Speed Demons: How Inconsistent Speeds on Urban Highways Cause Crashes
"GPS data reveals the hidden dangers of speed variance on elevated expressways."
Urban expressways are designed to move traffic quickly and efficiently through crowded cities. These high-speed roadways, with limited access and fewer interruptions, offer a seemingly safer alternative to surface streets. But the reality is more complex. While expressways aim to maintain consistent speeds, the ebb and flow of urban traffic often leads to significant speed variations, creating dangerous conditions.
Unlike traditional freeways, urban expressways feature closely spaced ramps connecting to surface roads, often with restricted lane configurations. This design, coupled with heavy traffic, makes expressways prone to bottlenecks and sudden slowdowns. The result is a constantly shifting landscape of speed, where drivers must navigate frequent changes in pace.
Now, a new study leverages GPS data to explore the impact of these speed variations on safety. By analyzing the movements of thousands of taxis on Shanghai's elevated expressways, researchers have uncovered a clear link between inconsistent speeds and increased crash risk. This article dives into their findings, revealing the hidden dangers of speed variance and offering insights into how to make urban expressways safer.
Where Road Safety Data Comes From
The Federal Highway Administration’s Safety Data Program integrates safety data across transportation modes and highlights safety in Chapter 2 of the National Transportation Statistics publication. NHTSA’s National Center for Statistics and Analysis (NCSA) provides analytical and statistical support to NHTSA and the wider highway safety community, drawing on its own inputs and data from other government agencies. FHWA’s Fatality and Injury Reporting System Tool (FIRST) lets users query crash data from NHTSA’s Fatality Analysis and Reporting System (FARS) and the NASS General Estimates System/Crash Report Sampling System (NASS-GES/CRSS). These sources describe data and reporting tools, but the supplied material does not give a current crash or injury total.
Established Safety Analysis Tools
FHWA promotes the Highway Safety Manual (HSM) as part of its Every Day Counts data-driven safety analysis initiative. AASHTOWare Safety Analyst implements many roadway safety management procedures included in Part B of the HSM, while FHWA also lists spreadsheet tools such as the Enhanced Interchange Safety Analysis Tool. A 2024 study proposed a safety analysis method based on a conflict extreme model to evaluate expressway exit areas with different exit forms. The supplied descriptions identify these methods but do not detail their limitations or compare their performance.
A 1966 Federal Safety Milestone
On September 9, 1966, about 200 people gathered in the White House Rose Garden as President Lyndon B. Johnson signed the Motor Traffic and Motor Vehicle Safety Act and the Highway Safety Act. Johnson noted that nearly three times as many Americans had died in traffic accidents in the 20th century as had died in wars. The National Safety Council describes early safety efforts as helping shape what later became the National Highway Traffic Safety Administration. Together, these accounts trace a shift from early reform toward federal institutions and continuing data-driven safety work.
The Perils of Speed Variance: Why Inconsistent Speeds Lead to Accidents
Previous research has identified several factors that contribute to crashes on urban expressways, including roadway design, traffic flow, and weather conditions. However, the role of speed variance – the degree to which speeds fluctuate along a roadway – has been difficult to quantify due to limitations in data collection.
- Spatial Speed Variance: Measures how much the speed changes across different locations on the road.
- Temporal Speed Variance: Captures the changes in speed at a specific location over time.
Speed Differences and Emerging Data Methods
A 2024 review of floating-car data research reports an optimal investment proportion of 3%–8% for floating cars. It also finds that sampling frequency significantly affects the accuracy of vehicle speed and heading-angle information, while having a weaker effect on positional parameters. A separate study of commercial truck risk using vehicle trajectories and in-vehicle monitoring data reports that trucks travel more slowly than other freeway vehicles, and that a further decrease in trucks’ average freeway speed increases their speed difference from surrounding traffic, making collision more likely. Research using the AVOID dataset reports higher injury severity in steering-direction scenes at 10–20 mph and even more severe injuries above 20 mph in traffic-through scenes.
Unsafe Behavior and Collision Severity
Research on urban expressways identifies unsafe driver behaviors as a common cause of crashes and analyzes how different behaviors influence crash duration using actual crash video images. A separate study developed an ordered logistic regression model to evaluate factors contributing to the severity of urban expressway collisions. The supplied descriptions therefore point to driver behavior and multiple contributing factors as relevant to crash outcomes, rather than establishing speed inconsistency as the sole explanation. They do not provide specific effect sizes or a direct comparison with speed variation.
Crash Cases Compared With Test Configurations
A comparison examined 442 University of Michigan CIREN crash cases against crash-test configurations used throughout the industry. The source establishes the number of cases and the comparison being made, but the supplied description does not report the results or identify which configurations matched or differed. It therefore supports the existence and scope of this comparison, not a conclusion about how well industry tests represent real crashes.
Slowing Down Accidents: Practical Steps for Safer Highways
The study's findings underscore the importance of addressing speed variance on urban expressways. Transportation planners and traffic engineers can use this information to develop strategies for mitigating crash risk and improving safety.
Risk in Interchange Weaving Areas
A study published October 1, 2025, focuses on urban expressway interchange (UEI) weaving areas, describing them as a special type of weaving section with a complex safety environment. Its purpose is to assess traffic safety risk and analyze lane-changing behavior in these areas. This makes lane changes a specific focus for safety assessment at urban expressway interchanges. The supplied source description does not include the study’s findings or expert conclusions.
Expressways in Urban Systems
A study of the expressway network design problem for multiple urban areas describes urban expressways as increasingly integral to urban systems as demand for fast and efficient travel grows. It characterizes expressways as large channels in urban networks with significant effects on traffic efficiency and cities’ long-term development. Separately, a 2025 wider-determinants approach to road safety argues for addressing broader social, economic, and environmental factors that influence safety outcomes. Together, these sources place roadway safety within wider transport networks and societal conditions.
Case Studies and Practical Safety Lessons
FHWA’s case study on the 2016 Minnesota Highway 10 Access Planning Study describes how the Minnesota Department of Transportation used quantitative performance analysis to develop project alternatives. Those alternatives aimed to achieve most of the safety and operational benefits of a freeway at significantly lower cost. The International Transport Forum’s report presents seven case studies of cities implementing data-driven road safety policies to share best practices. These examples connect safety analysis with planning choices and city-level policy, though the supplied descriptions do not report crash reductions or individual experiences.
One approach is to implement active traffic management systems (ATMS) that dynamically adjust speed limits based on real-time traffic conditions. These systems can help to smooth out speed variations and prevent sudden slowdowns, reducing the likelihood of accidents. Further, improving ramp designs and lane configurations may reduce bottlenecks and create a more consistent flow of traffic.
While this study focused on Shanghai's expressways, the lessons learned are applicable to urban highways around the world. By understanding the dangers of speed variance and taking proactive steps to manage it, we can create safer and more efficient transportation networks for all.