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Greenhouse gas (GHG) emission and excessive fuel consumption have become a pressing issue nowadays. Particularly, CO2 emissions from transportation account for approximately one-quarter of global e...

Freeway bottleneck identification is an essential component in the process of deploying mitigation strategies to reduce congestion at freeway bottlenecks. Most previous studies on bottleneck identification focus on recurrent bottlenecks, and limited work has been conducted to identify the locations of non-recurrent bottlenecks. Therefore, in this study, we propose a new travel time reliability (TTR) measurement and develop a freeway bottleneck identification method based on this measurement, which can identify with high probability not only recurrent bottlenecks but also the locations of non-recurrent bottlenecks. The TTR measurement is developed based on statistical distance between travel time distributions. Three statistical distance measurements, Jensen–Shannon divergence, Wasserstein distance, and Hellinger distance, are applied in the TTR measurement. The bottleneck identification method is evaluated in a case study on I-15 freeway corridor in Salt Lake City, Utah. The three statistical distance measurements show good consistency in ranking locations by the impacts of recurrent and non-recurrent congestion, especially for extreme cases with very high or low variation between travel time distributions. The recurrent bottlenecks identified in this study show their clustering characteristics, which is similar to the generating and dismissing process of recurrent congestion. The locations with high probability of non-recurrent bottlenecks scatter both spatially and temporally, which agrees with the random characteristic of non-recurrent congestion.

It is a common practice for transit lines with fluctuating passenger demands to use demand-driven bus scheduling to reduce passenger waiting time and avoid bus overcrowding. However, current literature on the demand-driven bus scheduling generally assumes fixed bus capacity and exclusively optimizes bus dispatch headways. With the advent of connected and autonomous vehicle technology and the introduction of autonomous minibus/shuttle, the joint design of bus capacity and dispatch headway holds promises to further improving the system efficiency while reducing operating and passenger costs. This paper formulates this problem as an integer nonlinear programming model for transit systems operating with mixed human-driven and autonomous buses. In such mixed operating environment, the model simultaneously considers: (1) dynamic capacity design of autonomous bus, i.e., autonomous buses with varying capacity can be obtained by assembling and/or dissembling multiple autonomous minibuses; (2) trajectory control of autonomous bus, i.e., autonomous bus can dynamically adjust its running time as a function of its forward and backward headways; and (3) stop-level passenger boarding and alighting behavior. The objective of the model is designed to balance the trade-off between the operating costs of dispatching different types of bus and the costs of increased passenger waiting time due to inadequate bus dispatching. The model is solved using a dynamic programming approach. We show that the proposed model is effective in reducing passenger waiting time and total operating cost. Sensitivity analysis is further conducted to explore the impact of miscellaneous factors on optimal dispatching decisions, such as penetration rate of autonomous bus, bus running time variation, and passenger demand level.

Microscopic traffic simulation is a well-established tool for the analysis of transportation systems, with a wide variety of applications in operations, safety, and planning. An essential c...

Bus holding is a widely used control method to regularize bus headways and reduce bus bunching. The method works in such a way by delaying buses at control points if their departure times or headways deviate from the planned ones. However, it may result in reduced bus commercial speeds and increased passenger onboard travel time. To avoid this problem, researchers have suggested that control points be spaced cautiously along the route such that only a few are needed. This study proposes a predictive headway-based bus holding strategy with dynamic control point importance ranking and selection based on the cooperative game theory. The framework considers not only individual control points’ impact but also the collective group control effects. Specifically, the proposed framework consists of two components: a performance model and a cooperative game model. The performance model predicts headway performances of all running buses when different control point combinations are in effect. Dynamic bus running times and passenger demands are reflected in the model. Then, these headway performances are passed to the cooperative game model with control points being players and improvements in headway performances compared with that under no holding control being the utility function. The game is solved by Myerson value, a concept that extends Shapley value used for the normal cooperative game and considers the cooperation structure and potential worth of coalitions. We use Myerson value to rank the importance of control points on regularizing headways, as it measures the average marginal utility contribution of a control point to all possible coalitions that exclude that point. We prove that Myerson value lies in the Ω-core of the game and thus satisfies allocation efficiency, individual and coalition rationality. The proposed framework is applied to target headway control and two-way-looking self-equalizing headway control. Simulation results show that the framework can significantly reduce passenger waiting time and bus headway variation.

Travel time reliability (TTR) is considered a critical piece of information in highway performance evaluation. The L02 project from Strategic Highway Research Program 2 (SHRP2) has developed a holistic method using statistical probability functions of travel time as the TTR measure to build highway performance evaluation and monitoring systems. Compared with single-value reliability measures, the L02 measure is able to identify sources of unreliability and quantify their associated impacts. To validate the adaptability of L02 measure, TTR analysis on the I-15 freeway corridor in Salt Lake City, Utah using probe data has been conducted. The result is compared against output from the quadrant-based TTR measure that is currently used by the Utah Department of Transportation. Through cross-validation, it is determined that the two suites of measures demonstrate good consistency in relation to reliability assessment and unreliability source diagnoses. In addition, the study provides a method to calibrate the quadrant-based TTR measure, and new critical values were developed based on the cross-validation.

An agent-based modeling for dynamic ridesharing in a multimodal network is proposed in this paper. The study aims to evaluate the performance of dynamic ridesharing system within a multimodal network and explore the competing mechanism between dynamic ridesharing and public transit, with the presence of managed lane facility. The modeling process simulates the interaction between travelers and the network, and applies a heuristic algorithm to model travelers' decision making process under uncertainty. The model is applicable to networks with varying demographics. Multiple scenarios based on the classic Sioux Falls network have been examined. The modeling results demonstrate that the effects of dynamic ridesharing on a network differ with traffic demand and market penetrations of various travel modes. In networks with high travel demand and low market penetration of public transit, the benefits of dynamic ridesharing system on reducing congestion and providing reliable travel time are quite limited. To enhance the effectiveness of dynamic ridesharing, traffic operators may consider project investments on managed lane facilities. In networks with high market penetration of public transit, dynamic ridesharing may attract large amounts of short distance trips and aggravate congestion, especially at the initial launching phase. Policy makers would want to ensure that the existing infrastructure is sufficient to accommodate the extra traffic induced by ridesharing. Ridesharing service providers might also consider proper strategies to avoid “abuse” of the system by short trips and accelerate the market penetration.

Snowplow trucks serve a crucial role in winter maintenance activities by removing, loading, and disposing of snow. A performance-monitoring and analysis process can assist transportation ag...

Social media platforms such as Facebook, Instagram, and Twitter have drastically altered the way information is generated and disseminated. These platforms allow their users to report events and express their opinions toward these events. The profusion of data generated through social media has proved to have the potential for improving the efficiency of existing traffic management systems and transportation analytics. This study complements existing literature by proposing a framework to evaluate transit riders’ opinion about quality of transit service using Twitter data. Although previous studies used keyword search to extract transit-related tweets, the extracted tweets can still be noisy and might not be relevant to transit quality of service at all. In this study, we leverage topic modeling, an unsupervised machine learning technique, to sift tweets that are relevant to the actual user experience of the transit system. Sentiment analysis is further performed based on the tweet-per-topic index we developed, to gauge transit riders’ feedback and explore the underlying reasons causing their dissatisfaction on the service. This framework can be potentially quite useful to transit agencies for user-oriented analysis and to assist with investment decision making.

Public transit agencies aim to improve services while reducing operating costs. Transit performance analysis, as the main approach used to assess operating cost and revenue, has received much atten...

The reliability, survivability and vulnerability of critical infrastructure systems has received significant attention of the past several decades. Transportation systems are among the many critical lifelines that urban areas and their associated communities are dependent upon. Disruptions to these systems have the potential to create significant human suffering and severe economic damage. As a result, the ability to proactively assess response and recovery options is critical for emergency preparedness in urban areas. Unfortunately, the vast majority of infrastructure disruption studies are deterministic in nature, exploring the impact of node and arc losses with pre-defined interdiction scenarios and ignoring the underlying dynamism of extreme events. We advance existing knowledge by presenting a probabilistic approach for simulating a range of disruption scenarios and for identifying critical links within the network. Specifically, our approach takes advantage of Monte Carlo simulation, network-wide demand modeling and regression analysis to address the probabilistic nature of disaster effects and the joint impacts of network link failures. Using Salt Lake County, Utah as the study area, the resulting analysis effectively identifies and ranks links based on their vulnerability and criticality. The proposed method is easily transferable to any transportation network, regardless of scale, topology or extreme event.

High Occupancy Toll (HOT) lane systems are considered one of most effective countermeasures to mitigate freeway congestion. Existing studies have largely focused on developing optimal tolling strategies to maximize the benefits of congestion pricing. Limited effort has been made to model the dynamic feedback mechanism of drivers’ responses to tolling. A thorough understanding of how the interactive relationship between demands (in both HOT lane and general purpose lanes) and toll rates evolves over time is necessary. The underlying mechanism can be used directly for guiding future HOT facilities investment decisions. This study builds upon the traditional vector autoregressive model and enables its parameters to be time-varying. Such a relaxation, namely, time-varying parameter vector autoregressive model (TVP-VAR), is used to answer the following two questions: (1) Is there a time varying effect between general purpose lane volume, HOT lane volume and dynamic toll rate? (2) If there is, how to quantify such time-varying interdependencies? Based on the empirical data from loop detectors and toll logs on Washington State Route 167 (SR167), we identified the existence of time-varying effects between drivers’ responses and toll rates, and quantified the evolving interactions amongst HOT demand, general purpose demand and tolling via time-varying impulse responses. In addition, we found that drivers’ perceptions on HOT lanes across distinct geographical locations are significantly different.

Shared mobility is an essential component of the larger sharing economy. Ride-hailing, bike-sharing, e-scooters, and other types of shared mobility continue to grow worldwide. Among these services is microtransit, a new transport mode that extends transit coverage within a region. Mobile devices enable microtransit services, aggregating riders and using real-time routing algorithms to group customers traveling in similar directions. Meanwhile, the newly emerged coronavirus, COVID-19, has radically reshaped the ridership behavior of all transit services, including microtransit. While existing research evaluates the performance of microtransit pilot programs before the pandemic, there is no information concerning the spatio-temporal pattern of microtransit activities under the impact of COVID-19. The purpose of this paper is to apply eigendecomposition and k-clique percolation methods to uncover the spatio-temporal patterns of microtransit trips. Further, we used these approaches to identify underlying communities using data from a pilot program in Salt Lake City, Utah. The resulting research offers insight into how COVID-19 altered travel behavior. Specifically, eigendecomposition delineated the homogeneity and heterogeneity of travel patterns across temporal dimensions. We identified first mile/last mile trips as a major source of variance in both pre- and post-COVID periods and that transit-dependent users prove to be inelastic despite the threat of COVID-19. The k-clique percolation method detected possible community formations and tracked how these communities evolved during the pandemic. In addition, we systematically analyzed overlapping communities and the network structure around shared nodes by using a clustering coefficient. The workflow developed in this research broadly is generalizable and valuable for understanding the unique spatio-temporal patterns of microtransit. The framework can also help transit agencies with performance evaluation, regional transport strategies, and optimal vehicle dispatching.

Public Transit Accessibility (PTA) analysis helps transit agencies and planners identify areas in need of transit service improvements and prioritize transit investments. To evaluate the accessibility of existing transit services and identify access gaps, it is critical to accurately estimate travel times between transit stops, which change throughout the day due to transit schedule variations. Commonly used methods in PTA ignore such temporal fluctuation. Moreover, these methods are unable to elucidate the causes of poor PTA. To address these issues, we first implemented an algorithm to effectively compute travel times at multiple departure times throughout the day in order to enable spatiotemporal PTA analysis. A series of indicators that are intuitive to interpret were developed to determine the varying causes of poor PTA and identify areas with immediate needs for improvements. We showcase the analytical framework using a transit network in the State of Utah operated by the Utah Transit Authority. The analysis is based solely on publicly-available open datasets, which makes it generally adaptable to other transit networks. Results can assist transit agencies with identifying areas in need of service improvement and prioritizing future investments.

A systematic approach to quantify Incident-Induced Delay (IID) is proposed in this study. The paper complements existing literature by developing a data-driven method to dynamically determine the spatiotemporal extent of individual incidents. The information construction process can be further used to uncover a variety of features that are associated with any specific incidents for optimal freeway management. Additionally, this study contributes two particular highlights: secondary incident identification and K-Nearest Neighbor (KNN) pattern matching. Secondary incident identification, as a pre-processing for IID estimation, disentangles the convoluted influences of subsequent incidents. The proposed method uses KNN pattern matching, an essentially heuristic search process to separate the delay solely induced by incidents from the recurrent congestion. The proposed algorithm on IID quantification was implemented on Interstate 15 in the state of Utah using data obtained from 2013. Results and implications are presented. Hot spot analysis is conducted that can be potentially used for incident mitigation and to inform investment decisions. The proposed methodology is easily transferable to any traffic operation system that has access to sensor data at a corridor level.

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.

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.

Access density is defined as the number of accesses per unit length along an arterial. Numerous studies conducted in various regions have indicated that access density has a significant inf...

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.

With a growing awareness of the importance of near-road air pollution and an increasing population of near-road pedestrians, it is imperative to “nowcast” near-road air quality conditions to the general public. This necessitates the building hourly predictive models that are both accurate and easy to use. This study demonstrates an approach to model the hourly near-road Black Carbon (BC) concentrations given on-road traffic information and current meteorological conditions using datasets from two urban sites in Seattle, Washington. The optimal set of prediction variables is determined with a Bayesian Model Averaging (BMA) method and three different model structures are further developed and compared by goodness-of-fit. An innovative approach is proposed to translate wind direction from numerical values to categorical variables with statistical significance. By modeling the autocorrelation within the BC time series using an AR(1) component, the model achieves a satisfactory prediction accuracy. The conditional heteroscedasticity and heavy-tailed distribution of the model residuals are successfully identified and modeled by the General Auto Regressive Conditional Heteroscedasticity (GARCH) model, which provides valuable insights to the interpretation of prediction results. The methodological procedure demonstrated in selecting and fine-tuning the model is computationally efficient and valuable for further implementation onto online platforms for near-road BC nowcasting. A comparison between the two sites also reveals the effectiveness of local freight regulation for mitigating the environmental impacts from a heavy truck fleet.

Maintenance 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...

This study examines the impact of a wide range of roadway geometric features on the severity outcomes of crashes occurred on rural two-lane highways. We argue that crash data have a hierarchical structure which needs to be addressed in modeling procedure. Moreover, most of previous studies ignored the impact of geometric features on crash types when developing crash severity models. We hypothesis that geometric features are more likely to determine crash type, and crash type together with other occupant, environmental and vehicle characteristics determine crash severity outcome. This paper presents an application of multilevel models to successfully capture both hierarchical structure of crash data and indirect impact of geometric features on crash severity. Using data collected in Illinois from 2007 to 2009, multilevel ordered logit model is developed to quantify the impact of geometric features and environmental conditions on crash severity outcome. Analysis results revealed that there is a significant variation in severity outcomes of crashes occurred across segments which verifies the presence of hierarchical structure. Lower risk of severe crashes is found to be associated with the presence of 10-ft lane and/or narrow shoulders, lower roadside hazard rate, higher driveway density, longer barrier length, and shorter barrier offset. The developed multilevel model offers greater consistency with data generating mechanism and can be utilized to evaluate safety effects of geometric design improvement projects.

Bike trajectories generated by the dockless bike-sharing service provides a great opportunity to explore users' travel behavior within the shared mobility transportation ecosystem. This paper proposes a new concept, namely biking islands, defined as geographical areas of interest with a high concentration of bike usage. Leveraging high-resolution trajectory data, biking islands are identified via percolation theory because of its suitability in describing the formation of clusters and critical road segments that have significant influence on biking behavior in an urban context. We showcase our methodology using the bike trajectory dataset provided by a market-leading company and use Shanghai, China as the study area. Results reveal a hierarchical structure of biking islands. With the increase of threshold, the biking islands start to shrink and split into various smaller ones. Larger biking islands are usually located in the central urban area of Shanghai and the Huangpu River acts as a natural barrier that impedes biking continuity across the region. Besides, the formation of biking islands is highly influenced by the surrounding land uses. The proposed concept and methods are not only helpful to understand travel behavior of cyclists and urban structures used for cycling, but also has the potential to support relevant urban and transportation planning, such as identifying designated non-motorized areas for cycling and biking facilities and pinpointing critical road segments that can improve the cycling efficiency of the entire network. Biking islands could be designed as designated areas for cycling where sufficient bike facilities are provided, and/or motorized transport modes are restricted or even prohibited, so as to ensure the convenience and safety of cyclists and support the development of bike-friendly cities.

The large-scale implementation of High-Speed Rail (HSR) network in China not only offers a new option for travelers’ mode choice, but also may influence, or even generate, the redistribution of demographic and economic activities. As has been observed over the past several years in other countries, the impact of HSR spans a wide range. However, few quantitative studies have been conducted to measure this impact. As a new attempt, this study uses accessibility analysis for quantifying the impact of China’s HSR network. Weighted average travel times and travel costs, contour measures, and potential accessibility are employed as indicators of accessibility at the macro or national level. Forty-nine major cities in the HSR network are used in the accessibility analysis. Accessibility quantification and spatial distribution analysis for the study cities are performed on a Geographical Information System (GIS) platform. Accessibilities associated with varying availabilities of HSR, conventional rail, and airline are estimated and compared. The selected indicators and computational methods are found effective in evaluating the accessibility impacts of HSR from different conceptualization strategies and perspectives. They also offer complementary information on accessibility capacity of the study cities created by the HSR network.

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.

Managed lane facilities, including high-occupancy vehicle (HOV) lanes, high-occupancy toll lanes, and express lanes, have become attractive tools for managing today's 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.