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Roadside landscape can be for a restorative function on ecology or for appreciation, but it may also bring clutter and traffic accidents. A literature review has identified that a lack of size regulation on roadside structures is the direct reason for this phenomenon. This paper aimed to investigate the optimum size solution. A Fuzzy Analytic Hierarchy Process method was used to evaluate the multi-criteria decision making problem. Evaluation indices focused on harmony with the environment, conspicuity, excitement, or depression degrees to viewers. Ten roadside structures in varied sizes formed the evaluation alternatives set. Experts gave their perceptual judgments on the alternative sets. The results revealed that roadside structures that are 25 m3–49 m3 have an excellent visual quality in the evaluation process and were considered to be the optimum size for roadside structures. The results could provide a theoretical basis for flexible landscape design in transportation corridors in China.

As a solution to the safety issues caused by a dilemma zone, multi-point advance detection schemes are typically used at high-speed signalized intersections to provide dilemma zone protection. Among many multi-point advance detection schemes, the Winston-Salem configuration, the SSITE configuration, the Beirele configuration, and the Bonneson configuration are among the most widely used schemes in the United States. This paper aims at evaluating the four detection schemes with regards to their safety and operational performances using the state-of-the-art dynamical dilemma zone model, a dilemma zone hazard model, and VISSIM micro-simulation platform. Particularly, the calibrated Dilemma Conflict Potential (DCP) model was used to replace the traditional "Number of Vehicles in Dilemma Zone" as the surrogate safety performance measure. When coupled with the dynamical dilemma zone model, the DCP model provides a more accurate dilemma hazard estimation, which cannot be achieved via the traditional dilemma zone model and safety estimation methods. The evaluation revealed that the Bonneson Configuration is the safest scheme among the four evaluated schemes. However, the excellent safety performance is at the cost of relatively higher delays. Importantly, the evaluation results provide analytical information for practitioners in selecting the appropriate dilemma zone protection method for addressing safety issues at high-speed signalized intersections.

Yellow light dilemma zone is one of the critical factors that possibly cause rear-end and right-angle crashes at high speed signalized intersections. It is an ideal objective of practical solutions to minimize the dilemma hazard while maximizing the operational efficiency. This study is intended to quantify the concerned objective by defining the combined cost for dilemma hazard and delay. A microscopic simulation based approach is applied to compute the dilemma hazard of each approaching vehicle. The operational efficiency is assessed by the overall intersection delay. An optimization framework is designed to minimize the combined cost for dilemma hazard and delay. An optimal detector configuration is obtained and evaluated via comparison with SSITE configuration, which is recommended by FHWA. The evaluation result concludes the superiority of the optimal detector configuration in terms of better safety and operational performances, as well as less combined cost for dilemma hazard and delay.

The accuracy of the loop-based vehicle classification under various traffic conditions is greatly dependent upon the capability of clarifying phases or states of traffic flow. One challenge lies in identifying traffic phases using variables that could be directly calculated from dual-loop data. In this paper, the authors present a hybrid method that incorporates level of service and K-means clustering methods for identifying traffic phases from dual-loop data. The authors apply the "phase representative variables" to represent traffic characteristics in the traffic flow phase identification algorithm. By using video vehicular trajectory data, they have successfully identified the traffic factors influencing vehicle classification accuracy under non-free traffic conditions and developed the innovative length-based vehicle classification models. The model evaluation results indicate that the accuracy of the estimated vehicle lengths has been increased from 42% to 92% under synchronized and stop-and-go traffic flow phases. The results also foster a better understanding of the traffic stream characteristics and associated theories to help further development of relevant microscopic simulation models with sensing traffic data sources.

At high speed signalized intersections, advance loop detectors are typically placed upstream the intersection to provide protection for yellow light dilemma zone (DZ). However, most widely used advance loop placement methods are typically based on the old study results and traditional computation of static DZ. To reflect DZ dynamics in loops placement, a new method specifically designed for dynamic DZ protection is urged to be developed. In this context, an alternative loops placement method which is developed based on the updated model of dynamic DZ is proposed and is evaluated with comparison to three classic loops placement methods. The results prove that the alternative method is superior to the classic Bonneson, Beirele, and SSITE layouts in terms of reducing DZ conflicts, intersection delay, and max-out occurrence percentage. It greatly enhances both safety and operation by providing effective and efficient protection for dynamic DZ.

Today, congestion migration is an important topic in the US for both freeway and arterial road systems. While freeway congestion has been widely studied, effectively measuring arterial congestion is still a developing topic, and is particularly important since more than 40 percent of US vehicle travel miles are resident in arterial roads. Arterial congestion measurement in the real world is complicated by technology challenges, such as limited arterial traffic detection capability, complex traffic flow patterns due to interruption of traffic signals, and different configurations of arterial traffic detectors. This paper investigates the effectiveness of arterial corridor congestion estimation using V+O (Volume plus Occupancy) data retrieved from the system detectors. Unlike the common stop-bar detectors, the system detectors are often used for "Responsive Traffic Plan Selection", where they are usually installed at a location immediate downstream of the intersection. The paper begins with a review of the existing efforts towards arterial congestion estimation, then focuses on the system detectors approach by looking at the effectiveness of the aggregated system detectors data for arterial congestion measurement. In comparing the field collected data with the simulation results, the conclusion illustrates that system detectors can provide reasonable congestion estimates and are sufficient to serve as the arterial congestion level estimation source for the Advanced Traveler Information System (ATIS) system.

The significance of vehicle-type-specific headway studies lies in the application of freeway capacity and safety analysis. However, data reliability plays an important role in the ability to understand the characteristics and microscopic modeling of freeway traffic. This study takes advantage of observation-based video data by using data extracting software VEVID to retrieve reliable vehicle trajectory data. The study focused on uniqueness of vehicle-type-specific headway under specific traffic states, as well as mixed headway. Data examination showed significant differences in vehicle-type-specific headways under average traffic conditions, uncongested flow and congested flow. Headways tend to be minimal in uncongested traffic flow, and maximal in congested traffic flow. In addition, the degree of discreteness by vehicle type headway differs significantly under different traffic conditions. Noticeably, in congested traffic, all types of headways tend to be less than reported in literature review results. In particular, Truck-Car and Truck-Truck headways behave considerably differently when traffic shifts between congested and uncongested conditions. The practical applications of these findings including improvement of microscopic car-following models, traffic state identification and vehicle classification.

Vehicle emission has been recognized as one of major contributors to air pollution in urban areas, which brings adverse effect to public health and to climate change. Air pollutant concentration is usually used as a measurement to evaluate the level of air quality, particularly in terms of environmental health. Modeling the affect of traffic to pollutant concentration helps better understand what and how traffic factors contribute to the air pollution and related public health problem. This paper presents a study of modeling Carbon Monoxide (CO) concentration, one regulated toxic air pollutant, using the data collected in four roadways surrounding the University of Cincinnati west campus. In this study, intersections and roadway segments are considered as point and line emission sources, respectively. CO emission strengths at point source and line sources are modeled by considering the impact of intersection delay, segment traffic volumes and speed, as well as weather and other environmental factors. The dispersion of CO emission from intersections and segments is modeled by integrating both classic line source model and point source models, and values of relevant contributing or factors are obtained by analysis of observed data. Finally, predicted CO concentrations resulted from the integrated model are compared with the observed CO concentrations at a sensor placed within the campus for model validation. The results indicate that concentrations predicated by the developed model are mostly close to the observed data, or have similar trend.

Dilemma zone is dynamically featured both in location and length at signalized intersections. This study presents a proof-of-concept development of the innovative methodology for characterizing the dynamics of dilemma zones using video-capture techniques. With availability of ground-truth video and extracted vehicle trajectory data, various dilemma zone contributing factors, such as vehicle type, speed at the onset of yellow indication, onset acceleration/deceleration, dilemma zone behavior (pass or stop), yellow duration, and arrival type can be identified and/or quantified. More importantly, the study quantitatively distinguishes and models the risky zone and option zone, both of which constitute a dilemma zone under varied speeds and yellow durations. The paper demonstrates a significant advancement in dilemma zone data analysis and modeling, which provides a solid basis for further research on modeling the dynamics of dilemma zones and loop detectors placement for dilemma zone protection problems in the future.