Traffic congestion is a common phenomenon in our daily lives that greatly costs society. A better understanding of the interaction between freeways and arterial streets may help traffic engineers and researchers improve the operation of existing facilities and deploy feasible traffic diversion plans to improve the usage of existing road capacity within a traffic network. This paper proposes a novel two-step approach to evaluate the interaction between freeways and arterial streets by comparing their performances. The first step is to identify freeway and arterial travel time pattern similarities via template matching techniques commonly used in computer vision. The interaction is quantified in the second step by using conditional probability theory. The result of the two-step process allows analysts to determine whether traffic diversion is possible or likely between freeways and parallel arterials. The city of Bellevue, Washington was selected as a case study site because of the availability of traffic sensor data. The results demonstrate that the analysis approach allows traffic analysts to more comprehensively observe the interaction between freeway and arterial performance by using existing data collection facilities. The quantitative interaction results provide a better understanding of diversion potential and are useful in incident response, individual route planning, and integrated corridor management. This approach can be applied to any city’s freeway-arterial network if reliable sources of travel time data are available.

Considering the peaking effect over a short time period is important because queuing might build up and take substantial time to discharge. Traditionally, the peak-hour factor has been used to quantify such a peaking effect. The Highway Capacity Manual (HCM) suggests a design value of 0.92 for congested urban areas and 0.88 for rural areas, if no field measurements are available. These default values give general guidelines, but they might be too coarse for practical usage. An effort is made to model the actual peak-hour factors as a function of volume-to-capacity ratio and the functional classification of roadways. A total of 1,669 data points were obtained for analysis. The results show that, among several functional forms, the simple power function established with functional classification of roadways can be used to explain 46% of data variation, which appears to be acceptable, given the significance of data variability. The 95th percentile confidence intervals on the mean estimates and the predictive limits are also provided. Compared to the HCM default value, the recommended peak-hour factors, in general, result in higher average intersection delays with the optimal signal control. Finally, model validation using data collected from two other geographical areas indicates the proposed prediction model is transferable.

A railroad grade crossing (RGC) is a spatial location where rail and highway users share the right-of-way. A significant number of traffic crashes and severe consequences at RGCs have signaled the need for appropriate models to investigate the key factors associated with the casualty risk level at an RGC in terms of the number of fatalities or injuries caused by one or more crashes in a specific time period. This study used a zero-inflated Poisson regression model to describe the relationship between the extra-zero count fatality or injury data and explanatory variables collected at 592 RGCs in Taiwan. The annual averaged daily traffic and the presence of Guidance Sign 31 were significantly associated with the probability of no fatality or injury encountered at an RGC; if an RGC was at risk of a fatality or injury, the number of daily trains, crossing angle, and Guidance Sign 31 significantly influenced the expected total number of fatalities or injuries caused by traffic crashes. The empirical results indicated that traffic exposure and traffic signage have significant effects on the risk levels of casualties at an RGC.

Average vehicle occupancy (AVO) has been used in various applications, including conversion of person trips and vehicle trips in travel demand modeling, evaluation and monitoring of congestion management strategies, and computation of person delays in traffic analysis. AVO data are traditionally collected in the field using the roadside windshield method and the carousel method. The accuracy of AVO estimates is of particular importance because in traffic analysis a relatively small percentage of AVO will convert to the same percentage of vehicles. This paper describes a detailed process for estimating AVOs at the individual location, facility-type, and county levels. A detailed sampling process designed to select data collection locations and dates on three different facility-types (surface streets, freeways, and toll facilities) is presented. To ensure that all areas are covered, the sampling was performed in proportion to roadway vehicle miles traveled (VMT) within individual census tracts over a one-year period. A comprehensive statistical analysis of the collected data was then demonstrated. The results showed no significant difference in the AVO estimates among the three facility-types. Also, no significant difference was found among the AVO estimates derived from facility-weighted composite values and those derived from averaging the simple means. The process presented in this paper provides a rigorous example for the collection and analysis of vehicle occupancy data.

This paper assesses the impact of exclusive truck facilities on urban freeway performance. A large-scale regional microscopic traffic simulation model is developed for morning and afternoon peak hours and is used to model two alternative truckway configurations in the Greater Toronto Area (GTA), including a truck-only highway and a truck lane conversion on Highway 401. Demand inputs to the microsimulation model are generated by a regional transportation demand model that provides origin-destination (OD) matrices for light, medium, and heavy trucks and passenger cars. Plug-ins are developed to represent a truck-only lane in the simulation environment. The simulation model is successfully calibrated to reflect observed road counts and to produce realistic congestion patterns. The effect of infrastructure changes on travel distances, travel times, exclusive truck lane usage, and travel speeds is assessed. Microscopic simulation allows queuing formation/dissipation and bottlenecks to be represented and allows for separate analysis of truck and car performance. Addition of a 4-lane truck-only highway results in greater travel time improvements for trucks, and it sometimes results in shifting of traffic bottlenecks on Highway 401 to downstream locations. Conversion of a freeway lane on Highway 401 results in increased congestion for passenger cars, but improved travel speeds for trucks. Both scenarios show truck facility usage ranges from 100 to 800 trucks per hour per direction.

Slab curvature, which represents the response of concrete pavement slabs to environmental loads, influences the location and magnitude of critical slab stresses and affects long-term pavement performance. The purpose of this study was to measure the changes in temperature and moisture profiles in a newly constructed concrete pavement and to determine both the overall deformed shapes of the slabs as well as the relative contributions of built-in and transient environmental effects over time. Data were collected from an instrumented jointed plain concrete pavement (JPCP) over a 2-year period. Slab curvatures were computed or predicted using measurements of temperature and moisture conditions in the slab, static strain measurements, and pavement surface profile measurements. It was found that the additional restraint provided by the dowel and tie bars does not appear to significantly reduce slab curvature resulting from daily temperature fluctuations or from reversible drying shrinkage. It does have a substantial effect on reducing slab curvature attributable to long-term drying shrinkage. It was also found that the Mechanistic-Empirical Pavement Design Guide default value is close to that measured in this study for the restrained slab but does not appear to be appropriate for unrestrained (i.e., undoweled) slabs.

In the last few years, there have been rapid developments and advances of technologies for pavement cracking surveys for both data acquisition and data interpretation. Although the high-speed and objective means of automated data collection and processing for pavement distress survey are appealing, many agencies are reluctant to adopt the new technology because either data quality is perceived as compromised or that there is lack of experience. However, there has been a slow but growing trend that more agencies began to realize that as long as a proper balance between data quality and full automation is made, the level of error in automation is acceptable. In addition, there is a lack of commonly used cracking protocols and limited analysis of data quality. This paper attempts to design an analysis method and apply real-world data sets under varying conditions for fully automated analysis. A statistical approach is used to compare results among manual, semiautomated, and fully automated procedures. The paper also presents the automated cracking survey methodologies and protocols used in the Automated Distress Analyzer (ADA) and the Digital Highway Data Vehicle (DHDV). The quality of automated cracking data is compared with manual processing results. The variability and the quality assurance procedures in the cracking surveys are discussed. It is demonstrated that fully automated processing of pavement cracking can be as useful as manual processing in addition to providing substantial savings.

Inaccessible transit stops prevent people with disabilities from using fixed-route transit services, thereby limiting their mobility. The Americans with Disabilities Act (ADA) prescribes the minimum accessibility requirements for transit stops for riders with disabilities. In addition, transit agencies may also choose to implement the “universal-design” paradigm, which involves higher design standards than current ADA requirements and includes amenities that are useful for all riders, such as shelters and lighting. Because of budget limitations, however, transit agencies can select only a limited number of transit stops for ADA improvements each year. To increase the impact of these improvements, it is desirable that transit stops be selected such that they will maximize the overall benefits to patrons with disabilities. This paper describes a multiobjective binary nonlinear programming model for selecting, within a limited annual budget, a priority set of transit stops for improvements. The model aims to achieve two objectives, namely, meeting the minimum ADA standards and the universal-design standards. Compared with the traditional approach of selecting stops for improvements, which are often based on either staff experience or requests from elected officials, the optimization approach described in this paper provides a more objective platform to identify better candidate stops for ADA improvements.

Transit agencies seeking to improve transit service delivery are increasingly considering the deployment of transit signal priority (TSP). However, the impact of TSP on transit service and on the general traffic stream is a function of many factors, including intersection geometry, signal timings, traffic demands, TSP strategies and parameters, transit vehicle headways, timing when transit vehicles arrive at the intersection, etc. Previous studies have shown that depending on these factors, the net impact of TSP in terms of vehicle or person delay can be positive or negative. Furthermore, because of financial constraints, transit agencies are often able to deploy TSP at only a portion of all of the candidate intersections. Consequently, there is a need to estimate the impact of TSP before implementation to assist in determining at which intersections TSP should be deployed. Currently, the impacts of TSP are often estimated by using microscopic simulation models. However, the application of these models is resource intensive and requires specialized expertise that is often not available in-house to transit agencies. In this paper, we propose and validate an analytical model for estimating the delay impacts of green extension and early green (red truncation) TSP strategies. The model is applied to individual intersections and can reflect the effect of coordination on arrivals to the intersection; however, it does not consider the impact of TSP on coordination at downstream intersections. The results show that the proposed model provides estimates of the delay impacts of TSP that closely match those obtained from microsimulation modeling analysis (using VISSIM) for volume-to-capacity (v/c) ratios up to 0.8. The proposed model is suitable for implementation within a spreadsheet and requires considerably less effort and technical expertise to apply than a typical microsimulation model and therefore may be a more suitable tool for transit agencies to use for prioritizing TSP deployment.