Journal of Transportation Engineering, Part A: Systems

The Federal Highway Administration requires each state department of transportation to have a traffic monitoring program for estimation of among others annual average daily traffic (AADT), a performance measure that guides fund-allocation to the states. Determination of AADT for each road section theoretically requires year-round counts to be made at all sections of a state’s monitored road network. Because this is not cost-effective, counts are made for a short duration on the majority of road sections, and these are adjusted to AADT estimates using seasonal factors (SFs) developed from data collected at the small number of road sections, called automatic traffic recorder (ATR) stations, at which year-round counts are made. Alternative methods exist for developing the SFs. One method makes use of only the most recent calendar years’ ATR data. A second makes use of multiple calendar years of ATR data, determining the final SFs as a simple mean of SFs from each of the calendar years. A third method, developed in this research, called the weighted-factor method, also makes use of multiple calendar years of ATR data but combines SFs from these calendar years taking into consideration their respective variances. The three methods were empirically investigated for the accuracy of the AADT estimates they yield, and the weighted-factor method found to yield the most accurate AADT-estimates.

In recent years, agencies across the United States have embraced the implementation of the flashing yellow arrow (FYA) for permissive left turns. Previous research determined that the FYA indication communicated the presence of permissive left turns more effectively compared with the circular green, which led to its inclusion in current guidelines. Despite the recent implementations and multiple research efforts to investigate the operations of FYA permissive indications, there remains a lack of national guidance on the definition of change and clearance intervals for transitioning between protected and permissive left turns at intersections operating permissive/protected left turns. This study investigates the connection between traditional signal phasing/design and human factors through both a driver comprehension computer-based static evaluation and a real-world field study to evaluate various practices employed across the United States. Overall, the survey results provide evidence to suggest similar comprehension levels between the FYA and circular green (CG) permissive left-turn indication sequences. The field evaluation yielded similar comprehension levels; however, drivers appeared to induce fewer potential conflicts when the all-red clearance interval was present. Initial evidence suggests potential safety benefits associated with including the all-red clearance interval upon transitioning between the protected and permissive left-turn phase with the application of a FYA.

The advent of driverless vehicles, including automobiles and buses, may considerably affect the competitiveness and ridership of public transportation services in negative as well as positive ways. Since driverless vehicles may be widely used in the fairly near future, public transit operators and transportation planners should prepare to deal with their anticipated effects. In this paper, (1) we formulate modular optimization models for both human-driven and automated bus services with fixed routes as well as flexible routes. These models are solved analytically. Their results can be used to abstract the essential characteristics of the transportation system and analyze system behavior. (2) We develop preliminary quantitative assessments of those effects, showing that without drivers, the introduction of automated vehicles can reduce the competitiveness of bus transit. (3) We conduct sensitivity analyses to explore how changes in input parameters affect the results. (4) We identify insights that may help prepare transit operators, transportation planners, and other transportation system stakeholders for effectively adapting to the introduction of driverless vehicles.

As natural and human-induced disasters have increased in recent years in number, magnitude, and impacts, the frequency of evacuation orders is expected to be accordingly raised in the future. Practice has shown, however, that many times, the respective operations face impediments, thus indicating the need for efficient tools to facilitate them. Intelligent transportation systems (ITS) have been recognized as a promising strategy in this direction. In this context, this study presents a comprehensive overview of current practices and research efforts regarding the use of ITS in real-time evacuation operations; the scope is to synthesize and critically discuss the state of the practice and state of the art on ITS-assisted decision making in evacuation management. Emphasis is placed on the modeling tools required for adequate representation of real-time evacuation conditions and on the role of emerging technologies in this field. Research findings are analyzed, challenges faced by transportation authorities are identified, and promising future directions are proposed. The paper concludes with a sociobehavioral analysis of the role of information provision in disaster settings, discussing its influence and the possible implications and complications arising, as well as reviewing relevant research studies from the evacuation literature.

This paper presents an approach to estimate the delay propagation in a high-speed railway (HSR) network under accident conditions. Considering the complexity and dynamics of HSR accidents, different levels of delay are discussed using clustering analysis, and the delay propagation is analyzed both forward and backward using different mathematical formulations. The forward delay propagation is described by a fitting distribution between arrival delay and forward resistance of accidents at different levels. The backward-delay-propagating mechanism is exploited by a combined model of horizontal propagation and vertical propagation under corresponding constraints of minimum headway and emergency measures. To integrate the forward- and the backward-propagating models, a global algorithmic framework is then designed with special consideration of routes separation. Finally, an applied case analysis of regional rail network in the Yangtze River Delta verifies the feasibility of the delay propagation algorithm. Through a validation test to accidents with detailed records, results suggest that the proposed algorithm can conservatively estimate the delay distribution with an accuracy greater than 75%.

This paper studies the effect dwelling mixed-traffic arterial Bus Rapid Transit (BRT-“Lite”) buses have on general traffic conditions and intersection capacity. Queue length and flow rate data collected from the busiest intersection along the bus route were used as proxies for quantifying traffic impacts. Two traffic scenarios were examined: (1) high volume oversaturated traffic; and (2) normal intersection conditions that were at or below saturation levels. Through use of linear regression models and a $k$-means clustering analysis—comparing traffic conditions before and after bus arrival as a function of green dwell time—it was found that BRT-Lite buses have no statistically significant impact on intersection performance or traffic capacity. This conclusion was further complemented by paired $t$-test results in which bus dwelling was found to result in an approximate change in both the average queue length and flow rate of only 1%. As a pilot study for the new BRT-Lite system, this research provides additional insights into the practical applicability of mixed traffic BRT systems and enriches the body of literature on related subjects.

Traffic stream characteristics are largely influenced by the presence of nonmotorized vehicles (NMVs) when mixed with the motorized vehicles in a non-lane-based heterogeneous traffic stream. The traffic stream operation becomes more chaotic with the high percentage of NMVs in a shared carriageway. Accordingly, in the present study, NMVs have been considered a friction factor influencing the traffic operations of motorized traffic. Speed-flow data has been collected through video graphic surveys at six urban arterial midblock sections. Among these sections, three have mixed motorized traffic; whereas, the remaining three sections have a significant proportion of NMVs mixed with motorized traffic. The data has been extracted from five-minute videos, which are further utilized to develop mathematical models relating the speed of each type of vehicle with the corresponding densities of all other vehicles present in the traffic mix. These models are solved for any predefined volume and proportions of traffic mix, and speed flow plots are developed to infer the capacity values. The capacity values are specifically compared with the capacity of a section restricted to motorized traffic, and it is found that the midblock capacity of urban arterials is reduced by 3.60%–35.82% when the percentage of NMVs are as low as 5% to as high as 25%, respectively. The study outcomes are useful for estimating the capacity of urban midblock sections in order to reduce the congestion due to NMVs on urban roadway networks.

Spatially aggregated data are prone to the effects of the modifiable areal unit problem, especially when the phenomenon of interest is not used as a criterion to define the areas of analysis. Further, these conditions can result in statistical inconsistency. The purpose of this study is to assess the influence of different scales of analysis on the statistical variables employed in macroscopic modeling of road safety for the city of Fortaleza, Brazil. Two sets of spatial configurations were created with different aggregation criteria as a means of detecting changes in the averages, standard deviations, correlations, and spatial autocorrelations of the variables. The results show that the statistical variation is not only dependent on the change of scale, but also on the format of the variable produced by the aggregation process.

Real-time anomaly detection at metro stations is a very important task with considerable implications for massive passenger flow organization and train timetable rescheduling. State-of-the-art studies tend to conduct passenger flow anomaly detection; however, they fail to provide more detailed analysis of anomaly combination at metro stations. The primary motivation of this study is to develop a systematic approach to identifying the nature of passenger flow anomalies and estimating their alarm levels dynamically. Firstly, a $K$-means algorithm combined with hierarchical clustering is used to extract incrementally updated typical clustered features. Secondly, anomaly detection indexes that contain both mutant and migration anomalies are designed to identify the time and category of passenger flow anomalies. Then, coordinated anomaly thresholds and corresponding alarm level are listed considering active safety management and passenger travel efficiency. Finally, one of the busiest stations in the Shanghai, China, metro network is selected to demonstrate the proposed method. Application results indicate that these real-time anomalies can be detected both efficiently and accurately in changing passenger flow conditions. The insightful features extracted and fast online computation ensure that the detection results can be applied to assist real-time decision making in prewarning management and optimizing passenger flow organization strategies.

A continuous green T-intersection (CGT) is an innovative intersection that can improve the through traffic capacity by allowing major-leg vehicles on the top side of the T-intersection to pass through without stopping. Recently, traffic engineers decided to cease CGT operations at several T-intersections in Florida because of traffic safety concerns, conversion to a four-legged intersection, pedestrians’ demands, and non-compliance with the latest Manual on Uniform Traffic Control Devices. In this study, safety effects of recent conversions of CGTs back to conventional T-intersections in Florida are explored. An empirical Bayes (EB) before-and-after study method is adopted. The results indicate significant reductions in total, fatal-and-injurious, rear-end, and CGT-related crashes by approximately 30%–50% following the conversion. To validate the results, a cross-sectional analysis was conducted with new data from four states. The results are consistent for total, fatal-and-injurious, and CGT-related crashes with those from the before-and-after study. The results also show that crashes at CGTs can be minimized by providing a physical separation between the acceleration lane for the merging vehicles and the CGT through lane, along with other factors. Because Florida’s T-intersections that were converted back to the conventional design from CGT had no physical separation, the results showed a significant safety improvement following the conversion. Therefore, the decision to cease CGT operations at the Florida study sites was supported from the safety aspect. We concluded that safety at CGTs can be a concern compared to that at non-CGTs; however, it can be significantly improved by providing appropriate countermeasures.

The dynamic and uncertain characters of traffic often make bus schedules erratic; therefore, planned synchronized public transit transfers do not always materialize. This research aims at reducing the operational deviation of buses, and thus increasing arrival reliability of vehicles as well as improving the occurrence of direct transfer. To achieve this objective, a mathematical model is developed to calculate the recommended parameters of real-time bus operation strategies according to real-time arrival time deviation data. A case study is conducted to demonstrate the effectiveness of the proposed approach in practice. Results illustrate that adopting this approach reduces the probability of bus bunching from 8.33% to 0, and increases the operational reliability of vehicles, although transit vehicles have a larger delay. Moreover, direct transfer can be guaranteed with our proposed approach.

Advancements in the field of telematics have empowered the development of connected vehicle (CV) applications, which optimize the energy and traffic efficiency of vehicular traffic. Over the past decade, various research groups have proposed different methodologies for the implementation of dynamic eco-driving near signalized intersections. This paper reviews the proposed methodologies laying emphasis on the control logic, optimization objectives, assumptions, and constraints of these models. The strengths and limitations of existing dynamic eco-driving models are discussed, and future research directions are proposed in relation to the opportunities emerging in the field of transportation and the technological barriers that may exist. Further, the methodologies implemented for the assessment of the impacts of dynamic eco-driving are critically reviewed.

Poor air quality in Alaska’s remote communities due to road dust is one of the top environmental concerns of residents in these communities. Most communities are disconnected from the road network, with community roads that are predominantly unpaved. In Alaska, high costs limit widespread paving of roads, leaving communities to rely on alternative dust control strategies. The goals for this study were to assess the magnitude and impact of the dust problem in rural Alaska and use a diversity of experience, including regulatory, research, engineering, and cultural, to develop a road-dust management approach for rural Alaska. The plan incorporates different levels of dust management: institutional controls, road watering, chemical dust suppressants, and road surface stabilization. Geographical zones where use of each different dust management level will be most appropriate are identified based on rainfall frequency. Approximately 50% of Alaska’s communities can manage road dust with institutional controls and road watering. Many of the road-dust management ideas presented are transferable to other global regions that experience similar economic and community access challenges as Alaska.

Access to public transit forms a vital part of the well-being of people with disabilities. However, people with disabilities continue to be challenged in accessing their local transit services. This study investigates the existence of any gaps in users’ needs and practitioners’ prioritization of accessibility features. Senior practitioners deemed experts in the field from cities in New Zealand were invited to participate. Data were analyzed using the analytic hierarchy process to determine the ranking of nine accessibility factors. The most important factors identified by practitioners are crossing facilities (0.19) followed by access to stops/stations (0.17), and quality of footpaths (0.13), all of which are parts of the built environment. The least important factors are vegetation (0.047) and information at stops (0.058), whereas users placed more weight on information at stops (0.097) and vegetation (0.089). Bus driver attitude accounted for the largest disparity in prioritization between the two groups with a difference of 0.137. The study highlights the need to shift prioritization closer to the needs of people with disabilities to eliminate barriers.

The time required to transfer between different transit lines is a critical component of passenger travel time. Although transit agencies attempt to design well-coordinated timetables among intersecting lines at the scheduling stage, an operational control method is necessary to maintain the coordinated transfers that may occasionally be disrupted due to unexpected delays of public transit (PT) vehicles. One possible and practical approach is Connection Protection (CP), which involves holding a transit unit in order to wait for another transit unit that is planned to provide a coordinated transfer but has been delayed. This study develops a CP model to apply holding control to a receiving vehicle trip for the purpose of protecting the scheduled connection against delay of a feeder trip. The study incorporates the probabilistic nature of transit operations in formulating the cost function of the model, and accordingly makes more robust decisions for controlling the PT system. The developed model is evaluated by means of a sensitivity analysis. The results show that the model improves transfer efficiency and reduces the waiting times of affected passengers while minimizing delays to other passengers.

Investigating the contributing factors to aggregated crashes as a function of trip variables combined with details of their purpose and transportation modes is important in the planning stage of a transportation network. The primary purpose of this study is to investigate the association between aggregated crashes, different trip modes, and trip purposes in traffic analysis zones (TAZs). Negative binomial (NB) and a geographically weighted Poisson regression (GWPR) were estimated for total and severe crashes separately. Compared to the traditional NB model, the GWPR model can better account for the spatial heterogeneity in data. Among different trip-related variables, home-based trips with the purpose of work and school by modes such as private vehicle, bike, walk to local bus, and park and ride had the most important impacts on aggregated crash data. Among roadway characteristics variables, length of freeways and major arterials were found to have significant association with total and severe crashes. The results of this study provide a better understanding of the effects of different modal shares and trip purposes on traffic safety.