The interrelationships between speed limits, geometry, and driver behavior: a proof-of-concept study utilizing naturalistic driving data

Speed management has been an extensive focus of traffic safety research dating back to the 1960\u27s. Research has generally shown crash risk to increase as the average speed of traffic increases and as the standard deviation of travel speeds increases within a traffic stream. However, research as to the effects of speed limits has been somewhat inconclusive. This study investigates how speed limits affect driver speed selection, as well as the resultant crash risk, while controlling for various confounding factors such as traffic volumes and roadway geometry. Data are obtained at very high resolution from a Naturalistic Driving Study (NDS) conducted as a part of the second Strategic Highway Research Program (SHRP 2). These data are integrated with a Roadway Information Database (RID), which provides extensive details as to roadway characteristics in the six-state study area (Florida, Indiana, New York, North Carolina, Pennsylvania, and Washington.) These sources are used to examine how driver speed selection varies among freeways with different posted speed limits, and how the likelihood of crash/near-crash events change with respect to various speed metrics. Regression models are estimated to assess three measures of interest: the average speed of vehicles during the time preceding crash, near-crash, and baseline (i.e., normal) driving events; the variation in travel speeds leading up to each event as quantified by the standard deviation in speeds over this period (i.e. the average acceleration/deceleration rate); and the probability of a specific event resulting in a crash or near-crash based on speed selection and other salient factors. Significant correlation was observed with respect to speed selection behavior among the same individuals and particularly within a single driving event. Mean speeds are shown to increase with speed limits. However, these increases are less pronounced at higher speed limits. Drivers tend to reduce their travel speeds along horizontal or vertical curves, under adverse weather conditions, and particularly under heavy congestion. Increases in average travel speed and the variability in travel speeds are both found to increase crash risk. Crash risk also increases on vertical curves and ramp junctions, as well as among the youngest and oldest age groups of drivers. Ultimately, this research provides an important demonstration of how naturalistic driving data may be leveraged to examine driver behavior and research questions of interest that are difficult or impractical through other empirical settings. The results also provide important insights that provide greater understanding of how drivers adapt their speed selection behavior in response to posted speed limits and other roadway characteristics

Investigating the Effects of Rainfall on Traffic Operations on Florida Freeways

Rainfall affects the performance of traffic operations and endangers safety. A common and conventional method (rain gauges) for rainfall measurements mostly provide precipitation records in hourly and 15-minute intervals. However, reliability, continuity, and wide area coverage pose challenges with this data collection method. There is also a greater likelihood for data misrepresentation in areas where short duration rainfall is predominant, i.e., reported values may not reflect the actual equivalent rainfall intensity during subintervals over the entire reporting period. With recent weather and climate patterns increasing in severity, there is a need for a more effective and reliable way of measuring rainfall data used for traffic analyses. This study deployed the use of precipitation radar data to investigate the spatiotemporal effect of rainfall on freeways in Jacksonville, Florida. The linear regression analysis suggests a speed reduction of 0.75%, 1.54%, and 2.25% for light, moderate, and heavy rainfall, respectively. Additionally, headways were observed to increase by 0.26%, 0.54%, and 0.79% for light, moderate, and heavy rainfall, respectively. Measuring precipitation from radar data in lieu of using rain gauges has potential for improving the quality of weather data used for transportation engineering purposes. This approach addresses limitations experienced with conventional rain data, especially since conventional collection methods generally do not reflect the spatiotemporal distribution of rainfall

Highway Cross Slope Measurement Using Airborne and Mobile LiDAR

Ensuring adequate pavement cross slope on highways can improve driver safety by reducing the potential for water sheeting and ponding. Collecting cross slope data is typically only based on small sample because efficient technology and means to collect accurate cross slope data has been evasive. The advent of Light Detection and Ranging (LiDAR) scanning technology has proven to be a valuable tool in the creation of 3D terrain models. Combined with other technologies such as Global Positioning Systems (GPS) and Inertial Measurement Unit devices (IMU) it is now possible to collect accurate 3D coordinate data in the form of a point cloud while the data collection system is moving. This study provides an evaluation of both Airborne LiDAR Scanning (ALS) and Mobile Terrestrial LiDAR Scanning (MTLS) systems regarding the accuracy and precision of collected cross slope data and documentation of procedures needed to calibrate, collect, and process this data. ALS data was collected by a single vendor on a section of freeway in Spartanburg, South Carolina and MTLS data was collected by six vendors on four roadway sections in South Carolina. The MTLS cross slopes were measured on 23 test stations using conventional surveying methods and compared with the LiDAR-extracted cross slopes. Results indicate that both adjusted and unadjusted MTLS derived cross slopes meets suggested cross slope accuracies (±0.2%). Unadjusted LiDAR data did incorporate corrections from an integrated inertial measurement unit, and high accuracy real-time kinematic GPS, however, was not post-processed adjusted with ground control points. Similarly, airborne LiDAR-extracted cross slopes was compared with conventional surveying measurement on five test stations along the freeway study section. Whereas, the ALS data accuracy was over the minimum acceptable error when two sides of the travel lanes were used to estimate the cross slope, the use of a fitted line to derive the cross slope provided accuracies similar to the MTLS systems. The levels of accuracy demonstrate that MTLS and ALS can be reliable methods for cross slope verification. Adoption of LiDAR would enable South Carolina Department of Transportation (SCDOT) or other highway agencies to proactively address cross slope and drainage issues. When rain falls on a pavement surface, the water depth that accumulates can result in hydroplaning. Previous research has not clearly defined a water depth at which hydroplaning occurs; however, there is considerable agreement that a water depth equal to 0.06 inches as the acceptable upper limit of water depth to minimize the possibility of hydroplaning. This research also explored the potential for hydroplaning with regard to the range of vehicle speed, tire tread depth, tire pressure, and pavement surface texture. Using the results of the sensitivity analysis to provide roadway context combined with MTLS derived cross slope data, SCDOT and other highway agencies can use a data driven approach to evaluate cross slopes and road segments that need corrective measures to minimize hydroplaning potential and enhance safety

Wide area detection system: Conceptual design study

An integrated sensor for traffic surveillance on mainline sections of urban freeways is described. Applicable imaging and processor technology is surveyed and the functional requirements for the sensors and the conceptual design of the breadboard sensors are given. Parameters measured by the sensors include lane density, speed, and volume. The freeway image is also used for incident diagnosis

An analysis of the crash risk and likelihood of engaging in a distraction while driving using naturalistic, time-series data

Distracted driving has become a severe threat to traffic safety due in large part to the proliferation of in-vehicle smart technologies, the ubiquity of cell phones, and a general societal shift towards constant mobility and connectivity. Research has consistently demonstrated adverse consequences to engaging in a distracting secondary behavior while operating a motor vehicle. Much of the prior research in this area has leveraged data from traffic simulators and police-reported crash data, resulting in estimates as to the impacts of distraction on crash risk. However, research has been more limited under actual driving conditions and there remain important gaps with respect to how distracted driving and the associated crash risks vary across drivers and roadway environments. This study addresses this gap by utilizing disaggregate time-series data from the second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) to conduct an in-depth investigation of various safety-focused aspects of distracted driving. The high resolution data were provided at 10 Hz resolution through a series of cameras and mechanical sensors. These operational data were integrated with geometric information from the companion Roadway Information Database (RID), as well as with data related to driver behavioral characteristics, risk perceptions, and risk-taking behavior from a series of participant surveys. Collectively, these sources resulted in a robust dataset of vehicle, roadway, weather, and driver behavioral parameters. Various aspects of distracted driving were investigated as a part of this analysis, including the effects of distraction on driving performance. More specifically, the effects of various types of distraction on driver speed selection behavior was examined. Additional analyses assessed how the prevalence of various types of distracting behaviors varied based upon driver characteristics, roadway geometry, traffic conditions, and environmental conditions. As a part of these investigations, a series of random effects linear and logistic regression models were estimated with the disaggregate time-series information. Risk models were also estimated to determine how various types of distractions impacted the likelihood of a crash or near-crash event. Ultimately, the results suggest that drivers generally adapt their behavior based upon the level of risk posed by various driving environments. These environmental factors, along with various driver-specific factors, were shown to influence speed selection, as well as proclivity for participating in various types of distracting behaviors. In turn, these distractions were found to exacerbate crash risks, with marked differences exhibited based upon the degree to which the distracting behaviors required drivers to direct their attention away from the primary driving task

IMPACT OF DYNAMIC MESSAGE SIGNS ON OCCURRENCE OF ROAD ACCIDENTS

Dynamic Message Signs (DMS) are key components of Advanced Traveler Information Systems to manage transportation networks, reduce congestion and improve safety through providing motorists with real-time information regarding downstream traffic conditions. While DMSs are intended to improve efficiency and safety of road networks, little has been done to study the effect of the signs on driver safety and their localized safety impacts. This thesis employs ground truth data as the basis to investigate the issue in State of Maryland in a four-year period (2007-2010). The results show no significant difference between the accident pattern in the proximity of DMSs and the onward adjacent segments. On-and-off study is also conducted on DMS operation status (on/off). The results converge with the previous analysis suggesting that there is no meaningful relationship between occurrence of accidents and presence of DMSs. Besides, statistical analysis on DMS characteristics and accidents in impact areas are performed

Improving Traffic Safety And Drivers\u27 Behavior In Reduced Visibility Conditions

This study is concerned with the safety risk of reduced visibility on roadways. Inclement weather events such as fog/smoke (FS), heavy rain (HR), high winds, etc, do affect every road by impacting pavement conditions, vehicle performance, visibility distance, and drivers’ behavior. Moreover, they affect travel demand, traffic safety, and traffic flow characteristics. Visibility in particular is critical to the task of driving and reduction in visibility due FS or other weather events such as HR is a major factor that affects safety and proper traffic operation. A real-time measurement of visibility and understanding drivers’ responses, when the visibility falls below certain acceptable level, may be helpful in reducing the chances of visibility-related crashes. In this regard, one way to improve safety under reduced visibility conditions (i.e., reduce the risk of visibility related crashes) is to improve drivers’ behavior under such adverse weather conditions. Therefore, one of objectives of this research was to investigate the factors affecting drivers’ stated behavior in adverse visibility conditions, and examine whether drivers rely on and follow advisory or warning messages displayed on portable changeable message signs (CMS) and/or variable speed limit (VSL) signs in different visibility, traffic conditions, and on two types of roadways; freeways and two-lane roads. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 566 drivers in Central Florida, USA. Several categorical data analysis techniques such as conditional distribution, odds’ ratio, and Chi-Square tests were applied. In addition, two modeling approaches; bivariate and multivariate probit models were estimated. The results revealed that gender, age, road type, visibility condition, and familiarity with VSL signs were the significant factors affecting the likelihood of reducing speed following CMS/VSL instructions in reduced visibility conditions. Other objectives of this survey study were to determine the content of messages that iv would achieve the best perceived safety and drivers’ compliance and to examine the best way to improve safety during these adverse visibility conditions. The results indicated that Caution-fog ahead-reduce speed was the best message and using CMS and VSL signs together was the best way to improve safety during such inclement weather situations. In addition, this research aimed to thoroughly examine drivers’ responses under low visibility conditions and quantify the impacts and values of various factors found to be related to drivers’ compliance and drivers’ satisfaction with VSL and CMS instructions in different visibility and traffic conditions. To achieve these goals, Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM) approaches were adopted. The results revealed that drivers’ satisfaction with VSL/CMS was the most significant factor that positively affected drivers’ compliance with advice or warning messages displayed on VSL/CMS signs under different fog conditions followed by driver factors. Moreover, it was found that roadway type affected drivers’ compliance to VSL instructions under medium and heavy fog conditions. Furthermore, drivers’ familiarity with VSL signs and driver factors were the significant factors affecting drivers’ satisfaction with VSL/CMS advice under reduced visibility conditions. Based on the findings of the survey-based study, several recommendations are suggested as guidelines to improve drivers’ behavior in such reduced visibility conditions by enhancing drivers’ compliance with VSL/CMS instructions. Underground loop detectors (LDs) are the most common freeway traffic surveillance technologies used for various intelligent transportation system (ITS) applications such as travel time estimation and crash detection. Recently, the emphasis in freeway management has been shifting towards using LDs data to develop real-time crash-risk assessment models. Numerous v studies have established statistical links between freeway crash risk and traffic flow characteristics. However, there is a lack of good understanding of the relationship between traffic flow variables (i.e. speed, volume and occupancy) and crashes that occur under reduced visibility (VR crashes). Thus, another objective of this research was to explore the occurrence of reduced visibility related (VR) crashes on freeways using real-time traffic surveillance data collected from loop detectors (LDs) and radar sensors. In addition, it examines the difference between VR crashes to those occurring at clear visibility conditions (CV crashes). To achieve these objectives, Random Forests (RF) and matched case-control logistic regression model were estimated. The results indicated that traffic flow variables leading to VR crashes are slightly different from those variables leading to CV crashes. It was found that, higher occupancy observed about half a mile between the nearest upstream and downstream stations increases the risk for both VR and CV crashes. Moreover, an increase of the average speed observed on the same half a mile increases the probability of VR crash. On the other hand, high speed variation coupled with lower average speed observed on the same half a mile increase the likelihood of CV crashes. Moreover, two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data is advantageous for predicting VR crashes; LDs or AVIs. Thus, this research attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two vi Expressways (SR 408 and SR 417). Also, it investigates which data is better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression using the historical VR crashes, LDs and AVI data. Regarding models estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 minute prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 minute prior to the crash time, affected the likelihood of VR crash occurrence. Argument concerning which traffic data (LDs or AVI) is better for predicting VR crashes is also provided and discussed

A deep machine learning approach for predicting freeway work zone delay using big data

The introduction of deep learning and big data analytics may significantly elevate the performance of traffic speed prediction. Work zones become one of the most critical factors causing congestion impact, which reduces the mobility as well as traffic safety. A comprehensive literature review on existing work zone delay prediction models (i.e., parametric, simulation and non-parametric models) is conducted in this research. The research shows the limitations of each model. Moreover, most previous modeling approaches did not consider user delay for connected freeways when predicting traffic speed under work zone conditions. This research proposes Deep Artificial Neural Network (Deep ANN) and Convolution Neural Network (CNN) traffic speed prediction models, for upstream freeway segments, including those on connected freeways, under work zone conditions. The developed models are able to identify the congestion on the connected links in addition to the upstream mainline segments. The models predict traffic speed with work zone conditions based on traffic volume approaching the work zone, speed during normal conditions, work zone capacity, distance from work zone, vertical road gradient, downstream traffic volume and type of freeway segment. Moreover, the previous efforts in non-parametric approaches did not consider a solution to the overfitting problem of Artificial Neural Network (ANN). The proposed Deep ANN and CNN models use a dropout regularization to mitigate the overfitting issues. When comparing the CNN model to the Deep ANN model and the results of the Work Zone Interactive Management APplication-Planning (WIMAP-P) model, the testing results show higher accuracy with the CNN model compared to the other two models. The CNN model has filters that extract useful inputs from previous layers and reduces the overfitting problems. Dropout regularization technique is used to prevent the co-adaptation of training data. The CNN model is calibrated by varying the number of neurons at each hidden layer, the number of hidden layers, the optimizer algorithm, the filter height and the filter stride. The results indicate that the CNN model outperforms Deep ANN and the model of WIMAP-P in predicting traffic speed under work zone conditions. While traditional efforts were conducted previously on predicting traffic congestion on the upstream freeway segments, the developed CNN model helps transportation agencies in planning for work zones by including both connected freeways and the upstream segments when predicting traffic speed under work zone conditions. Therefore, transportation agencies can prepare more accurate congestion mitigation plans, and provide more accurate user delay plans