C-V2X outperforms DSRC in road safety, reduces travel time and crash risk

Using real-time simulations of urban mobility in China and advanced car-following models, the team evaluated vehicle acceleration, travel time, time-to-collision (TTC), and data packet loss across various traffic volumes and autonomous vehicle (AV) penetration rates. Their findings suggest that C-V2X not only enhances traffic flow by reducing abrupt stops and unnecessary speed variations but also delivers measurable safety benefits by maintaining longer TTC intervals and fewer near-miss events.

Cellular Vehicle-to-Everything (C-V2X) communication technologies significantly improve road safety, vehicle responsiveness, and traffic efficiency in mixed and fully autonomous driving environments, according to a new study published in Sensors. Conducted by researchers from Shandong Jiaotong University, Don State Technical University, and Hiroshima University, the study compared C-V2X with Dedicated Short-Range Communications (DSRC) and found that the former reduced communication latency by over 99%, leading to faster decision-making and up to a 38% reduction in traffic conflicts.

Using real-time simulations of urban mobility in China and advanced car-following models, the team evaluated vehicle acceleration, travel time, time-to-collision (TTC), and data packet loss across various traffic volumes and autonomous vehicle (AV) penetration rates. Their findings suggest that C-V2X not only enhances traffic flow by reducing abrupt stops and unnecessary speed variations but also delivers measurable safety benefits by maintaining longer TTC intervals and fewer near-miss events.

The crux of this study titled "Cooperative Intelligent Transport Systems: The Impact of C-V2X Communication Technologies on Road Safety and Traffic Efficiency," was the development and validation of the Acceleration-Impairment Driver Model (AIDM), which integrates real-world communication flaws like packet loss and latency into vehicle acceleration logic. Unlike traditional models such as the Intelligent Driver Model (IDM) and Gipps model, which assume perfect communication, the AIDM incorporates two critical variables: the acceleration loss coefficient (γ) and stochastic error (ξ). These allow the model to simulate the degraded performance of DSRC and the superior responsiveness enabled by C-V2X.

Under simulation conditions, C-V2X vehicles were assigned a γ value of 3.1, reflecting high-fidelity communication with latency as low as 0.25 milliseconds. In contrast, DSRC vehicles had a γ value of 0.7, correlating to higher delays of up to 55 milliseconds. These seemingly small differences yielded significant real-world impacts: vehicles equipped with C-V2X reacted to braking events 25% faster, sustained more consistent speeds, and demonstrated 15% greater traffic flow efficiency.

The study modeled traffic across three congestion scenarios - moderate (1200 vehicles/h), dense (1400 vehicles/h), and near-capacity (1800 vehicles/h), on a 12 km section of the Beijing–Shanghai expressway. At 60% AV integration, conflict events dropped from 2684 to 1655 in moderate flow, from 3028 to 1917 in dense flow, and from 3310 to 2214 in near-capacity conditions. This represents reductions of 38%, 36.7%, and 33.1% respectively. Average travel times also improved notably, decreasing by 18.3%, 19.9%, and 26.1% across the same traffic volumes.

Researchers attribute these improvements to C-V2X's capacity to transmit real-time vehicle status updates, including position, acceleration, and intent, to surrounding vehicles and infrastructure. This exchange allows AVs to synchronize merging maneuvers, braking, and lane changes with much greater precision. In high-density environments, such coordination helps reduce shockwave braking and improves traffic fluidity.

A key safety metric, TTC, improved by 38% with C-V2X at 60% AV penetration, lowering collision risk by an estimated 26%. In terms of raw numbers, TTC for C-V2X vehicles was measured at 4.5 seconds, compared to 3.2 seconds under DSRC. Such extended reaction time provides AVs with the ability to decelerate more smoothly and predictively, essential factors in preventing rear-end collisions and managing speed harmonization.

Another major advantage cited by the study was C-V2X's superior performance under mixed traffic conditions. Even when only 25% to 30% of vehicles were autonomous, safety and efficiency gains were evident. This underscores the value of C-V2X in transitional periods where human-driven vehicles coexist with AVs. In such cases, roadside units and vehicle sensors estimated the arrival and merging behavior of human-driven cars to allow AVs to adjust safely.

While the cost of deploying C-V2X infrastructure was found to be 20–25% higher than DSRC, researchers emphasized that the long-term economic and social benefits far outweigh initial expenses. By reducing travel time, minimizing braking-induced traffic waves, and lowering accident rates, C-V2X can cut congestion-related costs by 30–35% over time.

The authors also simulated cooperative merging scenarios, where CAVs adjusted acceleration profiles using predictive communication. In full CAV environments, these systems relied on calculated safe time headways and speed harmonization to ensure collision-free merging at on-ramps. When human drivers were involved, AVs defaulted to more conservative behaviors, relying on roadside sensors to predict rather than coordinate maneuvers, a limitation the authors believe can be resolved with expanded V2X adoption.

To support its conclusions, the study conducted one-way ANOVA tests on conflict data, finding extremely strong statistical significance (p ≈ 4.46 × 10⁻¹⁷) in differences tied to γ values. These findings confirm that the model's improvements were not due to randomness, but rather a direct result of superior communication fidelity.

The SUMO simulation platform was used extensively in the study for its ability to represent multimodal traffic environments. Integrated with OMNET++ and ns-3 for modeling V2X communication conditions, SUMO allowed the researchers to simulate realistic delays, packet loss, and speed adaptation in both highway and urban roadways.