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  • Mobility and traffic research  (238)
  • 1
    Online Resource
    Online Resource
    Considering the Spatial Structure of the Road Network in Pavement Deterioration Modeling
    SAGE Publications ; 2024
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2678, No. 5 ( 2024-05), p. 153-161
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2678, No. 5 ( 2024-05), p. 153-161
    Abstract: Pavement deterioration modeling is important in providing information with respect to the future state of the road network and in determining the needs of preventive maintenance or rehabilitation treatments. This research incorporated the spatial dependence of the road network into pavement deterioration modeling through a graph neural network (GNN). The key motivation of using a GNN for pavement performance modeling is the ability to easily and directly exploit the rich structural information in the network. This paper explored if considering the spatial structure of the road network will improve the prediction performance of the deterioration models. The data used in this research comprises a large pavement condition dataset with more than a half million observations taken from the Pavement Management Information System maintained by the Texas Department of Transportation. The promising comparison results indicate that pavement deterioration prediction models perform better when the spatial relationship is considered.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981231188373
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2024
    detail.hit.zdb_id: 190260-X
    detail.hit.zdb_id: 2403378-9
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  • 2
    Online Resource
    Online Resource
    Reliable Traffic State Identification Using High-Resolution Data: A Consistent Offline-Online Dynamic Time Warping–Based Time Series Clustering Approach
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2677, No. 8 ( 2023-08), p. 509-524
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2677, No. 8 ( 2023-08), p. 509-524
    Abstract: Reliable real-time traffic state identification (TSI) provides key support for traffic management and control. Although substantial efforts have been devoted to TSI, considering the high dynamics and stochasticity of traffic flows, there remain challenges in providing reliable and consistent TSI results, especially in online network–level applications. In this study, we propose a time series clustering-based offline-online modeling framework for reliable TSI using high-resolution traffic data. Specifically, in the proposed framework, the offline module extracts representative traffic state patterns from massive historical data, which serve as the state references in the online module when performing real-time TSI with streaming information. Instead of point data, the proposed framework uses high-resolution traffic data in the form of time series, providing rich information on traffic flows and details on their short-term fluctuations and stable long-term trends. In the offline module, considering the fuzziness of traffic states, we introduce a fuzzy c-means based clustering method for offline traffic flow series clustering and traffic state pattern extraction, within which the dynamic time warping algorithm is adopted for measuring the similarity between different time series, and the optimal number of clusters is determined by a proposed critical segment–based method to reach consistent TSI in network-wide applications. In the online module, a dynamic programming–based real-time TSI approach is developed to produce reliable and smooth identification results. Extensive numerical experiments on a 20-mi-long freeway corridor in California, USA, were performed to validate the proposed framework. Results demonstrate the effectiveness of the proposed framework.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981231156916
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 190260-X
    detail.hit.zdb_id: 2403378-9
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  • 3
    Online Resource
    Online Resource
    Missing Pavement Performance Data Imputation Using Graph Neural Networks
    SAGE Publications ; 2022
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2676, No. 12 ( 2022-12), p. 409-419
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 12 ( 2022-12), p. 409-419
    Abstract: Pavement condition data is important for providing information on the current state of the network and determining the needs of preventive maintenance or rehabilitation treatments. However, the condition data set is often incomplete for various reasons such as measurement errors and non-periodic inspection intervals. Missing data, especially when missing systematically, presents loss of information, reduces statistical power, and introduces biased assessment. Existing practices in pavement management systems (PMS) usually discard entire cases with missing data or impute it through data correlation. This paper proposes a graph-based deep learning framework, convolutional graph neural networks, to tackle the missing data problem in PMS. Unlike other variants of neural networks, the proposed approach is able to capture the spatio-temporal relationship in data and to learn and reconstruct the missing data by combining information among neighboring sections. In the case study, pavement condition data from 4,446 sections managed by Texas Department of Transportation were used. Experiments show that the proposed model was able to outperform standard machine learning models when imputing the missing data.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981221095511
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 190260-X
    detail.hit.zdb_id: 2403378-9
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  • 4
    Online Resource
    Online Resource
    Hybrid Model for Prediction of Carbon Monoxide and Fine Particulate Matter Concentrations near a Road Intersection
    SAGE Publications ; 2015
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2503, No. 1 ( 2015-01), p. 29-38
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2503, No. 1 ( 2015-01), p. 29-38
    Abstract: Air quality time series near road intersections consist of complex linear and nonlinear patterns and are difficult to forecast. The backpropagation neural network (BPNN) has been applied for air quality forecasting in urban areas, but it has limited accuracy because of the inability to predict extreme events. This study proposed a novel hybrid model called GAWNN that combines a genetic algorithm and a wavelet neural network to improve forecast accuracy. The proposed model was examined through predicting the carbon monoxide (CO) and fine particulate matter (PM 2.5 ) concentrations near a road intersection. Before the predictions, principal component analysis was adopted to generate principal components as input variables to reduce data complexity and collinearity. Then the GAWNN model and the BPNN model were implemented. The comparative results indicated that GAWNN provided more reliable and accurate predictions of CO and PM 2.5 concentrations. The results also showed that GAWNN performed better than BPNN did in the capability of forecasting extreme concentrations. Furthermore, the spatial transferability of the GAWNN model was reasonably good despite a degenerated performance caused by the unavoidable difference between the training and test sites. These findings demonstrate the potential of the application of the proposed model to forecast the fine-scale trend of air pollution in the vicinity of a road intersection.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2503-04
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
    detail.hit.zdb_id: 190260-X
    detail.hit.zdb_id: 2403378-9
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  • 5
    Online Resource
    Online Resource
    Detection of Pavement Maintenance Treatments using Deep-Learning Network
    SAGE Publications ; 2021
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2675, No. 9 ( 2021-09), p. 1434-1443
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2675, No. 9 ( 2021-09), p. 1434-1443
    Abstract: Pavement maintenance and rehabilitation (M & R) records are important as they provide documentation that M & R treatment is being performed and completed appropriately. Moreover, the development of pavement performance models relies heavily on the quality of the condition data collected and on the M & R records. However, the history of pavement M & R activities is often missing or unavailable to highway agencies for many reasons. Without accurate M & R records, it is difficult to determine if a condition change between two consecutive inspections is the result of M & R intervention, deterioration, or measurement errors. In this paper, we employed deep-learning networks of a convolutional neural network (CNN) model, a long short-term memory (LSTM) model, and a CNN-LSTM combination model to automatically detect if an M & R treatment was applied to a pavement section during a given time period. Unlike conventional analysis methods so far followed, deep-learning techniques do not require any feature extraction. The maximum accuracy obtained for test data is 87.5% using CNN-LSTM.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981211007846
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 190260-X
    detail.hit.zdb_id: 2403378-9
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  • 6
    Online Resource
    Online Resource
    Comprehensive Approach to Allocate Reliability and Cost in Passenger Rail System Design
    SAGE Publications ; 2017
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2608, No. 1 ( 2017-01), p. 86-95
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2608, No. 1 ( 2017-01), p. 86-95
    Abstract: Rail system design and procurement comprise the process of identifying, acquiring, selecting, and purchasing the right products to form a rail system. To acquire a new system, various products with specific costs and reliability for each subsystem and Corresponding components are chosen from equipment suppliers. Planners must carefully examine the trade-offs between life-cycle cost (LCC), system reliability, and service reliability to allocate resources optimally. This study developed a comprehensive allocation process with four types of optimization models for passenger rail system design: ( a) maximization of system reliability, ( b) maximization of service reliability, ( c) minimization of LCC, and ( d) minimization of a combination of service unreliability (delay cost) and LCC. On the basis of the characteristics of a passenger rail system and possible alternatives, the proposed process can allocate LCC and service reliability optimally to determine the ideal investment plan for rail system design. Empirical case studies have demonstrated that the proposed optimization process and models can evaluate efficiently and successfully all possible alternatives and determine the best allocation among all subsystems and Corresponding components. This comprehensive approach can help users identify the ideal balance between cost and reliability to achieve an optimal rail system design.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2608-10
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2017
    detail.hit.zdb_id: 190260-X
    detail.hit.zdb_id: 2403378-9
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  • 7
    Online Resource
    Online Resource
    Nonmotorized Interference and Control Measures at Signalized Intersections in China
    SAGE Publications ; 2003
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 1846, No. 1 ( 2003-01), p. 44-49
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 1846, No. 1 ( 2003-01), p. 44-49
    Abstract: The adverse effects of bicycles and pedestrians on motor vehicle traffic in at-grade, signalized intersections under mixed-traffic conditions have been observed at several typical intersections in Beijing. Mixed bicycle and motor vehicle traffic is a major characteristic of urban transport in China and has led to serious congestion and capacity reduction in at-grade signalized intersections in urban areas. A method is presented to quantitatively measure nonmotorized effects, and values are recommended for adjusting the model to estimate the capacity of through vehicle lanes. Several temporal segregation solutions to mixed-traffic problems in at-grade signalized intersections are described that have proven cost-effective in several Chinese cities, and suggestions for their application are provided.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/1846-08
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2003
    detail.hit.zdb_id: 190260-X
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  • 8
    Online Resource
    Online Resource
    Modeling User Responses to Pricing : Simultaneous Route and Departure Time Network Equilibrium with Heterogeneous Users
    SAGE Publications ; 2008
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2085, No. 1 ( 2008-01), p. 124-135
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2085, No. 1 ( 2008-01), p. 124-135
    Abstract: In a previous contribution, the authors showed how to incorporate user heterogeneity in determining equilibrium route choices in a network in response to pricing. Presented here is a generalization of that framework to incorporate joint consideration of route and departure time as well as heterogeneity in a wider range of behavioral characteristics. A multicriterion simultaneous route and departure time user equilibrium (MSRDUE) model is presented, along with a simulation-based algorithm intended for practical network applications. The model explicitly considers heterogeneous users with different values of time (VOTs) and values of (early or late) schedule delay (VOESDs or VOLSDs) in their joint choice of departure times and paths characterized by a set of trip attributes that include travel time, out-of-pocket cost, and schedule delay cost. The problem is formulated as an infinite-dimensional variational inequality problem and solved by a column generation-based algorithmic framework that embeds (a) an extreme nondominated alternative-finding algorithm to obtain the VOT, VOESD, and VOLSD breakpoints that define multiple user classes and the associated least trip cost (joint departure time and path) alternative for each user class; (b) a traffic simulator to capture traffic flow dynamics and determine travel costs experienced; and (c) a path-swapping multiclass alternative flow-updating scheme to solve the restricted multiclass SRDUE problem defined by a subset of feasible alternatives. Application to an actual network illustrates the properties of the algorithm and underscores the importance of capturing user heterogeneity and temporal shifts in the appraisal of dynamic pricing schemes.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2085-14
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2008
    detail.hit.zdb_id: 190260-X
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  • 9
    Online Resource
    Online Resource
    Exploring the Influence of Urban Form on Work Travel Behavior with Agent-Based Modeling
    SAGE Publications ; 2008
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2082, No. 1 ( 2008-01), p. 132-140
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2082, No. 1 ( 2008-01), p. 132-140
    Abstract: This paper examines the effect of land use regulations on travel behavior by using agent-based modeling. A simulation model for a hypothetical urban area loosely based on the Chicago, Illinois, metropolitan area was used to study the impact of six land use regulation scenarios on transit use and urban form. The key features and techniques of the model development and the scenarios tested are described. The results from the simulations showed that although the land use regulations that were designed to increase the density near the transit station or in and near the urban core were able to achieve the intended land use patterns, they did not increase the transit mode share for the region in a significant manner. More detailed examination of the output revealed that as long as the rules for mode choice, the distribution of employment, and the transit network remained unchanged, land use regulations that affect residential locations produced limited effects on transit use.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2082-16
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2008
    detail.hit.zdb_id: 190260-X
    detail.hit.zdb_id: 2403378-9
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  • 10
    Online Resource
    Online Resource
    Ordinal Discrete Choice Analyses of Wisconsin Cross-Median Crash Severities
    SAGE Publications ; 2010
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2148, No. 1 ( 2010-01), p. 47-58
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2148, No. 1 ( 2010-01), p. 47-58
    Abstract: A cross-median crash (CMC), in which a vehicle crosses the median, is one of the most severe crashes because of the risk of colliding with an opposing vehicle. Ordinal discrete choice modeling efforts for investigating the nexus between the underlying severity propensity and miscellaneous roadway-safety-related factors for single- and multivehicle CMCs that occurred from 2001 to 2007 in Wisconsin are described. Ordinal logit (ORL) and probit (ORP) models were employed for the severity analyses. For multivehicle CMCs, the final ORP model found that road condition has a significant effect on severity. Adverse road conditions enhance the likelihood of a more severe consequence if a CMC occurs. Winter precipitation negatively affects CMC severity, and logically Wisconsin's geographical location plays a significant role. The final ORL model found that alcohol and drug use incurs more severe consequences when a CMC occurs. Both models found that more severe injuries occur on roadways posted with higher speed limits. The similarity and dissimilarity in findings by both models imply that it is necessary for safety researchers to apply distinct statistical methods when pursuing a comprehensive understanding of a study topic. The final ORP model for single-vehicle CMCs shows that alcohol and drug use, lane curvature, and unfriendly lighting conditions exacerbate the severity tendency if a CMC happens. A dry road surface is found to incur more severe consequences; this result implies that more severe single-vehicle CMCs are closely related to maintaining overly high speeds. All ORL regression models for single-vehicle CMCs were found statistically invalid. Median width and average daily traffic were found insignificant for both multivehicle and single-vehicle CMCs.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2148-06
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2010
    detail.hit.zdb_id: 190260-X
    detail.hit.zdb_id: 2403378-9
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