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This paper proposes an improved impedance function for roads with mixed traffic. It is known that only limited studies consider the impact of nonmotorized traffic on travel impedance of a road segment, and a comparison of the impedance considering nonmotorized traffic with the classic BPR function, which does not consider the former, is scarce. Most of the previous studies targeted road conditions in developed countries, where the presence of nonmotorized traffic is negligible, and therefore limited efforts have been invested to develop improved impedance function considering mixed traffic. To overcome this limitation, this paper develops an improved impedance function and carries out a case study for a road in the city of Wuhan, China. The improved impedance function explicitly considers the interaction between motorized and nonmotorized traffic. Taxi GPS data from the case study road is used to extract and analyze the travel time of the "probe vehicles" running through the sampled segment at any time during a sampling day. The capacity of the road segment is measured, and the traffic flow of motorized vehicles and nonmotorized vehicles on the segment is counted. Based on the above data, the classic BPR function and the improved one proposed in this paper are calibrated. After comparing and analyzing the observed road impedance based on both analytical and simulation results, the classic BPR function and the proposed impedance function, the proposed impedance function is found to be more accurate to simulate the observed road impedance, with the error reducing from 14.83 s with the classic BPR impedance function to 6.50 s with the improved function. The proposed impedance function possesses a simple structure and high flexibility, and the parameters calibrated in this paper can be applied to similar roads to provide more realistic impedance than the previous ones based on the classic BPR function. The calibrated improved impedance function's transferability to other similar roads is validated by applying it to another road and the results show that the percentage error between the predicted travel times and the observed ones is only 3.8%. [ABSTRACT FROM AUTHOR]

This paper presents a study aiming at understanding the relationship between the spatiotemporal characteristics of road weather conditions and a number of road weather information systems (RWIS) stations using real-world case studies. Semivariogram models are constructed to determine the spatial variability of road weather conditions, especially, autocorrelation range which describes a separation distance at which the measurements are no longer correlated to each other. An optimal RWIS density is then determined through an optimization process that minimizes the total inference errors across the underlying road network. The findings suggest that the regions with less varied topography tend to have a longer spatial correlation range than the regions with more varied topography. The study further reveals that the range of spatial autocorrelation is related to the optimal density of RWIS network - the region with a longer range requires fewer RWIS stations, than the region having a shorter range. [ABSTRACT FROM AUTHOR]

This paper presents a risk-based approach for classifying the road surface conditions of a highway network under winter weather events. A relative risk index (RRI) is developed to capture the effect of adverse weather conditions on the collision risk of a highway in reference to the normal driving conditions. Based on this index, multiple risk factors related to adverse winter weather conditions can be considered either jointly or separately. The index can also be used to aggregate different types of road conditions observed on any given route into a single class for risk-consistent condition classification and reporting. Two example applications are shown to illustrate the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a cost-benefit based approach to the problem of finding the optimal location and density of road weather information system (RWIS) stations over a regional road network. The novelty of the proposed method lies in the models that can be used to estimate the benefits of RWIS information, including reduced maintenance costs and collisions, thus determining the optimal number and location of a RWIS network. A case study based on the existing RWIS network in Northern Minnesota in the US is used to show the application of the proposed approach. Linear regression models are developed for the annual maintenance costs and the expected number of collisions that could occur on two types of highways delineated by the existence of a RWIS station nearby. The calibrated models are then applied to individual highway segments defined on the basis of a uniform grid system to determine the expected benefit of having a RWIS station installed. These benefits along with RWIS installation and maintenance costs are converted into net benefits and then the net present value, which are then used in determining the optimal number of RWIS stations and prioritizing the candidate locations. It was found from the case study that a total of 45 stations would provide the best return of investment with a 25 year net benefit of approximately $6.5 million and a life-cycle benefit-to-cost ratio of 3.5. [ABSTRACT FROM AUTHOR]