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Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITSs) usually require monitoring of massive road networks and gathering traffic data at a high spatial and temporal resolution. This leads to the accumulation of substantial data volumes, necessitating the development of more concise data representations. Approaches like principal component analysis (PCA), which operate within subspaces, can construct precise low-dimensional models. However, interpreting these models can be challenging, primarily because the principal components often encompass a multitude of links within the traffic network. To overcome this issue, this study presents a novel approach for representing and indexing network traffic conditions through weighted CUR matrix decomposition integrated with clustering analysis. The proposed approach selects a subset group of detectors from the original network to represent and index traffic condition through a matrix decomposition method, allowing for more efficient management and analysis. The proposed method is evaluated using traffic detector data from the city of Nashville, TN. The results demonstrate that the approach is effective in representing and indexing network traffic conditions, with high accuracy and efficiency. Overall, this study contributes to the field of network traffic monitoring by proposing a novel approach for representing massive traffic networks and exploring the effects of incorporating clustering into CUR decomposition. The proposed approach can help traffic analysts and practitioners to more efficiently manage and analyze traffic conditions, ultimately leading to more effective transportation systems. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) represent a new, growing segment of transportation research. While there have been prior studies and deployments of AVs worldwide, full autonomy in bus transit has gained interest among researchers and practitioners within the last decade, which presents an opportunity to synthesize early trends. Therefore, the objective of this paper is to provide a review of the latest research on fully autonomous buses to summarize findings and identify gaps needing future research. Forty studies were reviewed in detail, and five main themes were identified, which are (1) technology deployment; (2) user acceptance; (3) safety; (4) social and economic aspects; and (5) regulations, policies, and legal issues. The results reveal that most prior studies have focused on technology development, and the area of regulation and policy would benefit from additional study. Noteworthy differences between research in Europe and the United States were also identified. In Europe, large funded projects involving real-world deployments have focused on user acceptance, security and safety, costs, and related legal issues, whereas in the United States, research has primarily concentrated on simulation modelling with limited real-world deployments. The results of this review are important for policy-makers and researchers as AV technology continues to evolve and become more widely available., 1. Introduction Autonomous vehicle (AV) technology has grown and developed rapidly in recent decades. Accompanying this trend is increasing concern among professionals in related fields over how AVs will affect [...]

The study examined the extent of cross-disciplinarity in nanotechnology and transportation engineering research. Researchers in these two fields were determined from the web sites of the U.S. News and World Report top 100 schools in civil engineering and materials science. Web of Science searches for 2006 and 2007 articles were obtained and the journals in which the articles appeared were classified using Library of Congress call numbers. Nanotechnology was found to be more cross-disciplinary than transportation engineering. Implications for collection development and research assistance were given. (Contains 2 figures.)

Real-time traffic information such as traffic speed is essential to a variety of practical applications. Because of the high installation and maintenance cost of convention data collecting methods, transportation engineers have sought alternative data sources to monitor traffic. Probe-based traffic data, such as Waze traffic data, could serve as alternative data sources in traffic management, but this source has not been thoroughly explored nor evaluated. Using the 10.8 mile stretch of I-40 in Knoxville, Tennessee, we compared the speed measurements from both Waze and Remote Traffic Microwave Sensors (RTMS) over two months and explored the characteristics of Waze traffic speed data. These are the main findings: 1) These two datasets showed a similar pattern with slight differences. Waze speeds tend to be higher than RTMS speeds for high speed, while Waze speeds are more likely to be similar or even lower than RTMS speeds for low speed; 2) several factors affecting the speed differences between RTMS speeds and Waze speeds were identified, such as Waze speed value, time of day (peak hour vs. non-peak hour), AADT (Annual Average Daily Traffic), and segment length; and 3) Waze reported the same speed for several successive reporting periods if the real-time speed is not available. The effective Waze sampling period is about two to four minutes. Waze speeds had more real-time speed observations during congested times, indicating that Waze speeds are more reliable for congested scenarios. The findings may lead to a better understanding of this source of data and any resulting analysis. [ABSTRACT FROM AUTHOR]

This paper demonstrates that in addition to intracycle demand fluctuation, which is already a consideration in many delay models, intercycle demand variance also impacts average delay at signalized intersections. Webster-type delay models treat demand fluctuation over the whole analysis period, often 15 min or longer, as if it were just within a single cycle. Such an approach is fine if used judiciously, one might presume. However, results from Monte Carlo simulations with the incremental queue accumulation (IQA) method indicate that Webster-type delay models will underestimate the average delay under heavy traffic conditions. As unutilized capacity at a signalized intersection cannot be saved or carried over to be used by succeeding cycles when demand surges due to normal fluctuation, better understanding of the patterns of intercycle demand variance is important. Simulation results demonstrate that different patterns of intercycle demand variance can result in different levels of average delay. A low-to-high demand pattern will cause a higher average delay than a high-to-low pattern would, even though the overall demand level is exactly the same. It is therefore clear that neglecting intercycle demand variance may lead to significant inaccuracy and, hence, suboptimal signal timing decisions. DOI: 10.1061/(ASCE)0733-947X(2009)135:5(288) CE Database subject headings: Intersections; Traffic signals; Monte Carlo method; Delay time; Traffic capacity.

Evacuation plans are traditionally based on empirical knowledge and local experience. Many studies are underway to improve and, even, optimize the process of evacuation planning and operations. To this end, a commonly accepted set of well-defined measures of effectiveness (MOEs) for evacuation is necessary. To guide various optimization processes and assess their benefits, this paper examines MOEs in literature, whether implicitly assumed or explicitly defined, and subsequently proposes a comprehensive four-tier MOE framework, taking into consideration of evacuation time, cumulative exposure, and temporal/spatial risk factors. For each tier of MOE, the optimization formulation is presented. DOI: 10.1061/(ASCE)0733-9488(2007)133:1(3) CE Database subject headings: Safety; Security; Risk management; Optimization; Evacuation.

Maintenance activity on two-lane highways often requires the closure of one travel lane. Vehicles in both directions of travel are served via the one remaining lane. Under such conditions, alternating right of way into the highway's work zone is provided to each direction of travel. This so-called 'one-way traffic control' typically creates significant motorist delays. This paper describes a proposed technique for estimating vehicle delays and queue lengths on two-lane highways operating under one-way traffic control. Using parameter estimates derived through empirical data, the procedure estimates the average amount of time right of way is extended to each direction of travel during a given time period of operation. Average motorist delays and queue lengths occurring over the given time period are then computed using deterministic queueing theory. The proposed technique can aid highway agencies in determining the appropriate physical lengths and/or hours of operation for two-lane highway work-zone segments.