A Comprehensive Study on the Estimation of Freeway Travel Time Index and the Effect of Traffic Data Quality

Travel time reliability aims to quantify the variation of travel time by using the entire range of travel times for a given trip, for a selected time period over a selected horizon. A trip can occur over a segment, facility or any subset of the transportation network, for the purpose of calculating travel time reliability. As one of the most important performance measures, travel time reliability reports the number of trips that fail or succeed according to a predetermined standard. Unreliability is usually caused by the interaction of factors that influence travel times, such as fluctuations in demand due to daily or seasonal variation, or special events, traffic control devices, traffic incidents, inclement weather, work zones, and physical capacity. These factors collectively produce travel times that can be better presented by a probability distribution. A well-accepted measure of travel time reliability is the Travel Time Index (TTI) formulated as the ratio of travel time in the peak period to the travel time at free-flow conditions. In this thesis, the Travel Time Index values were calculated and compared from two different kinds of data sources: probe vehicles and fixed location detectors. Speed from vehicle probe data can be retrieved from the National Performance Management Research Dataset (NPMRDS) and the freeway segment speed can be calculated by dividing the segment length by the total travel time. Spot speed from fixed location detectors can be retrieved from the Wisconsin’s Archived Data Management Systems (ADMS), V-SPOC (Volume, Speed and Occupancy) which measures the speed at certain locations of a segment. The free flow speed also varies by data source. In the V-SPOC data, the posted speed limit is considered to be the free flow speed and in the NPMRDS data, the reference speed which is the 85th percentile speed of all observed sample speeds is considered to be the free flow speed. The effect of data quality on the TTI values is also examined in the thesis. Inductive loop detectors are a major source of traffic information, but they are often criticized for generating missing and faulty data which compromise real-time traffic control, operations, and management. There is no doubt that the quality of data will affect the accuracy of the calculation of Travel Time Index and its influence needs to be quantified. This study area was chosen to be the one that contains all different kinds road segments like basic, weaving, on ramp and off ramp segments. The result shows that the removal of invalid data improves the TTI index in the congested traffic conditions. Lastly, a traffic simulation application, FREEVAL-RL tool, was applied to calculate the Travel Time Index. The sensitivity analysis of some important parameters used in the FREEVAL-RL Tool was performed. Calibration procedure was designed and carried out for the tool to reflect the real-world scenarios such as are Capacity Adjustment Factor, jam density and capacity drop. The outcome of the calibrated model was consistently matched to the travel time distribution in terms of mean, 50th percentile, 80th percentile, 95th percentile Travel Time Index (TTI) reported in the NPMRDS data

A Comprehensive Study on the Estimation of Freeway Travel Time Index and the Effect of Traffic Data Quality

Travel time reliability aims to quantify the variation of travel time by using the entire range of travel times for a given trip, for a selected time period over a selected horizon. A trip can occur over a segment, facility or any subset of the transportation network, for the purpose of calculating travel time reliability. As one of the most important performance measures, travel time reliability reports the number of trips that fail or succeed according to a predetermined standard. Unreliability is usually caused by the interaction of factors that influence travel times, such as fluctuations in demand due to daily or seasonal variation, or special events, traffic control devices, traffic incidents, inclement weather, work zones, and physical capacity. These factors collectively produce travel times that can be better presented by a probability distribution. A well-accepted measure of travel time reliability is the Travel Time Index (TTI) formulated as the ratio of travel time in the peak period to the travel time at free-flow conditions. In this thesis, the Travel Time Index values were calculated and compared from two different kinds of data sources: probe vehicles and fixed location detectors. Speed from vehicle probe data can be retrieved from the National Performance Management Research Dataset (NPMRDS) and the freeway segment speed can be calculated by dividing the segment length by the total travel time. Spot speed from fixed location detectors can be retrieved from the Wisconsin’s Archived Data Management Systems (ADMS), V-SPOC (Volume, Speed and Occupancy) which measures the speed at certain locations of a segment. The free flow speed also varies by data source. In the V-SPOC data, the posted speed limit is considered to be the free flow speed and in the NPMRDS data, the reference speed which is the 85th percentile speed of all observed sample speeds is considered to be the free flow speed. The effect of data quality on the TTI values is also examined in the thesis. Inductive loop detectors are a major source of traffic information, but they are often criticized for generating missing and faulty data which compromise real-time traffic control, operations, and management. There is no doubt that the quality of data will affect the accuracy of the calculation of Travel Time Index and its influence needs to be quantified. This study area was chosen to be the one that contains all different kinds road segments like basic, weaving, on ramp and off ramp segments. The result shows that the removal of invalid data improves the TTI index in the congested traffic conditions. Lastly, a traffic simulation application, FREEVAL-RL tool, was applied to calculate the Travel Time Index. The sensitivity analysis of some important parameters used in the FREEVAL-RL Tool was performed. Calibration procedure was designed and carried out for the tool to reflect the real-world scenarios such as are Capacity Adjustment Factor, jam density and capacity drop. The outcome of the calibrated model was consistently matched to the travel time distribution in terms of mean, 50th percentile, 80th percentile, 95th percentile Travel Time Index (TTI) reported in the NPMRDS data

Advanced Quantitative Methods for Imminent Detection of Crash Prone Conditions and Safety Evaluation

Crashes can be accurately predicted through reliable data sources and rigorous statistical models; and prevented through data-driven, evidence-based traffic control strategies. Both predictive analysis and analysis to estimate the causal effect of traffic variables of real-time crashes are instrumental to crash prediction and a better understanding of the mechanism of crash occurrence. However, the research on the second analysis type is very limited for real-time crash prediction; and the conventional predictive analysis using inductive loop detector data has accuracy issues related to inconsistently and distantly spaced loop detectors. The effectiveness of traffic control strategies for improving safety performance cannot be measured and compared without an appropriate traffic simulation application. This dissertation is an attempt to address these research gaps. First, it conducts the propensity score based analysis to assess the causal effect of speed variation on crash occurrence using the crash data and ILD data. As a casual analysis method, the propensity score based model is applied to generate samples with similar covariate distributions in both high- and low-speed variation groups of all cases. Under this setting, the confounding effects are removed and the causal effect of speed variation can be obtained. Second, it conducts a predictive analysis on lane-change related crashes using lane-specific traffic data collected from three ILD stations near a crash location. The real-time traffic data for the two lanes – the vehicle’s lane (subject lane) and the lane to which that a vehicle intends to change (target lane) – are more closely related with lane-change related crashes, as opposed to congregated traffic data for all lanes. It is found that lane-specific variables are appropriate to study the lane-change frequency and the resulting lane-change related crashes. Third, it conducts a predictive analysis on real-time crashes using simulated traffic data. The purpose of using simulated traffic data rather than real data is to mitigate the temporal and spatial issues of detector data. The cell transmission model (CTM), a macroscopic simulation model, is employed to instrument the corridor with a uniform and close layout of virtual detector stations that measure traffic data when physical stations are not available. Traffic flow characteristics at the crash site are simulated by CTM 0-5 minutes prior to a crash. It shows that the simulated traffic data can improve the prediction performance by accounting for the spatial-tempo issue of ILD data. Fourth, it presents a novel approach to modeling freeway crashes using lane-specific simulated traffic data. The new model can not only account for the spatial-tempo issues of detector data but also account for heterogeneous traffic conditions across lanes using a lane-specific cell transmission model (LSCTM). The LSCTM illustrates both discretionary lane-changing (DLC) and mandatory lane-changing (MLC) activities. This new approach presents a viable alternative for utilizing traffic simulation models for safety analysis and evaluation. Last, it develops a crash prediction and prevention application (CPPA) based on simulated traffic data to detect crash-prone conditions and to help select the desirable traffic control strategies for crash prevention. The proposed application is tested in a case study with VSL strategies, and results show that the proposed crash prediction and prevention method could effectively detect crash-prone conditions and evaluate the safety and mobility impacts of various VSL alternatives before their deployment. In the future, the application will be more user-friendly and can provide both online traffic operations support as well as offline evaluation of various traffic control operations and methods

Multi-level Safety Performance Functions For High Speed Facilities

High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. iii At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized iv appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users

Traffic Crash Prediction Using Machine Learning Models

Traffic crashes account for most of casualties and injuries worldwide, and there has been growing concerns and studies regarding the contributing factors of traffic crashes. There are many factors causing or related to an occurrence of traffic crash, e.g., land use, traffic flow conditions, driver behavior and weather condition. This paper studied the spatial and temporal distribution of crashes on highway and developed real-time prediction models for crash occurrence. Traffic flow data, weather data, and crash data from multiple data sources were collected and processed to develop the model. Multiple machine learning models, such as SVM model and Decision Tree model, were used as the candidate models. It was found that weather, crash time, and traffic flow shortly prior to the crash occurrence are critical impacting factors for real-time crash prediction. The candidate models have low to moderate sensitivity to predict the crash occurrences due to limited sample size. To use the models in a traffic operations environment, a prediction tool with interactive map could be developed to proactively monitor crash hot spots and prepare staffing and resources for the potential crash occurrences

Methodological evolution and frontiers of identifying, modeling and preventing secondary crashes on highways

© 2018 Elsevier Ltd Secondary crashes (SCs) or crashes that occur within the boundaries of the impact area of prior, primary crashes are one of the incident types that frequently affect highway traffic operations and safety. Existing studies have made great efforts to explore the underlying mechanisms of SCs and relevant methodologies have been e volving over the last two decades concerning the identification, modeling, and prevention of these crashes. So far there is a lack of a detailed examination on the progress, lessons, and potential opportunities regarding existing achievements in SC-related studies. This paper provides a comprehensive investigation of the state-of-the-art approaches; examines their strengths and weaknesses; and provides guidance in exploiting new directions in SC-related research. It aims to support researchers and practitioners in understanding well-established approaches so as to further explore the frontiers. Published studies focused on SCs since 1997 have been identified, reviewed, and summarized. Key issues concentrated on the following aspects are discussed: (i) static/dynamic approaches to identify SCs; (ii) parametric/non-parametric models to analyze SC risk, and (iii) deployable countermeasures to prevent SCs. Based on the examined issues, needs, and challenges, this paper further provides insights into potential opportunities such as: (a) fusing data from multiple sources for SC identification, (b) using advanced learning algorithms for real-time SC analysis, and (c) deploying connected vehicles for SC prevention in future research. This paper contributes to the research community by providing a one-stop reference for research on secondary crashes

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent advancements in artificial intelligence, sensor fusion and algorithms have brought about the introduction of a proactive safety management system closer to reality. The basic prerequisite for developing such a system is to have a reliable crash prediction model that takes real-time traffic data as input and evaluates their association with crash risk. Since the early 21st century, several studies have focused on developing such models. Although the idea has considerably matured over time, the endeavours have been quite discrete and fragmented at best because the fundamental aspects of the overall modelling approach substantially vary. Therefore, a number of transitional challenges have to be identified and subsequently addressed before a ubiquitous proactive safety management system can be formulated, designed and implemented in real-world scenarios. This manuscript conducts a comprehensive review of existing real-time crash prediction models with the aim of illustrating the state-of-the-art and systematically synthesizing the thoughts presented in existing studies in order to facilitate its translation from an idea into a ready to use technology. Towards that journey, it conducts a systematic review by applying various text mining methods and topic modelling. Based on the findings, this paper ascertains the development pathways followed in various studies, formulates the ubiquitous design requirements of such models from existing studies and knowledge of similar systems. Finally, this study evaluates the universality and design compatibility of existing models. This paper is, therefore, expected to serve as a one stop knowledge source for facilitating a faster transition from the idea of real-time crash prediction models to a real-world operational proactive traffic safety management system

Impacts of speed variations on freeway crashes by severity and vehicle type

Speed variations are identified as potentially important predictors of freeway crash rates; however, their impacts on crashes are not entirely known. Existing findings tend to be inconsistent possibly because of the different definitions for speed variations, different crash type consideration or different modelling and data aggregation approaches. This study explores the relationships of speed variations with crashes on a freeway section in the UK. Crashes split by vehicle type (heavy and light vehicles) and by severity mode (killed/serious injury and slight injury crashes) are aggregated based on the similarities of the conditions just before their occurrence (condition-based approach) and modelled using Multivariate Poisson lognormal regression. The models control for speed variations along with other traffic and weather variables as well as their interactions. Speed variations are expressed as two separate variables namely the standard deviations of speed within the same lane and between-lanes over a five minute interval. The results, similar for all crash types (by coefficient significance and sign), suggest that crash rates increase as the within lane speed variations raise, especially at higher traffic volumes. Higher speeds coupled with greater volume and high between-lanes speed variation also increase crash likelihood. Overall, the results suggest that specific combinations of traffic characteristics increase the likelihood of crash occurrences rather than their individual effects. Identification of these specific crash prone conditions could improve our understanding of crash risk and would support the development of more efficient safety countermeasures

An Examination of Traffic Volume during Snow Events in Northeast Ohio

Snowfall presents a hazard to drivers by reducing visibility and increasing safe stopping distances. Some drivers cancel trips if snowfall is occurring or forecast, and traffic volumes often decrease on snowy days. Lake-effect snow is very localized and is thus hypothesized to have a lesser influence on traffic volume than synoptic-scale snow, which usually covers a broader areal extent. We analyze traffic volume in northeast Ohio during 25 snow events and use a matched-pair analysis to determine whether volumes differ between lake-effect and synoptic-scale snowfall in these regions. We also examine the rate at which traffic volume decreases during snow events by time of day and day of week. Results indicate that there is little difference in mean traffic volume decreases when comparing lake-effect and synoptic-scale snow. Hourly trends suggest that traffic volume is most sensitive to snowfall during the midday on weekdays and late afternoon on weekends and least sensitive to snowfall during the overnight hours. Findings presented herein can assist in transportation planning, risk analysis and roadway safety