Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway.

UNLABELLED: The Intergovernmental Panel on Climate Change (IPCC) estimates that baseline global GHG emissions may increase 25-90% from 2000 to 2030, with carbon dioxide (CO2 emissions growing 40-110% over the same period. On-road vehicles are a major source of CO2 emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, for example, carbon monoxide (CO), nitrogen oxides (NO(x)), and particulate matter (PM). Therefore, the need to accurately quantify transportation-related emissions from vehicles is essential. The new US. Environmental Protection Agency (EPA) mobile source emissions model, MOVES2010a (MOVES), can estimate vehicle emissions on a second-by-second basis, creating the opportunity to combine a microscopic traffic simulation model (such as VISSIM) with MOVES to obtain accurate results. This paper presents an examination of four different approaches to capture the environmental impacts of vehicular operations on a 10-mile stretch of Interstate 4 (I-4), an urban limited-access highway in Orlando, FL. First (at the most basic level), emissions were estimated for the entire 10-mile section "by hand" using one average traffic volume and average speed. Then three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-second link drive schedules (LDS), and second-by-second operating mode distributions (OPMODE). This paper analyzes how the various approaches affect predicted emissions of CO, NO(x), PM2.5, PM10, and CO2. The results demonstrate that obtaining precise and comprehensive operating mode distributions on a second-by-second basis provides more accurate emission estimates. Specifically, emission rates are highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach. IMPLICATIONS: Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways.

Investigating the correlation between sidewalks and pedestrian safety.

In recent years, pedestrian safety has emerged to be of major concern to selected States in the US and at the national level. With the transition to the mobility of people, pedestrian safety has become more prevalent for governmental agencies to address and prioritize for strategic implementation. It was crucial to address the pedestrian-vehicular conflict within Florida as it was reported as having the highest four pedestrian incident locations in the country. Many pedestrian sidewalks in Florida are not continuous and there is a concern among planners and engineers in the Florida Department of Transportation (FDOT) that these gaps constitute discontinuity of flow and are potentially posing threats to pedestrian safety. This paper investigates the correlation between pedestrian safety and sidewalk gaps within the Central Florida region. A sidewalk gap is defined as a length of a roadway segment with missing or absent sidewalk between existing sidewalks (before and after) along the same segment. "Missing" sidewalks are locations where the sidewalk is not only absent, but there is a desire to have one constructed or it would be used if it were constructed. The missing sidewalk can be on one side of the road or both sides. A wide array of Geographic Information Systems (GIS) information associated with the sidewalk gaps and the safety data were secured by FDOT. The data included all the Roadway Characteristics Inventory (RCI) within the district. Crash data combined with other geometric design data, traffic data, and census data were used to develop a statistical model. The results showed that presence of sidewalk along roadway segments is one of the main factors that have significant impact on the expected number of pedestrian crashes at a specific location. Other factors included daily traffic volumes, roadway category, specifically along urban two-way divided and undivided arterials with four to six lanes as well as the population within half-mile radius surrounding the crash location. The model results revealed that the Incident Risk Ratio (IRR) of a pedestrian crash along roadways with no sidewalk is 1.67 times greater than the likelihood of a crash with the presence of a sidewalk at 95% Confidence Interval (C.I.). The analysis also concluded that the likelihood of a pedestrian crash per mile along roadways with no sidewalk is three times greater than the likelihood of a crash per mile with the presence of a sidewalk.

Assessing the impact of converting roundabouts to traffic signals on vehicle emissions along an urban arterial corridor in Qatar.

The type of control at intersections has a major effect on the operation of any urban corridor. Different predefined procedures are available to calculate some of the main operational characteristics, such as capacity, delay, and level of service, in order to select the best type of control. However, there are other important factors that affect major arterials operational characteristics, factors that are not fully addressed, such as the impact of emissions. In this study, a microscopic simulation approach using VISSIM and MOVES was developed to assess the environmental effect of converting four three-lane roundabouts to signalized intersections along a heavily congested urban corridor in Qatar. A decision was made to switch all roundabouts to traffic signals for better operations. Preliminary results indicated that the signal control outperformed the roundabout in the range of 37% to 43% reduction in emissions. A more detailed analysis revealed that roundabout corridor operations' effects on emission rates are divergent from those of signalized corridors, particularly upstream and downstream of the intersections. Immediate roundabout upstream approaches are driver behavior dependent, characterized by substantial coasting at lower speeds and subsequent re-accelerating with less idling, described as acceleration events, which resulted in high emission rates, while signalized corridors are signal timing dependent, characterized by ample idling with less coasting and re-acceleration, resulting in reduced emission rates. The results also revealed that there was no significant difference between emission rates in the vicinity of the two types of control. Both recorded nearly the same emission rate. Implications: A microscopic simulation approach using VISSIM and MOVES was developed to assess the environmental effect of converting four three-lane roundabouts to signalized intersections along a heavily congested urban corridor in Doha, Qatar. Intersection geometries along with the control type have significant impact on emission rates and play a major role in assessing environmental impacts. US EPA MOVES was calibrated to Qatar conditions which can be used to estimate emission factors and quantify vehicular emissions along other corridors in the country. The results can also be beneficial for other countries within the region.

Validating a driving simulator using surrogate safety measures.

Traffic crash statistics and previous research have shown an increased risk of traffic crashes at signalized intersections. How to diagnose safety problems and develop effective countermeasures to reduce crash rate at intersections is a key task for traffic engineers and researchers. This study aims at investigating whether the driving simulator can be used as a valid tool to assess traffic safety at signalized intersections. In support of the research objective, this simulator validity study was conducted from two perspectives, a traffic parameter (speed) and a safety parameter (crash history). A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. A driving simulator experiment with eight scenarios at the intersection were conducted to determine if the subjects' speed behavior and traffic risk patterns in the driving simulator were similar to what were found at the real intersection. The experiment results showed that speed data observed from the field and in the simulator experiment both follow normal distributions and have equal means for each intersection approach, which validated the driving simulator in absolute terms. Furthermore, this study used an innovative approach of using surrogate safety measures from the simulator to contrast with the crash analysis for the field data. The simulator experiment results indicated that compared to the right-turn lane with the low rear-end crash history record (2 crashes), subjects showed a series of more risky behaviors at the right-turn lane with the high rear-end crash history record (16 crashes), including higher deceleration rate (1.80+/-1.20 m/s(2) versus 0.80+/-0.65 m/s(2)), higher non-stop right-turn rate on red (81.67% versus 57.63%), higher right-turn speed as stop line (18.38+/-8.90 km/h versus 14.68+/-6.04 km/h), shorter following distance (30.19+/-13.43 m versus 35.58+/-13.41 m), and higher rear-end probability (9/59=0.153 versus 2/60=0.033). Therefore, the relative validity of driving simulator was well established for the traffic safety studies at signalized intersections.

Effects of major-road vehicle speed and driver age and gender on left-turn gap acceptance.

Because the driver's gap-acceptance maneuver is a complex and risky driving behavior, it is a highly concerned topic for traffic safety and operation. Previous studies have mainly focused on the driver's gap acceptance decision itself but did not pay attention to the maneuver process and driving behaviors. Using a driving simulator experiment for left-turn gap acceptance at a stop-controlled intersection, this study evaluated the effects of major traffic speed and driver age and gender on gap acceptance behaviors. The experiment results illustrate relationships among drivers' left-turn gap decision, driver's acceleration rate, steering action, and the influence of the gap-acceptance maneuver on the vehicles in the major traffic stream. The experiment results identified an association between high crash risk and high traffic speed at stop-controlled intersections. The older drivers, especially older female drivers, displayed a conservative driving attitude as a compensation for reduced driving ability, but also showed to be the most vulnerable group for the relatively complex driving maneuvers.

Light truck vehicles (LTVs) contribution to rear-end collisions.

Light truck vehicles (LTVs), comprising light-duty trucks, vans, and sport-utility vehicles (SUVs) drive higher and wider than passenger cars which could affect the visibility for the following passenger car driver. This paper investigates the contribution of LTVs to rear-end collisions resulting from horizontal visibility blockage using the University of Central Florida sophisticated reconfigurable driving simulator. Indeed, a sudden stop of a leading LTV, in the shadow of the blindness of the succeeding passenger car driver, may deprive the latter of a sufficient response time, which may lead to high probability of a rear-end collision. To investigate this issue, two scenarios were developed in the UCF driving simulator. The first scenario serves as a base scenario where the simulator car follows a passenger car, and the second scenario serves as a test scenario, where the simulator car follows an LTV. The results obtained by comparing the scenarios showed that LTVs produce more rear-end collisions at unsignalized intersections due to horizontal visibility blockage and due to the resulting drivers' behavior when driving behind an LTV.

Analyses of rear-end crashes based on classification tree models.

OBJECTIVE: Signalized intersections are accident-prone areas especially for rear-end crashes due to the fact that the diversity of the braking behaviors of drivers increases during the signal change. The objective of this article is to improve knowledge of the relationship between rear-end crashes occurring at signalized intersections and a series of potential traffic risk factors classified by driver characteristics, environments, and vehicle types. METHODS: Based on the 2001 Florida crash database, the classification tree method and Quasi-induced exposure concept were used to perform the statistical analysis. Two binary classification tree models were developed in this study. One was used for the crash comparison between rear-end and non-rear-end to identify those specific trends of the rear-end crashes. The other was constructed for the comparison between striking vehicles/drivers (at-fault) and struck vehicles/drivers (not-at-fault) to find more complex crash pattern associated with the traffic attributes of driver, vehicle, and environment. RESULTS: The modeling results showed that the rear-end crashes are over-presented in the higher speed limits (45-55 mph); the rear-end crash propensity for daytime is apparently larger than nighttime; and the reduction of braking capacity due to wet and slippery road surface conditions would definitely contribute to rear-end crashes, especially at intersections with higher speed limits. The tree model segmented drivers into four homogeneous age groups: < 21 years, 21-31 years, 32-75 years, and > 75 years. The youngest driver group shows the largest crash propensity; in the 21-31 age group, the male drivers are over-involved in rear-end crashes under adverse weather conditions and the 32-75 years drivers driving large size vehicles have a larger crash propensity compared to those driving passenger vehicles. CONCLUSIONS: Combined with the quasi-induced exposure concept, the classification tree method is a proper statistical tool for traffic-safety analysis to investigate crash propensity. Compared to the logistic regression models, tree models have advantages for handling continuous independent variables and easily explaining the complex interaction effect with more than two independent variables. This research recommended that at signalized intersections with higher speed limits, reducing the speed limit to 40 mph efficiently contribute to a lower accident rate. Drivers involved in alcohol use may increase not only rear-end crash risk but also the driver injury severity. Education and enforcement countermeasures should focus on the driver group younger than 21 years. Further studies are suggested to compare crash risk distributions of the driver age for other main crash types to seek corresponding traffic countermeasures.

Discrepancy analysis of driving performance of taxi drivers and non-professional drivers for red-light running violation and crash avoidance at intersections.

Due to comfort, convenience, and flexibility, taxis have become increasingly more prevalent in China, especially in large cities. However, many violations and road crashes that occurred frequently were related to taxi drivers. This study aimed to investigate differences in driving performance between taxi drivers and non-professional drivers from the perspectives of red-light running violation and potential crash involvement based on a driving simulation experiment. Two typical scenarios were established in a driving simulator, which includes the red-light running violation scenario and the crash avoidance scenario. There were 49 participants, including 23 taxi drivers (14 males and 9 females) and 26 non-professional drivers (13 males and 13 females) recruited for this experiment. The driving simulation experiment results indicated that non-professional drivers paid more attention to red-light running violations in comparison to taxi drivers who had a higher probability of red-light running violation. Furthermore, it was found that taxi drivers were more inclined to turn the steering wheel in an attempt to avoid a potential collision and non-professional drivers had more abrupt deceleration behaviors when facing a potential crash. Moreover, the experiment results showed that taxi drivers had a smaller crash rate compared to non-professional drivers and had a better performance in terms of crash avoidance at the intersection.

A rear-end collision risk assessment model based on drivers' collision avoidance process under influences of cell phone use and gender-A driving simulator based study.

Driver's collision avoidance performance has a direct link to the collision risk and crash severity. Previous studies demonstrated that the distracted driving, such as using a cell phone while driving, disrupted the driver's performance on road. This study aimed to investigate the manner and extent to which cell phone use and driver's gender affected driving performance and collision risk in a rear-end collision avoidance process. Forty-two licensed drivers completed the driving simulation experiment in three phone use conditions: no phone use, hands-free, and hand-held, in which the drivers drove in a car-following situation with potential rear-end collision risks caused by the leading vehicle's sudden deceleration. Based on the experiment data, a rear-end collision risk assessment model was developed to assess the influence of cell phone use and driver's gender. The cell phone use and driver's gender were found to be significant factors that affected the braking performances in the rear-end collision avoidance process, including the brake reaction time, the deceleration adjusting time and the maximum deceleration rate. The minimum headway distance between the leading vehicle and the simulator during the rear-end collision avoidance process was the final output variable, which could be used to measure the rear-end collision risk and judge whether a collision occurred. The results showed that although cell phone use drivers took some compensatory behaviors in the collision avoidance process to reduce the mental workload, the collision risk in cell phone use conditions was still higher than that without the phone use. More importantly, the results proved that the hands-free condition did not eliminate the safety problem associated with distracted driving because it impaired the driving performance in the same way as much as the use of hand-held phones. In addition, the gender effect indicated that although female drivers had longer reaction time than male drivers in critical situation, they were more quickly in braking with larger maximum deceleration rate, and they tended to keep a larger safety margin with the leading vehicle compared to male drivers. The findings shed some light on the further development of advanced collision avoidance technologies and the targeted intervention strategies about cell phone use while driving.

Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model.

Multi-vehicle rear-end accidents constitute a substantial portion of the accidents occurring at signalized intersections. To examine the accident characteristics, this study utilized the 2001 Florida traffic accident data to investigate the accident propensity for different vehicle roles (striking or struck) that are involved in the accidents and identify the significant risk factors related to the traffic environment, the driver characteristics, and the vehicle types. The Quasi-induced exposure concept and the multiple logistic regression technique are used to perform this analysis. The results showed that seven road environment factors (number of lanes, divided/undivided highway, accident time, road surface condition, highway character, urban/rural, and speed limit), five factors related to striking role (vehicle type, driver age, alcohol/drug use, driver residence, and gender), and four factors related to struck role (vehicle type, driver age, driver residence, and gender) are significantly associated with the risk of rear-end accidents. Furthermore, the logistic regression technique confirmed several significant interaction effects between those risk factors.

Quality of traffic flow on urban arterial streets and its relationship with safety.

The two-fluid model for vehicular traffic flow explains the traffic on arterials as a mix of stopped and running vehicles. It describes the relationship between the vehicles' running speed and the fraction of running vehicles. The two parameters of the model essentially represent 'free flow' travel time and level of interaction among vehicles, and may be used to evaluate urban roadway networks and urban corridors with partially limited access. These parameters are influenced by not only the roadway characteristics but also by behavioral aspects of driver population, e.g., aggressiveness. Two-fluid models are estimated for eight arterial corridors in Orlando, FL for this study. The parameters of the two-fluid model were used to evaluate corridor level operations and the correlations of these parameters' with rates of crashes having different types/severity. Significant correlations were found between two-fluid parameters and rear-end and angle crash rates. Rate of severe crashes was also found to be significantly correlated with the model parameter signifying inter-vehicle interactions. While there is need for further analysis, the findings suggest that the two-fluid model parameters may have potential as surrogate measures for traffic safety on urban arterial streets.

Classification analysis of driver's stop/go decision and red-light running violation.

When the driver encounters a signal change from green to yellow, he is required to make a stop or go decision based on his speed and the distance to the stop bar. Making the wrong decision will lead to a red-light running violation or an abrupt stop at the intersection. In this study, a field data collection was conducted at a high-speed signalized intersection, where a video-based system with three cameras was used to record the drivers' behavior related to the onset of yellow. Observed data include drivers' stop/go decisions, red-light running violation, lane position in the highway, positions (leading/following) in the traffic flow, vehicle type, and vehicles' yellow-onset speeds and distances from the intersection. Further, classification tree models were applied to analyze how the probabilities of a stop or go decision and of red-light running are associated with the traffic parameters. The data analysis indicated that vehicle's distance from the intersection at the onset of yellow, operating speed, and position in the traffic flow are the most important predictors for both the stop/go decision and red-light running violation. This study illustrates that the tree models are helpful to recognize and predict how drivers make stop/go decisions and partake in red-light running violations corresponding to the traffic parameters.

Exploring precrash maneuvers using classification trees and random forests.

Taking evasive actions vis-à-vis critical traffic situations impending to motor vehicle crashes endows drivers an opportunity to avoid the crash occurrence or at least diminish its severity. This study explores the drivers, vehicles, and environments' characteristics associated with crash avoidance maneuvers (i.e., evasive actions or no evasive actions). Rear-end collisions, head-on collisions, and angle collisions are analyzed separately using decision trees and the significance of the variables on the binary response variable (evasive actions or no evasive actions) is determined. Moreover, the random forests method is employed to rank the importance of the drivers/vehicles/environments characteristics on crash avoidance maneuvers. According to the exploratory analyses' results, drivers' visibility obstruction, drivers' physical impairment, drivers' distraction are associated with crash avoidance maneuvers in all three types of accidents. Moreover, speed limit is associated with rear-end collisions' avoidance maneuvers and vehicle type is correlated with head-on collisions and angle collisions' avoidance maneuvers. It is recommended that future research investigates further the explored trends (e.g., physically impaired drivers, visibility obstruction) using driving simulators which may help in legislative initiatives and in-vehicle technology recommendations.

Analyses of factors of crash avoidance maneuvers using the general estimates system.

OBJECTIVE: Taking an effective corrective action to a critical traffic situation provides drivers an opportunity to avoid crash occurrence and minimize crash severity. The objective of this study is to investigate the relationship between the probability of taking corrective actions and the characteristics of drivers, vehicles, and driving environments. METHODS: Using the 2004 GES crash database, this study classified drivers who encountered critical traffic events (identified as P_CRASH3 in the GES database) into two pre-crash groups: corrective avoidance actions group and no corrective avoidance actions group. Single and multiple logistic regression analyses were performed to identify potential traffic factors associated with the probability of drivers taking corrective actions. RESULTS: The regression results showed that the driver/vehicle factors associated with the probability of taking corrective actions include: driver age, gender, alcohol use, drug use, physical impairments, distraction, sight obstruction, and vehicle type. In particular, older drivers, female drivers, drug/alcohol use, physical impairment, distraction, or poor visibility may increase the probability of failing to attempt to avoid crashes. Moreover, drivers of larger size vehicles are 42.5% more likely to take corrective avoidance actions than passenger car drivers. On the other hand, the significant environmental factors correlated with the drivers' crash avoidance maneuver include: highway type, number of lanes, divided/undivided highway, speed limit, highway alignment, highway profile, weather condition, and surface condition. Some adverse highway environmental factors, such as horizontal curves, vertical curves, worse weather conditions, and slippery road surface conditions are correlated with a higher probability of crash avoidance maneuvers. These results may seem counterintuitive but they can be explained by the fact that motorists may be more likely to drive cautiously in those adverse driving environments. CONCLUSIONS: The analyses revealed that drivers' distraction could be the highest risk factor leading to the failure of attempting to avoid crashes. Further analyses entailing distraction causes (e.g., cellular phone use) and their possible countermeasures need to be conducted. The age and gender factors are overrepresented in the "no avoidance maneuver." A possible solution could involve the integration of a new function in the current ITS technologies. A personalized system, which could be related to the expected type of maneuver for a driver with certain characteristics, would assist different drivers with different characteristics to avoid crashes. Further crash database studies are recommended to investigate the association of drivers' emergency maneuvers such as braking, steering, or their combination with crash severity.