Examining Route Diversion And Multiple Ramp Metering Strategies For Reducing Real-time Crash Risk On Urban Freeways

Recent research at the University of Central Florida addressing crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical models capable of calculating the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models yield the rear-end and lane-change crash risk along the freeway in real-time by using static information at various locations along the freeway as well as real-time traffic data that is obtained from the roadway. Because these models use the real-time traffic data, they are capable of calculating the respective crash risk values as the traffic flow changes along the freeway. The purpose of this study is to examine the potential of two Intelligent Transportation System strategies for reducing the crash risk along the freeway by changing the traffic flow parameters. The two ITS measures that are examined in this research are route diversion and ramp metering. Route diversion serves to change the traffic flow by keeping some vehicles from entering the freeway at one location and diverting them to another location where they may be more efficiently inserted into the freeway traffic stream. Ramp metering alters the traffic flow by delaying vehicles at the freeway on-ramps and only allowing a certain number of vehicles to enter at a time. The two strategies were tested by simulating a 36.25 mile section of the Interstate-4 network in the PARAMICS micro-simulation software. Various implementations of route diversion and ramp metering were then tested to determine not only the effects of each strategy but also how to best apply them to an urban freeway. Route diversion was found to decrease the overall rear-end and lane-change crash risk along the network at free-flow conditions to low levels of congestion. On average, the two crash risk measures were found to be reduced between the location where vehicles were diverted and the location where they were reinserted back into the network. However, a crash migration phenomenon was observed at higher levels of congestion as the crash risk would be greatly increased at the location where vehicles were reinserted back onto the network. Ramp metering in the downtown area was found to be beneficial during heavy congestion. Both coordinated and uncoordinated metering algorithms showed the potential to significantly decrease the crash risk at a network wide level. When the network is loaded with 100 percent of the vehicles the uncoordinated strategy performed the best at reducing the rear-end and lane-change crash risk values. The coordinated strategy was found to perform the best from a safety and operational perspective at moderate levels of congestion. Ramp metering also showed the potential for crash migration so care must be taken when implementing this strategy to ensure that drivers at certain locations are not put at unnecessary risk. When ramp metering is applied to the entire freeway network both the rear-end and lane-change crash risk is decreased further. ALINEA is found to be the best network-wide strategy at the 100 percent loading case while a combination of Zone and ALINEA provides the best safety results at the 90 percent loading case. It should also be noted that both route diversion and ramp metering were found to increase the overall network travel time. However, the best route diversion and ramp metering strategies were selected to ensure that the operational capabilities of the network were not sacrificed in order to increase the safety along the freeway. This was done by setting the maximum allowable travel time increase at 5% for any of the ITS strategies considered

Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates

Vulnerable road users (VRUs), such as pedestrians and bicyclists, are at a higher risk of being involved in crashes with motor vehicles, and crashes involving VRUs also are more likely to result in severe injuries or fatalities. Signalized intersections are a major safety concern for VRUs due to their complex and dynamic nature, highlighting the need to understand how these road users interact with motor vehicles and deploy evidence-based countermeasures to improve safety performance. Crashes involving VRUs are relatively infrequent, making it difficult to understand the underlying contributing factors. An alternative is to identify and use conflicts between VRUs and motorized vehicles as a surrogate for safety performance. Automatically detecting these conflicts using a video-based systems is a crucial step in developing smart infrastructure to enhance VRU safety. The Pennsylvania Department of Transportation conducted a study using video-based event monitoring system to assess VRU and motor vehicle interactions at fifteen signalized intersections across Pennsylvania to improve VRU safety performance. This research builds on that study to assess the reliability of automatically generated surrogates in predicting confirmed conflicts using advanced data-driven models. The surrogate data used for analysis include automatically collectable variables such as vehicular and VRU speeds, movements, post-encroachment time, in addition to manually collected variables like signal states, lighting, and weather conditions. The findings highlight the varying importance of specific surrogates in predicting true conflicts, some being more informative than others. The findings can assist transportation agencies to collect the right types of data to help prioritize infrastructure investments, such as bike lanes and crosswalks, and evaluate their effectiveness

Interchange Comparison Safety Tool User Guide

DTFH6116D00040This user guide is intended for use alongside the Federal Highway Administration (FHWA) report Safety Comparisons Between Interchange Types (forthcoming) and the spreadsheet tool FHWA Interchange Configuration Safety Comparison Tool. (1,2) This user guide provides an overview of the data needs and workflow for using the spreadsheet tool. Additionally, it provides information on the interchange configurations for which this tool can be used and the ranges of characteristics to which it applies. Further, this guide provides an overview for finding results within the tool

Safety Comparisons Between Interchange Types

DTFH6116D00040Documentation of potential safety performance impacts is required for Interchange Justification Reports (IJRs) to validate the need for new or modified interchanges.(2) Typically, IJRs are written early in the project planning and design process, with details generally consistent with conceptual design. As such, the design details required for using the Highway Safety Manual (HSM) Part C predictive models may not be known, only the general form of the interchanges.(1) Without these details, using the HSM models to develop an accurate prediction of crash frequency and severity of the design is difficult. In addition, aggregating site-by-site predictions may not fully capture the safety performance impacts when considering the project location as a whole. The Federal Highway Administration sought to develop planning-level models and tools to predict crash frequency and severity for an existing or proposed interchange to further explore and address an IJR application. This predictive model estimates crash frequency and severity for service interchange configurations comprising more than 75 percent of those typically considered in IJRs. The safety performance function includes freeway and crossroad annual average daily traffic (AADT) per lane, ramp AADT, and interchange configuration. The base estimate is adjusted for freeway lanes, crossroad lanes, area type, interchange skew, a nearby adjacent interchange gore, managed lanes, the number of crossroad left-turn lanes at terminals, and variation in ramp volumes

Financial Benefits of Proposed Access Management Treatments

Transportation access management is defined as systematic control of the design, spacing, operation, and locations of street connections, interchanges, driveways, and median openings on the roadway with the purpose of providing vehicle access while preserving the efficiency and safety of the entire transportation system. Access management is a proven method for maintaining and improving roadway capacity; traffic flow; and the safety of traffic, pedestrians, and bicyclists on rural and urban highways and streets. No locally calibrated tool existed that captures the complexity of the current and future public benefits of proposed access management for estimating the financial and other benefits and comparing them with the associated financial costs. Therefore, this study had three primary objectives: (1) develop and validate benefits estimation methodology, (2) compile and derive supporting data for benefits estimation methodology, and (3) develop a software tool for benefits estimation. The result is a simple, straightforward benefits estimation methodology focused on benefits related to traffic operations, traffic safety, environmental impacts, and project costs. The methodology is facilitated by the two spreadsheet software tools that implement the benefits estimation and the calculation of traffic safety benefits, with Synchro/SimTraffic utilized for estimation of traffic operations and environmental impacts

Accounting for Endogeneity in Maintenance Decisions and Overlay Thickness in a Pavement-Roughness Deterioration Model

Pavement deterioration models are an important part of any pavement management system. Many of these models suffer from endogeneity bias because of the inclusion of independent variables correlated with unobserved factors, which are captured by the model's error terms. Examples of such endogenous variables include pavement overlay thickness and maintenance and rehabilitation activities, both of which are not randomly chosen but are in fact decision variables selected by pavement engineers based on field conditions. Inclusion of these variables in a pavement deterioration model can result in biased and inconsistent model parameter estimates, leading to incorrect insights. Previous research has shown that continuous endogenous variables, such as pavement overlay thickness, can be corrected using auxiliary models to replace the endogenous variable with an instrumented variable that has lower correlation with the unobserved error term. Discrete endogenous variables, such as the type of maintenance and rehabilitation activities, have been accounted for by modeling the likelihood of each potential outcome and developing individual deterioration models for each of the potential responses. This paper proposes an alternative approach to accommodate discrete endogenous variables-the selectivity correction method-that allows a single model to incorporate the impacts of all discrete choices. This approach is applied to develop a pavement-roughness progression model that incorporates both continuous and discrete endogenous variables using field data from Washington State. The result is a roughness progression model with consistent parameter estimates, which have more realistic values than those obtained in previous studies that used the same data