic volume at the 95% confidence level. The results of these tests are presented in Tables 5–7. The t-test was used to assess the equality of the means of simulated and observed samples, and Levene’s test was used to assess the equality of variances. In Table 5, the number of observed samples is the number of travel time records obtained using the floating car method. Travel time is measured from the back of the queue on any ingress approach to 50 meters into an egress approach.Table 5
Validation of simulation model’s vehicle travel times through the study intersections.
IntersectionSample size (number)Travel time (second)Variation test (Levene)Mean test (t-test)ObservationSimulationObservationSimulation95% confidence intervalp value95% confidence intervalp valueMeanS.D.MeanS.D.Hashemieh-Fakuri122360103.117.497.113.5(0.476; 5.318)0.096(−2.30; 14.20)0.156Moallem-Daneshamooz19536059.922.462.617.3(0.246; 3.194)0.261(−6.49; 1.04)0.155Kooshesh-Fadaiyan250360208.656.5202.843(0.964; 4.931)0.073(−3.35; 14.83)0.215Gaz-Ebadi210360198.275.4194.372(0.178; 1.418)0.607(−12.43; 4.82)0.387Table 6
Validation of simulation model’s traffic volume at the study intersections.
IntersectionApproachSample size (number)Traffic volume (second)Variation test (Levene)Mean test (t-test)ObservationSimulationObservationSimulation95% confidence intervalp value95% confidence intervalp valueMeanS.D.MeanS.D.Hashemieh-FakuriNorth3636076.413.276.511.8(0.758; 2.065)0.485(−4.64; 4.48)0.971South3636056.91657.17.85(1.03; 3.360)0.091(−5.60; 5.32)0.959East3636092.418.892.711.4(1.061; 3.893)0.078(−6.64; 6.17)0.941West3636091.418.891.59.9(0.975; 4.783)0.110(−6.57; 6.24)0.960Moallem-DaneshamoozNorth3636060.29.3160.48.3(0.770; 2.080)0.449(−3.40; 3.01)0.904South3636089.622.789.312.5(0.901; 5.202)0.24(−7.49; 7.99)0.948East3636085.918.485.913.1(0.213; 3.061)0.172(−6.30; 6.29)0.999West3636094.937 895.123(0.988; 3.245)0.201(−3.19; 12.62)0.964Kooshesh-FadaiyanNorth36360105.713 6108.711.7(0.845; 2.425)0.228(−7.78; 1.71)0.203South3636078.713 47810.8(0.950; 2.621)0.085(−3.92; 5.29)0.765East363608215.58214.4(0.689; 1.788)0.817(−5.46; 5.22)0.964West3636091.613.691.69.9(1.097; 3.611)0.061(−4.75; 4.57)0.969Gaz-EbadiNorth3636093.511.689.710.3(0.89; 1.93)0.191(−1.73; 3.56)0.32South36360119.112.4117.313.2(0.239; 4.341)0.873(−2.54; 6.04)0.413East3636041.913 341.838.13(1.024; 3.234)0.083(−4.47; 4.59)0.979West3636081.218.480.213.7(0.728; 5.396)0.210(−5.27; 7.32)0.743Table 7
Validation of simulation model’s queue length at the study intersections.
IntersectionApproachSample size (number)Queue length (number)Variation test (Levene)Mean test (t-test)ObservationSimulationObservationSimulation95% confidence intervalp value95% confidence intervalp valueMeanS.D.MeanS.D.Hashemieh-FakuriNorth363607.62.258.62.38(0.538; 2.410)0.996(−1.811; 0.253)0.093South3636014.925.816.67.29(0.394; 1.412)0.262(−3.68; 0.38)0.108East3636010.972.510.92.84(0.537; 1.486)0.515(−0.852; 0.888)0.967West363608.832.659.622.17(0.915; 3.447)0.106(−1.692; 0.125)0.089Moallem-DaneshamoozNorth363604.081.795.571.92(0.663; 4.256)0.441(−2.112; 0.868)0.097South363607.755.288.44.36(0.668; 3.164)0.499(−2.459; 1.165)0.474East363606.281.978.815.22(0.103; 1.408)0.089(−3.291; 1.768)0.23West3636011.588.0510.17.5(0.698; 4.592)0.354(−1.30; 4.25)0.289Kooshesh-FadaiyanNorth3636016.196.1917.93.53(0.604; 5.690)0.073(−3.84; 0.37)0.104South3636024.410.519.611(0.527; 3.041)0.887(−1.12; 8.40)0.112East3636026.61327.218.9(0.294; 1.090)0.067(−5.18; 4.01)0.798West36360247.7122.29.82(0.446; 2.069)0.646(−0.96; 4.43)0.201Gaz-EbadiNorth3636036.259.8233.618(0.149; 1.405)0.180(−0.87; 6.25)0.135South3636044.57.0939.915.8(0.088; 1.300)0.211(−1.91; 7.21)0.081East3636025.612.72318.6(0.323; 1.098)0.146(−1.92; 7.09)0.254West3636021.510.319.513.6(0.441; 2.543)0.775(−0.323; 0.988)0.261In Tables6 and 7, the number of observed samples is 36 because samples were collected at 5-minute intervals over a three-hour period. However, the number of simulated samples is 360 because the simulation was repeated 10 times. The tests of equality of means and variances show that the results of the model are consistent with the observed data at the 95% confidence level. That is, all p values are greater than 0.05; thus, the null hypothesis (actual mean = simulated mean or actual variance = simulated variance) cannot be rejected.


## 3.5. Design of Experiments

The design of experiments (DOE) method was used to investigate the main and interaction effects of the three violations on the response variables through a set of experiments. Four levels are used for each of the three violations: (i) current situation (observed) or 0% decrease in violations, (ii) 30% decrease in violations, (iii) 60% decrease in violations, and (iv) 100% decrease in violations. The optimal number of required experiments is 13 based on the DOE D-optimality criterion. These experiments are presented in Table8.Table 8
Fractional factorial design of experiments.
Randomized experiment (scenario)JSC (%)SNI (%)OTL (%)10002030030030430005303030606007006086000960606010010001100100121000013100100100%: percent of reduction in traffic violations.


## 3.6. Simulation of Scenarios

To model the different scenarios (experiments) in Aimsun software, it is essential to have control variables. As such, the total number of vehicles and the total number of pedestrians were kept constant across scenarios, with only the fraction of vehicles and pedestrians that commit violations being varied. The number of violations was calculated by the formulay = (1 − α) x, where y is the number of violations at the target level of the experiment, x is the current number of violations (observed), and α is the target percentage of decrease. The parameter α was given values of 0, 0.3, 0.6, and 1. For each experiment, the number of vehicles/pedestrians that do not commit the violations was increased by α times x so that the total number of vehicles/pedestrians remains constant.Given the use of distribution functions for stopped headway (i.e., time between incidents) and duration in the simulation model for SNI violations, the mean and variance of headway and duration were estimated by bootstrap simulation with 200 replications. The formula for estimating the bootstrap mean and variance of sizen (number of violations) of the random variable x for B replications is(1)x˜i=∑j=1nxjn,i=1,2,…,B,x˜=∑i=1B∑j=1nxijB∗n,σ2˜i=∑j=1nxij−x˜i2n−1,σ2˜=∑i=1B∑j=1nxij−x˜i2B∗n−1.


## 4. Results

As mentioned previously, each intersection was simulated 10 times for each of the 13 scenarios listed in Table8; thus, a total of 520 simulations were performed. The simulation results were then analyzed using the response surface methodology (RSM) to investigate the main and interaction effects of reducing violations (JSC, SNI, and OTL) on travel time and fuel consumption ratio. In this study, statistical models that capture the relationship between violations and response variables were developed using Design Expert v.11. In addition, Design Expert was used to generate plots of RSM results.


### 4.1. Travel Time

To measure the improvement in travel time in terms of the reduction in violations, the best model fitted to the simulation results with the aid of RSM was a quadratic form. In this model, independent variables include the percentage of decrease of each traffic violation and the traffic volume, and the dependent (response) variable is the travel time ratio which is calculated by dividing the travel time obtained from the simulation model with reduction in violations by the observed travel time. The estimated coefficients of the travel time model are given in Table9. In this model, A, B, and C are parameters corresponding to the percentage of decrease in JSC, SNI, and OTL, respectively. The parameter D corresponds to the entering traffic volume at the intersection. The positive signs for parameters A and B suggest that as the percentage of decrease for these two violations increases, the travel time ratio is decreased. This finding corresponds to expectation. The negative sign for parameter C suggests that as the percentage of decrease for OTL violations increases, the travel time ratio is increased. A possible explanation for this result is that the configuration of the approaches is not well designed to handle the left-turn volume. Therefore, a fraction (illegal left turn) of the left-turning volume is forced to occupy the through lane to accomplish the left turn maneuver. Similarly, the positive sign for parameter D suggests that as the entering volume increases, so will the travel time ratio.Table 9
Effect of decrease in traffic violations and traffic volume on travel time ratio.
Model summaryMain effectsInteractive effectsVariableABCDADBDCDD2Model coefficient2.014.78−1.771.17−0.00019−0.000430.00015−0.00005Model form−6601 + 2.01A + 4.78B − 1.77C + 1.17D − 0.00019 AD − 0.00043 BD + 0.00015 CD − 0.00005D2P value0.0001SignificantR2R-squaredAdjR-squaredPredR-squared0.8320.8300.827Figure7 shows the trace plot of the model for the intermediate values where traffic volume = 11,700 and all three types of violations are decreased by 50%. −1 on the x-axis means that all the violations are present, and +1 means that all violations are removed (100% decrease). This plot shows the rate of decrease and increase in the response variable as the independent variable is varied from the reference point. The results from Figure 7 indicate that reducing violations has a greater positive effect on travel time ratio when the traffic volume (exogenous variable) is higher. As mentioned, reducing OTL violations has a negative impact on the travel time ratio.Figure 7
Trace plot for travel time ratio.Figure8 shows the interaction effects of the decreasing JSC, SNI, or OTL violations and traffic volume on the response variable. It can be seen that at any given traffic volume, as the number of JSC and SNI violation decreases, the travel time ratio also decreases. This finding is consistent with those found by Kladeftiras and Antoniou [11]. However, as the number of OTL violation decreases, the travel time ratio actually increases. This can be due to both poor signal timing and poor geometric design of the intersection. That is, the number of left-turn lanes on the approach is inadequate for the demand. If the left-turning vehicles wait for their turn to get into the left-turn lane, it will create a queue upstream of the signal and block the through traffic. Also, at any given level of violation, the travel time ratio decreases more as the traffic volume increases. For example, at 50% decrease in all violations, the travel time ratio at the traffic volume of 11,700 is 85%, but it is only 68% at the traffic volume of 12,000.Figure 8
Two-dimensional contour plots of entering traffic volume at the intersection versus (a) JSC, (b) SNI, and (c) OTL for travel time ratio.
(a)(b)(c)Figure9 shows the interaction effects of decreasing the percentage of two violations on the response variable when other variables are set to their intermediate values (11,700 vehicles for traffic volume and 50% reduction in violation). The results in Figure 9(a) indicate that reducing the JSC and SNI violations improves the travel time ratio. The results in Figures 9(b) and 9(c) indicate that reducing OTL violations has a negative impact on the travel time ratio. Therefore, to reach the same level of response in the presence of OTL violations, it is necessary to further reduce the other two violations (i.e., JSC and SNI).Figure 9
Two-dimensional contour plots of traffic violations at the intersection: (a) JSC versus SNI, (b) JSC versus OTL, and (c) SNI versus OTL for the travel time ratio.
(a)(b)(c)


### 4.2. Fuel Consumption

Table10 shows the coefficients of the model fitted based on the simulation data for fuel consumption. For this model, the dependent variable is defined as the fuel consumption ratio which is calculated by dividing fuel consumption (after/experiment) by fuel consumption (current/real condition) in percent. The trace plot of this model for the intermediate values (traffic volume = 11,700, 50% decrease in violations) is shown in Figure 10. The lines A, B, and C indicate the percentage decrease in JSC, SNI, and OTL, respectively, and D is the traffic volume crossing the intersection.Table 10
Effect of decrease in traffic violations and traffic volume on fuel consumption ratio.
Model summaryMain effectsTwo-way effectsVariableABCDADBDCDD2Model coefficient1.453.67−1.040.85−0.00013−0.000320.00009−0.00004Model form−4799 + 1.45∗ A + 3.67 ∗ B − 1.04 ∗ C + 0.85 ∗ D − 0.00013 ∗ AD − 0.00032 ∗ BD + 0.00009 ∗ CD − 0.00004 ∗ D2P value0.0001SignificantR2R-squaredAdjR-squaredPredR-squared0.8300.8280.825Figure 10
Trace plot for fuel consumption ratio.As shown in Figure10, the dependent variable is directly related to the decrease in JSC and SNI. Also, the exogenous variable (traffic volume) shows that the positive effect of the decrease in violations on fuel consumption increases with the traffic volume. As before, reducing the number of vehicles that stop on the straight-through lanes instead of the left-turn lane has a negative effect on fuel consumption ratio.Figure11 shows the contour plots of the interactive effects of the decrease in JSC (Figure 11(a)), SNI (Figure 11(b)), and OTL (Figure 11(c)) (independent variables) versus the exogenous variable (traffic volume) on the fuel consumption ratio. According to these plots, at any given traffic volume, decreasing the JSC and SNI violations reduces the fuel consumption ratio, but decreasing the OTL violation has the opposite effect. At any given level of violation, the fuel consumption ratio decreases as the traffic volume increases. For example, with a 50% decrease in violations, the travel time ratio is 90% at the traffic volume of 11,700 but 76% at the traffic volume of 12,000.Figure 11
Two-dimensional contour plots of entering traffic volume at the intersection versus (a) JSC, (b) SNI, and (c) OTL for fuel consumption ratio.
(a)(b)(c)The contour plots of the interactive effect of decreasing the two violations on the response variable when other variables are set to their intermediate values (11,700 for traffic volume and 50% for percentage decrease in violation) are presented in Figure12. In Figure 12(a), it can be seen that decreasing JSC and SNI violations reduces fuel consumption ratio, while as shown in Figures 12(b) and 12(c), decreasing OTL violation increases fuel consumption ratio.Figure 12
Two-dimensional contour plots of violations at intersections: (a) JSC versus SNI, (b) JSC versus OTL, and (c) SNI versus OTL for fuel consumption ratio.
(a)(b)(c)Figure13 shows a summary of the RSM results for travel time ratio obtained from 40 simulations (4 intersections × 10 runs) for the 13 scenarios at the traffic volume of 11,700 vehicles. The impact of SNI and JSC violations on fuel consumption is nearly identical to that of travel time ratio. Similarly, reducing OTL violation has a negative effect on fuel consumption ratio. When all three violations are removed, a travel time ratio of 70% is achieved, meaning 30% improvement in travel time. A similar result is observed for fuel consumption ratio when all traffic violations are removed.Figure 13
Travel time ratio versus percentage of decrease in violations.


## 4.1. Travel Time

To measure the improvement in travel time in terms of the reduction in violations, the best model fitted to the simulation results with the aid of RSM was a quadratic form. In this model, independent variables include the percentage of decrease of each traffic violation and the traffic volume, and the dependent (response) variable is the travel time ratio which is calculated by dividing the travel time obtained from the simulation model with reduction in violations by the observed travel time. The estimated coefficients of the travel time model are given in Table9. In this model, A, B, and C are parameters corresponding to the percentage of decrease in JSC, SNI, and OTL, respectively. The parameter D corresponds to the entering traffic volume at the intersection. The positive signs for parameters A and B suggest that as the percentage of decrease for these two violations increases, the travel time ratio is decreased. This finding corresponds to expectation. The negative sign for parameter C suggests that as the percentage of decrease for OTL violations increases, the travel time ratio is increased. A possible explanation for this result is that the configuration of the approaches is not well designed to handle the left-turn volume. Therefore, a fraction (illegal left turn) of the left-turning volume is forced to occupy the through lane to accomplish the left turn maneuver. Similarly, the positive sign for parameter D suggests that as the entering volume increases, so will the travel time ratio.Table 9
Effect of decrease in traffic violations and traffic volume on travel time ratio.
Model summaryMain effectsInteractive effectsVariableABCDADBDCDD2Model coefficient2.014.78−1.771.17−0.00019−0.000430.00015−0.00005Model form−6601 + 2.01A + 4.78B − 1.77C + 1.17D − 0.00019 AD − 0.00043 BD + 0.00015 CD − 0.00005D2P value0.0001SignificantR2R-squaredAdjR-squaredPredR-squared0.8320.8300.827Figure7 shows the trace plot of the model for the intermediate values where traffic volume = 11,700 and all three types of violations are decreased by 50%. −1 on the x-axis means that all the violations are present, and +1 means that all violations are removed (100% decrease). This plot shows the rate of decrease and increase in the response variable as the independent variable is varied from the reference point. The results from Figure 7 indicate that reducing violations has a greater positive effect on travel time ratio when the traffic volume (exogenous variable) is higher. As mentioned, reducing OTL violations has a negative impact on the travel time ratio.Figure 7
Trace plot for travel time ratio.Figure8 shows the interaction effects of the decreasing JSC, SNI, or OTL violations and traffic volume on the response variable. It can be seen that at any given traffic volume, as the number of JSC and SNI violation decreases, the travel time ratio also decreases. This finding is consistent with those found by Kladeftiras and Antoniou [11]. However, as the number of OTL violation decreases, the travel time ratio actually increases. This can be due to both poor signal timing and poor geometric design of the intersection. That is, the number of left-turn lanes on the approach is inadequate for the demand. If the left-turning vehicles wait for their turn to get into the left-turn lane, it will create a queue upstream of the signal and block the through traffic. Also, at any given level of violation, the travel time ratio decreases more as the traffic volume increases. For example, at 50% decrease in all violations, the travel time ratio at the traffic volume of 11,700 is 85%, but it is only 68% at the traffic volume of 12,000.Figure 8
Two-dimensional contour plots of entering traffic volume at the intersection versus (a) JSC, (b) SNI, and (c) OTL for travel time ratio.
(a)(b)(c)Figure9 shows the interaction effects of decreasing the percentage of two violations on the response variable when other variables are set to their intermediate values (11,700 vehicles for traffic volume and 50% reduction in violation). The results in Figure 9(a) indicate that reducing the JSC and SNI violations improves the travel time ratio. The results in Figures 9(b) and 9(c) indicate that reducing OTL violations has a negative impact on the travel time ratio. Therefore, to reach the same level of response in the presence of OTL violations, it is necessary to further reduce the other two violations (i.e., JSC and SNI).Figure 9
Two-dimensional contour plots of traffic violations at the intersection: (a) JSC versus SNI, (b) JSC versus OTL, and (c) SNI versus OTL for the travel time ratio.
(a)(b)(c)


## 4.2. Fuel Consumption

Table10 shows the coefficients of the model fitted based on the simulation data for fuel consumption. For this model, the dependent variable is defined as the fuel consumption ratio which is calculated by dividing fuel consumption (after/experiment) by fuel consumption (current/real condition) in percent. The trace plot of this model for the intermediate values (traffic volume = 11,700, 50% decrease in violations) is shown in Figure 10. The lines A, B, and C indicate the percentage decrease in JSC, SNI, and OTL, respectively, and D is the traffic volume crossing the intersection.Table 10
Effect of decrease in traffic violations and traffic volume on fuel consumption ratio.
Model summaryMain effectsTwo-way effectsVariableABCDADBDCDD2Model coefficient1.453.67−1.040.85−0.00013−0.000320.00009−0.00004Model form−4799 + 1.45∗ A + 3.67 ∗ B − 1.04 ∗ C + 0.85 ∗ D − 0.00013 ∗ AD − 0.00032 ∗ BD + 0.00009 ∗ CD − 0.00004 ∗ D2P value0.0001SignificantR2R-squaredAdjR-squaredPredR-squared0.8300.8280.825Figure 10
Trace plot for fuel consumption ratio.As shown in Figure10, the dependent variable is directly related to the decrease in JSC and SNI. Also, the exogenous variable (traffic volume) shows that the positive effect of the decrease in violations on fuel consumption increases with the traffic volume. As before, reducing the number of vehicles that stop on the straight-through lanes instead of the left-turn lane has a negative effect on fuel consumption ratio.Figure11 shows the contour plots of the interactive effects of the decrease in JSC (Figure 11(a)), SNI (Figure 11(b)), and OTL (Figure 11(c)) (independent variables) versus the exogenous variable (traffic volume) on the fuel consumption ratio. According to these plots, at any given traffic volume, decreasing the JSC and SNI violations reduces the fuel consumption ratio, but decreasing the OTL violation has the opposite effect. At any given level of violation, the fuel consumption ratio decreases as the traffic volume increases. For example, with a 50% decrease in violations, the travel time ratio is 90% at the traffic volume of 11,700 but 76% at the traffic volume of 12,000.Figure 11
Two-dimensional contour plots of entering traffic volume at the intersection versus (a) JSC, (b) SNI, and (c) OTL for fuel consumption ratio.
(a)(b)(c)The contour plots of the interactive effect of decreasing the two violations on the response variable when other variables are set to their intermediate values (11,700 for traffic volume and 50% for percentage decrease in violation) are presented in Figure12. In Figure 12(a), it can be seen that decreasing JSC and SNI violations reduces fuel consumption ratio, while as shown in Figures 12(b) and 12(c), decreasing OTL violation increases fuel consumption ratio.Figure 12
Two-dimensional contour plots of violations at intersections: (a) JSC versus SNI, (b) JSC versus OTL, and (c) SNI versus OTL for fuel consumption ratio.
(a)(b)(c)Figure13 shows a summary of the RSM results for travel time ratio obtained from 40 simulations (4 intersections × 10 runs) for the 13 scenarios at the traffic volume of 11,700 vehicles. The impact of SNI and JSC violations on fuel consumption is nearly identical to that of travel time ratio. Similarly, reducing OTL violation has a negative effect on fuel consumption ratio. When all three violations are removed, a travel time ratio of 70% is achieved, meaning 30% improvement in travel time. A similar result is observed for fuel consumption ratio when all traffic violations are removed.Figure 13
Travel time ratio versus percentage of decrease in violations.


## 5. Summary and Conclusions

This study investigated the effect of three common traffic violations (JSC, SNI, and OTL) at four-legged signalized intersections using a traffic microsimulation model and response surface methodology. The required data to investigate the effect of these violations on travel time and fuel consumption were collected from four signalized intersections in Mashhad over the 3-hour period from 10:30 to 13:30 (peak hours of traffic violations). After analyzing the collected data, the Aimsun software was used to simulate the intersections in their current condition, and the resulting model was calibrated and validated at the 95% confidence level. After running simulations for 13 different experimental designs, following the RSM procedure, the best model for the data was fitted withR2 = 0.83, and the results were used to examine the effect of the decrease in violations on travel time and fuel consumption ratio.The results showed that the dependent variables “travel time ratio” and “fuel consumption ratio” are affected not only by the independent variables (reduction ratio of violations) but also by the volume of traffic as the exogenous variable. It was found that decreasing two of the violations, namely, JSC and SNI, improves travel time and fuel consumption but decreasing OTL has the opposite effect. Furthermore, the larger the entering traffic volume, the more significant the effect of reducing violations on travel time and fuel consumption. It was estimated that for the 3-lane four-legged intersection with an intermediate traffic volume (11,700 vehicles), decreasing all violations by 30, 60, and 100% will reduce the travel time by 9, 18, and 30% and the fuel consumption by 6, 13, and 21%, respectively. The results indicate that reducing violations at intersections can have significant traffic, economic, and environmental benefits.This paper contributes to the existing literature by introducing the use of response surface methodology to allow for the investigation of both main effects and interaction effects when there are multiple violations (i.e., JSC, SNI, and OTL). The results of this study help traffic managers know about the impact of traffic violations on traffic indicators and highlight the need to reduce JSC and SNI violations at signalized intersections. This can be accomplished via enforcement, education, and awareness programs. A surprising fact discovered in this study is that reducing OTL violations will increase travel time and energy consumption of vehicles going through the intersections. This finding suggests that there may be many more intersections in the city of Mashhad that have insufficient left-turning capacity and/or inadequate signal timing to accommodate left-turning volume. As such, it is recommended that the transportation agency monitors OTL violations at intersections throughout the city and use this measure to determine which intersections need to be expanded and/or their signals be retimed.



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*Source: 2038167-2023-01-11.xml*