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

Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions

Fog is a critical external factor that threatens traffic safety on freeways. Variable speed limit (VSL) control can effectively harmonize vehicle speed and improve safety. However, most existing weather-related VSL controllers are limited to adapt to the dynamic traffic environment. This study developed optimal VSL control strategy under fog conditions with fully consideration of factors that affect traffic safety risks. The crash risk under fog conditions was estimated using a crash risk prediction model based on Bayesian logistic regression. The traffic flow with VSL control was simulated by a modified cell transmission model (MCTM). The optimal factors of VSL control were obtained by solving an optimization problem that coordinated safety and mobility with the help of the genetic algorithm. An example of I-405 in California, USA was designed to simulate and evaluate the effects of the proposed VSL control strategy. The optimal VSL control factors under fog conditions were compared with sunny conditions, and different placements of VSL signs were evaluated. Results showed that the optimal VSL control strategy under fog conditions changed the speed limit more cautiously. The VSL control under fog conditions in this study effectively reduced crash risks without significantly increasing travel time, which is up to 37.15% reduction of risks and only 0.48% increase of total travel time. The proposed VSL control strategy is expected to be of great use in the development of VSL systems to enhance freeway safety under fog conditions

Methods for Utilizing Connected Vehicle Data in Support of Traffic Bottleneck Management

The decision to select the best Intelligent Transportation System (ITS) technologies from available options has always been a challenging task. The availability of connected vehicle/automated vehicle (CV/AV) technologies in the near future is expected to add to the complexity of the ITS investment decision-making process. The goal of this research is to develop a multi-criteria decision-making analysis (MCDA) framework to support traffic agencies’ decision-making process with consideration of CV/AV technologies. The decision to select between technology alternatives is based on identified performance measures and criteria, and constraints associated with each technology. Methods inspired by the literature were developed for incident/bottleneck detection and back-of-queue (BOQ) estimation and warning based on connected vehicle (CV) technologies. The mobility benefits of incident/bottleneck detection with different technologies were assessed using microscopic simulation. The performance of technology alternatives was assessed using simulated CV and traffic detector data in a microscopic simulation environment to be used in the proposed MCDA method for the purpose of alternative selection. In addition to assessing performance measures, there are a number of constraints and risks that need to be assessed in the alternative selection process. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. This research utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied to an ITS investment case study to support freeway bottleneck management. The results of this dissertation indicate that utilizing CV data for freeway segments is significantly more cost-effective than using point detectors in detecting incidents and providing travel time estimates one year after CV technology becomes mandatory for all new vehicles and for corridors with moderate to heavy traffic. However, for corridors with light, there is a probability of CV deployment not being effective in the first few years due to low measurement reliability of travel times and high latency of incident detection, associated with smaller sample sizes of the collected data

Enhancing Freeway Safety through Intervening in Traffic Flow Dynamics Based on Variable Speed Limit Control

New technologies and traffic data sources provide great potential to extend advanced strategies in freeway safety research. The High Definition Monitoring System (HDMS) data contribute comprehensive and precise individual vehicle information. This paper proposes an innovative Variable Speed Limit (VSL) based approach to manage crash risks by intervening in traffic flow dynamics on freeways using HDMS data. We first conducted an empirical analysis on real-time crash risk estimation using a binary logistic regression model. Then, intensive microscopic simulations based on AIMSUN were carried out to explore the effects of various intervention strategies with respect to a 3-lane freeway stretch in China. Different speed limits with distinct compliance rates under specified traffic conditions have been simulated. By taking into account the trade-off between safety benefits and delay in travel time, the speed limit strategies were optimized under various traffic conditions and the model with gradient feedback produces more satisfactory performance in controlling real-time crash risks. Last, the results were integrated into lane management strategies. This research can provide new ideas and methods to reveal the freeway crash risk evolution and active traffic management. Document type: Articl

Traffic Conflict Analysis Under Fog Conditions Using Computer Simulation

The weather condition is a crucial influence factor on road safety issues. Fog is one of the most noticeable weather conditions, which has a significant impact on traffic safety. Such condition reduces the road\u27s visibility and consequently can affect drivers\u27 vision, perception, and judgments. The statistical data shows that many crashes are directly or indirectly caused by the low-visibility weather condition. Hence, it is necessary for road traffic engineers to study the relationship of road traffic accidents and their influence factors. Among these factors, the traffic volume and the speed limits in poor visibility areas are the primary reasons that can affect the types and occurring locations of road accidents. In this thesis, microscopic traffic simulation, through the use of VISSIM software, was used to study the road safety issue and its influencing factors due to limited visibility. A basic simulation model was built based on previously collected field data to simulate Interstate 4 (I-4)\u27s environment, geometry characteristics, and the basic traffic volume composition conditions. On the foundation of the basic simulation model, an experimental model was built to study the conflicts\u27 types and distribution places under several different scenarios. Taking into consideration the entire 4-mile study area on I-4, this area was divided into 3 segments: section 1 with clear visibility, fog area of low visibility, and section 2 with clear visibility. Lower speed limits in the fog area, which were less than the limits in no-fog areas, were set to investigate the different speed limits\u27 influence on the two main types of traffic conflicts: lane-change conflicts and rear-end conflicts. The experimental model generated several groups of traffic trajectory data files. The vehicle conflicts data were stored in these trajectory data files which, contains the conflict locations\u27 coordinates, conflict time, time-to-conflict, and post-encroachment-time among other measures. The Surrogate Safety Assessment Model (SSAM), developed by the Federal Highway Administration, was applied to analyze these conflict data. From the analysis results, it is found that the traffic volume is an important factor, which has a large effect on the number of conflicts. The number of lane-change and rear-end conflicts increases along with the traffic volume growth. Another finding is that the difference between the speed limits in the fog area and in the no-fog areas is another significant factor that impacts the conflicts\u27 frequency. Larger difference between the speed limits in two nearing road sections always leads to more accidents due to the inadequate reaction time for vehicle drivers to brake in time. And comparing to the scenarios that with the reduced speed limits in the low visibility zone, the condition that without the reduced speed limit has higher conflict number, which indicates that the it is necessary to put a lower speed limit in the fog zone which has a lower visibility. The results of this research have a certain reference value for studying the relationship between the road traffic conflicts and the impacts of different speed limits under fog condition. Overall, the findings of this research suggest follow up studies to further investigate possible relationships between conflicts as observed by simulation models and reported crashes in fog areas

Developing and Simulating a Communication Plan for Mitigation of Secondary Crashes: Leveraging Connected Vehicle Technologies

The Federal Highway Administration (FHWA) has identified secondary crashes (SCs) on United States (US) highways as one of the core transportation issues that needs to be addressed. These crashes contribute to increased property damage, injuries, and fatalities and a decline in traffic flow conditions on freeways and adjacent arterials. The purpose of this study was to 1) propose a communication plan that leverages connected vehicle (CV) technologies to increase awareness to road users to target the mitigation of SCs, and 2) to evaluate the potential benefits of the proposed communication plan with CV technologies in alleviating SCs. This study used VISSIM microscopic software to model a freeway road segment on Florida’s Turnpike system and Lyons Road, a parallel arterial. The software was used to replicate the proposed communication plan and CV applications to issue advisories, such as speed, lane-change, or detour advisory to drivers during an incident. A safety evaluation was performed using the Surrogate Safety Assessment Model (SSAM) software by importing trajectory files from VISSIM to analyze generated traffic conflicts. The change in the number of simulated conflicts was used to evaluate the mitigation of SCs. The results showed significant safety benefits using the proposed communication plan with CV technologies. A conflict reduction of up to 98% was observed with full penetration of CVs at low traffic volume. Statistical analysis indicated that different penetration rates of CVs were required to achieve significant safety benefits depending on the analyzed scenario, i.e., traffic volume, number of lanes closed, side of the road the lane is closed, and dissemination of detour advisory

Exploratory Analysis of Ramp Metering on Efficiency, and Safety of Freeways Using Microsimulation

The microscopic Verkehr In Städten – SIMulations Model (VISSIM) stochastic simulator program was used to explore the effectiveness of ramp metering on efficiency, Level of Service, and safety of freeways. Three different geometric configurations of ramp-freeway junctions were evaluated using different traffic volume conditions of the ramp and the freeway. Different signal timing scenarios were designed for the different traffic volume and geometric configuration scenarios. Calibration process was conducted for the collected traffic data that were obtained from cameras and detectors. Two-hundred-eighty models were built and run to explore the effectiveness of the performance and safety of the ramp meters on freeways. Average speed and average travel time of the vehicles passing a 3,000-ft long freeway segment were used as measures of effectiveness of the freeway efficiency evaluation. Average density in the ramp influence area was used to obtain the freeway level of service as a measure of effectiveness of the freeway capacity evaluation. Frequency, types, and severity of vehicle conflicts, which occurred on the 3,000-ft freeway segment, were used as measures of effectiveness of the freeway safety evaluation. The Surrogate Safety Assessment Model (SSAM) program, which was developed by the Federal Highway Administration (FHWA), was used to find the frequency and types of vehicle conflicts, while the severity of vehicle conflicts was separated by a designed method that was retrieved from the previous literature studies. Minitab statistical software was used for some tests such as normality test to determine the appropriate number of samples, and F-tests. A sensitivity analysis was also conducted for better understanding the effectiveness of two assumption changes on the results that were obtained from running the models. The assumptions were car following headway in the ramp influence area and traffic composition on the freeway. The findings of the study provided different results related to the different geometric configurations, signal timing designs, and traffic volumes. Ramp metering at the Type I geometric configuration provided positive effects on the efficiency and safety of the freeway when using the two designed signal timing scenarios when the freeway traffic volume was equal to or greater than 1,250 vehicle per hour per lane (vphpl) and the ramp traffic volume was equal to or greater than 800 vphpl. Ramp metering provided negative effects on the efficiency and safety of the freeway when using it for the Type II geometric configuration. In the geometric configuration of Type III, ramp metering using the signal timing of 2 seconds green and 4 seconds red provided the best efficiency and safety increases when the freeway traffic volume was equal to or greater than 1,250 vphpl and the ramp traffic volume was equal to or greater than 800 vphpl. Conclusively, ramp metering increases efficiency and improves safety of freeways only at specific situations regarding geometric configuration of the ramp-freeway junction type, traffic volume of the freeway and the ramp, and the designed traffic signal of the ramp meters

Field Implementation of Freeway Control

This dissertation presents a series of traffic management studies including freeway and intersection traffic modeling, estimation, control methodologies and field implementation tests. First, a traffic flow prediction method that combines the most recent traffic data with historical traffic data is studied. An autoregressive moving average with exogenous input (ARMAX) model is estimated on-line based on the most recent vehicle detector station (VDS) data. Results obtained using empirical freeway mainline and on-ramp data show that this method outperforms methods that rely only on the historical average of the data to perform a prediction, especially during days with unusual traffic flow demands. Second, two freeway control strategies: coordinated ramp metering (CRM) and variable speed advisory (VSA) are investigated and implemented in field tests. In the control of CRM study, the freeway is modeled by the cell transmission model (CTM) and the control problem is solved by the model predictive control (MPC) scheme. The proposed CRM is deployed in a segment of California State Route 99 Northbound (SR-99N) for a five-week field test. The test results shows that the freeway efficiency can be improved by 7.25% for morning peak hours. In the VSA control study, an advisory speed limit control is designed by using traffic flow stabilization of the Lighthill-Whitham-Richards (LWR) model. The proposed VSA is deployed in a segment of California State Route 78 Eastbound (SR-78E) for a five-week field test. The test results shows that the freeway efficiency can be improved by 8.71% for morning peak hours. Both control strategies indicate freeway efficiency improvement in congested traffic. Third, the large-scale signalized intersection traffic network control by offset optimization is also studied. The traffic network is described by a directed graph and the traffic dynamic is represented by continuous-time fluid queue model with sinusoidal arrival and departure rate assumption. The original non-convex offset optimization problem can be relaxed into a semidefinite program (SDP). The Burer-Monteiro (BM) method is used for solving the large SDP to avoid conic constraints. Two real-world traffic simulation networks, respectively in Manhattan, NY and in Pasadena, CA are constructed for demonstrating the BM method. Numerical simulation results indicate that BM method has good scalability and it can efficiently recover optimal solutions of the SDP