Impact of Connected Vehicle on Work Zone Network Safety through Dynamic Route Guidance

Despite enhanced safety strategies, in-vehicles technologies, and improvements in infrastructure, urban transportation networks are still accident-prone. Connected vehicle offers the possibility to exchange data with vehicles and infrastructure in an effort to improve safety. The main objective of the research reported in this paper is to evaluate the potential safety benefits of deploying a connected vehicle system on a traffic network in the presence of a work zone. The modeled connected vehicle system in the research reported in this paper uses vehicle-to-vehicle (VTV) communication to share information about work zone links and link travel times. Vehicles which receive work zone information will also modify their driving behavior by increasing awareness and decreasing aggressiveness. This paper also proposes a decaying average travel time dynamic route guidance algorithm which exhibits weighted information decay. Traffic microsimulation software is used to model the network and a C plugin is developed to implement connected vehicle in the simulation. The surrogate safety measure improved time to collision (TTC) is used to assess the safety of the network. Various market penetrations of connected vehicles were utilized along with three different behavior models to account for the uncertainty in driver response to connected vehicle information. The results show that network safety is strongly correlated with the behavior model used; conservative models yield conservative changes in network safety. The results also show that market penetrations of connected vehicles under 40% contribute to a safer traffic network, while market penetrations above 40% decrease network safety. The findings of the research reported in this paper indicate connected vehicle technology can have unintended consequences, as seen in decreased safety at high market penetrations, requiring researchers to develop additional applications to mitigate these effects.