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  • 1
    Online Resource
    Online Resource
    Spatiotemporal Interaction Pattern Recognition and Risk Evolution Analysis During Lane Changes
    Institute of Electrical and Electronics Engineers (IEEE) ; 2023
    In:  IEEE Transactions on Intelligent Transportation Systems Vol. 24, No. 6 ( 2023-6), p. 6663-6673
    Details
    In: IEEE Transactions on Intelligent Transportation Systems, Institute of Electrical and Electronics Engineers (IEEE), Vol. 24, No. 6 ( 2023-6), p. 6663-6673
    Type of Medium: Online Resource
    ISSN: 1524-9050 , 1558-0016
    URL: Article
    DOI: 10.1109/TITS.2022.3233809
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2023
    detail.hit.zdb_id: 2034300-0
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  • 2
    Online Resource
    Online Resource
    Multivariate analysis of car-following behavior data using a coupled hidden Markov model
    Elsevier BV ; 2022
    In:  Transportation Research Part C: Emerging Technologies Vol. 144 ( 2022-11), p. 103914-
    Details
    In: Transportation Research Part C: Emerging Technologies, Elsevier BV, Vol. 144 ( 2022-11), p. 103914-
    Type of Medium: Online Resource
    ISSN: 0968-090X
    URL: Article
    DOI: 10.1016/j.trc.2022.103914
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2015891-9
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  • 3
    Online Resource
    Online Resource
    Mixture modeling of freeway speed and headway data using multivariate skew- t distributions
    Informa UK Limited ; 2017
    In:  Transportmetrica A: Transport Science Vol. 13, No. 7 ( 2017-08-09), p. 657-678
    Details
    In: Transportmetrica A: Transport Science, Informa UK Limited, Vol. 13, No. 7 ( 2017-08-09), p. 657-678
    Type of Medium: Online Resource
    ISSN: 2324-9935 , 2324-9943
    URL: Article
    DOI: 10.1080/23249935.2017.1318973
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2017
    detail.hit.zdb_id: 2719295-7
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  • 4
    Online Resource
    Online Resource
    Origin-Destination-Based Travel Time Reliability under Different Rainfall Intensities: An Investigation Using Open-Source Data
    Hindawi Limited ; 2020
    In:  Journal of Advanced Transportation Vol. 2020 ( 2020-11-4), p. 1-12
    Details
    In: Journal of Advanced Transportation, Hindawi Limited, Vol. 2020 ( 2020-11-4), p. 1-12
    Abstract: Origin-destination- (O-D-) based travel time reliability (TTR) is fundamental to next-generation navigation tools aiming to provide both travel time and reliability information. While previous works are mostly focused on route-based TTR and use either ad hoc data or simulation in the analyses, this study uses open-source Uber Movement and Weather Underground data to systematically analyze the impact of rainfall intensity on O-D-based travel time reliability. The authors classified three years of travel time data in downtown Boston into one hundred origin-destination pairs and integrated them with the weather data (rain). A lognormal mixture model was applied to fit travel time distributions and calculate the buffer index. The median, trimmed mean, interquartile range, and one-way analysis of variance were used for quantification of the characteristics. The study found some results that tended to agree with the previous findings in the literature, such that, in general, rain reduces the O-D-based travel time reliability, and some seemed to be unique and worthy of discussion: firstly, although in general the reduction in travel time reliability gets larger as the intensity of rainfall increases, it appears that the change is more significant when rainfall intensity changes from light to moderate but becomes fairly marginal when it changes from normal to light or from moderate to extremely intensive; secondly, regardless of normal or rainy weather, the O-D-based travel time reliability and its consistency in different O-D pairs with similar average travel time always tend to improve along with the increase of average travel time. In addition to the technical findings, this study also contributes to the state of the art by promoting the application of real-world and publicly available data in TTR analyses.
    Type of Medium: Online Resource
    ISSN: 2042-3195 , 0197-6729
    URL: Article
    DOI: 10.1155/2020/8816020
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2553327-7
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  • 5
    Online Resource
    Online Resource
    Can Autonomous Vehicles Save Fuel? Findings from Field Experiments
    Hindawi Limited ; 2022
    In:  Journal of Advanced Transportation Vol. 2022 ( 2022-7-8), p. 1-12
    Details
    In: Journal of Advanced Transportation, Hindawi Limited, Vol. 2022 ( 2022-7-8), p. 1-12
    Abstract: The majority of the more recent studies have mainly focused on how to achieve energy-efficient goals by optimizing the driving behavior for human-driven vehicles or designing trajectory planning and tacking algorithms for autonomous vehicles. However, the energy-saving advantages of autonomous vehicles have not been quantitatively and theoretically explored. Therefore, this study aims to specifically clarify whether autonomous vehicles use less fuel than human-driven ones. First, the differences in driving behavior, regarding speed control, between autonomous vehicles and human-driven vehicles were compared. The most notable difference between them is that an autonomous vehicle can control the vehicle speed more effectively, with less speed fluctuations than a human driver. Subsequently, the influence of speed fluctuation on vehicle fuel consumption (L/100 km) was formulated based on the vehicle specific power (VSP) model. The mathematical deduction showed that the fuel consumption is proportional to the speed fluctuation under the same mean speed. Finally, simulation experiments were conducted under real scenarios. The simulation data showed that the fuel consumption increases almost linearly with the increase in speed fluctuation. Field experiments were also conducted on the fuel consumption of an autonomous vehicle under different driving modes. The experimental data showed that the fuel consumption also increases almost linearly with the increase in speed fluctuation. In the human-driven mode, the fuel consumption increased by 5.6% and 14.7%, respectively, compared with that in the autonomous mode at average speeds of 20 km/h and 40 km/h. Furthermore, the maximum fuel consumption was up to 60% more when the autonomous vehicle was driven by a driver, as the driving behavior displayed greater speed fluctuations.
    Type of Medium: Online Resource
    ISSN: 2042-3195 , 0197-6729
    URL: Article
    DOI: 10.1155/2022/2631692
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2553327-7
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  • 6
    Online Resource
    Online Resource
    iTRAQ-Based Quantitative Proteomic Analysis Reveals Proteomic Changes in Mycelium of Pleurotus ostreatus in Response to Heat Stress and Subsequent Recovery
    Frontiers Media SA ; 2018
    In:  Frontiers in Microbiology Vol. 9 ( 2018-10-9)
    Details
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 9 ( 2018-10-9)
    Type of Medium: Online Resource
    ISSN: 1664-302X
    URL: Article
    DOI: 10.3389/fmicb.2018.02368
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2018
    detail.hit.zdb_id: 2587354-4
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  • 7
    Online Resource
    Online Resource
    Identification of important charged residues for alkali cation exchange or pH regulation of NhaH, a Na+/H+ antiporter of Halobacillus dabanensis
    Elsevier BV ; 2013
    In:  Biochimica et Biophysica Acta (BBA) - Biomembranes Vol. 1828, No. 3 ( 2013-03), p. 997-1003
    Details
    In: Biochimica et Biophysica Acta (BBA) - Biomembranes, Elsevier BV, Vol. 1828, No. 3 ( 2013-03), p. 997-1003
    Type of Medium: Online Resource
    ISSN: 0005-2736
    URL: Article
    DOI: 10.1016/j.bbamem.2012.11.015
    RVK:
    WA 15000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2013
    detail.hit.zdb_id: 2209384-9
    SSG: 12
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  • 8
    Online Resource
    Online Resource
    Use of Skew-Normal and Skew-t Distributions for Mixture Modeling of Freeway Speed Data
    SAGE Publications ; 2011
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2260, No. 1 ( 2011-01), p. 67-75
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2260, No. 1 ( 2011-01), p. 67-75
    Abstract: Normal, lognormal, and other forms of distribution have been used to characterize speed data. Recently, several researchers have used the normal mixture model to fit the distribution of speed. To investigate the applicability of mixture models with other types of component density, a study was done that fits 24-h speed data collected on I-35 in Texas by using skew-normal and skew-t mixture models with an algorithm of expectation maximization type. The results show that a finite mixture of skew distributions can significantly improve the goodness of fit of speed data. Compared with normal distribution, skew-normal and skew-t distributions can accommodate skewness and excess kurtosis themselves; thus the skew mixture models require fewer components than normal mixture models to capture the asymmetry and bimodality present in speed data. The results of the study indicate that a two-component skew-t mixture model is the optimal model, and this model can better account for heterogeneity in the data. The study verifies that traffic flow condition is the main cause for heterogeneity in the 24-h speed data. The research methodology can be used to analyze freeway speed data characteristics. The findings can also be used in development and validation of microscopic simulation of freeway traffic.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2260-08
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2011
    detail.hit.zdb_id: 2403378-9
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  • 9
    Online Resource
    Online Resource
    Comparison of Sichel and Negative Binomial Models in Estimating Empirical Bayes Estimates
    SAGE Publications ; 2013
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2392, No. 1 ( 2013-01), p. 11-21
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2392, No. 1 ( 2013-01), p. 11-21
    Abstract: Traditionally, transportation safety analysts have used the empirical Bayes (EB) method to improve the estimate of the long-term mean of individual sites and to identify hotspot locations. The EB method combines two sources of information: (a) the expected number of crashes estimated by crash prediction models and (b) the observed number of crashes at individual sites. Because of the overdispersion commonly found in crash data, a negative binomial (NB) modeling framework has been used extensively in crash prediction estimation models. Recent studies have shown that the Sichel (SI) distribution provides a promising avenue for developing crash prediction models. The objective of this study was to examine the application of the SI model in calculating EB estimates. The study used crash data collected at four-lane undivided rural highways in Texas to develop SI models with fixed and varying dispersion terms. The results led to the following main conclusions: (a) the selection of the crash prediction model (i.e., the SI or the NB model) affected the value of the weight factor used for estimating the EB output and (b) the identification of hazardous sites, based on the EB method, could be different when the SI model was used. Finally, a simulation study that is designed to examine which crash prediction model can identify hotspots better is recommended for future research.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2392-02
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2013
    detail.hit.zdb_id: 2403378-9
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  • 10
    Online Resource
    Online Resource
    Crash Frequency Minimization with Severity Mitigation in Road Geometric Design Using Chance Constraint Programming Optimization
    Hindawi Limited ; 2023
    In:  Journal of Advanced Transportation Vol. 2023 ( 2023-7-4), p. 1-18
    Details
    In: Journal of Advanced Transportation, Hindawi Limited, Vol. 2023 ( 2023-7-4), p. 1-18
    Abstract: Crash frequency and crash severity are two major aspects of transportation safety. In this paper, we propose a decision-making scheme combining statistical analysis and optimization modeling to be used in transportation safety study. We conduct a safety analysis, a travel speed analysis, and an optimization analysis to develop a two-stage decision scheme to minimize crash frequency while mitigating crash severity, using data collected in urban environments in Lincoln, Nebraska. In the safety analysis and the travel speed analysis, we study the impact of lane width and other related road geometric design parameters on annual crash frequency and vehicle travel speed using count models and linear regression models, respectively. In the optimization analysis, the proposed two-stage stochastic programming model determines the lane width and other road geometric design parameters in the first stage, and then the posted road speed limit in the second stage for each scenario. To mitigate crash severity, we use a chance constraint to restrict a certain percentile of the vehicle travel speed to comply with the posted road speed limit. This two-stage decision scheme is shown to be effective for the data collected in Lincoln, Nebraska, when restricting the vehicle travel speed of up to two positive standard deviations from the mean travel speed to be under the posted speed limit. The application of a stochastic programming model that utilizes regression analysis results serves as an innovative decision scheme that effectively connects statistical analysis and optimization studies in road geometric design for transportation safety. Its objective is to minimize crash frequency while simultaneously mitigating crash severity. This methodology has extensive potential for application in various environments to assist in the reduction of both crash frequency and crash severity.
    Type of Medium: Online Resource
    ISSN: 2042-3195 , 0197-6729
    URL: Article
    DOI: 10.1155/2023/4798400
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2023
    detail.hit.zdb_id: 2553327-7
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