Browsing by Author "Li, Yingfeng"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Assessing the Safety Impact of Roadway Improvements Using Naturalistic Driving Data--Feasibility Study
    Li, Yingfeng; Medina, Alejandra; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2017-10-19)
    This project explored the feasibility of using Second Strategic Highway Research Program (SHRP 2) data, including the Roadway Information Database (RID), to evaluate the effectiveness of roadway safety improvements where traditional crash data are limited. The research team conducted two case studies based on naturalistic driving study (NDS) data from 200 trips. The two case studies evaluated the safety effects of (1) a paving project with newly installed pavement and markings, and (2) a median barrier replacement project with a newly installed and restored concrete median. A number of safety surrogate measures were used to develop a comprehensive understanding of how driver behavior changed with and without the safety treatment. The results from both case studies indicated that the roadway improvements had an impact on driver safety behavior, as indicated through the surrogate safety measures of speed, lateral and longitudinal accelerations, lane deviation, and car-following behavior. The two case studies illustrate two different methods for studying the effectiveness of roadway improvements on safety. The paving project case study compared driver behavior data collected at the project site after the roadway improvement with data from an adjacent site with similar roadway conditions but without the pavement improvement. The median barrier project case study compared data on the same segment of road before and after the improvement project. The two different methods illustrate the flexibility available with SHRP 2 safety data. In addition to the case studies, the research team also assessed the availability, suitability, and limitations of SHRP 2 and RID data for evaluating the safety impact of roadway improvements.
  • Thumbnail Image
    Assessment of the Performance of Light-Emitting Diode Roadway Lighting Technology
    Gibbons, Ronald B.; Li, Yingfeng; Meyer, Jason E. (Virginia Transportation Research Council, 2015-10)
    This study, championed by the Virginia Department of Transportation (VDOT) Traffic Engineering Division, involved a thorough investigation of light-emitting diode (LED) roadway lighting technology by testing six types of roadway luminaires (including housing and all components enclosed) in a laboratory environment and on the field over a 2-year period. The results showed that LED luminaires exhibited superior lighting and related qualities compared to high-pressure sodium luminaires. Different photometric characteristics were found among LED luminaires of different designs, indicating a careful selection considering light distribution and illuminance level is necessary for individual lighting applications. During the first 2 years of operation, the average light loss for the LED luminaires was 6% based on laboratory testing. The study also found that implementing LED technology systematically will result in a return on investment between 3.25 and 5.76 for different scenarios over a 25-year period due to savings in maintenance and energy consumption. The study resulted in the VDOT LED Roadway Luminaire Specification document and developed recommendations relevant to VDOT’s implementation of LED technology.
  • Thumbnail Image
    Evaluation of Innovative Approaches to Curve Delineation for Two-Lane Rural Roads
    Gibbons, Ronald B.; Flintsch, Alejandra Medina; Williams, Brian M.; Li, Yingfeng; Machiani, Sahar Ghanipoor; Bhagavathula, Rajaram (Virginia Transportation Research Council, 2018-06)
    Run-off-road crashes are a major problem for rural roads. These roads tend to be unlit, and drivers may have difficulty seeing or correctly predicting the curvature of horizontal curves. This leads to vehicles entering horizontal curves at speeds that are too high, which can often lead to vehicles running off the roadway. This study was designed to examine the effectiveness of a variety of active and passive curve warning and curve delineation systems on two two-lane rural roads to determine which is the most effective at reducing vehicle speeds and assisting lane-keeping. The study consisted of a human-factors study, as well as an observational study. There were nine curves examined in the study on two road sections in Southwest Virginia. The human-factors study included participants whose speed and lane position were tracked as they drove through eight curves, both before and after new treatments were installed in each of the eight curves. The observational study examined the speed and lane position of traffic on all the curves before and after the installation of the new treatments. The observational study included a curve on a road near the primary study section. The results of the study were mixed, with every tested system leading to some reductions in speed or encroachments at some parts of the curve while also leading to increases in the same values at other parts of the curve. No clear difference was discovered between passive and active systems or between delineation and warning systems. The study recommends that in addition to a safety assessment, specific curve characteristics and budget should be the main considerations in the selection of a treatment for a curve.
  • Thumbnail Image
    Integrating the Adaptive Lighting Database with SHRP 2 Naturalistic Driving Data
    Li, Yingfeng; Gibbons, Ronald B.; Flintsch, Alejandra Medina (National Surface Transportation Safety Center for Excellence, 2015-09-16)
    This report details efforts to integrate the Adaptive Lighting Database (ALD) with the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) and the Roadway Information Database. The ALD provides detailed in situ lighting performance data and associated safety, traffic, and roadway data for seven states: Washington (WA), North Carolina (NC), California (CA), Delaware (DE), Minnesota (MN), Vermont (VT), and Virginia (VA). The SHRP 2 database provides naturalistic driving data from a large-scale study carried out at six sites around the nation: Bloomington, Indiana; central Pennsylvania; Tampa Bay, Florida; Buffalo, New York; Durham, North Carolina; and Seattle, Washington. The RID, which was developed as part of the SHRP 2 program, provides detailed traffic and roadway information for the SHRP 2 sites. The integration of these datasets would make it possible for researchers to investigate relationships between different lighting characteristics, roadway configurations, and roadway safety. With this objective in mind, the research team developed an in-depth description of the NDS database structure, data elements, and database relationships; documented in detail the data entities, format, and content of the ALD; and developed and demonstrated two Geographic Information System (GIS) approaches for integrating NDS and ALD data. The two GIS approaches target different needs and requirements. The first approach involved data integration directly between the ALD and NDS time series data. By matching both lighting and time series data points onto the same roadway network, simple spatial joins or linear referencing mechanisms could be used to relate individual points from both datasets. The approach involved both existing and custom tools developed on the ArcGIS platform. Data in both databases could then be integrated through spatial and relational joins. The researchers used data for the State of Washington to demonstrate the approach and associated advantages and challenges. Time series data representing approximately 2,800 nighttime NDS trips were matched to the ALD roadway network. The second approach involved data integration of the ALD and NDS data based on the roadway segments in the RID. Within the NDS database, time series data points were matched to a digital map of the roadway network defined by links (uniquely identified by a LINKID), directly isolating time series data on the links of interest and eliminating the need for additional spatial processing. The RID roadway network was then matched with the ALD roadway network and each ALD roadway segment was assigned the LINKID of the corresponding RID roadway segment, allowing relational database joins to be used, which are many orders of magnitude faster than spatial joins. To demonstrate this approach, the research team used a draft version of the RID roadway data and lighting data for the State of North Carolina.
  • Thumbnail Image
    Understanding Fixed Object Crashes with SHRP2 Naturalistic Driving Study Data
    Hao, Haiyan (Virginia Tech, 2018-08-30)
    Fixed-object crashes have long time been considered as major roadway safety concerns. While previous relevant studies tended to address such crashes in the context of roadway departures, and heavily relied on police-reported accidents data, this study integrated the SHRP2 NDS and RID data for analyses, which fully depicted the prior to, during, and after crash scenarios. A total of 1,639 crash, near-crash events, and 1,050 baseline events were acquired. Three analysis methods: logistic regression, support vector machine (SVM) and artificial neural network (ANN) were employed for two responses: crash occurrence and severity level. Logistic regression analyses identified 16 and 10 significant variables with significance levels of 0.1, relevant to driver, roadway, environment, etc. for two responses respectively. The logistic regression analyses led to a series of findings regarding the effects of explanatory variables on fixed-object event occurrence and associated severity level. SVM classifiers and ANN models were also constructed to predict these two responses. Sensitivity analyses were performed for SVM classifiers to infer the contributing effects of input variables. All three methods obtained satisfactory prediction performance, that was around 88% for fixed-object event occurrence and 75% for event severity level, which indicated the effectiveness of NDS event data on depicting crash scenarios and roadway safety analyses.