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  • Mobility and traffic research  (18)
  • 1
    Online Resource
    Online Resource
    Development of a Safety Assessment Tool for Long-Span Bridges : Case Study of Sutong Bridge in China
    SAGE Publications ; 2014
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2451, No. 1 ( 2014-01), p. 97-102
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2451, No. 1 ( 2014-01), p. 97-102
    Abstract: This paper provides a safety assessment tool (SAT) for long-span bridges in China. The authors selected the Sutong Yangtze River Bridge, one of the largest long-span bridges in China, for a case study. Currently, safety assessment of long-span bridges in China is primarily based on human judgment. No effective method exists for estimating the traffic crash risk of long-span bridges under various conditions; this situation creates difficulty in providing effective safety control strategy and developing counter-measures. In this study, the Traffic Software Integrated System was used to simulate the traffic on the Sutong Bridge. A safety assessment model (SAM) was developed from four contributing factors: traffic volume, free-flow speed, percentage of heavy vehicles, and weather conditions. SAM was used as the basis for developing an SAT that could aid policy makers and traffic professionals in better understanding of traffic safety of the Sutong Bridge: (a) a risk map was created to indicate different safety levels of the bridge under various conditions and (b) a user interface was designed to allow users to identify the safety level of the bridge quickly and easily.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2451-11
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2014
    detail.hit.zdb_id: 2403378-9
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  • 2
    Online Resource
    Online Resource
    Online Implementation and Evaluation of Weather-Responsive Coordinated Signal Timing Operations
    SAGE Publications ; 2015
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2488, No. 1 ( 2015-01), p. 71-86
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2488, No. 1 ( 2015-01), p. 71-86
    Abstract: This paper presents the development and application of weather-responsive traffic management strategies and tools to support coordinated signal timing operations with traffic estimation and prediction system (TREPS) models. First, a systematic framework for implementing and evaluating traffic signal operations under severe weather conditions was developed, and activities for planning, preparing, and deploying signal operations were identified in real-time traffic management center (TMC) operations. Next, weather-responsive coordinated signal plans were designed and evaluated with the TREPS method and a locally calibrated network. Online implementation and evaluation was conducted in Salt Lake City, Utah—the first documented online application of TREPS to support coordinated signal operation in inclement weather. The analysis results confirm that the deployed TREPS, which is based on DYNASMART-X, is able to help TMC operators test appropriate signal timing plans proactively under different weather forecasts before deployment and is capable of using real-time measurements to improve the quality and accuracy of the system's estimations and future predictions through detectors and roadside sensor coverage.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2488-08
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
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  • 3
    Online Resource
    Online Resource
    Use of Social Networking Data to Explore Activity and Destination Choice Behavior in Two Metropolitan Areas
    SAGE Publications ; 2016
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2566, No. 1 ( 2016-01), p. 71-82
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2566, No. 1 ( 2016-01), p. 71-82
    Abstract: On the basis of data from online location-based social networks, the spatiality of destinations in the context of social networks and the influence of social networks on travelers’ destination choices are explored through check-in behavior. Analysis results show that social relationships play a role in travelers’ destination choices and that distance plays a strong role in social networks and in location choice. Comparison of check-in behavior of travelers in two social networks identified in two metropolitan areas (Chicago, Illinois, and New York City) and examination of interactions in the largest communities in each social network indicate that the denser a social network is, the greater the likelihood that travelers will be influenced by their friends in their choice of destination. However, travelers’ own experiences appear to exert greater influence on their decision making than do friendships.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2566-08
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2016
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  • 4
    Online Resource
    Online Resource
    Methodology of Applying Heavy Weight Deflectometer for Calculation of Runway Pavement Classification Number
    SAGE Publications ; 2007
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 1990, No. 1 ( 2007-01), p. 57-64
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 1990, No. 1 ( 2007-01), p. 57-64
    Abstract: The International Civil Aviation Organization requires every airport that serves commercial airline operations to publish its pavement classification number (PCN) in its own aeronautical information publication. This number is defined as a number expressing the bearing strength of a pavement for unrestricted operations. Likewise, each airline must provide the aircraft classification number (ACN) that corresponds to each type of aircraft it operates. Only when the aircraft's ACN is less than the airport's PCN is the aircraft allowed to land with its maximum landing weight. Otherwise, the aircraft is restricted to a certain weight limit. There are two ways to determine the PCN: the using aircraft method and the technical method. Many airports chose to determine the PCN by the using aircraft method because of its simplicity. However, the technical method, which can specify the pavement structural bearing capacity more accurately, can give a more precise PCN than the using aircraft method. An in-depth description of the establishment of a technical methodology for determining PCNs by applying the heavy weight deflectometer field data is presented. A practical case study of a runway with two kinds of slab thickness mixed with two types of subgrade strengths is introduced for calculating the PCN through the established methodology.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/1990-07
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2007
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  • 5
    Online Resource
    Online Resource
    Understanding Ridesplitting Behavior with Interpretable Machine Learning Models Using Chicago Transportation Network Company Data
    SAGE Publications ; 2022
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2676, No. 2 ( 2022-02), p. 83-99
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 2 ( 2022-02), p. 83-99
    Abstract: As congestion levels increase in cities, it is important to analyze people’s choices of different services provided by transportation network companies (TNCs). Using machine learning techniques in conjunction with large TNC data, this paper focuses on uncovering complex relationships underlying ridesplitting market share. A real-world dataset provided by TNCs in Chicago is used in analyzing ridesourcing trips from November 2018 to December 2019 to understand trends in the city. Aggregated origin–destination trip-level characteristics, such as mean cost, mean time, and travel time reliability, are extracted and combined with origin–destination community-level characteristics. Three tree-based algorithms are then utilized to model the market share of ridesplitting trips. The most significant factors are extracted as well as their marginal effect on ridesplitting behavior, using partial dependency plots for interpretation of the machine learning model results. The results suggest that, overall, community-level factors are as or more important than trip-level characteristics. Additionally, the percentage of White people highly affects ridesplitting market share as well as the percentage of bachelor’s degree holders and households with two people residing in them. Travel time reliability and cost variability are also deemed more important than travel time and cost savings. Finally, the potential impact of taxes, crimes, cultural differences, and comfort is discussed in driving the market share, and suggestions are presented for future research and data collection attempts.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981211036363
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
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  • 6
    Online Resource
    Online Resource
    Comparative Study of Predictive Analysis Methods to Estimate Bridge Response
    SAGE Publications ; 2019
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2673, No. 9 ( 2019-09), p. 365-376
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2673, No. 9 ( 2019-09), p. 365-376
    Abstract: Monitoring bridge performance is crucial to ensure safety and allocate resources in a cost-effective manner. This paper aims to reduce the gap between researchers and practitioners by showing how predictive analytics can be employed in the process of distilling operational information out of bridge monitoring data. Furthermore, it has the goal to aid infrastructure owners and managers in evaluating bridge performance over time and making data-driven decisions to prolong the life of the structure. To achieve this goal, the paper presents a comparative study of three predictive analysis models to estimate bridge response to heavy trucks: multilinear regression, artificial neural network, and regression tree. Following this comparison, an alternative strategy, based on the analysis of influential observations, is proposed. This approach brings together predictive power with other important capabilities such as explanatory capabilities and interpretability. The test bed structure is a short-span highway bridge which was monitored for 3 years using weigh-in-motion (traffic data) and structural health monitoring (bridge data) systems.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198119843866
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
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  • 7
    Online Resource
    Online Resource
    Gaussian Processes for Short-Term Traffic Volume Forecasting
    SAGE Publications ; 2010
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2165, No. 1 ( 2010-01), p. 69-78
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2165, No. 1 ( 2010-01), p. 69-78
    Abstract: The accurate modeling and forecasting of traffic flow data such as volume and travel time are critical to intelligent transportation systems. Many forecasting models have been developed for this purpose since the 1970s. Recently kernel-based machine learning methods such as support vector machines (SVMs) have gained special attention in traffic flow modeling and other time series analyses because of their outstanding generalization capability and superior nonlinear approximation. In this study, a novel kernel-based machine learning method, the Gaussian processes (GPs) model, was proposed to perform short-term traffic flow forecasting. This GP model was evaluated and compared with SVMs and autoregressive integrated moving average (ARIMA) models based on four sets of traffic volume data collected from three interstate highways in Seattle, Washington. The comparative results showed that the GP and SVM models consistently outperformed the ARIMA model. This study also showed that because the GP model is formulated in a full Bayesian framework, it can allow for explicit probabilistic interpretation of forecasting outputs. This capacity gives the GP an advantage over SVMs to model and forecast traffic flow.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2165-08
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2010
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  • 8
    Online Resource
    Online Resource
    From Personal Attitudes to Public Opinion : Information Diffusion in Social Networks Toward Sustainable Transportation
    SAGE Publications ; 2014
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2430, No. 1 ( 2014-01), p. 28-37
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2430, No. 1 ( 2014-01), p. 28-37
    Abstract: A model of a social network–based attitude diffusion system in the context of activity and travel choice behavior is presented. The principal mechanisms contributing to attitude formation were identified, and mathematical models to capture these processes were developed. The primary contributions of this research are (a) the modeling of attitude diffusion according to social and learning mechanisms and (b) the evolution of these attitudes over time in a lattice neighborhood social network. The agent-based framework presented is sufficiently general and flexible to allow the building of a more complete representation of information diffusion and attitude formation within activity and travel behavior choice dimensions, for example, mode choice or departure time choice. The framework allows the extension of the presented approach with additional social network structures, information sources, and social interaction mechanisms in the physical and virtual realms or the extension and modification of the presented approach to simulate the impact of information-based management strategies.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2430-04
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2014
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  • 9
    Online Resource
    Online Resource
    K-Prototypes Segmentation Analysis on Large-Scale Ridesourcing Trip Data
    SAGE Publications ; 2020
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2674, No. 9 ( 2020-09), p. 383-394
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2674, No. 9 ( 2020-09), p. 383-394
    Abstract: Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they affect travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examined emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data were matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcing’s role in Chicago’s mobility system. The goal was to investigate the systematic variations in patronage of ridehailing. K-prototypes was utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output was a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses the implications of the identified clusters related to affordability, equity, and competition with transit.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198120929338
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
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  • 10
    Online Resource
    Online Resource
    Privately Owned Autonomous Vehicle Optimization Model Development and Integration with Activity-Based Modeling and Dynamic Traffic Assignment Framework
    SAGE Publications ; 2019
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2673, No. 10 ( 2019-10), p. 683-695
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2673, No. 10 ( 2019-10), p. 683-695
    Abstract: This paper presents a first-order approach integrated with activity-based modeling and dynamic traffic assignment framework to model the impact of autonomous vehicles on household travel and activity schedules. By considering shared rides among household members, mode choices, re-planning of departure times, and the rescheduling of activity sequences, two optimization models—basic personal owned autonomous vehicle (POAV) model and enhanced POAV model—are presented. The proposed approach is tested for the different models at the household level with different household sizes. The activity schedules of each household were generated in the Chicago sub-area network. The results show that each POAV can effectively replace multiple conventional vehicles, however, using POAV will lead to more vehicle miles traveled because of detour trips. The proposed enhanced POAV model considers mode choice decision with a household-based approach instead of a trip-based approach to capture the impacts of repositioning trips on mode choice. The results show that, if the generalized travel cost of POAV remains at the same level as conventional vehicles, more passengers will choose to use transit because the repositioning trips increase the total cost.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198119852072
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2403378-9
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