The Metaverse is a concept used to refer to a virtual world that exists in parallel to the physical world. It has grown from a conceptual level to having real applications in virtual reality games. The applicability of the Metaverse in numerous sectors like marketing, education, social, and even advertising exists. However, there exists little or no work on Metaverse applicability to the transportation industry. Data-driven intelligent transportation systems (DDITS) aim to provide more intelligent systems based on exploiting data. This paper reviews the concepts and features of the Metaverse. Also, the review goes over three dominant DDITS challenges: vehicle fault detection and repair, testing new technologies, and anti-theft systems. In addition, it highlights prospective Metaverse solutions that apply to the DDITS. Buttressing the utility of Metaverse in DDITS, this paper presents two major case studies: the invisible to visible (I2V) and the Metaverse on Wheels (MoW) technologies. Finally, the influence, limitations, and open issues of Metaverse applications to DDITS are discussed. 

Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems

. Due to their widespread and continuous expansion, transportation networks are considerably exposed to natural hazards such as earthquakes, floods, landslides or hurricanes. The vulnerability of specific segments and structures among bridges, tunnels, pumps or storage tanks can translate not only into direct losses but also into significant indirect losses at the systemic level. Cascading effects such as post-event traffic congestion, building debris or tsunamis can contribute to an even greater level of risk. To support the effort of modeling the natural hazards' implications at the full
transportation network scale, we developed a new applicable framework,
relying on (i) GIS to define, analyze and represent transportation networks; (ii) methods for determining the probability of network segments to fail due to natural-hazard effects; (iii) Monte Carlo simulation for multiple scenario generation; (iv) methods to analyze the implications of connectivity loss on emergency intervention times and transit disruption; and (v) correlations with other vulnerability and risk indicators. Currently, the framework is integrated into ArcGIS Desktop as a toolbox entitled “Network-risk”, which makes use of the ModelBuilder functions and is free to download and modify. Network-risk is an attempt to bring together interdisciplinary research with the goal of creating an automated solution to deliver insights on how a transportation network can be affected by natural hazards, directly and indirectly, assisting in risk evaluation and mitigation planning. In this article we present and test Network-risk at the full urban scale for the road network of Bucharest. This city is one of Europe's most exposed capitals to earthquakes, with high seismic-hazard values and a vulnerable building stock but also significant traffic congestion problems not yet accounted for in risk analyses and risk reduction strategies.

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Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania

A significant difference in the behavior of heterogeneous and undisciplined traffic streams is observed when compared with the conventional traffic flow. Most of the existing traffic flow models are developed considering the traffic stream with strict lane discipline. Several studies from South Asian countries have reported high heterogeneity in the traffic stream with weak or no lane discipline. This study, for the first time, develops fundamental traffic flow diagrams for the heterogeneous and undisciplined traffic stream by analyzing traffic videos captured using Unmanned Aerial Vehicle (UAV) in Karachi, Pakistan. UAV-based speed-density data is modeled using a weighted least-square regression technique, and stochastic fundamental diagrams (FDs) are developed to represent the entire range of speed-density data. The stochastic FDs are used to determine the 85th percentile speeds to implement speed limits on major urban arterials. The multi-modal FDs show a significant difference in the behavior of different modes in the traffic stream. The aggressive behavior of motorbike riders, which put them at a higher risk of accidents, highlights the need for policy measures to enforce discipline in the traffic stream. The main contribution of this study is the utilization of a UAV-based geospatial analysis technique for accurate extraction of longitudinal and lateral distances between vehicles to determine the relationship between macroscopic and microscopic parameters of traffic flow. This study shows that lateral gaps between vehicles are inversely related to traffic density. The longitudinal gaps observed for local heterogeneous traffic show a significant difference with the longitudinal gaps estimated using a standard car-following model. The macroscopic and microscopic models for heterogeneous and undisciplined traffic flow presented in this study could be useful in developing novel traffic flow models and calibrating the existing microscopic/ macroscopic traffic flow models for the traffic streams with similar heterogeneity and lane behavior.

On the fundamental diagram and driving behavior modeling of heterogeneous traffic flow using UAV-based data

Traffic streams in many developing countries consist of various modes of transport, with high heterogeneity in driver behavior. Modeling these types of traffic streams, in which traffic rules (spee...

Width-Based Cell Transmission Model for Heterogeneous and Undisciplined Traffic Streams:

The article addresses the problem of transportation efficiency reduction in Moscow due to road blocking. Special attention is paid to unauthorized blocking that occurs as a result of omission by vehicle owners. We present factors affecting efficiency reduction and evaluate their effects.

Effects of road blocking on traffic flows in Moscow

Car following models replicate the behaviour of a car following another car. In mixed traffic conditions, leader-follower vehicle types are not only car-car cases but also there are different combinations of vehicles (e.g., car-two wheeler and heavy vehicle-auto-rickshaw). Due to weak lane discipline, the follower is unable to strictly follow the lead vehicle. The other types of following behaviour like staggered following, following between vehicles, etc. are also observed under mixed traffic conditions. Depending on speed and position of vehicles in front, the subject vehicle may follow a leading vehicle with varying levels of lateral separation or stagger. The nature, extent and impact of such interactions need to be investigated for characterizing following behaviour under mixed traffic conditions. Hence, the present study focuses on analysis and modelling of vehicle following behaviour under mixed traffic conditions. The current study uses an open access mixed traffic trajectory data collected on an urban arterial road in Chennai city, India. The collected dataset includes 3005 vehicle trajectories with a total of 111,629 observations. To identify the different types of following behaviour, the extent of overlap (%) between leader-follower pair was used. The vehicle following behaviour was classified into three types: 1) Car Following 2) Staggered following and 3) Following between two vehicles. The vehicle following behaviour were analysed for different categories of vehicles and also, for different types of follower-leader pair. When the follower is a smaller size vehicle compared to a leader, it generally follows the leader. It was observed that when the vehicle size increases, staggered following behaviour of vehicle also increases. Subject vehicle chooses a following behaviour based on size of follower and leader, speeds of follower and leader, longitudinal gap between follower and leader, types of follower and leader. Multinomial Logistic Regression model was used to model the choices of vehicle following behaviour. This study has interesting implications in identifying vehicle-specific and vehicle following parameters for car following models to be adopted under mixed traffic and non-lane discipline traffic conditions.

Sehle Basheer

Papers

Analysis and modeling of vehicle following behavior in mixed traffic conditions