Journal of Transportation Engineering, Part A: Systems

The well-known spatiotemporal traffic diagram is a popular and powerful tool in the field of transportation research and practice. It is an important basis of analyzing traffic conditions, identifying bottlenecks, and controlling and routing traffic. Traditionally, the spatiotemporal diagram is constructed by using stationary detector data, and little research has focused on construction using widely existing floating car data (FCD). Therefore, this paper proposes a data-driven method to construct the spatiotemporal diagram by using FCD. The method is completely based on FCD without the aid of map-matching and geographic information system tools. Two real-world road networks in Beijing are taken as examples to demonstrate the method. The method is validated by comparing instantaneous speed contained by individual trajectories with aggregated speed in the spatiotemporal diagrams. The method helps to understand traffic dynamics from FCD, and then aids to carry out various transportation researches and applications.

A significant portion of highway infrastructure life-cycle costs can be attributed to maintenance expenditures, particularly in the later years of an infrastructure’s life. These expenditures are a key input for agency functions including cost allocation, financial planning and needs assessment, and investment evaluation. Thus, agencies seek improved techniques for forecasting future maintenance expenditures. Evidence from empirical data suggests that maintenance expenditure data exhibits significant spatial and temporal variations. If this is not addressed, the analysis could be impaired by disturbance correlation and yield potentially inefficient model estimates. In addressing this issue this paper introduces and validates (for highway pavement sections) a random-effects panel-data methodology to account for variations in routine highway maintenance expenditures at different locations and years. The results suggest that the annual maintenance expense (AMEX) values are: $250–$1,120/lane-km [$400–$1,800/lane-mile(mi)] for interstates, $125–$650/lane-km ($200–$1,000/lane-mi) for U.S. routes, and $125–$310/lane-km ($200–$500/lane-mi) for state roads, depending on the pavement age, surface type, climate, and traffic.

This paper presents the latest results from a noncontact ultrasonic rail inspection system developed at the University of California, San Diego (UCSD) under the auspices of the Federal Railroad Administration. Noncontact rail probing is an attractive feature that could potentially allow for new possibilities. The UCSD system utilizes a focused air-coupled transmitter, symmetrically placed air-coupled receivers, and a real-time statistical algorithm to maximize true outliers (defects) and minimize false positives. Two field tests were conducted at the Rail Defect Test Facility of the Transportation Technology Center in Pueblo, Colorado, at test speeds from 1.6 to $24\mathrm{km}/\mathrm{h}$ (1 to $15\mathrm{mi}/\mathrm{h}$). The results of these tests, evaluated in terms of probability of detection versus probability of false alarms (receiver operating characteristic curves) indicate a good detection performance at the speeds of 1.6 and $8\mathrm{km}/\mathrm{h}$, with a much poorer performance at the speeds of 16 and $24\mathrm{km}/\mathrm{h}$. Possible reasons for the performance degradation at the higher speeds are discussed, and future work aimed at mitigating these issues is proposed.

The concept of goal-oriented analysis of transportation system performance presented in this paper captures multiple stakeholder and sociopolitical views of system performance, while simultaneously augmenting or maximizing value in multiple contexts (e.g., different regions) and at multiple scales (e.g., state and regional). This study applies the concept of goal-oriented analysis of transportation system performance to evaluate the state of selected highway corridors of significance in the state of Georgia. A corridor’s performance is assessed as it traverses different regions first for varying contextual priorities. A similar analysis is conducted for the selected corridors as they traverse different regions, using statewide goals. The results are compared to assess similarities and differences, and the findings analyzed to demonstrate the significance of considering regional priorities within analyses to achieve statewide goals. In particular, the results indicate that there are potential cobenefits to be gained in setting context-sensitive targets and allocating resources effectively, and potential value in additional data collection to enhance such analyses.

On-site pavement structural-performance monitoring is a challenging task. High accuracy, large coverage, and good reliability of the embedded sensors are the key factors to ensure monitoring efficiency. Most of the sensors for pavement structures are currently pointwise. This paper introduces a shape-measurement sensor based on a fiber-Bragg-grating (FBG) sensing technique for pavement structure, which can provide large-scale layer shape measurement using one FBG sensing element. It can bear the high compaction force and high temperatures during pavement construction. The core sensing element can be replaced after embedding and therefore can fulfill the lifecycle performance-monitoring requirement. The sensor can be applied for long-term pavement rutting or substructure-settlement monitoring. Different kinds of lab tests and numerical modeling have been carried out to study the performance of the proposed sensors. The proposed sensor has also been embedded in a real highway structure and the monitoring data have demonstrated its effectiveness for future large-scale practical application.

Four analytical models to estimate percent time spent following (PTSF) on two-lane highways were tested with data from 16 sites in Brazil. Besides percent following, the models are based on shockwave analysis, moving-bottleneck theory, or queuing theory. As direct measurement of the PTSF is impractical, PTSF values produced by a simulation program were the basis for comparison (observed PTSF values). The quality of the PTSF estimates produced by the four models was tested using 442 observations and four goodness-of-fit functions [mean normalized error (MNE), mean absolute normalized error (MANE), root-mean squared normalized error (RMSNE), and correlation coefficient $r$]. Although some degree of correlation between the estimated and observed PTSF values was detected, none of the four models was able to provide reliable estimates of PTSF, as indicated by high mean normalized errors (around 35%) and mean absolute normalized errors that were, on average, close to 50%.

Adaptive traffic signal control (ATCS) reduces congestion, improves travel time reliability, and prolongs the effectiveness of traffic signal timing. The objective of this paper is to demonstrate different data sources and analysis techniques to compare the arterial performance for before, during, and after ATCS implementation. This paper presents several analyses using field-collected travel time data and simulation modeling techniques. The paper demonstrates that different sources of travel time data can be used to characterize the corridor performance under ATCS. Although corridor travel time is the traditional way to assess the improvements from the newer technologies, other measures need to be looked at for objective evaluation of the new systems. It is recommended to use multiple evaluation techniques for better decision making as every technique has its own limitations. The methodologies discussed will help transportation engineers, planners, and policymakers to understand what analysis can be conducted at any point of an ATCS deployment to evaluate the system performance. As policies and timings are adjusted, this feedback is an effective way to continuously monitor the ATCS and quantify benefits.

In an effort to assist transportation professionals with the evaluation of wider economic benefits (WEB) of highway improvements, this paper provides important insights into the open-source AASHTO EconWorks wider economic benefits analysis tools using three case studies located in Indiana. The paper makes a significant contribution to the state of practice by providing an in-depth discussion of the lessons learned during the Federal Highway Administration (FHWA) implementation assistance program in Indiana. The paper briefly presents the theoretical background related to the development of the tools and explains data needs and sources. The discussion of the case studies primarily focuses on the sensitivity of the tools’ results to critical input parameters (such as volume to capacity ratio and productivity elasticity), which has not been addressed in previous studies. Lastly, the paper elaborates on the applicability and limitations of the tools, and also discusses their potential integration into an agency’s decision-making process. The inferences from this case study can be useful for state departments of transportation (DOTs), metropolitan planning organizations (MPOs), and other organizations interested in quantifying the potential productivity gains associated with planned or future highway projects.

Vehicle merging is a tactical, dynamic optimizing process with a series of decision-making operations. Existing microscopic lane-changing simulation models have been criticized that they do not well capture the important sequential decision-making process during merging because of the lack of deeply investigating field data. This study first analyzes merging tactics based on noise-filtered next-generation simulation (NGSIM) data, and then a new combined sequential decision-making model was proposed for candidate gap generation, targeting gap selection, merge location choice, and acceleration decisions of the merging vehicles. This sequential decision-making model could dynamically simulate the merging vehicles’ choice of targeting gaps and merging tactics under the effects of changing traffic conditions. How the merging vehicles behave after rejecting their current adjacent gap and synchronize with the speed of the main-lane traffic during merging is embodied in this model. Such aspects were oversimplified and against reality in existing merging models. The proposed new merging model was calibrated and validated with a U.S. Highway 101 (U.S.101) data set. The superiority of this new framework is highlighted in comparison with other two merging models. The results demonstrate that the proposed model has an acceptable accuracy to reproduce the trajectories of merging vehicles. The simulated vehicles’ trajectories show that the gap-selection model and targeting gap approaching algorithm can well simulate the driving behavior of the merging vehicle, which carries gap-rejection experience. The empirical field data analysis and model calibration results in this study provide several useful thoughts on building merging models to capture more real-world freeway merging behaviors.