Use of Connected Vehicles to Characterize Ride Quality

Abstract

The United States rely on the performance of more than four million miles of roadways to sustain its economic growth and to support the dynamic mobility needs of its growing population. The funding gap to build and maintain roadways is ever widening. Hence, the continuous deterioration of roads from weathering and usage poses significant challenges. Transportation agencies measure ride quality as the primary indicator of roadway performance. The international roughness index is the prevalent measure of ride quality that agencies use to assess and forecast maintenance needs. Most jurisdictions utilize a laser-based inertial profiler to produce the index. However, technical, practical, and budget constraints preclude their use for some facilities, particularly local and unpaved roads that make up more than 90% of the road network in the US. This study expands on previous work that developed a method to transform sensor data from many connected vehicles to characterize ride quality continuously, for all facility types, and at any speed. The case studies used a certified and calibrated inertial profiler to produce the international roughness index. A smartphone aboard the inertial profiler produced simultaneously the roughness index of the connected vehicle method. The results validate the direct proportionality relationship between the inertial profiler and connected vehicle methods within a margin-of-error that diminished below 5% and 2% after 30 and 80 traversal samples, respectively. Use of Connected Vehicles to Characterize Ride QualityRaj Bridgelall et al.Page 3/17 INTRODUCTIONRide quality refers to the degree that a vehicle protects its occupants from factors that decrease ride comfort. The road impact factors (RIF) are uneven surfaces and anomalies such as potholes, cracks, and utility covers. The driver impact factors (DIF) are behaviors such as abrupt braking, rapid acceleration, weaving, and speeding around curves. Hence, the RIF and the DIF can induce motions and noises that cause rider discomfort. The vehicle impact factors (VIF) affect how riders perceive the disturbances from RIF and DIF. The VIF are a strong function of the vehicle suspension and handling characteristics but they can also include other factors such as features of furniture design, interior aesthetics, and entertainment. Altogether, these factors result in the overall ride quality experienced. Highway agencies narrow the definition of ride quality to the RIF and use a model of the vehicle suspension system, called the Golden Car, to standardize the VIF as a fixed suspension response that dampens vibrations from road roughness (1). The Golden Car approximates the suspension response of vehicles typical of the 1980s, which is around the time-period that practitioners agreed on the approach. Nearly all regular passenger and commercial motor vehicles, regardless of their size and weight, provide similar suspension responses because manufacturers design them to attenuate vibrations within a common range of frequencies that cause hu...

Effects of smartphone sensor variability in road roughness evaluation

“…The use of smartphones has become a potential alternative approach to measure roadway roughness to reduce cost, increase monitoring frequency, and increase coverage networkwide (Forslöf and Jones 2015). Most smartphone based methods for roughness measurement use the built-in sensors of smartphones, including the global positioning system (GPS) receiver, three-dimensional accelerometer with gyroscope, and ground speed sensor (Bridgelall et al 2016b). The statistical model for road roughness improves when combining data from multiple sensors rather than taking only the magnitude from an accelerometer, referenced to an average speed (Douangphachanh and Oneyama 2014).…”

“…Shorter window lengths are more sensitive to GPS positioning error (Douangphachanh and Oneyama 2014). Previous studies demonstrated that for any window size, the mean value of RIF-indices is directly proportional to the IRI, at any given speed (Bridgelall et al 2016b).…”

Response-based methods to measure road surface irregularity: a state-of-the-art review

“…This can be done with the help of the future development of sensor technology. Lastly, the intensive on-going research on RIF and TWIT [95][96][97][98][99][100][101] as the alternatives for IRI in connected vehicle environment will be promising for large-scale implementation.…”

“…Regarding new roughness index, a speed-independent road impact factor -RIF (individual vehicle) and its corresponding time-wavelength-intensity-transform -TWIT (vehicle groups) for connected vehicles were established using advanced signal processing in [94]. Further studies were conducted intensively to investigate and validate the RIF regarding sampling rate selection [95], localisation [96,97], RIF-IRI proportionality [98], deterioration forecasts in consideration of suspension parameter variances [99], stop-andgo conditions [100], and wavelength sensitivity [101].…”

Road infrastructure design towards passenger ride comfort for autonomous public transport

“…Whereas, the frequent turning movements of buses with curves, intersections and roundabouts can result in a higher range of ride discomfort from uncomfortable, very uncomfortable to extremely uncomfortable. In another context such as rural road or mining roads [98], the effects of the road surface are more significant than urban roads.…”