基于车载测试的CNG汽车微观排放特性研究

为了分析CNG汽车道路排放的微观特性,采用车载排放测试系统,并选取市区主要道路为试验路段进行了实车道路试验。通过试验获取典型车辆的排放数据及行驶状态,分析车辆在实际行驶中的排放特性,为预测不同交通状态下CNG汽车的排放特性和区域排放清单建立提供参考。     

沥青混凝土路面就地热再生技术在高速公路施工中的应用

通过就地热再生试验路段的实施,对沥青路面回收材料(RAP)进行评价。确定了矿料级配设计、再生剂用量、最佳新沥青用量、新混合掺料量、工艺控制和质量要点,为沥青混土路面就地热再生技术方案的确定提供了依据。     

山区高速公路路基路面排水方案设计探讨

为探索山区高速公路路基路面排水方案设计,文章以广西某高速公路为背景工程.通过详细的水文计算分析,确定不同路段排水结构所需构造尺寸,并以此为依据,设计出不同路段的路基路面排水方案,为同类型项目排水方案设计提供参考与借鉴。     

基于公路安全防护工程高风险路段分析

本文对公路安全防护工程的背景及风险评估的原理进行阐述,以广东省某市国省干线进行现场调研,通过基础数据采集、数据标准化处理、风险值计算,得出该市管理区域内国省干线的高风险路段,提高了路段管理单位重视程度,为以后公路养护提供依据。     

路面垫层施工技术在公路工程中的应用

为研究路面垫层施工技术在公路工程中的应用,本文介绍垫层施工技术所用原材料和施工工艺,依托实际工程,使用三种不同集料配合比制备拌和料铺设试验路段A、试验路段B、试验路段C,通过试验检测试验路段A、B、C弯沉值和渗水系数.研究结果表明:密实型连续级配碎石铺设路面垫层可以有效减小行车道弯沉值;试验路段A垫层弯沉值远远小于试验...     

快速路平曲线半径对交通排放的影响分析

本文主要研究了快速路平曲线半径与交通特征参数对车速和交通排放的影响。根据合肥市四条典型快速路平曲线路段实测数据,建立了断面车速预测模型,并选择COPERT Ⅳ排放模型对合肥市轻型机动车排放因子进行测算。基于模型模拟数据,分析了行驶速度与平曲线半径对排放因子的影响,拟合了污染物CO排放因子与速度的函数关系。最后基于断面车速预测模型,建立了合肥市平曲线路段轻型车污染物排放量预测模型,并进行实例计算。     

基于交通大数据的动态交通状态预测及全局路径规划研究

为提高动态交通状态预测的准确性,对基于交通大数据的动态交通状态预测及全局路径规划进行研究。以交通大数据为基础,获取指定路段的属性信息与采集信息,通过对样本数据进行描述,得到该路段的试验数据。在具体的预测和分析阶段,构建基于交通大数据的预测模型,实现对动态交通状态的可靠预测。试验结果表明,时间特征值对交通流量的影响较大,某一时间段的交通状态数据可为全局路径规划提供数据源,有利于相关人员作出科学决策,可更好地满足当前智慧交通管理新需求。     

沥青路面车辙钻芯取样试验研究

文章通过对某试验路段车辙凹陷位置及隆起位置进行钻芯取样试验,分析了沥青路面车辙不同位置处芯样的厚度、毛体积密度、油石比、级配的变化规律,为高速公路沥青路面车辙研究提供参考。     

山区高速公路长大下坡路段避险车道 设置位置研究

本文在分析了避险车道现状问题和安全影响因素的基础上,通过货车在雅西高速公路上的制动鼓温度变化实车试验,建立货车在山区高速公路长大下坡路段行驶时的制动鼓温度变化模型,然后在试验的基础上建立一套适用于山区高速公路的避险车道设置位置理论计算方法,以此来确定避险车道的设置位置和设置间距.     

高速公路连续长下坡路段安全评价与整治措施研究

《高速公路连续长下坡路段安全评价与整治措施研究》项目针对山区公路连续长下坡路段事故率高、伤亡率大、交通安全设施设置针对性不强等问题,通过大量事故调研及资料调查,采用汽车行驶动力学、仿真计算等技术手段对长下坡路段的安全水平评价方法等内容进行系统研究。文章着重介绍了该项目的研究现状、研究内容、技术路线、关键技术、研究成果及创新点。     

Impact of horizontal geometric design of two-lane rural roads on vehicle co2 emissions

In 2014, highway vehicles accounted for 72.8% of all Greenhouse Gases emissions from transportation in Europe. In the United States (US), emissions follow a similar trend. Although many initiatives try to mitigate emissions by focusing on traffic operations, little is known about the relationship between emissions and road design. It is feasible that some designs may increase average flow speed and reduce accelerations, consequently minimizing emissions.This study aims to evaluate the impact of road horizontal alignment on CO₂ emissions produced by passenger cars using a new methodology based on naturalistic data collection. Individual continuous speed profiles were collected from actual drivers along eleven two-lane rural road sections that were divided into 29 homogeneous road segments. The CO₂ emission rate for each homogeneous road segment was estimated as the average of CO₂ emission rates of all vehicles driving, estimated by applying the VT-Micro model.The analysis concluded that CO₂ emission rates increase with the Curvature Change Rate. Smooth road segments normally allowed drivers to reach higher speeds and maintain them with fewer accelerations. Additionally, smother segments required less time to cover the same distance, so emissions per length were lower. It was also observed that low mean speeds produce high CO₂ emission rates and they increase even more on roads with high speed dispersions.Based on this data, several regression models were calibrated for different vehicle types to estimate CO₂ emissions on a specific road segment. These results could be used to incorporate sustainability principles to highway geometric design.     

Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data

Road segments with identical site-specific attributes often exhibit significantly different crash counts due to unobserved reasons. The extent of unobserved heterogeneity associated with a road feature is to be estimated prior to selecting the relevant safety treatment. Moreover, crash count data is often over-dispersed and spatially correlated. This paper proposes a spatial negative binomial specification with random parameters for modeling crash counts of contiguous road segments. The unobserved heterogeneity is incorporated using a finite multi-variate normal mixture prior on the random parameters; this allows for non-normality, skewness in the distribution of the random parameters, facilitates correlation across the random parameters, and relaxes any distributional assumptions. The model extracts the inherent groups of road segments with crash counts that are equally sensitive to the road attributes on an average; the heterogeneity within these groups is also allowed in the proposed framework. The specification simultaneously accounts for potential spatial correlation of the crash counts from neighboring road segments. A Gibbs sampling framework is proposed that leverages recent theoretical developments on data-augmentation algorithms, and elegantly sidesteps many of the computational difficulties usually associated with Bayesian inference of count models. Empirical results suggests the presence of two latent groups and spatial correlation within the study road network. Road features with significantly different effect on crash counts across two latent groups of road segments were identified.     

Likelihood-based offline map matching of GPS recordings using global trace information

In batch map matching the objective is to derive from a time series of position data the sequence of road segments visited by the traveler for posterior analysis. Taking into account the limited accuracy of both the map and the measurement devices several different movements over network links may have generated the observed measurements. The set of candidate solutions can be reduced by adding assumptions about the traveller’s behavior (e.g. respecting speed limits, using shortest paths, etc.). The set of feasible assumptions however, is constrained by the intended posterior analysis of the link sequences produced by map matching. This paper proposes a method that only uses the spatio-temporal information contained in the input data (GPS recordings) not reduced by any additional assumption.The method partitions the trace of GPS recordings so that all recordings in a part are chronologically consecutive and match the same set of road segments. Each such trace part leads to a collection of partial routes that can be qualified by their likelihood to have generated the trace part. Since the trace parts are chronologically ordered, an acyclic directed graph can be used to find the best chain of partial routes. It is used to enumerate candidate solutions to the map matching problem.Qualification based on behavioral assumptions is added in a separate later stage. Separating the stages helps to make the underlying assumptions explicit and adaptable to the purpose of the map matched results. The proposed technique is a multi-hypothesis technique (MHT) that does not discard any hypothesized path until the second stage.A road network extracted from OpenStreetMap (OSM) is used. In order to validate the method, synthetic realistic GPS traces were generated from randomly generated routes for different combinations of device accuracy and recording period. Comparing the base truth to the map matched link sequences shows that the proposed technique achieves a state of the art accuracy level.     

Urban traffic flow prediction: a spatio‐temporal variable selection‐based approach

Short‐term traffic flow prediction in urban area remains a difficult yet important problem in intelligent transportation systems. Current spatio‐temporal‐based urban traffic flow prediction techniques trend aims to discover the relationship between adjacent upstream and downstream road segments using specific models, while in this paper, we advocate to exploit the spatial and temporal information from all available road segments in a partial road network. However, the available traffic states can be high dimensional for high‐density road networks. Therefore, we propose a spatio‐temporal variable selection‐based support vector regression (VS‐SVR) model fed with the high‐dimensional traffic data collected from all available road segments. Our prediction model can be presented as a two‐stage framework. In the first stage, we employ the multivariate adaptive regression splines model to select a set of predictors most related to the target one from the high‐dimensional spatio‐temporal variables, and different weights are assigned to the selected predictors. In the second stage, the kernel learning method, support vector regression, is trained on the weighted variables. The experimental results on the real‐world traffic volume collected from a sub‐area of Shanghai, China, demonstrate that the proposed spatio‐temporal VS‐SVR model outperforms the state‐of‐the‐art. Copyright © 2015 John Wiley & Sons, Ltd.     

Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data

Nowadays, the massive car-hailing data has become a popular source for analyzing traffic operation and road congestion status, which unfortunately has seldom been extended to capture detailed on-road traffic emissions. This study aims to investigate the relationship between road traffic emissions and the related built environment factors, as well as land uses. The Computer Program to Calculate Emissions from Road Transport (COPERT) model from European Environment Agency (EEA) was introduced to estimate the 24-h NOx emission pattern of road segments with the parameters extracted from Didi massive trajectory data. Then, the temporal Fuzzy C-Means (FCM) Clustering was used to classify road segments based on the 24-h emission rates, while Geographical Detector and MORAN’s I were introduced to verify the impact of built environment on line source emissions and the similarity of emissions generated from the nearby road segments. As a result, the spatial autoregressive moving average (SARMA) regression model was incorporated to assess the impact of selected built environment factors on the road segment emission rate based on the probabilistic results from FCM. It was found that short road length, being close to city center, high density of bus stations, more ramps nearby and high proportion of residential or commercial land would substantially increase the emission rate. Finally, the 24-h atmospheric NO₂ concentrations were obtained from the environmental monitor stations, to calculate the time variational trend by comparing with the line source traffic emissions, which to some extent explains the contribution of on-road traffic to the overall atmospheric pollution. Result of this study could guide urban planning, so as to avoid transportation related built environment attributes which may contribute to serious atmospheric environment pollutions.     

A tensor-based Bayesian probabilistic model for citywide personalized travel time estimation

Urban travel time information is of great importance for many levels of traffic management and operation. This paper develops a tensor-based Bayesian probabilistic model for citywide and personalized travel time estimation, using the large-scale and sparse GPS trajectories generated by taxicabs. Combined with the knowledge learned from historical trajectories, travel times of different drivers on all road segments in some time slots are modeled with a 3-order tensor. This tensor-based modeling approach incorporates both the spatial correlation between different road segments and the person-specific variation between different drivers, as well as the coarse-grain temporal correlation between recent and historical traffic conditions and the fine-grain temporal correlation between different time slots. To account for the variability caused by the intrinsic uncertainties in urban road network, each travel time entry in the built tensor is treated as a variable following a log-normal distribution. With the help of the fully Bayesian treatment, the model achieves automatic hyper-parameter tuning and model complexity controlling, and therefore the problem of over-fitting is prevented even when the used data is large-scale and sparse. The proposed model is applied to a real case study on the citywide road network of Beijing, China, using the large-scale and sparse GPS trajectories collected from over 32,670 taxicabs for a period of two months. Empirical results of extensive experiments demonstrate that the proposed model provides an effective and robust approach for urban travel time estimation and outperforms the considered competing methods.     

Accident cost saving and highway attributes

Two semi-logarithmic regression models are developed to estimate accident rates and accident costs, respectively, for rural non-interstate highways in the state of Iowa. Data on 21,224 accidents occurring between 1989 and 1991 on 17,767 road segments are used in the analysis. Seven road attributes of these road segments are included as predictor variables. Applying the resulting regression models to a rather typical highway upgrade situation, the present value of the accident cost saving is computed. The sensitivity of the estimated cost saving to values for fatal, personal injury, and property damage only accidents is tested.Because factors other than road characteristics greatly influence accident costs, the models developed in this research explain a limited amount of the variance in these costs among road segments. Results of the analysis indicate that the most important attribute associated with accident costs is average daily traffic per lane, followed by conditions requiring passing restrictions and the sharpness of curves. Varying the values for the three categories of accidents shows that results are far more sensitive to the value of personal injuries than fatalities. The feasibility of using predictive models of accident costs in benefit-cost analyses of highway investments is demonstrated.     

Allocation of onroad mobile emissions to road segments for air toxics modeling in an urban area

Dispersion models are useful tools for setting emission control priorities and developing strategies for reducing air toxics emissions. Previous methodologies for modeling hazardous air pollutant emissions for onroad mobile sources are based on using spatial surrogates to allocate county level emissions to grid cells. A disadvantage of this process is that it spreads onroad emissions throughout a grid cell instead of along actual road locations. High local concentrations may be underestimated near major roadways, which are often clustered in urban centers. Here, we describe a methodology which utilizes a Geographic Information System to allocate benzene emissions to major road segments in an urban area and model the segments as elongated area sources. The Industrial Source Complex Short Term dispersion model is run using both gridded and link-based emissions to evaluate the effect of improved spatial allocation of emissions on ambient modeled benzene concentrations. Allocating onroad mobile emissions to road segments improves the agreement between modeled concentrations when compared with monitor observations, and also results in higher estimated concentrations in the urban center.     

Generating lane-based intersection maps from crowdsourcing big trace data

Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data.     

Estimating arterial link speed using conventional road detectors

Abstract

In response to an initiative to develop an advanced traffic information system in Bangkok, this paper explores practical guidelines for the optimal location of road sensors, such that the data collected on spot speeds reflect an entire link's average speed. In particular, the authors use microsimulation software to investigate optimal detector locations, using the sum of squared errors and root mean squared errors. The analysis hypothesizes that road segments are 0.4, 0.6, 0.8, 1.0, 2.0 and 3.0 km in length and are specially designed to replicate typical arterial streets in Bangkok. The results show that a single detector location can produce good estimates of link speed only for segments that are shorter than 1.0 km. For distances of 1.0 km or more, the results suggest that two detectors be used for good link speed estimates under all traffic conditions.