高速公路行车风险路侧感知系统的设备优化布设

Devices’optimal deployment of roadside sensing system for expressway driving risk

采用最大信息系数相关性检验遴选行车风险度量指标,提出基于信息熵理论的行车风险多指标度量与融合方法;构建以获取行车风险熵最大为目标,考虑系统建设成本、设备检测范围的路侧感知设备布设优化模型.基于多车道高速公路车辆行驶轨迹,通过算例获得在不同预算约束条件下的最优路侧设备布设方案,分析设备选型、传统的等间距设备布设方法、原始数据噪声等因素对路侧感知系统获取道路行车风险能力的影响.结果表明,路侧感知系统建设经费增加与系统行车风险获取能力提升呈现边际效用递减规律,相较于设备数量过多或过少的情况,设置数量适中的路侧感知设备费效比更高,优化后的设备布设方案的费效比比等间距布设方案的费效比高,原始数据测量误差不超过10%不会影响设备优化布设方案的计算结果.

The driving risk measurement indexes were selected based on the correlation test results of maximum information coefficient.A multi-indicator calculation and fusion method for driving risk was designed based on an information entropy theory.An optimal deployment model of roadside sensing devices,aiming to maximize the value of driving risk entropy collected by the devices,was developed which took construction cost and device detection range as constraints.Taking a multi-lane expressway driving trajectory as data,the optimal deployment schemes of roadside sensing devices under different budget constraints was calculated.The factors affecting the capacity of the roadside sensing system to capture the roadway driving risk were analyzed,such as the device type selection,the traditional scheme of uniformly-spaced device deployment,and the original data noise.Results show that the increasing of construction budget of the roadside sensing system and the system’s abilities of sensing driving risk improvement follow the law of diminish marginal effect.Comparing with the situations where there are too many or too few devices,a moderate number of sensing devices has a higher cost-effectiveness ratio.The cost-effectiveness ratio of the optimal device deployment scheme is higher than that of the uniformly-spaced device scheme.Less than 10%original data detection error does not affect the calculation results of the optimal device deployment scheme.