Xiaoyue Cathy Liu

Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways

This study analyzes driver injury severities for single-vehicle crashes occurring in rural and urban areas using data collected in New Mexico from 2010 to 2011. Nested logit models and mixed logit models are developed in order to account for the correlation between severity categories (No injury, Possible injury, Visible injury, Incapacitating injury and fatality) and individual heterogeneity among drivers. Various factors, such as crash and environment characteristics, geometric features, and driver behavior are examined in this study. Nested logit model and mixed logit model reveal similar results in terms of identifying contributing factors for driver injury severities. In the analysis of urban crashes, only the nested logit model is presented since no random parameter is found in the mixed logit model. The results indicate that significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. There are 5 variables found only significant in the rural model and six significant variables identified only in the urban crash model. These findings can help transportation agencies develop effective policies or appropriate strategies to reduce injury severity resulting from single-vehicle crashes.

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation.

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.

Modeling Traffic Flow Dynamics on Managed Lane Facility: Approach Based on Cell Transmission Model

With the increasing attention paid to environmental effects and sustainable infrastructure, transportation agencies are now seeking more cost-effective and environmentally friendly countermeasures against traffic congestion than the traditional roadway expansion. Managed lane (ML) systems, as an innovative strategy for managing the roadway conditions in real time, have been gaining increasing popularity in the recent decade. However, the unique characteristics of ML facilities, such as the frictional effects between general purpose lanes (GPLs) and their adjacent ML, are not well studied and modeled. This paper investigates the interaction between GPLs and MLs, as buffer-separated ML facilities are readily affected by congestion in the adjacent GPLs. The frictional effect is quantified through development of speed–flow curves for the ML facility. A traffic flow model is developed on the basis of the cell transmission model incorporating this frictional effect to model the traffic evolution on the ML facility. This model is capable of replicating the traffic flow pattern, including congestion onset, propagation, and dissipation at a macroscopic level. The model also captures the effect of GPL congestion on the ML. This model can serve as the underlying ML traffic flow model in future research for evaluating the effectiveness of different tolling strategies.

Operational Performance and Speed-Flow Relationships for Basic Managed Lane Segments

Managed lane facilities, including high-occupancy vehicle (HOV) lanes, high-occupancy toll lanes, and express lanes, have become attractive tools for managing todays transportation system. Although managed lanes, specifically, HOV lanes, have existed for several decades, there has been little documentation of their traffic flow behaviors. Because these facilities tend to take on a variety of configurations, including different numbers of managed lanes and separation types from abutting general purpose lanes, transportation engineers should understand the traffic flow differences among facility types. An understanding of the interaction between managed lanes and parallel general purpose lanes is also needed for assessing the performance of managed lanes. A study was done to investigate the performance and traffic flow behavior of managed lane facilities at sites across the country. Traffic flow behavior for five facility types, based on separation type and number of lanes, was analyzed. Factors such as frictional impact on the managed lane—caused by poor performance of the general purpose lanes and slow vehicle effects on managed lane facilities where passing is prohibited—were considered in development of a set of speed–flow curves for each of the five basic facility types. These speed–flow relationships are expressed similarly to basic freeway segments in the Highway Capacity Manual and can be readily incorporated into the manual for predicting the performance of combined general purpose and managed lane facilities.

Quantifying Cross-Weave Impact on Capacity Reduction for Freeway Facilities with Managed Lanes

With the increasing concerns about environmental effects and the sustainability of roadway capacity expansion, transportation agencies are seeking alternative solutions to mitigate congestion. Managed lanes (MLs) promote person throughput on freeways and manage congestion through improving efficiency. The ML concept therefore has been gaining popularity in past decades. However, the lack of guidance on evaluating the performance of the ML facility poses challenges for agencies wanting to design and implement the strategy effectively. Many MLs are designed to be left-lane concurrent; vehicles entering the freeway from general purpose (GP) lane on-ramps need to cross weave over multiple GP lanes to access the ML. These weaving vehicles will have a negative effect on the operating performance of the parallel GP lanes. This paper investigates this cross-weaving effect as a function of different roadway geometric configurations as well as traffic conditions. A microscopic simulation model was built and calibrated on the basis of video data collected along I-635 in Dallas, Texas. Multiple scenarios were tested to explore the effect of the following parameters: number of GP lanes, cross-weave demand, and cross-weaving length. A set of capacity adjustment factors was determined to account for this effect as a function of those parameters. Results showed that the capacity-reducing effect was higher with a reduction in cross-weaving length, an increase in the number of GP lanes, or a rise in cross-weave demand volumes. The results are important in evaluating the operational performance of freeway segments in the presence of concurrent GP lanes and MLs in a Highway Capacity Manual context.

Deterministic Approach to Managed Lane Analysis on Freeways in Context of Highway Capacity Manual

The building of managed lanes parallel to general purpose lanes is an increasingly common approach to optimizing freeway capacity. Managed lanes allow agencies to classify customers and assign a portion of the freeway capacity to them. With no methodology in the Highway Capacity Manual (HCM) for analyzing these facilities, analysts rely on more time-consuming simulation analyses. A methodology is presented for estimating the performance of a parallel system of general purpose and managed lane facilities in an HCM context based on NCHRP Project 3–96. The methodology defines new managed lane segment types to use in an HCM analytical framework and is associated with a new set of speed–flow curves. It is sensitive to the number of lanes and the type of separation between managed lanes and general purpose lanes. The method introduces the concept of parallel lane groups of general purpose and managed lanes and thus can account for speed reduction in managed lanes caused by congestion in adjacent general purpose lanes. The method was implemented in a computational engine, FREEVAL-ML, which was built on the freeway facilities method in HCM 2010 but which was updated to incorporate inputs and outputs of the managed lane components. The geometry of two existing managed lane facilities in Washington State is used to illustrate the method, demonstrating the applicability of the analytical framework to real-world facilities.

Genetic Algorithm and Regression-Based Model for Analyzing Fare Payment Structure and Transit Dwell Time

The time that buses spend at stops, also called dwell time (DT), has a direct effect on transit service reliability and operational efficiency. A practical, coherent, and quantitative DT modeling approach is needed to identify the factors that contribute most to DT. Commonly used methods for studying DT to date involve manually collected field data or the use of automatic sensors to gather information on factors influencing DT. These approaches have often suffered from limited sample sizes or the inability to provide information on nonelectronic fare payment methods (e.g., cash payment and prepaid passes), which can contribute significantly to DT. To address these gaps, this study developed a genetic algorithm and regression-based modeling approach first to estimate transit fare transactions that do not have electronic records and then to quantify the effect of a number of factors on DT. Integrating information from multiple data sources, the combined approach of optimization and regression analysis offers a data-driven evaluation of existing fare payment structures and their individual effects on DT. With the 35M bus rapid transit line operated by the Utah Transit Authority as a case study, the method demonstrates the robustness and strong prediction power in DT modeling. Results quantify the magnitude of advantages of offboard over onboard fare collections and offer some insights into the operational effects of station placement, design, and the built environment. The modeling approach is transferable to any transit route or system that is equipped with automatic passenger counters. The fare payment analysis can assist transit agencies with service optimization and performance assessments.

Data-Driven Geospatial-Enabled Transportation Platform for Freeway Performance Analysis

The burgeoning field of big data nowadays has motivated the development of innovative architecture for better exploiting and exploring huge amount of multidisciplinary data. Inspired by the concept of eScience, the on-line transportation platform Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net) is developed in this study for the purpose of transportation data sharing, integration, visualization, and analysis. The major research goal of the DRIVE Net system can be summarized in threefold. First, it provides the repository service to facilitate data sharing and integration. Second, the system is capable of visualizing large sets of transportation data, helping users to perceive and understand the data. Third, the interactive and computational functionalities built into the DRIVE Net allow users to perform a variety of statistical modeling and analysis on multiple data sources, assisting with users to draw meaningful inferences and to make informed decisions. This research thus developed such an eScience platform addressing the aforementioned challenges for transportation applications. To particularly demonstrate the analytical capability of DRIVE Net, a new approach that automates real-time freeway performance measurement is developed and implemented onto the system. The proposed method provides quantitative evaluation of network-wide freeway performance to facilitate decision making in transportation operations and management.

An efficient General Transit Feed Specification (GTFS) enabled algorithm for dynamic transit accessibility analysis

The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis.

Spatial Sampling with Fisher Information for Optimal Maintenance Management and Quality Assurance

AbstractMaintenance management has been relying heavily on collecting asset condition information to plan for maintenance activities and budget allocation. Data collection is often conducted on a s...