This paper provides a comprehensive survey of vehicular Visible Light Communication (VLC) methodologies, highlighting their potential in Intelligent Transportation Systems (ITSs) for improving road safety, traffic flow, and passenger comfort. VLC, which uses white light-emitting diodes (LEDs) to transmit data and illumination, offers advantages such as safety for humans and electronic devices, unlicensed bandwidth, energy efficiency, and low cost. It is particularly suitable for vehicular communication due to the widespread use of LEDs in traffic lights, vehicle headlights, and taillights. However, vehicular VLC faces challenges such as line-of-sight (LoS) blockage, limited transmission range, and interference from environmental factors like weather and artificial lighting. The paper discusses various aspects of vehicular VLC, including system components, signal models, potential applications, and environmental challenges. It reviews existing studies on VLC channel modeling, emphasizing the need for realistic models that account for factors like road reflections, weather conditions, and asymmetric light patterns. The paper also explores emerging research areas such as reconfigurable intelligent surfaces (RISs), multi-hop relaying, and digital twins, which can enhance performance, reliability, and scalability in vehicular VLC systems. Key applications of vehicular VLC include vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communications, which can improve safety, traffic management, and information exchange between vehicles and infrastructure. The paper also discusses the integration of VLC with other technologies like millimeter wave (MMW) networks and the application of artificial intelligence (AI) and machine learning (ML) algorithms to optimize resource allocation and enhance security in vehicular VLC systems. The survey highlights the importance of developing realistic channel models for vehicular VLC to address challenges such as signal interference, limited range, and environmental factors. It also emphasizes the potential of VLC as a safe, efficient, and scalable alternative to traditional radio frequency (RF) communication in vehicular networks. The paper concludes that further research is needed to improve VLC channel models and explore new technologies to enhance the performance and reliability of vehicular VLC systems.This paper provides a comprehensive survey of vehicular Visible Light Communication (VLC) methodologies, highlighting their potential in Intelligent Transportation Systems (ITSs) for improving road safety, traffic flow, and passenger comfort. VLC, which uses white light-emitting diodes (LEDs) to transmit data and illumination, offers advantages such as safety for humans and electronic devices, unlicensed bandwidth, energy efficiency, and low cost. It is particularly suitable for vehicular communication due to the widespread use of LEDs in traffic lights, vehicle headlights, and taillights. However, vehicular VLC faces challenges such as line-of-sight (LoS) blockage, limited transmission range, and interference from environmental factors like weather and artificial lighting. The paper discusses various aspects of vehicular VLC, including system components, signal models, potential applications, and environmental challenges. It reviews existing studies on VLC channel modeling, emphasizing the need for realistic models that account for factors like road reflections, weather conditions, and asymmetric light patterns. The paper also explores emerging research areas such as reconfigurable intelligent surfaces (RISs), multi-hop relaying, and digital twins, which can enhance performance, reliability, and scalability in vehicular VLC systems. Key applications of vehicular VLC include vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communications, which can improve safety, traffic management, and information exchange between vehicles and infrastructure. The paper also discusses the integration of VLC with other technologies like millimeter wave (MMW) networks and the application of artificial intelligence (AI) and machine learning (ML) algorithms to optimize resource allocation and enhance security in vehicular VLC systems. The survey highlights the importance of developing realistic channel models for vehicular VLC to address challenges such as signal interference, limited range, and environmental factors. It also emphasizes the potential of VLC as a safe, efficient, and scalable alternative to traditional radio frequency (RF) communication in vehicular networks. The paper concludes that further research is needed to improve VLC channel models and explore new technologies to enhance the performance and reliability of vehicular VLC systems.