Narrow Results

The Wind–Vehicle–Bridge (WVB) coupling method is extensively applied in engineering practices for the dynamic analysis of railway bridges. However, research on the dynamic behaviors of the vehicle on curved bridges in wind conditions remains limited due to the complexities of interactions involved. In this study, the railway alignment and an actual curved bridge were chosen and modeled. The framework of the WVB system was detailed, and four excitation cases were investigated, considering mean wind, fluctuating wind and track irregularities. Three distinct scenarios, including vehicle on a curved bridge subjected to Inward Blowing Wind (IBW), Outward Blowing Wind (OBW) and vehicle on a straight bridge subjected to wind, were explored. The main conclusions are as follows: (1) wind direction significantly affects wheel unloading rate, lateral force, and derailment coefficient; (2) under mean wind condition, critical wind speed increases with the vehicle speed increasing under IBW, while it decreases with the vehicle speed increasing under OBW; (3) Combined wind and track irregularities reveal that wind direction significantly influences dynamic responses, and lower speeds are not necessarily equal to safer conditions for vehicles on curved bridges.

This paper presents a method for estimating vehicle speed using two roadside passive infrared (PIR) sensors whose optical axes are parallel to each other and perpendicular to the moving direction of vehicles. The vehicle speed was calculated based on the time lag between the two signals received by the PIR sensors, which was evaluated by using cross-correlation analysis. The experiments show that the method has an error of less than 5 km/h over a speed range of 20–60 km/h.

This paper presents a method of measuring transportation vehicle’s speed via analog signals of a couple of pyroelectric infrared sensors. The measuring system is located along the roadside with the assumption that optical axes of the sensors are parallel to each other and perpendicular to the moving direction of vehicles. A cross-correlation method combined with interpolation has been used in order to determine the time delay between two output signals, thus defining the traveling speed of the target. The experiment results show that the proposed method has an error of less than 5 km/h over the speed range of 20–90 km/h.

Vehicle speeds frequently fluctuate due to the prevailing heterogeneous traffic conditions on Indian roads. Accordingly, traffic noise levels are affected by different noise sources that depend on various vehicular and roadway characteristics. In order to simulate the actual vehicle noise generation at the possible speeds on Indian roads, an integrated method has been developed in this study to quantify the engine and tire–road noise levels. The governing parameters considered for the pass-by noise quantification include vehicle speed, type of pavement and gear shift/gear transmission. The measured A-weighted noise levels [LAmax (dB)] revealed that tire–road noise levels increased with the rise in vehicle speeds irrespective of the vehicle type and type of the pavement. Further, the tire–road noise levels quantified through the new methodology closely matched the noise levels measured by the standard coast-by method. The cross-over speeds for engine propulsion noise and tire–road interaction noise occur at much lower speeds on the cement concrete pavements compared to the asphalt pavements. On a decisive note, the perspective of measuring the roadside noise levels coupled with an engine propulsion noise measurement as reported in this study is first of its kind and can be used for noise measurements on critical urban roads by priming with the conventional pass-by methods.

This paper presents a method for estimating vehicle speed using two roadside passive infrared (PIR) sensors whose optical axes are parallel to each other and perpendicular to the moving direction of vehicles. The vehicle speed was calculated based on the time lag between the two signals received by the PIR sensors, which was evaluated by using cross-correlation analysis. The experiments show that the method has an error of less than 5 km/h over a speed range of 20−60 km/h.