Evaluation of Coordinated Ramp Metering (CRM) Implemented By Caltrans

Coordinated ramp metering (CRM) is a critical component of smart freeway corridors that rely on real-time traffic data from ramps and freeway mainline to improve decision-making by the motorists and Traffic Management Center (TMC) personnel. CRM uses an algorithm that considers real-time traffic volumes on freeway mainline and ramps and then adjusts the metering rates on the ramps accordingly for optimal flow along the entire corridor. Improving capacity through smart corridors is less costly and easier to deploy than freeway widening due to high costs associated with right-of-way acquisition and construction. Nevertheless, conversion to smart corridors still represents a sizable investment for public agencies. However, in the U.S. there have been limited evaluations of smart corridors in general, and CRM in particular, based on real operational data. This project examined the recent Smart Corridor implementation on Interstate 80 (I-80) in the Bay Area and State Route 99 (SR-99, SR99) in Sacramento based on travel time reliability measures, efficiency measures, and before-and-after safety evaluation using the Empirical Bayes (EB) approach. As such, this evaluation represents the most complete before-and-after evaluation of such systems. The reliability measures include buffer index, planning time, and measures from the literature that account for both the skew and width of the travel time distribution. For efficiency, the study estimates the ratio of vehicle miles traveled vs. vehicle hour traveled. The research contextualizes before-and-after comparisons for efficiency and reliability measures through similar measures from another corridor (i.e., the control corridor of I-280 in District 4 and I-5 in District 3) from the same region, which did not have CRM implemented. The results show there has been an improvement in freeway operation based on efficiency data. Post-CRM implementation, travel time reliability measures do not show a similar improvement. The report also provides a counterfactual estimate of expected crashes in the post-implementation period, which can be compared with the actual number of crashes in the “after” period to evaluate effectiveness

Exploring the Potentials of Using Crowdsourced Waze Data in Traffic Management: Characteristics and Reliability

Real-time traffic information is essential to a variety of practical applications. To obtain traffic data, various traffic monitoring devices, such as loop detectors, infrastructure-mounted sensors, and cameras, have been installed on road networks. However, transportation agencies have sought alternative data sources to monitor traffic, due to the high installation and maintenance cost of conventional data collecting methods. Recently, crowdsourced traffic data has become available and is widely considered to have great potential in intelligent transportation systems. Waze is a crowdsourcing traffic application that enables users to share real-time traffic information. Waze data, including passively collected speed data and actively reported user reports, is valuable for traffic management but has not been explored or evaluated extensively. This dissertation evaluated and explored the potential of Waze data in traffic management from different perspectives. First, this dissertation evaluated and explored Waze traffic speed to understand the characteristics and reliability of Waze traffic speed data. Second, a calibration-free incident detection algorithm with traffic speed data on freeways was proposed, and the results were compared with other commonly used algorithms. Third, a spatial and temporal quality analysis of Waze accident reports to better understand their quality and accuracy was performed. Last, the dissertation proposed a network-based clustering algorithm to identify secondary crashes with Waze user reports, and a case study was performed to demonstrate the applicability of our method and the potential of crowdsourced Waze user reports

The use of real-time connected vehicles and HERE data in developing an automated freeway incident detection algorithm

Traffic incidents cause severe problems on roadways. About 6.3 million highway crashes are reported annually only in the United States, among which more than 32,000 are fatal crashes. Reducing the risk of traffic incidents is key to effective traffic incident management (TIM). Quick detection of unexpected traffic incidents on roadways contribute to quick clearance and hence improve safety. Existing techniques for the detection of freeway incidents are not reliable. This study focuses on exploring the potential of emerging connected vehicles (CV) technology in automated freeway incident detection in the mixed traffic environment. The study aims at developing an automated freeway incident detection algorithm that will take advantage of the CV technology in providing fast and reliable incident detection. Lee Roy Selmon Expressway was chosen for this study because of the THEA CV data availability. The findings of the study show that emerging CV technology generates data that are useful for automated freeway incident detection, although the market penetration rate was low (6.46%). The algorithm performance in terms of detection rate (DR) and false alarm rate (FAR) indicated that CV data resulted into 31.71% DR and zero FAR while HERE yielded a 70.95% DR and 9.02% FAR. Based on Pearson’s correlation analysis, the incidents detected by the CV data were found to be similar to the ones detected by the HERE data. The statistical comparison by ANOVA shows that there is a difference in the algorithm’s detection time when using CV data and HERE data. 17.07% of all incidents were detected quicker when using CV data compared to HERE data, while 7.32% were detected quicker when using HERE data compared to CV data

Real-time Traffic State Assessment using Multi-source Data

The normal flow of traffic is impeded by abnormal events and the impacts of the events extend over time and space. In recent years, with the rapid growth of multi-source data, traffic researchers seek to leverage those data to identify the spatial-temporal dynamics of traffic flow and proactively manage abnormal traffic conditions. However, the characteristics of data collected by different techniques have not been fully understood. To this end, this study presents a series of studies to provide insight to data from different sources and to dynamically detect real-time traffic states utilizing those data. Speed is one of the three traffic fundamental parameters in traffic flow theory that describe traffic flow states. While the speed collection techniques evolve over the past decades, the average speed calculation method has not been updated. The first section of this study pointed out the traditional harmonic mean-based average speed calculation method can produce erroneous results for probe-based data. A new speed calculation method based on the fundamental definition was proposed instead. The second section evaluated the spatial-temporal accuracy of a different type of crowdsourced data - crowdsourced user reports and revealed Waze user behavior. Based on the evaluation results, a traffic detection system was developed to support the dynamic detection of incidents and traffic queues. A critical problem with current automatic incident detection algorithms (AIDs) which limits their application in practice is their heavy calibration requirements. The third section solved this problem by proposing a selfevaluation module that determines the occurrence of traffic incidents and serves as an autocalibration procedure. Following the incident detection, the fourth section proposed a clustering algorithm to detect the spatial-temporal movements of congestion by clustering crowdsource reports. This study contributes to the understanding of fundamental parameters and expands the knowledge of multi-source data. It has implications for future speed, flow, and density calculation with data collection technique advancements. Additionally, the proposed dynamic algorithms allow the system to run automatically with minimum human intervention thus promote the intelligence of the traffic operation system. The algorithms not only apply to incident and queue detection but also apply to a variety of detection systems

Simulation Exploration of the Potential of Connected Vehicles in Mitigating Secondary Crashes

Secondary crashes (SCs) on freeways are a major concern for traffic incident management systems. Studies have shown that their occurrence is significant and can lead to deterioration of traffic flow conditions on freeways in addition to injury and fatalities, albeit their magnitudes are relatively low when compared to primary crashes. Due to the limited nature of crash data in analyzing freeway SCs, surrogate measures provide an alternative for safety analysis for freeway analysis using conflict analysis. Connected Vehicles (CVs) have seen compelling technological advancements since the concept was introduced in the 1990s. In recent years, CVs have emerged as a feasible application with many safety benefits especially in the urban areas, that can be deployed in masses imminently. This study used a freeway model of a road segment in Florida’s Turnpike system in VISSIM microscopic simulation software to generate trajectory files for conflict analysis in SSAM software, to analyze potential benefits of CVs in mitigating SCs. The results showed how SCs could potentially be reduced with traffic conflicts being decreased by up to 90% at full 100% composition of CVs in the traffic stream. The results also portrayed how at only 25% CV composition, there was a significant reduction of conflicts up to 70% in low traffic volumes and up to 50% in higher traffic volumes. The statistical analysis showed that the difference in average time-to-collision surrogate measure used in deriving conflicts was significant at all levels of CV composition

Highway Performance and Time-Sensitive Industries

Communities and states are using every means available to them to attract and retain economic activity. One such strategy is to plan for the changing needs of new and existing businesses. In the past two decades, firms have come to view time as one of their most precious resources. Some businesses have adopted efficiently timed production methods like just-in-time, in which inventory and safety stock are minimized; deliveries of intermediate goods at all stages of production are synchronized with suppliers so that at no point do products linger. Highway projects that reduce unanticipated delays enhance the ability of time sensitive businesses to maintain closely timed production and sales schedules. Perhaps the most important type of delay in this context is that produced by incidents, which are events that disrupt normal traffic flow. In addition to accidents, incidents include stalled vehicles, debris on the road, or other impediments to orderly flow. While they are rare events, incidents do happen, and they can greatly affect travel times, especially on roads operating at near capacity. Highway improvements can reduce the likelihood of incidents and reduce the severity of impacts when incidents do occur. How to measure increases in highway system performance for time-sensitive businesses when these systems are upgraded is a focus of this monograph. We begin by examining the changes in the business environment that precipitated the movement toward time-sensitive production. Then, from an extensive survey, we conclude that although businesses in Iowa are somewhat less time-sensitive than businesses in many other places, Iowa’s businesses anticipate tighter production schedules in the future. We show how traffic incidents and incident-produced congestion erode highway performance for time-sensitive industries. An analysis of the causes and consequences of incident-produced delays provides the foundation for our model of incident-produced delay, which we have developed to gauge highway performance for time-sensitive firms. This research was a joint effort between researchers at the University of Iowa Public Policy Center and Iowa State University’s Department of Transportation and Logistics. The Iowa Department of Transportation provided funding for this project

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

ASSESSING FREEWAY CRASH RISK USING CROWDSOURCED WAZE INCIDENT ALERTS

Traffic data obtained through crowdsourcing are becoming more accessible to traffic agencies due to advancements in smartphone technology. Traffic managers aim to use this data to complement their conventional sources of data and provide additional context in their analysis. In this study, Waze incident alerts are integrated with GPS-Probe speed data and Kentucky State Police (KSP) crashes to assess their impact on traffic flow and safety on freeways in Kentucky. The analysis showed that the presence of a vehicle on the shoulder is associated with about 36.7% of freeway crashes in Kentucky. The presence of a vehicle on the shoulder coupled with congestion were 11.7% of the crashes. As such, the correlation between vehicle on shoulder, congestion and crashes was significant. Albeit present within the vicinity of 7.4% of crashes, the presence of a vehicle in the travel lane did not show as having a significant correlation with crashes. Linking Waze crash alerts with crashes and assessing their spatiotemporal patterns, it is found that Waze crashes are spatially accurate and hence could be used as an alternate source for identifying crashes, sometimes earlier, in Kentucky and hence cutting down incident response and clearance times. The data used in this study and the analytical methods employed offer much needed insight into the potential of crowdsourced traffic incident data for traffic monitoring to ensure safety

Evaluating the Performance of Cooperative Merging Assistance System for Aging Drivers

Freeway merging maneuvers demand considerable attention by drivers and are among the more complex operations drivers must perform on freeways. Aging drivers, a growing population in the United States, face added challenges when merging. This study utilized Vissim models created in a previous study that modeled the behavior of aging drivers during freeway merging. An algorithm for Cooperative Merging Assistance System (CMAS) that utilizes Connected Vehicle (CV) technology was developed in this study. The Vissim models were created for two interchanges on I-75 in Fort Myers, Florida, each with different geometric characteristics. Acceleration lane lengths of 1000ft and 1500ft were analyzed in this study, and the CV environment was created in Vissim through the Component Object Model (COM) Interface. A sensitivity analysis was conducted by varying CV penetration rates, composition of aging on-ramp drivers, and mainline and on-ramp traffic flows to analyze the effects of CV technology under different levels of service (LOSs). Merging location, merging speed and vehicle interaction states (braking for lane change, emergency stop and cooperative braking) together with deceleration rate were the measures of effectiveness (MOEs) considered. Findings showed the number of aging drivers merging late onto the freeway can be decreased by up to 60.0% when CMAS was employed, while there was no significant change in merging speed at 95% confidence level when CMAS was employed. Furthermore, the results showed that CMAS reduced the percentages of aging drivers braking for lane change or emergency stop and also hard braking by up to 100% for low traffic conditions (LOS A and B). A maximum reduction of 82.2% was observed for cooperative braking of mainline vehicles when CMAS was employed. The reductions in interaction states were significant at 95% confidence level according to Mann-Kendall trend test

Analyzing Benefits of Connected Vehicle Technologies During Incidents on Freeways and Diversion Strategies Implementation: A Microsimulation-Based Case Study of Florida\u27s Turnpike

The full utilization of connected vehicles (CVs) is highly anticipated to become a reality soon. As CVs become increasingly prevalent in our roadway network, connected technologies have enormous potential to improve safety. This study conducted a microscopic simulation to quantify the benefits of CVs in improving freeway safety along a 7.8-mile section on Florida’s Turnpike (SR-91) system. The simulation incorporated driver compliance behavior in a CV environment. The simulation was implemented via an existing VISSIM network model partially developed by the Florida Department of Transportation (FDOT). In addition, the study analyzed how CVs would assist in detour operations as a strategy for congestion management during traffic incidents on freeways. The Surrogate Safety Assessment Model (SSAM) software was used to evaluate the benefits of CVs based on time-to-collision (TTC) as the performance measure. The TTC was evaluated at various CV market penetration rates (MPRs) of 0%, 25%, 50%, 75%, and 100%. The results showed a decreasing trend of conflicts for morning and evening peak hours, especially from 25% to 100% CV MPRs. The benefits were statistically significant at a 95% confidence level for high CV MPR (above 25%). Upon an incident on the freeway, at higher CV MPRs simulations, the detour strategy seemed to reduce travel time on the freeway. Besides, the detour strategy was more helpful when the incident clearance duration lasted more than 30 minutes. Findings from this study may help the incident management process prepare for detour strategies based on the severity of the incident at hand and could explain the importance of CVs in supporting warning and management strategies for drivers to improve safety on freeways. Keywords: Conflicts, Connected Vehicles, Driver Compliance Rate, Detour, Incident Modeling, Safety Surrogate Measure