Journal of Transportation Engineering, Part A: Systems

Different types of trains may have substantially dissimilar characteristics, which result in various capacity effects. The concept of base train equivalent (BTE) has been previously proposed to standardize different train types into a universal unit, i.e., a base train unit (BTU). However, the previously developed delay-based model suffers from consistency issues before and after BTU conversion, and its application is limited to only two train types. Thus, this study proposes a new concept of delay-based BTE computation and corresponding BTE models. The dynamic BTE model considers volume and heterogeneity to fully reflect the actual capacity effect of nonbase trains. The fixed BTE model identifies the most appropriate BTE value at a particular traffic heterogeneity. Results of the case studies demonstrate that the proposed method can address scenarios with all types of traffic mixes and multiple train types. The unit of delay-based rail capacity can be converted into a standard unit using the proposed models. The effect of an extra train can be effectively assessed, and the capacity evaluation from different lines or systems can be analyzed and compared.

In recent years, with the advancement in traffic sensing, data storage, and communication technologies, the availability and diversity of transportation data have increased substantially. When the volume and variety of traffic data increase dramatically, integrating multisource traffic data to conduct traffic analysis is becoming a challenging task. The heterogeneous spatiotemporal resolutions of traffic data and the lack of standard geospatial representations of multisource data are the main hurdles for solving the traffic data-integration problem. In this study, to overcome these challenges, a transportation data-integration framework based on a uniform geospatial roadway referencing layer is proposed. In the framework, on the basis of traffic sensors’ locations and sensing areas, transportation-related data are classified into four categories, including on-road segment-based data, off-road segment-based data, on-road point-based data, and off-road point-based data. Four data-integration solutions are proposed accordingly. An iterative map conflation algorithm as a core component of the framework is proposed for integrating the on-road segment-based data. The overall integration performance of the four types of data and the efficiency of the iterative map conflation algorithm in terms of percentage of integrated roadway segments and computation time are analyzed. To produce efficient transportation analytics, the proposed framework is implemented on an interactive data-driven transportation analytics platform. Based on the implemented framework, several case studies of real-world transportation data analytics are presented and discussed.

This work investigates whether the user-generated data from multiple sources, such as smart cards and social media, can be used to identify main mobility/activity patterns based solely on geo-tagged information. To perform such an analysis, automated models are developed to (1) retrieve user mobility patterns from historical, user-generated data logs, (2) categorize users based on the similarity of their observed mobility patterns, and (3) predict the travel distances of users for participating in future activities. For testing purposes, user-generated data sets from smart card logs and Twitter profiles collected between November 2013 and February 2015 in London are used. User-generated data from 200 smart card and 32 active Twitter users are collected and 6 main clusters are identified based on the mobility/activity pattern similarities of users. Results show that it is possible to integrate data logs from multiple sources to capture the main mobility/activity patterns observed in an area. Results also reveal that the accuracy of the predicted travel distance of one user’s trip can be significantly improved if the user’s previous activities are considered in the prediction process.

A right-hook (RH) crash is a common type of bicycle–motor vehicle crash that occurs between a right-turning vehicle and through-moving bicycle at an intersection in right-hand driving countries. Despite the frequency and severity of this crash type, no significant driver-performance based evidence of the causes of RH crashes at signalized intersections was found in the literature. This study examined the driver’s visual attention in a right-turning scenario at signalized intersections with bicycle lanes but no exclusive right-turning lanes while interacting with a bicyclist to develop an understanding of RH crash causality. Fifty-one participants in 21 simulated road scenarios performed a right-turning maneuver at a signalized intersection while conflicting with traffic, pedestrians, and bicyclists. Overall, a total of 820 (41 × 20) observable right-turn maneuvers with visual attention data were analyzed. The results show that in the presence of conflicting oncoming left-turning vehicular traffic, drivers spent less visual attention on the approaching bicyclist, thus, making them less likely to be detected by the driver. The presence of oncoming left-turning traffic and the bicyclist’s speed and relative position, and conflicting pedestrians were found likely to increase the risk of RH crashes. The results of the current study will help identify effective crash mitigation strategies that may include improving the vehicle–human interface or the implementation of design treatments in the road environment to improve driver and bicyclist performance.

Urban traffic prediction is a challenging task due to the complexity of urban networks. Many studies have been conducted to improve the prediction accuracy, but the limitation still remains that their accuracy varies with location and time due to lack of understanding. To overcome this limitation, it is necessary to investigate in depth the various phenomena that change the traffic flow patterns. Among the phenomena, this study aims to analyze the effect of inherent variation in a link and spatiotemporal dependency between links in predicting travel speed in urban networks and to identify the factors that influence the two phenomena. The results show that the variation and dependency have significant differences according to locations. The results also indicate that the effects of the two phenomena vary depending on the prediction horizon of the prediction model and suggest to consider both the variation and dependency in short-term prediction but focus on only the variation in long-term prediction. The authors also identify the factors that affect the two phenomena and recommend guidelines for urban traffic prediction.

Wood transportation costs have significant impacts on timber investment returns. To optimize transportation costs, a practical design of the route network is necessary for log trucks that ship wood material from timberlands to wood mills. Road conditions resulting from various road maintenance policies dramatically affect speed limits, log-truck route networks, and transportation costs. The effects of road maintenance policies on truck route selection and user/agency cost trade-offs have not been deeply researched. By considering forest-road maintenance policies, this study intends to provide accurate information on timber-processing service coverage and log-truck route selections for forest companies or subcontracted fleets of log trucks. This research also conducts an environmental analysis to assess the effects of various road maintenance policies on consumed energy and greenhouse gas emissions of log trucks. With current maintenance policies, the results indicate that only between 33% and 51% of timber areas in Oregon can be accessed or harvested by existing nearby wood mills. However, application of the proposed road maintenance policy in this paper would increase accessibility to timberlands by up to 104%. The cost of log transportation also decreases by 16%–34%. The methodology developed in this study can lead to improvements in forest road preservation management systems to achieve the full benefits of reducing log-truck emissions and total transportation costs.

Railway alignment optimization is a large-scale and time-consuming civil engineering problem. To solve it, a three-dimensional distance transform (3D-DT) algorithm, which is a variant of the three-dimensional Euclidean distance transform (3D-EDT), was previously designed. However, that algorithm is quite computationally intensive. In addition, the 3D-DT is inherently sequential, and it is thus challenging to parallelize. Thus, this study focuses on improving the sequential 3D-DT by transforming it into a parallel one. First, existing representative parallel EDT methods are reviewed and assessed. Then the railway alignment optimization model and the sequential 3D-DT are described. After that, critical execution properties of the 3D-DT that significantly influence its parallelization are explored in depth. Lastly, a novel so-called parallel linkage method is presented. This parallel implementation, which is developed using the OpenMP library, is highly effective and scalable by fully exploiting the parallelism of the algorithm. Using this parallel 3D-DT method, a large-scale, real-world railway case is tested and analyzed in detail. The outcomes verify that the proposed parallel method can accelerate the optimization process significantly without reducing the quality of computation results.

This study proposes a solution framework for operational analysis and financial assessment of transit services that considers the passenger behavior and the elasticity of transit demand to service characteristics. The proposed solution framework integrates a dynamic transit passenger assignment model (Fast-Trips) with a mode choice model and a service design module, and iterates these methods until an equilibrium between fares and frequencies is reached. The solution framework was implemented for a newly conceived intercity transit service in Arizona, and the system performance was studied for multiple fare policy and frequency design scenarios. The results showed that the scenarios with designed-oriented frequencies had lower ratios of revenue to operating cost ($R/C$) compared with those in which frequencies were set based on the passenger path-choice behaviors and route usage, which emphasizes the importance of considering elastic transit demand in network and service designs. The sensitivity analysis also indicated that there are multiple ways to achieve a certain $R/C$ ratio, and therefore it is the other objectives and the operator’s priorities that define the final design and service characteristics.

In recent times, amplified pedestrian-vehicle interaction, especially at urban midblock crossings, have ensued in higher risks and fatalities during pedestrian crossings. This situation further exacerbates under heterogeneous traffic conditions, where indiscriminate mix of vehicles with diverse static and dynamic characteristics in conjunction with loose lane discipline adds another dimension to the already multifaceted pedestrian-vehicle interaction phenomena. Because pedestrian crossing behavior is significantly governed by traffic flow characteristics, pedestrian characteristics, land-use, road geometry, and sociodemographic characteristics, it becomes a prerequisite to investigate pedestrian–vehicle interaction at microscopic levels for the design of efficient and safe crossing facilities. Therefore, in this regard, the objective of the current study is to develop warrants for pedestrian crossing facilities at urban midblock sections under mixed-traffic conditions prevailing in countries such as India. To comprehend this objective, pedestrian–vehicle (${\mathrm{PV}}^2$) conflict analysis, augmenting both macroscopic and microscopic pedestrian crossing characteristics, is carried out at nine different unprotected midblock crosswalks located in different parts of India. Video-graphic surveys were conducted, and both macroscopic and microscopic characteristics were extracted at minute levels from the recorded video. Thereafter, level of service (LOS) thresholds are developed based on the extracted microscopic characteristics covering both operational (pedestrian delay) and safety (safety margin) aspects. The developed LOS thresholds were then correlated with pedestrian–vehicle conflict (${\mathrm{PV}}^2$) values to develop pedestrian crossing warrants. The developed pedestrian crossing warrants can act as practical recommendations in identifying requisite pedestrian crossing facilities based on the observed pedestrian–vehicle interaction and pedestrian behavior for a given roadway and traffic condition.

Research on track quality behavior has been extensively published, and many different approaches have been presented to describe the process of track quality. The research goal of this paper is to form a basis for a data-driven tamping prediction based on track quality analyses over time. A research database containing asset information, executed maintenance tasks, and measuring data of some 4,400 km of track of the Austrian rail network in a time sequence of 16 years is available. The modified standard deviation of vertical track geometry is identified as an ideal track quality indicator for planning and predicting tamping tasks in Austria in the context of this research. Further analyses show that a linear regression function is best suited for describing track quality between two tamping tasks and shows the best accuracy for predicting track quality in the future. An algorithm was developed by means of the linear regression function that enables analyses of track quality behavior over time for long time series and the whole network. This includes track quality before and after tamping tasks as well as deterioration rates. In the future, these basics have to be combined with further technical evaluations to detect an optimal intervention limit. Furthermore, economical and operational considerations must be incorporated to find the optimal tamping strategy under the given conditions.

The distribution of travel times in high density urban environments is observed to be multimodal as a result of random demand fluctuations, nonrecurrent incidents, and other interruptions. Conventional travel time measures that use indices from unimodal distributions, such as average speed, cannot accurately reflect true traffic conditions in the network. Finite mixture models (FMMs) are a natural choice to represent the distribution of travel times in such settings. In this study, travel times in Midtown Manhattan collected from radio frequency identification device (RFID) transponders are used to test and validate three FMMs. The three models are the Poisson mixture, the Gaussian mixture, and the Gamma mixture. The first two are fitted using the expectation-maximization algorithm and the third using sparse approximation techniques. The Gaussian and Gamma mixture models are demonstrated as capturing the clustering in the travel time data. The Gamma mixture is demonstrated as being slightly superior in terms of generalizability to out-of-sample test data. This case study indicates the potential for a feasible performance measure of the status of urban traffic that is frequently interrupted by signal controls.