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

Short-Term Travel Time Prediction on Freeways

Short-term travel time prediction supports the implementation of proactive traffic management and control strategies to alleviate if not prevent congestion and enable rational route choices and traffic mode selections to enhance travel mobility and safety. Over the last decade, Bluetooth technology has been increasingly used in collecting travel time data due to the technology’s advantages over conventional detection techniques in terms of direct travel time measurement, anonymous detection, and cost-effectiveness. However, similar to many other Automatic Vehicle Identification (AVI) technologies, Bluetooth technology has some limitations in measuring travel time information including 1) Bluetooth technology cannot associate travel time measurements with different traffic streams or facilities, therefore, the facility-specific travel time information is not directly available from Bluetooth measurements; 2) Bluetooth travel time measurements are influenced by measurement lag, because the travel time associated with vehicles that have not reached the downstream Bluetooth detector location cannot be taken at the instant of analysis. Freeway sections may include multiple distinct traffic stream (i.e., facilities) moving in the same direction of travel under a number of scenarios including: (1) a freeway section that contain both a High Occupancy Vehicle (HOV) or High Occupancy Toll (HOT) lane and several general purpose lanes (GPL); (2) a freeway section with a nearby parallel service roadway; (3) a freeway section in which there exist physically separated lanes (e.g. express versus collector lanes); or (4) a freeway section in which a fraction of the lanes are used by vehicles to access an off ramp. In this research, two different methods were proposed in estimating facility-specific travel times from Bluetooth measurements. Method 1 applies the Anderson-Darling test in matching the distribution of real-time Bluetooth travel time measurements with reference measurements. Method 2 first clusters the travel time measurements using the K-means algorithm, and then associates the clusters with facilities using traffic flow model. The performances of these two proposed methods have been evaluated against a Benchmark method using simulation data. A sensitivity analysis was also performed to understand the impacts of traffic conditions on the performance of different models. Based on the results, Method 2 is recommended when the physical barriers or law enforcement prevent drivers from freely switching between the underlying facilities; however, when the roadway functions as a self-correcting system allowing vehicles to freely switching between underlying facilities, the Benchmark method, which assumes one facility always operating faster than the other facility, is recommended for application. The Bluetooth travel time measurement lag leads to delayed detection of traffic condition variations and travel time changes, especially during congestion and transition periods or when consecutive Bluetooth detectors are placed far apart. In order to alleviate the travel time measurement lag, this research proposed to use non-lagged Bluetooth measurements (e.g., the number of repetitive detections for each vehicle and the time a vehicle spent in the detection zone) for inferring traffic stream states in the vicinity of the Bluetooth detectors. Two model structures including the analytical model and the statistical model have been proposed to estimate the traffic conditions based on non-lagged Bluetooth measurements. The results showed that the proposed RUSBoost classification tree achieved over 94% overall accuracy in predicting traffic conditions as congested or uncongested. When modeling traffic conditions as three traffic states (i.e., the free-flow state, the transition state, and the congested state) using the RUSBoost classification tree, the overall accuracy was 67.2%; however, the accuracy in predicting the congested traffic state was improved from 84.7% of the two state model to 87.7%. Because traffic state information enables the travel time prediction model to more timely detect the changes in traffic conditions, both the two-state model and the three-state model have been evaluated in developing travel time prediction models in this research. The Random Forest model was the main algorithm adopted in training travel time prediction models using both travel time measurements and inferred traffic states. Using historical Bluetooth data as inputs, the model results proved that the inclusion of traffic states information consistently lead to better travel time prediction results in terms of lower root mean square errors (improved by over 11%), lower 90th percentile absolute relative error ARE (improved by over 12%), and lower standard deviations of ARE (improved by over 15%) compared to other model structures without traffic states as inputs. In addition, the impact of traffic state inclusion on travel time prediction accuracy as a function of Bluetooth detector spacing was also examined using simulation data. The results showed that the segment length of 4~8 km is optimal in terms of the improvement from using traffic state information in travel time prediction models

Value of Travel Time Reliability Part II: A Study of Tradeoffs Between Travel Reliability, Congestion Mitigation Strategies and Emissions

Capacity, demand, and vehicle based emissions reduction strategies are compared for several pollutants employing aggregate US congestion and vehicle fleet condition data. We find that congestion mitigation does not inevitably lead to reduced emissions; the net effect of mitigation depends on the balance of induced travel demand and increased vehicle efficiency that in turn depend on the pollutant, congestion level, and fleet composition. In the long run, capacity-based congestion improvements within certain speed intervals can reasonably be expected to increase emissions of CO2e, CO, and NOx through increased vehicle travel volume. Better opportunities for emissions reductions exist for HC and PM2.5 emissions, and on more heavily congested arterials. Advanced-efficiency vehicles with emissions rates that are less sensitive to congestion than conventional vehicles generate less emissions co-benefits from congestion mitigation

Doctor of Philosophy

dissertationTraffic congestion occurs because the available capacity cannot serve the desired demand on a portion of the roadway at a particular time. Major sources of congestion include recurring bottlenecks, incidents, work zones, inclement weather, poor signal timing, and day-to-day fluctuations in normal traffic demand. This dissertation addresses a series of critical and challenging issues in evaluating the benefits of Advanced Traveler Information Strategies under different uncertainty modeling approaches are integrated in this dissertation, namely: mathematical programming, dynamic simulation and analytical approximation. The proposed models aim to 1) represent static-state network user equilibrium conditions, knowledge quality and accessibility of traveler information systems under both stochastic capacity and stochastic demand distributions; 2) characterize day-to-day learning behavior with different information groups under stochastic capacity and 3) quantify travel time variability from stochastic capacity distribution functions on critical bottlenecks. First, a nonlinear optimization-based conceptual framework is proposed for incorporating stochastic capacity, stochastic demand, travel time performance functions and varying degrees of traveler knowledge in an advanced traveler information provision environment. This method categorizes commuters into two classes: (1) those with access to perfect traffic information every day, and (2) those with knowledge of the expected traffic conditions across different days. Using a gap function framework, two mathematical programming models are further formulated to describe the route choice behavior of the perfect information and expected travel time user classes under stochastic day-dependent travel time. This dissertation also presents adaptive day-to-day traveler learning and route choice behavioral models under the travel time variability. To account for different levels of information availability and cognitive limitations of individual travelers, a set of "bounded rationality" rules are adapted to describe route choice rules for a traffic system with inherent process noise and different information provision strategies. In addition, this dissertation investigates a fundamental problem of quantifying travel time variability from its root sources: stochastic capacity and demand variations that follow commonly used log-normal distributions. The proposed models provide theoretically rigorous and practically usefully tools to understand the causes of travel time unreliability and evaluate the system-wide benefit of reducing demand and capacity variability

No More Freeways: Urban Land Use-Transportation Dynamics without Freeway Capacity Expansion

Observations of the various limitations of freeway capacity expansion have led to a provocative planning and policy question – What if we completely stop building additional freeway capacity. From a theoretical perspective, as a freeway transportation network matures, there exists a saturation point beyond which any additional freeway capacity would only be counterproductive from a welfare point of view, and worsen the existing urban transportation problems. Traditional benefit/cost analysis of individual freeway capacity expansion projects often ignores long-term induced demand and land use changes and does not represent a systems approach to this important theoretical issue. From a practical perspective, a no-more-freeway policy can relieve transportation funds for other potentially more effective usages, such as improving urban arterial street system, improving transit level of service and coverage, implementing demand management and pricing strategies, and facilitating more efficient land use patterns (e.g. high density in-fill and transit-oriented developments). This research answers the following critical land use-transportation planning questions. Improved knowledge on these issues should benefit planers and decision-makers who pursue mobility and sustainability objectives and have the power to shape future cities. (1). Under what conditions will freeway capacity expansion become counterproductive to urban planning objectives (where is the saturation point)? (2). How will land use and transportation evolve under a “No-More-Freeway” policy? (3). What are the implications of such a policy on congestion, land use efficiency, transportation finance, and social welfare? (4). What is the impact of a less restrictive “No-More-Freeway” policy that only allows private-section freeway investments and relieves public-section freeway investments for other compelling transportation needs. The analysis in this project builds upon a modeling tool, ABSOLUTE, developed by the P.I. in previous research projects. ABSOLUTE is an Agent-Based Simulator Of Land Use-Transportation Evolution, which translates planning policies such as the “No-More- Freeway” policy into alternative urban growth paths and possibly urban growth equilibria (land use and transportation system equilibria). Due to the “Small Start” nature of this OTREC project, the analysis focuses primarily on stylized urban areas, and empirical analysis of the “No-More- Freeway” policy is only conducted for one policy scenario on the Twin Cities, MN, area

Accounting for peak shifting in traditional cost-benefit analysis

When cost-benefit analysis fails to account for peak-shifting the benefits of road improvement options are miscalculated. Using theory from transportation economics, we derive a simple model that disaggregates the average daily equilibrium into peak, counter-peak, and off-peak equilibria. This paper demonstrates how accounting for peak-shifting improves the performance of cost-benefit analysis.Transportation Demand, Transportation Supply, Congestion, Cost/Benefit Analysis, Planning Policy

Optimization of path based sensor spacing on a freeway segment for travel time prediction during incidents

Congestion on freeways is increasing and a key source of it is non-recurring incidents. Accurate vehicle travel time predictions are needed during these incidents in order for roadway users to make informed trip decisions. Path based sensors are becoming a leading technology in gathering real-time travel time data. The data is used to make travel time predictions that are then provided through various means, such as dynamic message signs, to roadway users. These types of sensor are located at stationary points along a roadway and collect individual vehicle travel time data from vehicles as they drive pass the sensors. The accuracy of the predictions, in terms of representing future travel times, is dependent on many factors including the sensor spacing along the roadway, the duration and location of a traffic incident, and the uncongested and congested traffic speeds and traffic flows. Understanding the relationship between the travel time prediction accuracy and the different variables is necessary to optimize sensor spacing. In addition, because incidents occur at different times of the day, have varying durations, occur at different locations, and cause different capacity reductions depending on the severity of the incident, the sensor spacing cannot be based on one incident scenario. Instead, multiple incident scenarios, along with the probability of each occurring, needs to be taken into account. Path based sensor spacing during incidents on a freeway segment is optimized in this dissertation. In addition, the marginal benefit of additional sensors is calculated. A mathematical model and a solution methodology are developed. The mathematical model applies macroscopic traffic principles and shock wave theory. It calculates the travel time prediction error by sensor spacing during an incident on a freeway segment. The solution algorithm consists of four main steps. First, historical incident data for the roadway are gathered. Second, the mathematical model is applied to determine the average travel time prediction error by sensor spacing for each of the historical incidents. Third, the weighted average travel time prediction error by sensor spacing is calculated, which considers all the possible incidents and the frequency of each occurring. Fourth, the optimal spacing is chosen which minimizes the weighted average error. The applicability of the model and solution methodology is demonstrated through a case study of a ten mile freeway segment in Northern New Jersey