Transportation Research Record: Journal of the Transportation Research Board

Diagnostic methods in the context of the Highway Safety Manual (HSM) aim to identify abnormalities or patterns in crash occurrence which may provide an important clue to an effective remedy. These methods are used to assess the nature of the safety problem and on its basis select a countermeasure for which there is a known crash modification factor. Two approaches are presently used to perform diagnostic examinations: the test of proportions and crash type-specific safety performance functions (SPFs). The test of proportions uses empirical proportions (diagnostic norms) of crash types and crash attributes within congestion strata, the total number of crashes, and the number of observed crashes of a specific type to compute the cumulative probability of the observed outcome. Crash type-specific SPFs are used to identify sites at which the observed frequency or severity of specific crash types is higher than expected. This paper examines the strengths of both methods by applying them to the same datasets and comparing the results. The findings suggest that the test of proportions works well in identifying locations with a single disproportionally frequent crash type. Crash type-specific SPFs can identify locations having multiple crash types with an elevated frequency, but will not identify locations having crash patterns susceptible to correction but not having elevated counts. Findings also suggest that the degree of stratification in diagnostic norms influences the number of overlapping sites detected by both methods.

Modeling shipment size for intra-city shipments is a subject that has not been sufficiently addressed in past research, despite its growing importance in disaggregate freight modeling. While past research on shipment size estimation mainly focuses on inter-city shipments, intra-city shipments differ from them in various aspects. In filling this research gap, this study estimates shipment size models using the records of intra-city shipments, identifying the effects of factors and heterogeneity on the shipment size selection mechanism. The estimated coefficients are also compared against their theoretical values derived from a conceptual economic order quantity model. The estimated empirical models highlight the characteristics of intra-city shipments and indicate the importance of both receiver function and commodity type, and also vehicle operation type, in capturing the nuances of the selection mechanism among intra-city shipments.

The pedestrian hybrid beacon (PHB) is a traffic control device used at pedestrian crossings. It was first included in the 2009 Manual on Uniform Traffic Control Devices and was based on the HAWK developed in Tucson, Arizona. The focus of an Arizona Department of Transportation research study was the investigation of the use of PHBs on higher-speed roads. Ten locations in Arizona representing higher operating speed conditions (85th percentile speed ranging from 44 to 54 mph) were selected for inclusion in this study. Data were collected using a multiple video camera setup. The final dataset reflected about 40 h of video data and included 1,214 pedestrians or cyclists crossing at PHBs. Overall, driver yielding for these 10 sites averaged 97%, which is similar to driver-yielding rates for PHBs installed on lower-speed streets.

On October 21, 2018, a Puyuma express train went overspeed through a sharp curve and derailed in Yilan, Taiwan. This accident resulted in 18 fatalities and 267 injuries. Although such accidents occur once in a while worldwide, this case of an overspeed derailment from a train-set equipped with an automatic train protection (ATP) system (similar to the function of Positive Train Control (PTC) in the U.S.) is rare. A temporary investigation team was appointed by the Executive Yuan, the highest administrative organ in Taiwan, and the investigation was completed within 2 months. This paper presents the process, analysis, findings, and recommendations from the accident investigation. The accident was first analyzed using fault tree analysis to identify potential causes and contributing factors of this derailment. The results were then categorized into layers of defenses by using a Swiss cheese model. We further extended the original Swiss cheese model to a “time-dependent Swiss cheese model” to demonstrate how the barriers were penetrated at different times by incorporating the timestamps of important events. Another modified Swiss cheese model called “causal relationship Swiss cheese model” was presented to further demonstrate the causal relationships. With the proposed process and models, the immediate causes and contributing factors were quickly identified and presented in a way that could be easily understood by the general public. The results showed that the ATP system (or the PTC) cannot guarantee 100% safety. A review of the safety culture and corresponding procedures is important to ensure the safety of railway operations.

Reclaimed asphalt pavement (RAP) has been used in micro-surfacing mixtures with good promise and improved sustainability. However, no systematic study has been conducted to analyze the chemical components of the RAP micro-surfacing mixture when blending virgin binder with RAP binder, and to confirm the reasonableness of using RAP micro-surfacing mixtures. Based on a gap-graded method, this paper explored the chemical properties of RAP micro-surfacing mixtures using attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR) and fluorescence microscopy, and explained the blending mechanism between the RAP and the cold mixture. The blending efficiency and effective styrene butadiene rubber (SBR) concentration were quantified based on various chemical component parameters. The results showed that partial blending existed for RAP micro-surfacing mixtures. It was found that the diffusion happened from the outer layer of the RAP mixture as a result of the coating of emulsion, and could continuously and gradually happen for the RAP mixture, giving higher blending efficiency for a high RAP content mixture. The addition of RAP makes SBR particles more dispersible. Including rejuvenators and increasing curing time could improve the blending efficiency and the effective modifier concentration.

This paper proposes a general network performance model (NPM) for monitoring the performance of urban rail systems using smart card data. NPM is a schedule-based network loading model with strict capacity constraints and boarding priorities. It distributes passengers over the network given origin-destination demand, operations, route choice, and effective train capacity. A Bayesian simulation-based optimization method for calibrating the effective train capacity is introduced, which explicitly recognizes that capacity may be different at different stations depending on congestion levels. Case studies with data from the Mass Transit Railway network in Hong Kong are used to validate the model and illustrate its applicability. NPM is validated using survey data on left-behind passengers and exiting passenger flow extracted from smart card data. The use of NPM for performance monitoring is demonstrated by analyzing the spatial-temporal crowding patterns in the system and evaluating dispatching strategies.

Estimation of vehicular emissions at network level is a prominent issue in transportation planning and management of urban areas. For large networks, macroscopic emission models are preferred because of their simplicity. However, these models do not consider traffic flow dynamics that significantly affect emissions production. This study proposes a network-level emission modeling framework based on the network-wide fundamental diagram (NFD), via integrating the NFD properties with an existing microscopic emission model. The NFD and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogeneous vehicle types with different emission generation rates in a network-level model. This framework is applied to the large-scale network of Chicago as well as its central business district. Non-linear and support vector regression models are developed using simulated trajectory data of 13 simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from the underlying microscopic emissions model regardless of the simulation tool that is used to calibrate the network-level emissions model. The microscopic traffic simulation is appropriate for smaller networks, while mesoscopic traffic simulation is a proper means to calibrate models for larger networks. The proposed model is also used to demonstrate the relationship between macroscopic emissions and flow characteristics in the form of a network emissions diagram. The results of this study provide a tool for planners to analyze vehicular emissions in real time and find optimal policies to control the level of emissions in large cities.

Ann Arbor Connected Vehicle Test Environment (AACVTE) is the world’s largest operational, real-world deployment of connected vehicles (CVs) and connected infrastructure, with over 2,500 vehicles and 74 infrastructure sites, including intersections, midblocks, and highway ramps. The AACVTE generates a massive amount of data on a scale not seen in the traditional transportation systems, which provides a unique opportunity for developing a wide range of connected vehicle (CV) applications. This paper introduces a data infrastructure that processes the CV data and provides interfaces to support real-time or near real-time CV applications. There are three major components of the data infrastructure: data receiving, data pre-processing, and visualization including the performance measurements generation. The data processing algorithms include signal phasing and timing (SPaT) data compression, lane phase mapping identification, trajectory data map matching, and global positioning system (GPS) coordinates conversion. Simple performance measures are derived from the processed data, including the time–space diagram, vehicle delay, and observed queue length. Finally, a web-based interface is designed to visualize the data. A list of potential CV applications including traffic state estimation, traffic control, and safety, which can be built on this connected data infrastructure is discussed.

Fine aggregate matrix (FAM) has been regarded as a key constituent of asphalt mixtures. Although several design methods have been recently proposed to prepare FAM specimens that represent the materials contained within the asphalt concretes, the volumetric characteristics of FAM may not be uniformly distributed within samples compacted using devices such as the Superpave gyratory compactor (SGC). This can mislead the mechanical analyses conducted with testing specimens that are typically extracted from different locations of the compacted samples, as they can present varying volumetric characteristics. This study employs the advanced micro-computed tomography (CT) scan technique to evaluate the air void distribution within FAM specimens extracted from different locations of SGC samples compacted with distinct air void contents. Rheological tests are performed in a dynamic shear rheometer to determine the dynamic shear modulus of the testing specimens. A statistical analysis is conducted to evaluate potential correlations between the volumetric and the rheological characteristics of the FAMs and to identify locations within the SGC samples from which small cylindrical specimens with similar characteristics can be extracted and used for testing. The adoption of advanced techniques such as the CT scan is essential for the advancement of the knowledge on the complex characteristics of asphalt mixtures, and may facilitate the fabrication of FAMs that are more representative of those that comprise asphalt concretes, as well as allow the use of similar replicates in mechanical tests.

As part of the efforts by Wyoming Technology Transfer Center (WYT2/LTAP) to develop a gravel roads management system (GRMS) in Wyoming, this research study developed a user-friendly tool, using JavaScript, which implements an optimization model based on genetic algorithms (GA). The developed tool will help decision makers and local agencies in managing gravel roads efficiently. Using this tool, a decision maker will be able to identify the most appropriate treatment type for each road, based on service level, estimated project costs, predicted road conditions, and whether to fund a project or not. The optimization model aims to maximize the overall condition of the gravel roads network subject to the average daily traffic (ADT) on each road. The developed tool can be applied to large-scale optimization problems (i.e., gravel roads network). The tool operates with minimal data requirements that are in line with procedures regularly followed at these agencies. In addition to having an engineered outcome, this tool can help local agencies in allocating their limited available funds efficiently, enhancing the planning process, maximizing the social welfare of the local economy, and promoting a sense of general satisfaction within the local community. A case study using data from Laramie County was used to validate this tool. The initial results were promising and in line with previous efforts to manage gravel roads in Wyoming.

The most common test methods used to evaluate alkali-silica reaction (ASR) are the concrete prism test (CPT) and the accelerated mortar bar test (AMBT). However, these tests were not found to be entirely reliable in predicting the performance of concrete under field conditions, especially when supplementary cementitious materials (SCMs) are used. Recently, two new test methods, the miniature concrete prism test (MCPT) and the concrete cylinder test (CCT), have been proposed but still need to be benchmarked with results from outdoor exposed blocks. In this paper, the results from the MCPT, CCT, CPT and exposed blocks are compared and their ability to properly evaluate the expected behavior of these mixtures in service with regard to ASR is discussed. Here, the results of mixtures made with four reactive aggregates: Spratt, Placitas (coarse aggregates), Wright, and Jobe (fine aggregates) and SCMs (fly ashes Classes F or C, slag cement, or silica fume) at different levels of cement replacement or lithium nitrate are presented. For these mixtures, only the MCPT was capable of properly classifying the efficiency of the ASR preventive measures, as compared with the long-term results obtained from the exposed blocks.

Practitioners and researchers in the paving industry have highlighted the importance of the adoption of reliability-based pavement design. The goal of developing reliable pavements with optimum performance over their design life has become a key factor to be considered during both pavement design and construction processes. This requires the adoption of statistical and probabilistic-based analyses for the formulation of the properties and behavior of pavement materials. Thus, many researchers worked on the quantification and modeling of the uncertainty caused by the inherent variability in pavement materials in general and that of asphalt concrete (AC) in particular. The dynamic modulus (|E*|), a fundamental property for mechanistic-empirical and purely mechanistic pavement designs, has been proven to have a significant level of uncertainty that is dependent on climatic and traffic loading conditions. The main objective of this study is to investigate the effect of the AC mixture properties and components on the uncertainty in the |E*| mastercurve. This objective is achieved by conducting an experimental program incorporating four different mixtures having the same material sources but different binder types and gradations. Monte Carlo simulations are used to model the uncertainty of |E*| for each of these mixtures. The paper shows that the uncertainty is dependent on mixture type, as the presence of larger nominal maximum aggregate size, modified binder, or additive can increase the uncertainty in the |E*| mastercurve, especially at high temperatures or slow loading rates. The uncertainty is proven to be material related and not imposed by the testing instrumentation.

When a driver stops for a pedestrian, the pedestrian may be struck by a second driver traveling in the same direction of travel in the next lane, a scenario known as a multiple-threat crash. Prior studies primarily focused on yielding distance as a proxy measure for measuring multiple-threat risk. This paper details a multifaceted high visibility enforcement program with an emphasis on reducing multiple-threat risks to pedestrians, by directly measuring observed multiple-threat passing at unsignalized, marked crosswalks. The objective of the study was to increase driver compliance with crosswalk laws and reduce multiple-threat passing. The second objective of the study was to determine which other factors are predictive of multiple-threat passing rates. At 16 selected sites, coders observed driver behavior with special attention given to any drivers who passed a stopped or yielding vehicle in the same direction of travel. For baseline measurements, multiple-threat passing was observed at 11.86% of crossings. After sustained education, enforcement, and engineering efforts across several months, not only did driver yielding rates improve, but multiple-threat passing declined to 3.17% at the end of the program. Furthermore, the analysis indicated that advance stop lines are directly associated with fewer multiple-threat passes and that higher speeds are associated with more multiple-threat passes. This finding demonstrates the efficacy of this intervention approach not only on increasing yielding for pedestrians but also for reducing the risk of multiple-threat crashes.

One of the most important aspects of improving public bus transport attractiveness is reducing overcrowding in buses. However, most of the mathematical models that focus on designing bus services minimize the total social cost without considering the overcrowding discomfort. Further, they are mostly non-linear in nature and use heuristic and meta-heuristic approaches. Thus, they are difficult to understand and use by practitioners. This work addresses these gaps through models that include overcrowding discomfort and are also easy to implement and contextualize by practitioners. The authors develop one non-linear and two linear models to determine the optimum frequency of buses and apply them over a network of 34 routes of Delhi. The results reveal that the existing number of buses in Delhi is not sufficient to cater to the existing travel demand of peak hours, even after their optimum allocation. The authors also present a step-by-step procedure to enable practitioners to determine the minimum additional number of buses required to reduce the target discomfort and waiting time.

Traffic control policies aim at reducing the negative externalities that ever-growing demand is causing on transportation networks, such as congestion and pollutant emissions. To achieve these goals, strategies coordinating and aligning the effects of several individual traffic controllers have received increasing attention in research and development in the past decades. However, a considerable gap still exists between the desired and experienced performance of advanced dynamic traffic management systems, resulting in failure to completely prevent the increasing peak-hour congestion in main urban areas worldwide. In this work we contribute to assessing whether this gap might be tied to inefficient network design, rather than algorithmic prowess. Based upon our earlier work, we investigate whether a trend can be found between determining locations of controllers in a network following control theoretical insights, and try to confirm our earlier intuitions when dealing with dynamic traffic assignment, featuring accurate propagation and spillback dynamics. To achieve these goals, we extend an existing synthetic network generation tool to allow us to test this hypothesis on real-life-like road networks, and extend our previously developed algorithms for the controller location problems allowing for sufficient generalization. Test results are presented on a simpler deterministic scenario and on 240 randomly generated networks, showcasing that placing controllers following controllability-based principles is advantageous from the perspective of model-based dynamic traffic management applications.

The paper investigates the impacts and barriers posed by connected autonomous vehicles (CAVs) for pedestrians with visual impairment. This study uses a customized web-based survey of visually impaired people from Canada and abroad. Collected data are used to estimate econometric models to identify the critical factors that affect the level of trust in CAVs and the preference for using CAVs from the visually impaired individuals’ perspective. Separate models are estimated for Canadian and non-Canadian samples, as Canadian and non-Canadian participants show some differences in perception and positive attitude towards CAVs. The models reveal that the majority of the respondents prefer to get feedback and alerts from CAVs. Congenitally blind Canadians are less likely to trust CAVs, but non-Canadian congenital blinds tend to trust CAVs. The models also indicate that the respondents who experienced being near an accident with an electric vehicle (EV) are less likely to choose CAVs. Respondents who rely on mobile applications and technology-based devices for navigating purposes tend to trust CAVs. Blind people who rely on conventional navigation tools (e.g., white cane, guide dog, etc.) are less likely to be the users of CAVs. Gender effect is visible, as the female participants tend not to trust CAVs. In relation to policy recommendations, subsidies should be provided to various advocacy groups to offer orientation and mobility (O&M) training services, which are pivotal to educate how to use technology-based navigational services. Also, automobile manufacturers should be enforced to add acoustic vehicle alert systems (AVAS) to both EVs and CAVs.

The urban Sustainable Development Goal (SDG) includes the target to provide “access to safe, affordable, accessible and sustainable transport systems for all” by 2030. However, debate exists around the best indicator to measure this target, and few actual measurements exist. This is in part because basic transit data are missing from many of the world’s cities, including in Africa where popular or “informal” systems dominate. This paper explores how to make progress in measuring indicators for the SDG transport target using Nairobi’s minibus system, matatus, as a case study. We partially measure the SDG indicator for the city as currently defined by the UN and then compare the SDG measurement to a location-based accessibility indicator that incorporates income data, travel times, and land-use considerations for Nairobi’s highly monocentric spatial urban form. We show that although the SDG analysis suggests generally favorable transit coverage, it also points to underlying transport inequalities for low-income residents. The more fine-grained location-based accessibility analysis reveals rapidly decreasing accessibility to opportunities as distance increases from the city’s central business district. This accessibility-based analysis further highlights income-based transport inequalities, identifying opportunities for improving integrated transport for residents living on the city’s near and far peripheries. Improving non-motorized transport access for those living in low-income areas with high access potential would also be important to improve access. We recommend that cities start using open-source software and open data to measure a variety of indicators needed for data-driven policy, to meet SDG 11.2 and go further to improve access to opportunities for all residents.

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) deflection measurements; however, the stationary nature of the FWD requires lane closure and traffic control. In recent years, traffic speed deflection devices such as the traffic speed deflectometer (TSD), which can continuously measure pavement surface deflections at traffic speed, have been introduced. In this study, a mechanistic-based approach was developed to convert TSD deflection measurements into the equivalent FWD deflections. The proposed approach uses 3D-Move software to calculate the theoretical deflection bowls corresponding to FWD and TSD loading configurations. Since 3D-Move requires the definition of the constitutive behaviors of the pavement layers, cores were extracted from 13 sections in Louisiana and were tested in the laboratory to estimate the dynamic complex modulus of asphalt concrete. The 3D-Move generated deflection bowls were validated with field TSD and FWD data with acceptable accuracy. A parametric study was then conducted using the validated 3D-Move model; the parametric study consisted of simulating pavement designs with varying thicknesses and material properties and their corresponding FWD and TSD surface deflections were calculated. The results obtained from the parametric study were then incorporated into a Windows-based software application, which uses artificial neural network as the regression algorithm to convert TSD deflections to their corresponding FWD deflections. This conversion would allow backcalculation of layer moduli using TSD-measured deflections, as equivalent FWD deflections can be used with readily available tools to backcalculate the layer moduli.

Gap acceptance is one of the crucial components of lane-changing analysis and an important parameter in microsimulation modeling. Drivers’ poor gap judgment, and failure to accept a necessary safety gap, make it one of the major causes of lane-changing crashes on roadways. Several studies have been conducted to investigate lane-changing gap acceptance behavior; however, very few studies examined the behavior in complex real-world situations, such as in naturalistic settings. This study examined lane-changing gap acceptance behavior from the big Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) datasets using a nonparametric multivariate adaptive regression splines (MARS) approach to better understand the complex effects of different factors in gap acceptance behavior. The study developed a unique methodology to identify lane-changing events of the non-NDS-vehicles using the front-mounted radar data from NDS vehicles and extract necessary parameters for analyzing gap acceptance behavior. In addition, surrogate measures of safety, that is, time-to-collision (TTC), was utilized to understand the impact of lane-changing on the NDS following vehicle safety. Moreover, different distributions of gap acceptance were fitted to identify the trend of gap acceptance behavior. The results from the MARS model revealed that different factors including relative speed between lane-changing vehicle (LCV) and lead vehicle (LV)/following vehicle (FV), traffic conditions, acceleration of LCV and FV, and roadway geometric characteristics have significant effects on gap acceptance behavior. The results of this study have significant implications, which could be used in microsimulation model calibration and safety improvements in connected and autonomous vehicles (CAV).

The U.S. encompasses over 4 million roadway miles, with about half of them located in seasonal frost areas. Roads are especially susceptible to damage when the subsurface is saturated with water (i.e., spring thaw). Spring load restrictions (SLR) are important for maintaining the integrity of roads. Routine determinations of road freeze–thaw (FT) state are limited to vertically embedded temperature data probes (TDP). Although TDPs are valuable to departments of transportation for determining SLRs, TDPs only represent individual points within the road network and are costly to install and maintain. Recent updates to spaceborne technology and algorithms made physically based retrievals of FT conditions possible at improved accuracy and temporal resolution (every 3 days) and have potential use for assisting with SLRs. Although instruments such as NASA’s Soil Moisture Active Passive (SMAP) platform cannot resolve individual roads, past comparisons have shown good correspondence with TDPs. The main objective of this study is to provide information on the potential value of NASA’s SMAP FT tool to supplement other methods for making seasonal load restriction decisions. Results are compared against data and protocols by the Minnesota Department of Transportation (MnDOT) at 10 sites in Minnesota, over four winter seasons (2016–2019). Results show that even when a simple criterion is used—the date of the third consecutive thaw from the SMAP afternoon retrieval—those dates typically fell within a week of MnDOT road postings (61% of the time). In addition, SMAP FT typically matches TDP FT states throughout the year (79% of the time).

Research progress has been made in recent years in developing service or performance indicators (SPI) or methods to better measure or evaluate service or quality for pedestrians in a local context. The majority of SPIs relate objective (measurable) characteristics or attributes of the right-of-way, with the user’s perceived output variable (e.g., perceived comfort). Traditionally, these methods do not consider the user’s perspective of the input variables. However, there is evidence that the direct contact of pedestrians with the environment justifies an exploration of the contribution of perceptions to that end. This study explores the power of user perception onsite to explain the sidewalk quality of service (QoS), compared to physical and other measurable traditional inputs. Information of physical characteristics, traffic, and perceptions were acquired in 30 different urban rights of way in Bogota, Colombia. By comparing the explanatory power and the goodness of fit of different scenario models, perceptions have been found to be an important predictor to pedestrian perception of QoS. In the light of the results, this paper provides a generalized conceptual framework to explain QoS that complements the existing one and discusses the implication of the use of QoS as the outcome variable.

Surface cracks directly influence the integrity of asphalt pavement structures, the durability of the pavement, and driving safety. Assessment of cracking distress is of vital importance for pavement maintenance and rehabilitation. Compared with other methods, the spectral analysis of surface waves (SASW) method, a seismic wave-based nondestructive method, has advantages in estimating the deterioration of modulus and quantitatively evaluating the depth and severity of surface cracks by means of the Rayleigh wave propagation characteristic. The objectives of this paper are to monitor long-term attenuation characteristics of in-situ modulus of a semi-rigid asphalt pavement, to determine when the surface-opening cracks will occur as associated to the degree of modulus reduction, and to assess the depth of surface-opening crack through the dispersion characteristics of Rayleigh wave propagation using the SASW method. First, a general trend of modulus deterioration of asphalt layer was developed in an accelerated pavement testing (APT). Then the factors affecting the dispersion characteristics of the Rayleigh wave were determined through theoretical derivation. Finally, a series of experimental tests on a pavement segment with well-controlled surface-opening cracks was performed to explore how a surface-opening crack in asphalt pavement would vertically influence the propagation of Rayleigh waves at different crack widths. It was found that the general trend of modulus deterioration could be divided into four stages, and the surface-opening cracks occurred in the fourth stage with 40–50% modulus reduction rate. In addition, the relationship between crack depth and the shortest wavelength in the Rayleigh wave dispersion curve was developed according to different crack widths.

In the metro station, passengers’ psychological state can be affected by space congestion, slow walking speed, increasing queue length, and other factors, resulting in a time-lapse effect, which can be described as people feeling that they spend more time than the actual. To measure the effect, this paper develops congestion indexes for the walking and queuing areas in metro stations, and designs stated preference (SP) and revealed preference (RP) surveys and scene simulation experiments. Based on the survey and experiment data, we attempt to describe the psychological state and space–time perception of passengers in metro stations and propose quantitative models of congestion indexes at different service levels. Furthermore, on the basis of these models, thresholds of walking distance and waiting time can be calculated for different service levels. The results can provide a reference for real-time passenger flow monitoring and theoretical supports for metro operators to measure passenger flow status and adopt passenger flow management strategies under different conditions. Overall, this study offers promising insights into passenger flow monitoring and management, but some limitations need to be addressed in future work.

Gravity load paths of high-skew bridges differ from the ones with no skew. High skew can also lead to stresses or displacements that adversely affect service performance. This paper demonstrates the effects of skew on bridges through finite element analyses, bridge inspections, and statistical analyses. Five deck-girder type bridges with and without skew were inspected. A database of more than 1,400 deck-girder type bridges was analyzed to seek relationships between skew and National Bridge Inventory (NBI) ratings. Practices of Departments of Transportation (DOT) were compared with each other and to provisions of AASHTO LRFD Bridge Design Specifications (BDS). Acute deck corner cracking and bridge movements were documented on some high-skew bridges. Field inspections and database analyses showed that not all high-skew bridges have performance issues, and NBI ratings are in general not sensitive to skew. This is likely because of many factors affecting performance and certain details mitigating skew effects.

Fire resistant design of both structural and non-structural components in road tunnels is predicated on the determination of fire demand intensity. Current practice typically uses a conservative, deterministic fire curve that does not necessarily provide a representative evaluation of the spatial and temporal distribution of thermal demands in tunnels that are caused by large vehicle fires. This paper proposes a tunnel-specific probabilistic framework for evaluating vehicle fire frequency and intensity based on tunnel geometry and traffic information. The framework leverages a fast-running computational tool that has been previously developed by the authors for calculating fire-induced heat flux exposure on tunnel liners because of enclosed vehicle fires. The likelihood of a vehicular fire and the associated fire size distribution are used to generate probabilistic distributions of total fire exposure for the reinforced concrete tunnel liner. Critical heat flux values according to these probabilistic distributions are then used to assess reductions in concrete material strength and resulting losses in the structural performance of the system. A case study of the Fort Pitt Tunnel in Pittsburgh, PA, is included for demonstration. The proposed framework enables decision making regarding design and renovation of tunnels for fire resistance as well as post-fire inspection by quantifying the risk of capacity reduction in the concrete liner because of a realistic range of fire hazard intensities.

The rate of fatalities at signalized intersections involving heavy vehicles is nearly five times higher than for passenger vehicles in the US. Previous studies in the US have found that heavy vehicles are twice as likely to violate a red light compared with passenger vehicles. Current technologies leverage setback detection to extend green time for a particular phase and are based upon typical deceleration rates for passenger cars. Furthermore, dilemma zone detectors are not effective when the max out time expires and forces the onset of yellow. This study proposes the use of connected vehicle (CV) technology to trigger force gap out (FGO) before a vehicle is expected to arrive within the dilemma zone limit at max out time. The method leverages position data from basic safety messages (BSMs) to map-match virtual waypoints located up to 1,050 ft in advance of the stop bar. For a 55 mph approach, field tests determined that using a 6 ft waypoint radius at 50 ft spacings would be sufficient to match 95% of BSM data within a 5% lag threshold of 0.59 s. The study estimates that FGOs reduce dilemma zone incursions by 34% for one approach and had no impact for the other. For both approaches, the total dilemma zone incursions decreased from 310 to 225. Although virtual waypoints were used for evaluating FGO, the study concludes by recommending that trajectory-based processing logic be incorporated into controllers for more robust support of dilemma zone and other emerging CV applications.

The purpose of this research was to evaluate the interaction of left-turn and opposing through traffic volumes for permitted and protected left-turn phasing at intersections and develop boundaries that help identify when to switch from permitted to protected phasing at signalized intersections. Permitted phasing allows vehicles to turn left after yielding to opposing vehicles; protected phasing provides an exclusive phase for vehicles to turn left without opposition; and protected-permitted phasing combines these phasing alternatives, allowing both permitted and protected turning movements. Intersections with 1, 2, and 3 opposing-lane configurations with permitted and protected-permitted models (split into green times of 10, 15, and 20 s) were analyzed for a total of 12 simulation models. Each model was divided into 100–225 different volume scenarios, with incremental increases in left-turn and opposing volumes. By exporting trajectory files from VISSIM and importing these into the Surrogate Safety Assessment Model, crossing conflicts for each volume combination in each model were extracted. MATLAB was then used to create contour maps representing the number of crossing conflicts per hour associated with different combinations of left-turn and opposing volume. Basic decision boundaries were examined in each contour map. Statistical analysis software was used to perform a linear regression analysis on transformed data and to develop natural log-based equations that form the decision boundaries for each configuration and phase alternative. These equations were graphed and final decision boundaries developed for the 1-, 2-, and 3-lane configurations between permitted and protected-permitted phasing as well as between protected-permitted and protected phasing.

In the 2000s, the Utah Department of Transportation (UDOT) began implementing technological enhancements to reduce the fatality rate of pedestrians involved in crashes. Although these enhancements appeared to be successful at improving safety, there was a need to evaluate their effectiveness. This research evaluates the safety impacts of several pedestrian crossing enhancements using Utah-specific compliance rates of drivers as a surrogate safety measure. This study analyzes enhanced pedestrian crossings to determine the factors that affect the compliance of driver yielding in Utah and provides a statistical analysis to prove the significance of each factor on compliance. The results show that the “High-intensity Activated crossWalK” (HAWK) is more effective at reducing the probability of a non-compliant event compared with an overhead flashing beacon (OFB), and that an OFB is more effective at reducing the probability of a non-compliant event compared with a rectangular rapid flashing beacon (RRFB) or an overhead rectangular rapid flashing beacon (ORRFB). The results show that adding a pedestrian enhancement to a marked crosswalk at a location with five lanes and speed limit between 35 mph and 45 mph can increase compliance rate by 97% for the HAWK, 77% for the OFB, and 57% for the RRFB and ORRFB.

Permanent deformation is an essential criterion for evaluating pavement performance. In an accelerated pavement testing (APT) experiment at the Virginia Tech Transportation Institute, a laser profiler, multi-depth deflectometer (MDD), and forensic investigation were used to measure the permanent deformation of a pavement system. This paper analyzes the permanent deformation measured via the three methods during the APT experiment. The major concern among the three methods is applicability rather than accuracy. To measure surface deformation, the laser profiler is a more practical method than MDD in APT, as it can scan the whole surface instead of just one point. To measure the deformation within a pavement structure, MDD provides deformation development throughout the whole experiment, which is helpful for a deeper understanding of pavement materials and structures. However, MDD is also more expensive and requires significant installation effort and maintenance during the experiment compared with forensic investigation, which also needs to be considered.

The goal of this study was to evaluate two connection types for reinforced concrete two-way rebar hinges and to assess their seismic performance if incorporated in accelerated bridge construction applications. Two large-scale models of bridge systems were tested on shake tables; the first utilized a pocket connection with the opening preformed in the column and the second implemented a socket connection with the opening preformed in the footing. Both bridges were subjected to multiple ground motions ranging from 30% to 225% of the design level earthquake. Rebar hinge behavior during the earthquakes including interface slippage, rotation, reinforcement strains, observed damage, and bent forces were used to evaluate the connections and their relative merit and to make recommendation for implementation in the field.

Joints, wide cracks, and poor quality concretes facilitate the intrusion of chlorides, causing corrosion in bridge decks and substructures that limit the service lives. Distress in deck concretes can adversely affect ride quality and structural integrity. The objective of this study was to eliminate the joints in existing bridges and to improve the surface conditions of the decks by overlays. Two parallel bridges in Virginia were selected for study. The performance of the closure pours and overlays was observed for 4–5 years. Joints were replaced with closure pours (also known as link slabs) consisting of fiber-reinforced concretes resistant to wide cracking and intrusion of solutions. Polyvinyl alcohol, polypropylene, and steel fibers were used in the closure pours; a compressive strength of 3,000 psi (pounds per square inch) at 24 h was sought. In the overlays, silica fume concrete alone and with shrinkage reducing admixture, lightweight coarse aggregate, and lightweight fine aggregate was investigated for crack control and low permeability, and compared with the control of latex-modified concrete with rapid set cement. A compressive strength of 3,000 psi at 3 days was sought. Test results and surveys showed that satisfactory strengths and permeability were achieved; the closure pours containing steel and polyvinyl alcohol fibers had tight cracks (most less than 0.1 mm with a few up to 0.2 mm). All overlays were performing well except for one section placed in adverse weather conditions and exposed to a truck fire. There were a few areas patched where poor surface preparation had led to delamination.

In this paper, a new in situ method for determining the structural rolling resistance (SRR), defined as the dissipated energy caused by deformation of the pavement when subjected to a moving load, is presented. The method is based on the relation between SRR and the slope of the deflection basin under a moving load. Using the Traffic Speed Deflectometer, the deflection slope is measured at several positions behind and in front of the right rear-end tire pair of a full-size truck trailer while driving under realistic conditions. The deflection slope directly under the tire is estimated from a linear interpolation between the two nearest sensors. A set of data from a test road segment located in Denmark is analyzed and the SRR coefficients are found to be in the range 0.005% to 0.05%. The deflection slope measurements have a high reproducibility (repeated measurements agree within standard deviations of 4% to 10%) with high spatial resolution, and the method for calculating SRR from these measurements has the clear advantage that it requires no knowledge or model of the pavement structure or viscoelastic properties. Numerical simulations of pavement response show that the proposed interpolation method tends to underestimate the actual SRR, and better estimates can be obtained by other interpolation schemes.

Texas Department of Transportation (TxDOT) projects have been experiencing significant delays. Some of these delays can be rooted to the inaccurate estimation of the contract time. This research presents a preliminary framework for the development of a computer-based system designed to determine a realistic contract duration for TxDOT projects. In addition to traditional deterministic scheduling, the system also performs probabilistic scheduling using the program evaluation and review technique. The system also incorporates an interactive database containing a list of various highway construction activities and their productivity rates; the database is used to estimate the durations of project activities. The system was deployed to reschedule some previous TxDOT projects, and the results were statistically compared and analyzed. The results show that this system can significantly improve the estimate of the contract time. This framework lays the foundation for the development of a more advanced contract time determination system based on probabilistic scheduling.

Aging has a significant effect on performance of asphalt materials. Reliable characterization of asphalt binder properties with aging is crucial to improving asphalt binder specifications as well as modification and formulation methods. The objective of this study is to correlate the laboratory conditioning methods with field aging using evolution of binder rheological parameters with time and pavement depth. Loose mixtures are aged in the lab (5 and 12 days aging at 95°C, and 24 h at 135°C) and recovered binder rheological properties are compared with those from different layers of field cores. The virgin binder results with 20 h pressure aging vessel (PAV) aging are also included. Binder testing is conducted using a dynamic shear rheometer with a 4 mm plate over a wide range of frequencies and temperatures. Rheological parameters calculated from the master curves, performance grade system, and binder Christensen–Anderson–Marasteanu model are used to evaluate changes with aging. The field aging gradient is evaluated, and the laboratory conditioning durations corresponding with the field aging durations at different pavement depths are calculated. The results show that 5 days of aging can simulate around 8 years of field aging (in New Hampshire) for the top 12.5 mm pavement, and 12 days’ aging can simulate approximately 20 years; 20 h PAV binder aging is not adequate to capture the long-term performance of the pavement. This study provides a way to optimize the laboratory conditioning durations and evaluate the performance of asphalt material with respect to pavement life (time) and depth (location) within the pavement structure.

Unmanned aerial vehicles (UAVs) are being increasingly implemented in a range of applications. Their low payload capacity and ability to overcome congested road networks enables them to provide fast delivery services for urgent high-value low-volume cargo. This work investigates the economic viability of integrating UAVs into urban hospital supply chains. In doing so, a strategic model that determines the optimal configuration of supporting infrastructure for urgent UAV delivery between hospitals is proposed. The model incorporates a tailored facility location algorithm that selects an optimal number of hubs given a set of candidates and determines the number of UAVs required to fulfill total demand. The objective is to minimize the total cost of implementation, computed as the sum of generalized, battery, vehicle, and hub establishment costs. The model is applied to a case study based on the establishment of a UAV delivery network for deliveries between National Health Service (NHS) hospitals in London. A baseline scenario is also developed using current NHS vehicles for delivery. Results demonstrate that UAV-based delivery provides significant reductions in operational costs compared with the baseline. Furthermore, the analysis indicates the location of hubs is more significant to the solution optimality than any increase in range or payload.

In the past decade, transportation network companies (TNCs) such as Uber, Lyft, and Via have established themselves as a viable transportation alternative to other modes. However, the popularity of these services has come with a fair share of criticism for their negative externalities such as increasing vehicle miles traveled and congestion in cities. Pooled ride-hailing trips, in which all or a part of two individual (or group) trips are combined in and served by a single vehicle, have the potential to reduce these externalities. Pooling of rides is an effective solution to reduce congestion and travel cost, but pooled rides still represent a small percentage of the total trips served (and miles driven) by TNCs relative to single-occupancy (and without customer) vehicle miles. Both TNCs and cities alike will benefit from understanding what factors encourage or deter pooling a ride-hailing trip. In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ride-hailing trips. Multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.

This paper presents the mechanical properties of alkali activated concrete (AAC) cured at 70°C for 24 h. The AAC mixtures were synthesized using five class C fly ashes (FAs) having different chemical and physical properties. Sodium hydroxide (SS) and sodium silicate (SH) were used as the alkali activators in this study. A conventional concrete (CC) mixture, having a compressive strength of 34.5 MPa, was synthesized using ordinary Portland cement (OPC) mixture for comparison purposes. The slump, as well as the compressive, tensile splitting, and flexural strengths were investigated at different concrete ages up to 28 days. The results revealed that with increasing the calcium content in an FA used to synthesized AAC mixture, the slump value and the mechanical properties decreased. All AAC mixtures reached approximately 92% of their 28-day compressive strength after 1 day compared with only 29% in the case of CC. Therefore, AAC can be used in applications where rapid strength gain is required, such as urgent repair, precast industry, and so forth. The measured data was also used to develop a set of equations to accurately predict the splitting tensile and flexural strengths.

The recent proliferation of bike share operations has augmented established docked systems in major cities with several stationless operators. By relaxing control of where the bikes may be located, the stationless systems are more agile but less certain. We hypothesize that a stationless bike share system reduces access and egress distance while increasing unreliability, offering a trade-off from the customer’s perspective. This work presents a framework for quantifying the trade-off between expected trip time and variability in trip time for stationed and stationless bike share systems. The systems are modeled subject to shared assumptions where possible, and the trade-off is measured for about 1,000 simulated journeys corresponding to a 1 h simulation. Sensitivity to the shared assumptions is tested to support the generalizability of the results. The findings indicate that stationed systems have higher expected user costs and lower variance in user cost. As expected, the user cost distributions are asymmetrical. This analysis supports the context-specific adoption of stationed or stationless bike share operations based on user attributes (trip purpose, walk speed, destination choice, etc.) and operator attributes (budget for bicycles, support for public transport, value on reliability, etc.).

Unsafe driving behaviors, driver limitations, and conditions that lead to a crash are usually referred to as driver errors. Even though driver errors are widely cited as a critical reason for crash occurrence in crash reports and safety literature, the discussion on their consequences is limited. This study aims to quantify the effect of driver errors on crash injury severity. To assist this investigation, driver errors were categorized as sequential events in a driving task. Possible combinations of driver error categories were created and ranked based on statistical dependences between error combinations and injury severity levels. Binary logit models were then developed to show that typical variables used to model injury severity such as driver characteristics, roadway characteristics, environmental factors, and crash characteristics are inadequate to explain driver errors, especially the complicated ones. Next, ordinal probit models were applied to quantify the effect of driver errors on injury severity for rural crashes. Superior model performance is observed when driver error combinations were modeled along with typical crash variables to predict the injury outcome. Modeling results also illustrate that more severe crashes tend to occur when the driver makes multiple mistakes. Therefore, incorporating driver errors in crash injury severity prediction not only improves prediction accuracy but also enhances our understanding of what error(s) may lead to more severe injuries so that safety interventions can be recommended accordingly.

Adequate quality of subgrade under patched areas can extend the service life of concrete pavements and reduce their maintenance costs. In this study, the performance of various subgrade stabilization scenarios was evaluated and compared with each other in a full-scale laboratory-based setup. For this purpose, a test box with the footprint of 6 × 6 ft and a height of 4 ft was constructed using C steel channels. The test box was used to investigate the effects of various types of soil stabilization methods, such as chemical (cement) stabilization, use of aggregate base course (ABC), geogrid (GG) and geotextile (GT) with ABC, GT with cement-stabilized soil, GT with in-situ compacted soil, flowable fill, and lean concrete. The test results showed that all stabilization techniques successfully improved the performance of the subgrade layer by decreasing the deformation under the fatigue loading representing a single axle load of 9,000 lbf/tire. The use of GT with aggregate-based layers was found to significantly reduce the amount of settlement. Subgrade layers stabilized with GG also experienced lower values of deformations compared with the unmodified (control) section. However, GG was not as effective as GT. The use of cement-treated aggregate and lean concrete reduced the deformations to negligible levels.

Arterials are important transportation facilities, undertaking the two functions of mobility and accessibility. In the urban area, signalized intersections along arterials are usually closely spaced and bear heavy traffic pressure. Capacities of intersections can be reduced by downstream intersections even without having spillback. This effect will be accumulated and amplified back along the traffic direction and may lead to severe congestion in the upstream intersection which can be frequently observed, especially during peak hours. However, existing traffic simulators cannot capture this phenomenon accurately because they ignore the capacity drop before spillback happens. In this study, downstream influence is quantified by a virtual optimal speed (v_op). v_op is the speed by which the upstream platoon reaches the endpoint of the queue ahead when the last vehicle in the downstream queue just starts. Based on that, two piecewise regression models, for start-up lost time and saturation flow rate, are formulated to estimate the capacity reduction. These regression models are further introduced to improve the modified cell transmission model (CTM). The result of the simulation experiment shows that the proposed CTM model has better performance in simulating traffic flow on signalized arterials than the existing CTM, especially in reproducing the traffic congestion in upstream. The analysis emphasized the importance of considering the downstream influence when simulating the traffic on signalized arterials. Finally, a sensitivity analysis is designed to further reveal the causes of upstream congestion.

Traffic incidents, as non-recurrent events, are one of the major causes of congestion in the transportation network. To mitigate the impacts of such incidents and to recover the performance of transportation systems as safely and quickly as possible, most responsible agencies over the past decades have implemented various traffic incident management systems, and an incident duration prediction model is one of the key components to estimate the impact of time-varying incidents on the network. Many studies have been undertaken to develop a robust prediction model of incident duration, but they have struggled to provide a reliable and accurate estimation result because of various data and modeling issues, such as a highly skewed distribution, complex correlations, heteroscedasticity, many outliers, and so forth. This study proposes an outlier analysis process for estimating the outlier-ness of each detected incident and utilizing such outlier information to improve accuracy of prediction of incident duration estimation and detect any system deficiency. An ensemble modeling technique and various outlier detection methodologies have been used to estimate the outlier-ness, and a hybrid association rule mining method has been applied to classify the detected outliers as anomalies or noises. Lastly, through the model evaluation and application example in this study, we can conclude that the proposed outlier analysis process can improve the accuracy of incident duration estimation and detect the potential system deficiencies associated with incident response, data recording, resource management, and so forth.

The tugboat is the vessel that helps to maneuver large ships for berthing and un-berthing operations. To achieve efficient tugboat operations, investigating the features of tugboat activities is of crucial importance. This study aims to use automatic identification system (AIS) data to identify the maneuver services and analyze the characteristics of tugboat activities. A two-stage algorithm is developed to extract the time, locations, and involved tugboats for berthing and un-berthing operations from AIS data. The AIS data from Tianjin port, China, are used in the case study to demonstrate the effectiveness of the proposed method and analyze the pattern of tugboat activities. First, some important features of tugboat jobs are presented, such as the daily number of jobs and the spatial distribution of jobs. Then, a temporal and spatial analysis is conducted to investigate tugboat assignment, service time, tugboat utilization, and locations of berthing and un-berthing operations. The obtained results and implications could shed light on the deployment of tugboat berths, tugboat scheduling, and evaluation of tugboat fleet operation.

This paper explores the application of count models to represent the relationship between flight disruptions and weather. Throughout the world, flights are regularly disrupted by delays at airports and in the terminal airspace, and less frequently by diversions and cancelations. Many delay studies have been conducted for large American and European airports, in part due to the availability of high-quality data. However, such high-quality data is not as readily available for other airports throughout the world. In this study, excess-zero count models are built using a publicly available dataset for Iqaluit Airport (YFB) in Northern Canada, to determine the influence of different weather components on disruption counts. Visibility and crosswind speeds are shown to have the largest influence on flight disruptions. The models are also applied using Aviation System Performance Metrics (ASPM) flight data for Anchorage Airport (ANC) in Alaska; the data is systematically degraded to match completeness of the Iqaluit data to test the models. The results verify that an excess-zero model using incomplete data yields results similar to that of a count model with complete data, demonstrating that an excess-zero model can overcome data incompleteness to yield acceptable results. Although count models have been applied extensively in the transportation literature, the authors believe this to be the first application to flight disruptions, and the first quantitative model of operations at a northern Canadian airport. This paper demonstrates that challenges in data availability—the case for most airports throughout the world—can be addressed with novel statistical modeling applications, and thus, delay studies can be conducted for almost any airport.

This paper proposes the new offset diamond interchange (ODI) as an alternative design which shows potential in mitigating the limitations of failing service interchanges in relation to traffic operation. Vehicle travel time was considered as the primary measure of effectiveness (MOE) for the traffic operation analysis, while the maximum queue lengths were also analyzed. A comprehensive series of VISSIM simulation scenarios was conducted to evaluate the new ODI design in different situations of traffic demand, turning-traffic ratios, traffic distribution, and traffic composition. The results were compared with five existing and four other new interchange designs. The new ODI performed statistically significantly better than all existing designs, including the conventional diamond, parclo A, and the diverging diamond interchange (DDI). The ODI design was found vulnerable in relation to the maximum queue produced in high turning conditions with the unbalanced traffic distribution; however, the threat could be diminished by increasing the storage lengths about 80 ft. Right-of-way (ROW) costs may make the ODI a poor alternative in replacing a standard diamond, but since the ODI fits the footprint of a parclo A it could work well in replacing those interchanges.

Connected vehicle (CV) application developers need a development platform to build, test, and debug real-world CV applications, such as safety, mobility, and environmental applications, in edge-centric cyber-physical system (CPS). The objective of this paper is to develop and evaluate a scalable and secure CV application development platform (CVDeP) that enables application developers to build, test, and debug CV applications in real-time while meeting the functional requirements of any CV applications. The efficacy of the CVDeP was evaluated using two types of CV applications (one safety and one mobility application) and they were validated through field experiments at the South Carolina Connected Vehicle Testbed (SC-CVT). The analyses show that the CVDeP satisfies the functional requirements in relation to latency and throughput of the selected CV applications while maintaining the scalability and security of the platform and applications.

Urban deliveries are traditionally carried out with vans or trucks. These vehicles tend to face parking difficulties in dense urban areas, leading to traffic congestion. Smaller and nimbler vehicles by design, such as cargo-cycles, struggle to compete in distance range and carrying capacity. However, a system of cargo-cycles complemented with strategically located cargo-storing hubs can overcome some limitations of the cargo-cycles. Past research provides a limited perspective on how demand characteristics and parking conditions in urban areas are related to potential benefits of this system. To fill this gap, we propose a model to simulate the performance of different operational scenarios—a truck-only scenario and a cargo-cycle with mobile hubs scenario—under different delivery demand and parking conditions. We apply the model to a case study using data synthesized from observed freight-carrier demand in Singapore. The exploration of alternative demand scenarios informs how demand characteristics influence the viability of the solution. Furthermore, a sensitivity analysis clarifies the contributing factors to the demonstrated results. The combination of cargo-cycles and hubs can achieve progressive reductions in kilometers-traveled and hours-traveled up to around densities of 150 deliveries/km², beyond which savings taper off. Whereas the reduction in kilometers-traveled is influenced by the the carrying capacity of the cargo-cycle, the reduction in hours-traveled is related to to the cargo-cycle ability to effectively decrease the parking dwell time by reducing, for instance, the time spent searching for parking and the time spent walking to a delivery destination.

The Federal Highway Administration (FHWA)’s Pavement Policy as codified in 23 CFR 626 states, “Pavement shall be designed to accommodate current and predicted traffic needs in a safe, durable, and cost effective manner” to be eligible for federal highway funding. To meet this requirement, state highway agencies have developed pavement type determination (PTD) policies, also known as pavement type selection, and implemented pavement management. Iowa Department of Transportation (DOT)’s PTD has been in place for many years; but in 2018, Iowa DOT looked at enhancing their PTD process to address gaps between past practice and best practice. Among the enhancements, user benefit as defined by pavement smoothness was utilized when net present value (NPV) alone could not definitively distinguish a preferred alternative. The smoothness benefit would become the divisor in a cost–benefit (C/B) ratio that would be used to determine the preferred alternate for the PTD. The cost portion of the ratio would remain the NPV of agency costs for the construction and projected rehabilitations during the analysis period. After a literature review and interviews of comparable state DOTs, several modifications to Iowa DOT’s PTD and the C/B ratio were analyzed and adopted. The modifications range from accepted practice changes, such as the use of a longer analysis period (50 years), to unconventional techniques, such as the consideration of smoothness. Iowa DOT believes these changes provide a more robust PTD. They are also considering additional improvements based upon additional research and policy making.

A public transit system with multiple fixed bus lines faces non-uniform fluctuating passenger demand, both spatial and temporal. This non-uniformity warrants the use of public transit operational strategies to achieve efficiency. This study proposes a methodology for optimizing the operational integration of multiple bus lines to address the spatial non-uniformity of passenger demand by applying five operational strategies: full-route operation, short turn, limited stop, deadheading, and a mixture of either two or three of the latter three strategies. The operational strategies to be developed improve the efficiency of bus lines and accommodate the observed passenger demand in the most favorable manner, that is, through the consideration of passengers’ preferences with the objective of the minimum resulting cost. The methodology is first applied to a sample problem, and then to a real-life case study of multiple bus lines in Dalian, China. The results obtained demonstrate that the effectiveness of combined strategies is higher than that of any single strategy. In the real-life bus line case, a combination of strategies without considering deadheading trips saves four vehicles in comparison with the full-route operation scenario. The anticipated number of vehicles is further reduced by three by the introduction of the deadheading trip strategy, resulting in greater public transit system efficiency.

Ensuring that the available sight distance (ASD) on highways meets the minimum requirements of geometric design standards is crucial for safe and efficient operation of highways. Current practices of ASD assessment using design software or through site visits are labor intensive, time consuming, and traffic disruptive. Thus, this paper introduces a fully automated algorithm that allows large-scale assessment of ASD in three-dimensional (3D) space on highways utilizing mobile light detection and ranging (LiDAR) data. The algorithm was tested on LiDAR data of highway segments in Alberta, Canada. The results showed that the algorithm was highly accurate in detecting sight distance limitations at the defined regions and, in all cases, the driver’s vision was restricted by the pavement surface on vertical crest curves. In the case of combined vertical and horizontal curves, the vertical crest curve was found to be the controlling element in sight distance deficiencies. In addition, the assessment of historical collision data revealed clusters along the regions defined with ASD limitations, indicating that restrictions in drivers’ vision could have contributed to the collision occurrence.

Urban rail systems frequently suffer from unexpected service disruptions, which can result in severe delays and user dissatisfaction. “Bus bridging” is the strategy most commonly applied in responding to rail service interruptions in North America and Europe. Buses are pulled from regular routes and dispatched to serve as shuttles along the disrupted rail segment until regular train service is restored. In determining the required number of buses and source routes, most transit agencies rely on ad hoc approaches based on operational experience and constraints, which do not necessarily alleviate the extensive delays and queue build-ups at affected stations, nor do they minimize system-wide impacts in an optimal manner. This paper proposes a genetic algorithm-based optimization model to determine the optimal number of shuttle buses and route allocation to minimize overall subway- and bus rider delay for any given rail disruption incident. The generated optimal solutions were sensitive to bus-bay capacity constraints along the shuttle service corridor of any given disrupted subway segment, utilizing methods found in the Transit Capacity and Quality of Service Manual. The model was used in an analysis of real-world incident data obtained from the Toronto Transit Commission and supplemented by other passenger and travel time data. The bus bridging toolkit showed strong potential to produce efficient shuttle response plans that reduced the transit user delays by more than 50% while ensuring minimum queue formation at disrupted stations and maximizing the utilization of shuttle buses.

While public transit network design has a wide literature, the study of line planning and route generation under uncertainty is not so well covered. Such uncertainty is present in planning for emerging transit technologies or operating models in which demand data is largely unavailable to make predictions on. In such circumstances, this paper proposes a sequential route generation process in which an operator periodically expands the route set and receives ridership feedback. Using this sensor loop, a reinforcement learning-based route generation methodology is proposed to support line planning for emerging technologies. The method makes use of contextual bandit problems to explore different routes to invest in while optimizing the operating cost or demand served. Two experiments are conducted. They (1) prove that the algorithm is better than random choice; and (2) show good performance with a gap of 3.7% relative to a heuristic solution to an oracle policy.

Thirteen different line-laser high-speed inertial profilers from four different manufacturers were recently tested at the Florida Department of Transportation (FDOT) Inertial Profiler Test Track. The hot-mix asphalt (HMA) track incorporates both dense and open-graded sections with international roughness index (IRI) values ranging from 34 to 104 in./mi. A cross-correlation analysis was performed on the resulting ride data. The accuracy comparison was performed using a SurPro reference profiler. The profilers as a group met the AASHTO R 56 cross-correlation criteria on each section except on a smooth, open-graded section. The profilers as a group met the repeatability cross-correlation on this section, but did not meet the accuracy cross-correlation requirement. This paper presents a description of the testing program, data collection efforts and subsequent analyses and findings.

The study purpose is to utilize communication technologies in automated transportation data collection. The goal is to find a solution for positioning beacons transmitting wireless signals suitable for traffic data collection. The technique developed in this paper for positioning is based on the strength of Bluetooth signals transmitted by beacons, creating radio maps, and applying an algorithm called k-nearest neighbors (k-NN). Four Bluetooth signal scanners and a beacon were used in the experiments in an intersection and its adjacent streets. Numerous stations and scanners arrangements were tested for enhanced accuracy. The results confirm the functionality of the technique in positioning based on wireless signals. The modifications in the set-ups and arrangements clearly indicate that increasing the distance between the stations on the radio maps, along with meeting the minimum positioning accuracy requirements and making the arrangement of stations and scanners asymmetric, can enhance the accuracy level of positioning by reducing the error probability of the algorithm and causing more distinction in the radio maps. It was observed that by increasing the distance between the stations in each lane, from 3 m to 5 m, and making the stations and scanners asymmetric in arrangement, the positioning percentage with an error of 5 m or less may be increased to 90% from 73.5%. Also, the accuracy of positioning tends to decrease as the distance between the beacon and the scanners increases. This paper studies both stationary and moving beacons. It was found that although positioning of stationary beacons can be done with a precision of up to 90% with an error of 5 m or less if the stations and scanners are properly arranged, the positioning of moving beacons is more challenging.

Many subway systems operating near capacity face challenges in meeting reliability and level of service targets. This paper examines the effectiveness of various strategies to relieve congestion and increase capacity using a microscopic, agent-based, urban heavy rail simulation model. The Massachusetts Bay Transportation Authority’s (MBTA) Red Line serves as the testbed for the analysis. The Red Line operates very close to its capacity. Bottlenecks on the Red Line and possible strategies to mitigate them are discussed, including skip-stop, station consolidation, and dwell time control. The results show that, compared with the no strategy case, skip-stop and consolidation are effective in reducing runtimes and passenger journey times, increasing train throughput, and maintaining headway regularity during peak periods. Performance under these two strategies is also robust to dispatching irregularity and increases in passenger demand. The dwell time control strategy mitigates congestion and disturbances in operations to some extent, but is less effective and robust.

Intercity railway system operation on national holidays can be challenging because of possible surging demand. This study proposes an analysis framework to investigate railway system ridership data on national holidays, seeking to attain better understanding of relevant intercity trip patterns, so as to enable enhanced preparation and response before and during national holidays. The ridership data are analyzed in the form of Origin–Destination (O-D) tables and regarded as pictures of N × N pixels, where N is the number of the considered stations/cities in a railway system. The framework primarily adopts a deep auto-encoder to process these pictures to reduce data dimensions and abstracting key features within these pictorial data. Based on the abstracted features, k-means clustering is then conducted to categorize the O-D tables with similar trip patterns into the same group. Further, a discrete outcome model based on logistic regression is developed on the clustering results to enhance the interpretation of the trip pattern in each group and identify the significant holiday-related characteristics and external factors that can affect the trip pattern generation. The ridership data of Taiwan Railways Administration associated with 38 national holidays from January 2014 to August 2018 are analyzed. The analysis results highlight insightful interpretation in relation to clustered trip patterns and relevant trip characterization relative to various national holidays. The proposed framework and developed discrete outcome model are also validated, showing 85% correct assignments of O-D tables to the groups of relevant trip patterns.

Resilient modulus (Mr) is a critical input for pavement design as it is the main property used to evaluate the contribution of subgrade to the overall pavement structure. Considering this, practitioners need simple and accurate ways to determine the Mr of in-situ subgrade without the need for expensive and time-consuming testing. The objective of this study is to develop a generalized regression prediction model for in-situ Mr of subgrades, compare it with established prediction models, and assess the model’s predictions on pavement performance using the Mechanistic-Empirical Pavement Design Guide (Pavement ME). The prediction model was built using field data from 30 pavement sections studied in the Long Term Pavement Performance (LTPP) Seasonal Monitoring Program where backcalculated modulus from falling weight deflectometer testing, in-situ moisture contents, and subgrade material properties were considered in the model. Based on the results, it was found that liquid limit, plasticity index, WPI (the product of percent passing #200 and plasticity index), percent coarse sand, percent fine sand, percent silt, percent clay, moisture content, and their respective interactions were significant predictors of in-situ Mr values. The findings showed that the generalized regression approach was able to predict Mr more accurately than predictions from the Witczak model. To assess the application of the predictive model on pavement performance, three LTPP sections located in New York, South Dakota, and Texas were analyzed to predict the rutting performance based on Mr values obtained from the developed generalized prediction model and those obtained from the current Pavement ME model and then compared with rut depths measured in the field. The findings showed that, for coarse-grained subgrades that have a low degree of plasticity, the generalized regression model predicted rutting performance similar to the embedded Pavement ME model. For fine-grained subgrades, the developed model tends to predict lower rut depths which were closer to the field measured rut depths. Overall, the generalized regression approach was successfully applied to create a simple, practical, cost-effective and accurate Mr prediction model that can be used to estimate the stiffness of subgrades when designing and evaluating pavements.