公路测速校准仪研制探讨

一.概述 在高速公路和城市道路上安装了不少“电子警察”，它使用非接触方式测速监视道路上行驶的机动车速度．一旦测到超过规定的车速．就起动数码相机拍摄记录机动车牌照，供警方作为执法依据。由于长期使用，测速仪内的元件老化，以及环境影响等，造成测速装置本身不准，带来误判的事也常有发生。

浅谈如何使公路测速更加人性化

就当前公路测速中存在的问题,提出了一些使公路测速更加人性化的措施。

Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

Construction of crash prediction model of freeway basic segment based on interactive influence of explanatory variables

In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.

Travel time prediction model of freeway based on gradient boosting decision tree

To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.

Effect of Traffic Speed on Stresses and Strains in Asphalt Perpetual Pavement

Increasing traffic volumes and loads as well as public expectation for a long-lasting transportation infrastructure have necessitated designing perpetual pavements. The KDOT （Kansas Department of Transportation） conducted a field trial to investigate the suitability of perpetual pavement concept for Kansas highway pavements. The experiment involved construction of four thick pavement structures. To verify the approach of designing perpetual pavements on the basis of an endurance strain limit, the pavements were instrumented with gauges for measuring tensile strains at the bottom of asphalt base layers at various speeds. Pavements were also instrumented with pressure cells to measure stress on the top of subgrade. Pavement response measurements under known vehicle load were performed in August 2006. FWD （Falling-weight deflectometer） was also used to collect deflection data at 15 m intervals on the same date. FWD first-sensor （center） deflections were normalized and corrected to 20 ℃ temperature based on measured mid-depth pavement temperature. The result shows that strain and stress measurements show significant amount of variations. Measurements in the thickest section are the most consistent. The higher the traffic speed, the lower the strains and stresses. The difference between strains and stresses at 30 kmhar and 65 km/hr is higher than the difference between 65 km/hr and 95 kin/hr. This shows the effect of speed on stresses and strains decreases as the speed increases. Softer binder in the asphalt base layer results in lower strains, which confirms that softer binder results in higher fatigue life.

Measuring speed consistency for freeway diverge areas using factor analysis

Although either absolute speed or speed difference can be considered as a measure for speed consistency, few researches consider both in practice. The factor analysis method was introduced to extract an optimal number of factors from numerous original measures. The freeway diverging zone was divided into four elements, namely the upstream, the diverge area, the downstream and the exit ramp. Operating speeds together with individual vehicle speeds were collected at each element with radar guns. Following the factor analysis procedure, two factors, which explain 96.722% of the variance in the original data, were retained from the initial seven speed measures. According to the loadings after Varimax rotation, the two factors are clearly classified into two categories. The first category is named "speed scale" reflecting the absolute speed, and the other one is named "speed dispersion" interpreting speed discreteness. Then, the weighted score of speed consistency for each diverge area is given in terms of linear combination of the two retained factors. To facilitate the level classification of speed consistency, the weighted scores are normalized in the range of (0, 1.0). The criterion for speed consistency classification is given as 0≤F N <0.30, good consistency; 0.30≤F N <0.60, fair consistency; 0.60≤ F N ≤1.00, poor consistency. The validation by comparing with previously developed measures shows that the proposed measure is acceptable in evaluating speed consistency.

Evaluation of Rock Fall Hazards Using Lidar Technology

Lidar （light detection and ranging） is a relatively new technology that is being used in many aspects of geology and engineering, including researching the potential for rock falls on highway rock cuts. At Missouri University of Science and Technology, we are developing methods for measuring joint orientations remotely and quantifying the raveling process. Measuring joint orientations remotely along highways is safer, more accurate and can result in larger and more accurate data sets, including measurements from otherwise inaccessible areas. Measuring the nature of rock raveling will provide the data needed to begin the process of modeling the rock raveling process. In both cases, terrestrial lidar scanning is used to generate large point clouds of coordinate triplets representing the surface of the rock cut. Automated algorithms have been developed to organize the lidar data, register successive images without survey control, and removal of vegetation and non-rock artifacts. In the first case, we look for planar elements, identify the plane and calculate the orientations. In the second case, we take a series of scans over time and use sophisticated change detection algorithms to calculate the numbers and volumes of rock that has fallen off the rock face.

Monitoring Freeway Incident Detection Using a Hotelling T2 Control Chart

In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.