How does age affect driving behavior according to the research?

The study found that driver age significantly impacts several car-following parameters like acceleration and deceleration rates, suggesting that younger and older drivers have distinct behavioral patterns. Accounting for these age-related differences can lead to more accurate traffic simulations.

In what ways does income level influence driving styles as highlighted in the study?

Income level influences driving behavior, as variations in car-following parameters are observed across different income brackets. This suggests that socioeconomic factors can play a role in shaping driving habits. Ignoring income-based differences may result in less realistic traffic models.

What role does driving experience, measured by miles driven, play in shaping car-following behavior?

The research indicated that miles driven in the past year, acting as a proxy for driving experience, strongly correlates with several car-following parameters. Experienced drivers tend to exhibit different patterns of acceleration, deceleration, and spacing. Incorporating this experience factor can refine the accuracy of traffic flow simulations.

Did the study find gender to be a significant factor in car-following behavior? If not, what factors were found to be more relevant?

While gender was examined, it did not emerge as a primary factor in explaining differences in car-following behavior. The statistically significant differences observed were less pronounced than those related to age, income, or experience. The study focuses more on the impact of age, income, and experience as the main differentiating factors.

Where did the data come from for this research, and how did that data help the study achieve its goals related to driving behavior?

The Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) provided the data for this research. The data included 728 trips from 85 drivers, which allowed researchers to analyze real-world driving behavior and calibrate car-following models like Gipps, Intelligent Driver Model (IDM), and Wiedemann 99 (W99). This dataset's diversity was crucial for uncovering patterns related to age, income, and experience.

Surreal illustration of cars morphing into human figures, representing varied driving behaviors.

Decoding Driver Behavior: How Age, Income, and Experience Shape Your Driving Style

Lena Voss in Tech & Innovation September 2025 • 4 min read.

"Uncover the hidden factors influencing how we drive, from age and income to miles driven, and how these insights can reshape traffic management."

Microsimulation models are revolutionizing how we understand and manage transportation systems. By simulating individual driving behaviors, these models help us predict the impact of new technologies, policies, and infrastructure designs. At the heart of these models are car-following algorithms, which attempt to replicate how drivers respond to the vehicles around them.

However, one of the biggest challenges in creating accurate microsimulations is accounting for the vast differences in driving behavior from person to person. Some drivers are naturally more aggressive, while others are more cautious. Factors like age, gender, income, and experience can all play a role in shaping an individual’s driving style. Ignoring these differences can lead to inaccurate and unreliable model predictions.

Recent research leverages data from the Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) to explore how driver attributes influence car-following behavior. By analyzing data from a diverse group of drivers, researchers are uncovering patterns that could help us create more realistic and effective traffic simulations.

Unmasking the Key Influencers of Driving Behavior

Surreal illustration of cars morphing into human figures, representing varied driving behaviors.

The study focuses on three widely used car-following models—Gipps, Intelligent Driver Model (IDM), and Wiedemann 99 (W99)—calibrating them against a dataset of 728 trips from 85 drivers in the SHRP2 NDS. The goal was to determine how well these models could replicate real-world driving behavior and whether certain driver attributes could help explain variations in model parameters.

Researchers categorized drivers based on age, gender, income, and miles driven in the past year, then compared the model parameters for each group. Statistical tests were used to identify significant differences in driving behavior between these subgroups. The results revealed several key insights:

Age Matters: Driver age significantly impacts several car-following parameters, indicating that younger and older drivers exhibit distinct behavioral patterns. For example, acceleration and deceleration rates vary considerably across age groups.
Income Influences Driving Style: Income level also plays a role in shaping driving behavior. Different income brackets exhibit variations in car-following parameters, suggesting that socioeconomic factors can influence driving habits.
Experience Counts: Miles driven in the past year, a proxy for driving experience, shows a strong correlation with several car-following parameters. More experienced drivers tend to exhibit different patterns of acceleration, deceleration, and spacing.
Gender's Limited Role: Contrary to some expectations, gender does not appear to be a primary factor in explaining differences in car-following behavior. While some statistically significant differences were observed, they were less pronounced than those related to age, income, or experience.

Interestingly, the study found that no single car-following model consistently outperformed the others across all driver categories. This suggests that the best model for simulating traffic flow may depend on the specific mix of drivers on the road. Moreover, combining adjacent age groups with similar parameter values can simplify model structures without losing predictive power. Similarly, income categories can be refined to create more concise and effective stratifications.

Future Directions: Towards More Realistic Traffic Simulations

This research underscores the importance of accounting for driver heterogeneity in traffic simulation models. By incorporating driver attributes such as age, income, and experience, we can create more realistic and reliable simulations that better reflect real-world traffic conditions. Future research will delve deeper into the intersectionality of these attributes and explore additional factors that may influence driving behavior, like risk-taking tendencies and sensation-seeking behavior.