As per IRC 37, the design of flexible pavement requires the accurate prediction of resilient modulus for bituminous concrete. This, determination of M_r requires specialized test equipment, which may not be available in many laboratories. Therefore, it would be rather reasonable to create a model that could estimate M_r from easy to estimate volumetric parameters and other parameters obtained from sample conventional tests. This study aims to investigate the effect of different binders (VG30, VG40, PMB and CRMB), binder contents, volumetric parameters (air voids) and temperature on Resilient Modulus ( M_r ) in Bituminous Concrete (BC) course and Dense Bituminous Macadam (DBM) course. The samples were compacted using the Marshall Compacter and subsequently the volumetric parameters were calculated. The Indirect Tensile Strength (ITS) test was conducted to calculate the ITS and Toughness. The Repeated Load Indirect Tension Test was conducted to find out the Resilient modulus ( M_r ) values for all mixes. The regression analysis was performed using the Microsoft Excel tool, and the relationship between resilient modulus, volumetric parameters and ITS was developed. Using the final model, Resilient Modulus of mixtures may be predicted from volumetric parameters and indirect tensile strength under comparable or different testing conditions.
An autonomous vehicle (AV) or driverless car ensures safety using advanced sensor and positioning technologies with little or no human input. With numerous emerging technologies, it is important to know the potential capacity effects of AVs to aid our decision-making process with future investments. As AVs are not widely used especially in developing countries like India, it is difficult to study the behaviour of AVs and their interactions with other vehicles in the field. Hence, the mid-block section of a two-lane highway incorporating AVs is simulated using VISSM in this study to analyse how AVs impact the capacity of the highway. Passenger cars are replaced by autonomous cars in the model and the model is calibrated using travel time as measure, and the change in the capacity for various penetration rates of AV is estimated. The study gave promising outputs in terms of capacity enhancement and found that the introduction of the AVs can enable better utilization of the available road space.
The paper presents the performance of premix carpet for low-volume roads in terms of structural capacity and functional properties. The structural capacity was evaluated using falling weight deflectometer (FWD). International roughness index (IRI) was used as a measure of the functional property. A total of 26 sections with a total road length of 110 km were selected for the study. At each test location, pavement composition was determined by excavating test pit along the edge of the shoulder. Subsequently, FWD test was conducted. Deflections were measured at 8 radial distances, ranging from 0 to 1500 mm. Deflection values ranged between 475 and 1129 μm below the loading plate and 30–224 μm at radial distance of 1500 mm. Using deflection readings, structural number (SN) and deflection bowl parameters including surface curvature index (SCI), base damage index (BDI), base curvature index (BCI) and AREA were calculated. Then, IRI was measured using Roughometer III device. IRI progression with time indicated that for a trigger value of 4.62 m/km, premix carpet roads would require maintenance after 41 months of service life. In addition, a good correlation between the deflection bowl parameters and IRI was observed. So, the limits of IRI were used to propose the recommended range of deflection bowl parameters. It was found that when the values of SCI, BDI and BCI increase to more than 313, 152 and 58, respectively, or AREA and SN reduce to less than 447 and 17, respectively, the pavement exhibits poor condition and would require rehabilitation.
This paper explores a novel approach to dividing a traffic region (network) into sub-regions for efficient traffic control among the areas. The macroscopic flow diagram (MFD) in each of these sub-regions, referred to as sub-MFD, can then be used to determine the macro-state of that sub-region and subsequently design controllers. The region division is based on the theory of complex networks. We exploit the inherent network characteristics through the PageRank centrality algorithm to identify the most significant nodes in the traffic network. We use these significant nodes as the seeds for a Voronoi diagram-based partitioning mechanism of the network. A feedback linearization-based controller is then presented, which controls the traffic flow between the sub-regions. A case study is performed for the Manhattan area in New York City to demonstrate the network partitioning approach; the control approach is demonstrated through a toy example containing two sub-regions.
The estimation of the gap acceptance parameter for right-turning movements at uncontrolled T-intersections is the main focus of this paper. It takes a lot of effort to investigate an intersection that is unsignallised or is uncontrolled. In general, a driver's perception of the priority at an intersection is based on the volume of traffic, the design of the intersection, and the speed of the vehicle on main and minor approaches. Three uncontrolled T-intersections in the field were recorded as video in order to acquire the data. Accepted gaps, rejected gaps, vehicle types, and traffic composition are the data that were extracted from the video. The accepted and rejected gaps are fitted with various statistical distributions. The best-fitted model is recommended for the accepted and rejected gap based on goodness of fit tools, and the critical gap is estimated. Additionally, the critical gap value determined by best-fitted distributions (log-normal distribution) is compared to the values determined using Indo-HCM (Indo-HCM: Indian Highway Capacity Manual (Indo-HCM). CSIR-Central Road Res. Institute, New Delhi. (2017)). The value of the gap obtained using detailed traffic parameters can be further used for risk prediction to reduce the severity of crashes at uncontrolled intersections under mixed traffic conditions.
