Rapid developments in various frontiers of telecommunications and information technologies could enable the development of next-generation Intelligent Transportation Systems (ITS) that rely on inter-vehicle communications (IVC) to disseminate time-critical and location-based traffic information. In this study, we present a new model for computing the instantaneous multihop connectivity and end node probability for vehicles along a line in a transportation network, where the distribution of vehicles may be non-uniform, and vehicles' positions may depend on each other. With given locations of all vehicles, the proposed model can be used to estimate the connectivity when vehicles have different probabilities to be equipped with IVC units. With the model, we study how the distribution patterns of vehicles can affect information propagation, formulate the choice of the location of a road-side station as anoptimization problem, and examine the time-dependent connectivity properties on an inhomogeneous ring road. The new models yield results consistent with those in the literature but are simpler in formulation and computation. The new model for estimating multihop connectivity of an IVC network with realistic trac patterns have both theoretical and practical contributions in understanding and designing communication routing protocols, vehicle-infrastructure integration systems, and so on.

The connectivity of vehicular ad hoc networks (VANets) can be affected by the special distribution patterns, usually dependent and non-uniform, of vehicles in a transportation network. In this study, we introduce a new framework for computing the connectivity in a VANet for continuous distribution patterns of communication nodes on a line in a transportation network. Such distribution patterns can be estimated from traffic densities obtained through loop detectors or other detectors. When communication nodes follow homogeneous Poisson distributions, we obtain a new closed-form solution to connectivity; when distribution patterns of communication nodes are given by spatial renewal processes, we derive an approximate closedform solution to the connectivity; and when communication nodes follow non-homogeneous Poisson distributions, we propose a recursive model of connectivity. For a shock-wave traffic, we demonstrate the consistency between analytical results with those simulated with ns-2, acommunication simulator. With the developed models, we also discuss the impacts on connectivity of road-side stations and different distribution patterns of vehicles. Given continuous traffic conditions, the connectivity model could be helpful for designing routing protocols in VANets and implementing vehicle-infrastructure integration systems. Limitations and future research related to this study are discussed in the conclusion section.

Transportation system produces a large percentage of local pollutants including hydrocarbons (HC), carbon monoxide (CO), carbon dioxide from switching to alternative fuels, one measure would be to apply information and communication technologies to help us drive more smoothly so as to decrease pollutants emissions. This paper studies potential benefits of two green driving strategies based on inter-vehicle communication (IVC). Here green driving strategies are similar to intelligent speed adaptation , but we assume that an IVC-equipped vehicle is able to receive detailed trajectory information from other such vehicles with the help of IVC. For the purpose of evaluation, we integrate Newell’s car-following model and VT-Micro to establish a simulation platform. Market penetration rates of IVC-equipped vehicles and delivery delays of messages are two prominent features of IVC systems. We simulate stop-and-go traffic to calculate potential reductions in air pollutant emissions and fuel consumption under different market penetration rates and delivery delays. Results show that significant savings under frequent stop-and-go traffic conditions may be obtained with our strategies (HC: -88.3%, CO: -95.8%, NOx: -91.5%, CO2: -36.3%, Fuel Consumption: -71.3%) for the same travel time and almost the same overall travel distance. It is also shown that relatively large savings can be achieved even for a market penetration rate as low as 1% and communication delays larger than 2 minutes. In the future we will investigate environmental benefits of green driving strategies for more traffic scenarios and realistic communication scenarios.

Regulators concerned with traffic related emissions on large networks should consider allowing modelers to use mesoscopic traffic models (such as the MCDKW model) that can adequately represent congestion along with appropriate emissions models. This would simplify regulatory analyses, reduce errors, and cut costs.

We couple EMFAC with a dynamic mesoscopic traffic model to create an efficient tool for generating information about traffic dynamics and emissions of various pollutants (CO2, PM10, NOX, and TOG) on large scale networks. Our traffic flow model is the multi-commodity discrete kinematic wave (MCDKW) model, which is rooted in the cell transmission model but allows variable cell sizes for more efficient computations. This approach allows us to estimate traffic emissions and characteristics with a precision similar to microscopic simulation but much faster. To assess the performance of this tool, we analyze traffic and emissions on a large freeway network located between the ports of Los Angeles/Long Beach and downtown Los Angeles. Comparisons of our mesoscopic simulation results with microscopic simulations generated by TransModeler under both congested and free flow conditions show that hourly emission estimates of our mesoscopic model are within 4 to 15 percent of microscopic results with a computation time divided by a factor of 6 or more. Our approach provides policymakers with a tool more efficient than microsimulation for analyzing the effectiveness of regional policies designed to reduce air pollution from motor vehicles.