Simulation Study Reveals an Improvement in Average Travel Time Up to 22 Percent When Connected Vehicles Are Equipped with Route Guidance Features.

Microsimulation Analysis Used to Assess Travel Time and Emissions Impacts for Connected Vehicles with Re-routing Capabilities in Baltimore, Maryland.

Date Posted

Investigating the Effect of Connected Vehicles (CV) Route Guidance on Mobility and Equity

Summary Information

Connected vehicles (CVs) have the potential to optimize traffic mobility and enhance the safety of drivers and pedestrians, save energy, and reduce emissions. Researchers evaluated the system-wide mobility and equity effects of CVs with route guidance systems using a microscopic traffic simulation model replicating the behavior of CVs in a medium urban road network. Market penetration rates (MPRs) from 0 percent to 100 percent CVs were tested in the model with four vehicle types: conventional (non-CV) car, conventional bus, connected car, and connected bus. Several re-routing strategies were studied for the morning peak hour, using a road network from the Ednor Gardens-Lakeside area of Baltimore, Maryland with three north-south roadway links and two east-west links.


The system-wide mobility effects of CV equipped vehicles with route guidance features on mobility and equity were analyzed. Three different levels of traffic were simulated, a moderate case consisting of the base traffic volumes, and low (20 percent lower) and high (20 percent higher) volume cases. Vehicle-to-vehicle (V2V) interactions in the traffic model included simulations of an incident in the middle of the network with re-routed CVs, and modeled CV attributes, such as physical features, reaction time, standard gap, maximum / minimum acceleration, normal deceleration, sensitivity factor, and overtaking maneuver time. A travel time matrix was calculated using speed data from the vehicles, and volumes were retrieved from the traffic simulation software. To calculate air pollution coefficients, an instantaneous traffic emission model formula was calculated based on literature. In addition, network-level equity and the effects of dynamic re-routing of CVs on different paths were calculated using average travel times of each vehicle type for 15 different origin and destination pairs. The research team also included a dynamic bus lane for CV buses in the model, with 15-minute and 30-minute exclusive bus lanes to assess travel time differences before and after the implementation of the bus lane.


Overall, the simulation results showed that scenarios including CVs with route guidance features had lower total delay time, total emissions, and average travel time of re-routing paths.

  • CV scenarios reduced the total delay time by 21.2, 20.6, and 14.9 percent for the low, moderate, and high traffic groups, respectively, compared to non-CV scenarios.
  • The gradual deployment of CVs reduced the Carbon Dioxide (CO2), Nitrogen Oxides (NOx), Particulate Matter (PM) and Volatile Organic Compounds (VOC) emissions by up to 6.3, 7.6, 28.5, and 11.2 percent, respectively.
  • The simulation results showed that a 15-minute and 30-minute dynamic CV bus lane decreased the average travel time by 11.9 and 16.4 percent for CV buses, respectively. The dynamic re-routing test results showed that as the number of re-routing CV cars increased, the average travel time in re-routing paths decreased. Compared with a scenario with CV MPR of 10 percent, re-routing paths with 100 percent MPR lowered the average travel time by 22 percent.
  • The findings of the network-wide travel time analysis, which was used to support an equity assessment, indicated that high CV MPR helped non-peak periods more than peak period traffic.
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