Measurement uncertainty determination and curve-fitting algorithms for development of accurate digital maps for advanced driver assistance systems

Digital maps can provide support for numerous advanced driver assistance systems (ADAS) aimed at improving road safety. These new uses require more highly detailed and precise maps. The use of a datalog vehicle to collect roadway geometry data can fulfil these specifications. This paper presents fast, accurate measurement with an on-board inertial system together with a method to evaluate measurement uncertainty, particularly for any variables obtained indirectly. It also presents an algorithm for segmentation and fitting geometric curves to the experimental points, following current highway design standards. The algorithms have been applied to real road measurements. Segmentation has been done in straight alignments, circular curves and transition curves whose characteristic parameters are calculated. It has been seen that with a very small data set it is possible to reconstruct the measured geometry with few discrepancies regarding the experimental points.     

隧道加强照明配光优化技术研究

公路隧道照明中相比中间段基本照明而言,加强照明采用传统宽光曲线照明的性价比并不高,较长照射路径反而易造成过多的光通量损耗。为此,本文采用数值模拟的方法对隧道加强照明进行仿真,对比收光型和非收(宽)光型两种配光曲线的照明计算结果,分析收光曲线的照明效能并进一步优化配置。研究结论为:加强照明收光型配光曲线比非收光型配光曲线更节能,当路面平均照度要求为1 500 lx时,非收光曲线照明需求光通量高出收光曲线照明15.8%;收光曲线照明时路面照度总均匀度并不比非收光曲线照明低;对称型收光曲线拱顶单光带布设时,横向偏0.45 m时照明效果最佳。     

Infrastructure based approach for roadway departure risk assessment

This paper presents a warning device to prevent the roadway departure of light vehicles while cornering. The proposed risk assessment methodology is based on recent works from the authors (Rey et al., 2011b,a; Rey, 2010). Given the random variability arising from the driver, the vehicle and the infrastructure at the entrance to the curve, a probabilistic strategy is adopted to assess the roadway departure risk. The infrastructure-based methodology enables the real curve characteristics to be considered and an alarm triggering decision to be made. Two safety criteria are tested and the potential safety benefits of the proposed warning device are evaluated. Contrary to existing roadway departure warning systems, the proposed approach does not require extra equipment for vehicles; it only requires that the measuring and warning devices be part of the road infrastructure, which is a great advantage in terms of economic cost.     

A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation

This paper proposes a combined usage of microscopic traffic simulation and Extreme Value Theory (EVT) for safety evaluation. Ten urban intersections in Fengxian District in Shanghai were selected in the study and three calibration strategies were applied to develop simulation models for each intersection: a base strategy with fundamental data input, a semi-calibration strategy adjusting driver behavior parameters based on Measures of Effectiveness (MOE), and a full-calibration strategy altering driver behavior parameters by both MOE and Measures of Safety (MOS). SSAM was used to extract simulated conflict data from vehicle trajectory files from VISSIM and video-based data collection was introduced to assist trained observers to collect field conflict data. EVT-based methods were then employed to model both simulated/field conflict data and derive the Estimated Annual Crash Frequency (EACF), used as Surrogate Safety Measures (SSM). PET was used for EVT measurement for three conflict types: crossing, rear-end, and lane change. EACFs based on three simulation calibration strategies were compared with field-based EACF, conventional SSM based on Traffic Conflict Techniques (TCT), and actual crash frequency, in terms of direct correlation, rank correlation, and prediction accuracy. The results showed that, MOS should be considered during simulation model calibration and EACF based on the full-calibration strategy appeared to be a better choice for simulation-based safety evaluation, compared to other candidate safety measures. In general, the combined usage of microscopic traffic simulation and EVT is a promising tool for safety evaluation.     

A systems approach to area traffic control

Since the first pilot scheme for area‐traffic control was introduced in the city of Montreal (1959–60), computer control of traffic in urban areas through the adaptation of existing traffic‐signal systems has been provided to an increasing extent. This area of work may pose problems for the professional traffic engineer whose background in computer technology and general digital electronics may be limited.

In considering the engineering implications of such schemes a systems approach is important and is adopted here. Three existing and representative schemes are briefly mentioned in order to outline basic features. A more detailed examination of the various system elements follows with mention of data collection and transmission, and the role of the control computer.

The paper continues with a reconsideration of the three representative schemes in the light of the detailed treatment of system components. It concludes with a tentative assessment of the present position of area traffic control schemes and some suggestions as to the future development.     

公路隧道照明照明灯具配置优选研究

高速公路隧道的营运环境越来越受到社会各界的关注,隧道照明系统对行车的安全性和舒适性有着重要的影响。本文基于大型照明分析软件对隧道照明灯具的安装高度和间距进行了精确的计算机仿真,给出了隧道内照明系统的最佳间距和最佳高度,为合理设计和配置隧道照明灯具提供参考。     

多传感器的车辆信号灯故障动态监测方法研究

车辆信号灯对行车安全有重要的警示作用,现有的载货汽车不具备完善的信号灯故障动态监测预警功能。为在车辆行驶过程中对信号灯故障进行实时动态监测预警,设计了一种基于单片机控制、多传感器输入的车辆信号灯故障监测预警装置,基于信号灯工作原理的差异分别提出了信号灯故障0-1监测法、积分判别法和最小值判别法。以解放赛龙CA1169PK2L2EA80重型载货汽车为试验车,Freescale MC9S12XEP100单片机为车载终端构建了车辆信号灯故障监测试验系统平台。利用TES-1339R照度计分析了不同光照强度的外界光源对系统监测结果的影响,并采用故障预设的试验方法针对制动和转向信号灯分别采用不同的故障判别方法进行实车试验。试验结果表明:0-1监测法可以准确监测制动灯故障信号;积分判别法和最小值判别法适用于转向灯故障监测,最小值判别法较积分判别法监测转向灯故障分辨率高。该系统实现了车辆信号灯故障实时动态监测及声光预警,为预防由信号灯故障引发的道路交通事故提供了一种新方法。     

A mixed logit model for predicting exit choice during building evacuations

Knowledge on human behaviour in emergency is crucial to increase the safety of buildings and transportation systems. Decision making during evacuations implies different choices, of which one of the most important concerns is the escape route. The choice of a route may involve local decisions on alternative exits from an enclosed environment. This study investigates the effect of environmental (presence of smoke, emergency lighting and distance of exit) and social factors (interaction with evacuees close to the exits and with those near the decision-maker) on local exit choice. This goal is pursued using an online stated preference survey carried out making use of non-immersive virtual reality. A sample of 1503 participants is obtained and a mixed logit model is calibrated using these data. The model shows that the presence of smoke, emergency lighting, distance of exit, number of evacuees near the exits and the decision-maker and flow of evacuees through the exits significantly affect local exit choice. Moreover, the model indicates that decision making is affected by a high degree of behavioural uncertainty. Our findings support the improvement of evacuation models and the accuracy of their results, which can assist in designing and managing building and transportation systems. The main aim of this study is to enrich the understanding of how local exit choices are made and how behavioural uncertainty affects these choices.     

Recursive estimation of average vehicle time headway using single inductive loop detector data

Vehicle time headway is an important traffic parameter. It affects roadway safety, capacity, and level of service. Single inductive loop detectors are widely deployed in road networks, supplying a wealth of information on the current status of traffic flow. In this paper, we perform Bayesian analysis to online estimate average vehicle time headway using the data collected from a single inductive loop detector. We consider three different scenarios, i.e. light, congested, and disturbed traffic conditions, and have developed a set of unified recursive estimation equations that can be applied to all three scenarios. The computational overhead of updating the estimate is kept to a minimum. The developed recursive method provides an efficient way for the online monitoring of roadway safety and level of service. The method is illustrated using a simulation study and real traffic data.     

Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks

Cooperative Adaptive Cruise Control (CACC) systems have the potential to increase roadway capacity and mitigate traffic congestion thanks to the short following distance enabled by inter-vehicle communication. However, due to limitations in acceleration and deceleration capabilities of CACC systems, deactivation and switch to ACC or human-driven mode will take place when conditions are outside the operational design domain. Given the lack of elaborate models on this interaction, existing CACC traffic flow models have not yet been able to reproduce realistic CACC vehicle behaviour and pay little attention to the influence of system deactivation on traffic flow at bottlenecks. This study aims to gain insights into the influence of CACC on highway operations at merging bottlenecks by using a realistic CACC model that captures driver-system interactions and string length limits. We conduct systematic traffic simulations for various CACC market penetration rates (MPR) to derive free-flow capacity and queue discharge rate of the merging section and compare these to the capacity of a homogeneous pipeline section. The results show that an increased CACC MPR can indeed increase the roadway capacity. However, the resulting capacity in the merging bottleneck is much lower than the pipeline capacity and capacity drop persists in bottleneck scenarios at all CACC MPR levels. It is also found that CACC increases flow heterogeneity due to the switch among different operation modes. A microscopic investigation of the CACC operational mode and trajectories reveals a close relation between CACC deactivation, traffic congestion and flow heterogeneity.     

A GIS-Based traffic accident information system

This paper describes the process of developing a GIS-based traffic accident information system. A roadway network in Kent County, Delaware, was selected to demonstrate the graphic database. The development of the graphic and attribute databases on a workstation environment is described. Software was developed to graphically represent accident information pertaining to any point on the roadway network, and outputs for typical case scenarios are presented. The software also has the capability of displaying historical accident information pertaining to the site. This aspect would be helpful to the planner or designer in studying the impact of a particular roadway design with respect to safety. Some thoughts for future extensions of this study are also presented. Finally, it is concluded that a carefully designed GIS system can efficiently help transportation professionals with traffic safety studies and that its capabilities can be extended to include other areas such as pavement and inventory management, transit applications, and executive information systems.     

An integrated solution for lane level irregular driving detection on highways

Global Navigation Satellite Systems (GNSS) has been widely used in the provision of Intelligent Transportation System (ITS) services. Current meter level system availability can fulfill the road level applications, such as route guide, fleet management and traffic control. However, meter level of system performance is not sufficient for the advanced safety applications. These lane level safety applications requires centimeter/decimeter positioning accuracy, with high integrity, continuity and availability include lane control, collision avoidance and intelligent speed assistance, etc. Detecting lane level irregular driving behavior is the basic requirement for these safety related ITS applications. The two major issues involved in the lane level irregular driving identification are accessing to high accuracy positioning and vehicle dynamic parameters and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated solution for the lane level irregular driving detection. Access to high accuracy positioning is enabled by GNSS and Inertial Navigation System (INS) integration using filtering with precise vehicle motion models and lane information. The detection of different types of irregular driving behaviour is based on the application of a Fuzzy Inference System (FIS). The evaluation of the designed integrated systems in the field test shows that 0.5 m accuracy positioning source is required for lane level irregular driving detection algorithm and the designed system can detect irregular driving styles.     

Real time traffic flow outlier detection using short-term traffic conditional variance prediction

Outliers in traffic flow series represent uncommon events occurring in the roadway systems and outlier detection and investigation will help to unravel the mechanism of such events. However, studies on outlier detection and investigations are fairly limited in transportation field where a vast volume of traffic condition data has been collected from traffic monitoring devices installed in many roadway systems. Based on an online algorithm that has the ability of jointly predict the level and the conditional variance of the traffic flow series, a real time outlier detection method is proposed and implemented. Using real world data collected from four regions in both the United States and the United Kingdom, it was found that outliers can be detected using the proposed detection strategy. In addition, through a comparative experimental study, it was shown that the information contained in the outliers should be assimilated into the forecasting system to enhance its ability of adapting to the changing patterns of the traffic flow series. Moreover, the investigation into the effects of outliers on the forecasting system structure showed a significant connection between the outliers and the forecasting system parameters changes. General conclusions are provided concerning the analyses with future work recommended to investigate the underlying outlier generating mechanism and outlier treatment strategy in transportation applications.     

A random forests approach to prioritize Highway Safety Manual (HSM) variables for data collection

The Highway Safety Manual (HSM) recommends using the empirical Bayes method with locally derived calibration factors to predict an agency's safety performance. The data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics information. Many of these data variables are currently unavailable in most of the agencies' databases. Furthermore, it is not economically feasible to collect and maintain all the HSM data variables. This study aims to prioritize the HSM calibration variables based on their impact on crash predictions. Prioritization would help to identify influential variables for which data could be collected and maintained for continued updates, and thereby reduce intensive data collection efforts. Data were first collected for all the HSM variables from over 2400 miles of urban and suburban arterial road networks in Florida. Using 5 years (2008–2012) of crash data, a random forests data mining approach was then applied to measure the importance of each variable in crash frequency predictions for five different urban and suburban arterial facilities including two‐lane undivided, three‐lane with a two‐way left‐turn lane, four‐lane undivided, four‐lane divided, and five‐lane with a two‐way left‐turn lane. Two heuristic approaches were adopted to prioritize the variables: (i) simple ranking based on individual relative influence of variables; and (ii) clustering based on relative influence of variables within a specific range. Traffic volume was found as the most influential variable. Roadside object density, minor commercial driveway density, and minor residential driveway density variables were the other variables with significant influence on crash predictions. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluation of forced flows on freeways with single‐loop detectors

This paper documents the development of a simple method for identifying and/or predicting freeway congestion using single loop detection systems. The proposed algorithm is simple and easy to incorporate into most freeway management systems. The Washington State Department of Transportation's Traffic Systems Management Center (TSMC) sponsored the original study. The investigation also led to a recommendation to replace the original TSMC definition of congestion or forced flow conditions with a more reliable indicator. Although, the TSMC has recently implemented a more advanced prediction system based on fuzzy set theory and neural networks to further identify patterns and rules for ramp metering strategies, the findings presented here continue to be constructive to freeway managers looking for quick and easy analyses that rely solely on single‐loop detection systems. The Seattle Area freeway study section used for the original study was the portion of mainline 1–5 northbound starting at the downtown Seattle Station 108 and ending at the Mountlake Terrace Station 193. Several days' worth of volume and lane‐occupancy data were collected for the afternoon time period from 2:30 p.m. to 6:30 p.m. Time intervals of 20 seconds were chosen for each data collection period. Important products of this research include the following:

• simple, and more reliable criterion for the definition of “bottleneck” or forced flow conditions than that originally used by the TSMC.
• simple, and reliable criterion for predicting impending “bottlenecks” or forced flow conditions.
• A proposed variable for improved selection of the appropriate metering rate. (Further analysis of the use of this variable for determining metering rates is recommended for future studies.

The proposed criteria are simple and easy to incorporate into current freeway management computer systems. Further investigation of freeway performance measurement using volume and occupancy data obtained from single‐loop systems is currently being performed.     

Definition of wind blowers for vehicles testing at chassis-dyno facilities using a CFD approach

The need to increase measurement accuracy of fuel consumption and pollutant emissions in vehicles is forcing the market to develop chassis-dyno test cells that reproduce on-road conditions realistically.Air-cooling is key to vehicle performance. It is therefore critical that the design of a test cell guarantees realistic cooling of all vehicle components, as important errors in fuel consumption and emissions measurements may otherwise arise. In a test-room, a blower placed in front of the vehicle supplies the cooling air. While there are some guidelines in the literature for the selection of fans required for emissions measurements for standard driving cycles, the information for designing the air supply system for specific tests in other areas is scarce.New Real Driving Emissions (RDE) legislation will force manufacturers to perform on-road measurements of pollutants. This represents a significant challenge due to the variability of conditions coming from non-controlled parameters. In order to optimize vehicles, different tests are performed in cells equipped with a chassis-dyno where the on-road flow field around the vehicle is reproduced as closely as possible.This work provides some guidelines for the definition of the airflow supply system of chassis-dyno facilities for vehicle optimization tests, based on a CFD analysis of the flow characteristics around the vehicle. By comparison with the solution obtained for a vehicle in real road driving conditions, the exit section of the blower and the distance between the blower exit and the car that best reproduce realistic on-road flow conditions in a test room are determined.     

A zone design methodology for national freight origin–destination data and transportation modeling

The zone system used for freight data collection and the geographic resolution of published data has a significant impact on analysis and planning. The majority of existing freight model zones are created in an ad hoc way. In this paper, a new model-based design method is introduced to develop freight zones for the continental USA. It focuses on two methodology issues: (1) the criteria that represent the desired properties of a zone system and (2) the constraints that govern the shape, size, and continuity of zones. The method is applied to the continental USA by optimizing an interzonal travel distance weighted by freight flows using county-level freight data. Several optimal national-level freight zone systems with different numbers of zones are developed. The results indicate that a 300-zone system provides a balance between the number of zones and optimization measures where the currently available public freight data are provided with approximately 100 zones.     

Identifying the onset of congestion rapidly with existing traffic detectors

From an operations standpoint the most important function of a traffic surveillance system is determining reliably whether the facility is free flowing or congested. The second most important function is responding rapidly when the facility becomes congested. These functions are complicated by the fact that conventional vehicle detectors are only capable of monitoring discrete points along the roadway while incidents may occur at any location on the facility. The point detectors are typically placed at least one-third of a mile apart and conditions between the detectors must be inferred from the local measurements.This paper presents a new approach for traffic surveillance that addresses these issues. It uses existing dual loop detector stations to match vehicle measurements between stations and monitor the entire roadway. Rather than expending a considerable effort to detect congested conditions, the research employs a relatively simple strategy to look for free flow traffic. Whenever a unique vehicle passes the downstream station, the algorithm looks to see if a similar vehicle passed the upstream station within a time window that is bounded by feasible travel times. The approach provides vehicle reidentification and travel time measurement on freeways during free flow and through the onset of congestion. If desired, other algorithms can be used with the same detectors to provide similar measurements during congested conditions. The work should prove beneficial for traffic management and traveler information applications, while promising to be deployable in the short term.     

A traffic noise model for road intersections in the city of Cartagena de Indias,Colombia

Road traffic noise models are fundamental tools for designing and implementing appropriate prevention plans to minimize and control noise levels in urban areas. The objective of this study is to develop a traffic noise model to simulate the average equivalent sound pressure level at road intersections based on traffic flow and site characteristics, in the city of Cartagena de Indias (Cartagena), Colombia. Motorcycles are included as an additional vehicle category since they represent more than 30% of the total traffic flow and a distinctive source of noise that needs to be characterized. Noise measurements are collected using a sound level meter Type II. The data analysis leads to the development of noise maps and a general mathematical model for the city of Cartagena, Colombia, which correlates the sound levels as a function of vehicle flow within road intersections. The highest noise levels were 79.7 dB(A) for the road intersection María Auxiliadora during the week (business days) and 77.7 dB(A) for the road intersection India Catalina during weekends (non-business days). Although traffic and noise are naturally related, the intersections with higher vehicle flow did not have the highest noise levels. The roadway noise for these intersections in the city of Cartagena exceeds current limit standards. The roadway noise model is able to satisfactorily predict noise emissions for road intersections in the city of Cartagena, Colombia.     

Online Prediction of Travel Time: Experience From a Pilot Trial

Abstract

This study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements. The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system. However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data. The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements.