Evaluating the Impact of Human-Driven and Autonomous Vehicles in Adverse Weather Conditions Using a Verkehr in Städten—SIMulationsmodell (VISSIM) and Surrogate Safety Assessment Model (SSAM)
Abstract
:1. Introduction
1.1. Verkehr in Städten—SIMulations Model (PTV VISSIM)
1.2. Surrogate Safety Assessment Model (SSAM)
1.3. SSAM Simulation with VISSIM
2. Literature Review on Different Parameters Used in PTV VISSIM Microsimulations
2.1. Mobility Impact of AVs
2.2. Driving Behavior’s Impact on Safety
2.3. Effect of Weather
2.3.1. Impact of Weather and Driving Conditions on Driver Behavior
2.3.2. Modeling Driving Behavior and Traffic Flow in a Variety of Weather Scenarios
2.3.3. Modeling Speed Distribution of Vehicles Under Various Weather Situations
2.3.4. Performance of AVs Under Adverse Weather
2.4. Rural Area and Safety Analysis
2.5. Research Gaps Identified
2.5.1. Lack of Real-World Validation of SSAM Results
Method Used | Advantage | Disadvantage |
---|---|---|
Field study [76,80,81] | Simple to use; more reliable than many other objective measurements. | Variability among and across observers; high expense; labor-intensive. |
Computer vision methods [77,78,79,82] | Automatically identify traffic conflicts; economical; trustworthy; and effective. | High standards for video quality; still in the early stages of development. |
Driving in a naturalistic manner [83,84,85] | Permits the investigation of uncommon safety scenarios, such as collision and conflict scenarios. | Restricted data size: event sorting is time-consuming; data are safeguarded and not entirely accessible to the research community. |
2.5.2. Inconsistencies in Weather Impact Modeling
2.5.3. Limited Work on Mixed Traffic in Developing Countries
3. Discussion
4. Future Research Opportunities
4.1. VISSIM Integration with AI
4.2. AVs in Heterogenous and Mixed Traffic Environments
4.3. Real-Time SSAM Integration with Live Traffic
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Description | Impact |
---|---|---|
CC0 | Distance at standstill | Influences minimum spacing between vehicles |
CC1 | Headway time | Higher values imply more cautious following behavior |
CC2 | Following variation | Affects longitudinal oscillation in vehicle movement |
CC3 | Threshold for entering following | Determines perception–reaction threshold |
CC4 | Negative following threshold | Defines lower bound of speed difference for following |
CC5 | Positive following threshold | Defines upper bound of speed difference for following |
CC6 | Speed dependency of oscillation | Higher values cause greater speed oscillation |
CC7 | Oscillation acceleration | Indicates acceleration during oscillatory movements |
CC8 | Standstill acceleration | Acceleration from stationary state |
CC9 | Acceleration at 80 km/h | Desired acceleration at higher speeds |
Author | VISSIM Model | Geometry | Important Conclusions |
---|---|---|---|
[37] | W99 and lane change | Intersection | Examining how AVs may affect potential conflicts. There will be substantial safety improvements from AVs. The rate of collisions was found to have decreased. The network is safer when there are more AVs installed. |
[66] | W 99 and lane change | Freeway | Examining the impact of traffic characteristics on lane change for safety. Setting an immense speed dispersion results in more frequent lane changes, while a small speed distribution results in fewer lane changes. |
[67] | W 99 and lane change | Freeway | Investigating the application of ramp metering (RM) on a highway. The average wait time was most successfully decreased by the signal on the ramp with the shortest red time. The metering rate on highways is influenced by traffic conditions, and this strategy improved average speed the most. |
[32] | W99 | Intersections and roundabouts | The impacts of AV on safety are assessed through simulation. With increased penetration, AVs greatly improve safety. |
[68] | W74 | Arterial | Predicting emergency vehicle (EV) routes and travel times. Calibration and validation considerably improved the accuracy of travel time estimation. EVs’ limited mobility necessitated a more dynamic PCU at high flow rates. |
[69] | W 99 | Freeway | Considerate consequences of aggressive driving. Close following, abrupt lane changes, and quick deceleration are examples of destructive driving that raises the possibility of an accident with another car. |
[70] | W 99 and lane change | Intersection | Determining different CAV penetration rates. Significant increases in safety are among the advantages of raising CAV penetration rates in traffic flow. |
[71] | W 99 and W 74 | Roadway | Simulated vehicle behavior in mixed traffic conditions. The trajectories show that the hysteresis phenomenon occurs among vehicles even under mixed traffic conditions. The technique of replicating high-speed roads with W 99 models and urban roads with W 74 models is severely opposed by the study. According to the study’s findings, both theories are very consistent. |
[72] | W 99 and W 74 and lane change | Freeway | Examined how traffic flow distribution inside a lane is affected by car-following and lane-change characteristics. In Wiedemann’s model, the parameters CC3 and CC1 play a vital role in determining a vehicle’s lane-change headway. In the W 99 scenario, CC1 plays a substantial role, while the bxadd and bxmult parameters have little effect on lane flow distribution in W 74. |
[73] | W99 | Freeway | The reliability of route time can be predicted by examining the distribution of time headway and standstill distance. Incorporating stochastic elements for time headway and standstill distance into car tracking models enhances the precision and efficacy of assessing travel time reliability metrics. |
Author | Key Parameter | Finding | Limitations |
---|---|---|---|
Huang et al. [74] | Mobility, inclement weather | When compared with clear weather, snowy conditions had the greatest impact on traffic flow, increasing stop counts by 7.5 times and delay times by 2.5 times. When compared with clear weather, heavy, dense fog significantly increases the total amount of stop (1.8 times more) and stoppage durations (2.9 times more). Whereas rainy weather results in a 1.3-fold increase in delay durations and a 2.37-fold increase in the frequency of stops compared with clear weather. | Environmental impact on adverse weather conditions not studied. |
Fujiu et al. [75] | Rural area delay time AV | Autonomous vehicles’ effects on traffic flow are highly dependent on the amount of mixing and the type of traffic, such as urban or rural. When compared with simply autonomous vehicles, the combination of non-vehicular traffic, such as cyclists and pedestrians, with AVs increases the OD delay time. | Only weekday mornings were analyzed. Utilizing only delay time as an evaluation index, no sensitivity analyses were conducted. |
Khashayarfard and Nassiri [37] | AV, safety | Accident risk might be lowered by up to 93% if all AVs were present in traffic flow. Use the traffic conflicts TTC and DRAC. | Does not employ MTTC or PET or any other surrogate measures. Evaluation of how AVs affect variations in demand and applying them to every situation was not carried out; no sensitivity analysis was conducted. |
Park et al. [69] | AV, urban road, traffic flow, road capacity | Traffic flow improved as AV penetration increased, and the average delay decreased by up to 31%. Connections with three or four lanes also significantly increased the delay, as was to be expected. When AV adoption reached 100%, the roadway network could handle 40% more traffic in terms of increased road capacity. | The model’s parameters were not precisely calibrated. Minor passageways were not as well-calibrated, and the main corridors were the focus. The study assumed homogenous behavior of AVs. Does not investigate how adding AVs to microscopic simulation models affects the behavior of human drivers. |
Hammit et al. [53] | Adverse weather driving behavior on NDS SHRP2 trips | Improvement in speed at capacity and density are observed in snowy, moderate, and heavy rain environments and no capacity change and reduction in density is observed for fog, very light rain, and light rain. | Excludes considering the variability of drivers within each weather situation. Does not assess how driving behavior changes from favorable to adverse weather circumstances. |
Chen et al. [55] | Adverse weather Traffic flow characteristic | The study found that poor weather has a consistent impact on traffic flow characteristics. The developed method can overcome the current limitation of the field data-based methodology. | Here only car-following behavior is tested but lane-change and overtaking behavior are not considered. No other traffic parameters are considered; only the volume of traffic is considered. |
Zhang et al. [62] | Adverse weather conditions AV sensors | The impact of unfavorable weather conditions on AV sensors including LiDAR, GPS, cameras, and radar is reviewed in this research. Additionally, they suggested a novel model that considers both the backscatter and attenuation effects to describe the rain impact on millimeter-wave radar. According to the modeling results, intense rains may decrease a millimeter-wave radar’s detection range by up to 55%. | The radar receiver experiences noise problems due to the radar’s large bandwidth. The radar’s optimum beamwidth, according to the function requirements, should be employed. Adaptable power transmission should be indicated according to the function region and weather circumstances. |
Khan et al. [58] | Driver behavior in general; speed selection in clear and foggy weather | Driver speed selection behavior is significantly influenced by weather-related factors such as visibility, fog, and surface conditions, since sensor-based technology (AV) is less vulnerable to bad weather. On motorways, fog can result in rear-end and lane-deviation accidents by affecting a driver’s observation of speed and visibility of objects on the road. | Does not use different age group representative sample in speed selection during foggy weather. Driver’s behavior in selection of speed and acceleration during adverse weather is neglected. |
Morando et al. [32] | SSAM, AV | AVs improve safety through high penetration rates, regardless of traveling with shorter headways to increase roadway capacity and reduce delays. AVs reduce conflicts at signalized intersections by 20% to 65%, with penetration rates (PRs) ranging from 50% to 100%. With 100% AV PR, roundabout conflicts decrease by 29% to 64%. | Does not investigate the effects of V2V safety technologies. Traffic conflicts in this analysis were solely related to TTC and PET. Consider including more SSMs to reinforce the approach’s validity. Additional testing with diverse network configurations, traffic situations, and AV penetration rates may be necessary. |
Khavas et al. [57] | Inclement weather | Which VISSIM input parameters are most capable of generating a traffic stream with the attributes connected to the weather category can be ascertained using the model proposed in this study. | Does not utilize the AVs under different traffic flow and inclement weather. |
Ghasemzadeh et al. [58] | Weather; driver behavior | Standard deviation of lane location was greatly increased by heavy rain. Compared with drivers in clear weather, drivers in heavy rain are approximately 3.8 times more probable to have a higher average deviation of lane position. They further concluded that drivers are better at maintaining their lanes on roads with greater speeds. | Limitations of this study included the limited sample size and the lack of demographic and NDS vehicle data (Naturalistic Driving Research Data). |
Fan et al. [19] | Freeway merge area SSAM | After two stages of calibration, the mean absolute prevent error (MAPE) for all conflicts was found to have decreased from 78.1% to 33.4%. In particular, the MAPE value decreased from 79.5% to 35.8% for lane-change conflicts in addition to 76.6% to 33.5% for rear-end conflicts. | Does not apply safety assessment study to unsignalized intersections and freeway diverging regions. Does not discover consistency among the simulated and the observed traffic conflicts along with use of calibration process using numerous performance measurements. |
Behavior of AVs | Compared to Human-Driven Vehicles Under All Weather Scenerios |
---|---|
Degree of caution based on CC1 and CC2 | Lower |
Degree of perception reaction based on CC3 | Higher |
Degree of sensitivity to the dec/acc of following vehicle based on CC4/CC5 | Lower |
Speed dependency of oscillation based on CC6 | Lower |
Degree of acceleration oscillation based on CC7 | Lower |
Degree of standstill acceleration based on CC8 | Higher |
Parameters | AV1 [89] | AV2 [90] | HV |
---|---|---|---|
CC0 | 0.50 | 0.75 | 1.5 |
CC1 | 0.50 | 0.45 | 0.9 |
CC2 | 0 | 2 | 4 |
CC4 | 0 | −0.1 | −0.35 |
CC5 | 0 | 0.1 | 0.35 |
CC6 | 0 | 0 | 11.44 |
CC7 | 0.45 | 0.25 | 0.25 |
CC8 | 3.9 | 3.5 | 3.5 |
Look ahead distance | 10 | 2 | 2 |
Parameters | AV | HV |
---|---|---|
Location | Cities, country roads, highways, and urban areas | Cities, country roads, highways, and urban areas |
Weather | Can be used in various weather | Cannot be used in adverse weather |
Speed | Increase | Decrease |
Delays, stoppage frequency | Decrease | Increase |
Traffic congestion, accidents | Decrease | Increase |
Dependability of travel duration | Increase | Increase |
Driver distraction | Minimum | High |
Road capacity | increase | decrease |
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Ahmed, T.; Ali, A.; Huang, Y.; Lu, P. Evaluating the Impact of Human-Driven and Autonomous Vehicles in Adverse Weather Conditions Using a Verkehr in Städten—SIMulationsmodell (VISSIM) and Surrogate Safety Assessment Model (SSAM). Electronics 2025, 14, 2046. https://doi.org/10.3390/electronics14102046
Ahmed T, Ali A, Huang Y, Lu P. Evaluating the Impact of Human-Driven and Autonomous Vehicles in Adverse Weather Conditions Using a Verkehr in Städten—SIMulationsmodell (VISSIM) and Surrogate Safety Assessment Model (SSAM). Electronics. 2025; 14(10):2046. https://doi.org/10.3390/electronics14102046
Chicago/Turabian StyleAhmed, Talha, Asad Ali, Ying Huang, and Pan Lu. 2025. "Evaluating the Impact of Human-Driven and Autonomous Vehicles in Adverse Weather Conditions Using a Verkehr in Städten—SIMulationsmodell (VISSIM) and Surrogate Safety Assessment Model (SSAM)" Electronics 14, no. 10: 2046. https://doi.org/10.3390/electronics14102046
APA StyleAhmed, T., Ali, A., Huang, Y., & Lu, P. (2025). Evaluating the Impact of Human-Driven and Autonomous Vehicles in Adverse Weather Conditions Using a Verkehr in Städten—SIMulationsmodell (VISSIM) and Surrogate Safety Assessment Model (SSAM). Electronics, 14(10), 2046. https://doi.org/10.3390/electronics14102046