Road Grade Estimation for On-Road Vehicle Emissions Modeling Using Light Detection and Ranging Data

Abstract

Vehicle-specific power (VSP) is useful for explaining a substantial portion of variability in real-world vehicle emissions, such as those measured with portable emissions monitoring systems (PEMS). VSP is a function of vehicle speed, acceleration, and road grade. Road grade is shown to significantly affect estimates of both VSP and of real-world emissions via sensitivity analysis and analysis of empirical data. However, road grade is difficult to measure reliably using PEMS. Therefore, alternative methods for estimating road grade were identified and compared. A preferred method for estimating road grade was explored in more detail based on light detection and ranging (LIDAR) data. The method includes buffering LIDAR data onto roadway maps using a geographic information system tool, defining segments of roadway based on criteria pertaining to vertical curvature, quantification of roadway elevations within the buffered segments, and estimation of road grade and banking by fitting a plane to each segment. Factors influencing errors in road grade estimates are discussed. The method was evaluated by application to selected interstate highways and comparison to design drawing data. The development and application of LIDAR-based road grade data are demonstrated via a case study using PEMS data collected in the Research Triangle Park, NC, area. LIDAR data are shown to be reliable and accurate for road grade estimation for vehicle emissions modeling.

In-Use Measurement of Activity, Energy Use, and Emissions of a Plug-in Hybrid Electric Vehicle

“…Road grade is a factor that influences vehicle-specific power (VSP) demand and is strongly correlated with the energy consumption of a vehicle. 9,10 The amount of power consumed to recharge the plug-in battery after the field measurements was measured using a watt-hour meter, as shown in Figure 3(h).…”

A Lagrangian Relaxation-Based Solution Method for a Green Vehicle Routing Problem to Minimize Greenhouse Gas Emissions

“…x iik = 0, for ∀i, ∀k (19) x ijk ∈ {0, 1}, for ∀i, ∀j, ∀k (20) u ik ∈R, for ∀i, ∀k (21) Objective Function (9) minimizes the total GHG emissions by K vehicles. Note that x ijk , v ij , r ij , m ij , t i , and t j are variables determined by selecting a set of vehicle routes.…”

“…Constraint (19) prevents a vehicle from leaving and returning to the same customer location. Finally, binary integrity is specified by Constraint (20). Constraint (21) defines the auxiliary variables in Constraint (18).…”

Research on In-vehicle, Real-time Measurement of Road Grades

“…경사계 대신 IMU (Inertia Measurement Unit)를 이용하는 방법 [4] 이나 LIDAR DATA를 이용하는 방법 [5] 또는GPS 센서를 이용하 는 방법 [6] 등의 선행 연구가 이루어져 있지만, 이 연구들의 경우에 는 측정오차에 대한 검증 없이 단순한 방법 제시에 한정되어 있다. …”