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  • Mobility and traffic research  (14)
  • 1
    Online Resource
    Online Resource
    Impact of Cooperative Adaptive Cruise Control on a Multilane Highway under a Differentiated Per-Lane Speed Limit Policy
    SAGE Publications ; 2021
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2675, No. 10 ( 2021-10), p. 353-366
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2675, No. 10 ( 2021-10), p. 353-366
    Abstract: Cooperative adaptive cruise control (CACC) has drawn wide attention in recent years for its potential throughput benefit, as it is a promising intermediate technology to the highly connected and automated vehicles. The impact of CACC on multilane highways has been the subject of several studies, but they assumed traffic under a uniform speed limit. Recent research has revealed that traffic performs differently under a differentiated per-lane speed limit (DPLSL) policy with heavy vehicle (HV) restricted lanes. Whether the benefits of CACC still remain under a DPLSL policy has not been explored. This study developed cellular automaton models to incorporate CACC-equipped and non-equipped vehicles (i.e., passenger cars, HVs) on a two-way eight-lane highway with a DPLSL. Results shown throughputs by lane increase up to 78.5% as the CACC car market penetration rate (MPR) rises. Such increases became sharper (i.e., ≥10%) for inner lanes (i.e., HV restricted lanes) and outer lanes after reaching a 40% and a 60% CACC car MPR, separately. Moreover, HVs induced a 1.5% to 15.7% throughput reduction across lanes even under higher CACC car MPRs (i.e., 60%, 80%). This DPLSL policy may cause the lanes to experience a throughput penalty when they are adjacent to lanes with a different speed limit as the MPR of CACC cars rises. Lastly, in traffic with a 60% CACC car MPR, increases are brought further by considering 10% of HV with CACC, especially on those HV non-restricted lanes. The study is helpful for policy makers to further prepare for the prevalence of CACC in the forthcoming years.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981211011475
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
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  • 2
    Online Resource
    Online Resource
    Delay Propagation in Large Railway Networks with Data-Driven Bayesian Modeling
    SAGE Publications ; 2021
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2675, No. 11 ( 2021-11), p. 472-485
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2675, No. 11 ( 2021-11), p. 472-485
    Abstract: Reliability and punctuality are the key evaluation criteria in railway service for both passengers and operators. Delays spanning over spatial and temporal dimensions significantly affect the reliability and punctuality level of train operation. The optimization of capacity utilization and timetable design requires the prediction of the reliability and punctuality level of train operations, which is determined by train delays and delay propagation. To predict the punctuality level of train operations, the distributions of arrival and departure delays must be estimated as realistically as possible by taking into account the complex railway network structure and different types of delays caused by route conflict and connected trips. This paper aims to predict the propagation of delays on the railway network in the Greater Sydney area by developing a conditional Bayesian model. In the model, the propagation satisfies the Markov property if one can predict future delay propagation in the network based solely on its present state just as well as one could knowing the process’s full history, so that it is independent of such historical procedures. Meanwhile, we consider the throughput estimation for the cases of delay caused by interchange line conflicts and train connection in this model. To the best of the authors’ knowledge, this is the first work of data-driven delay propagation modeling that examines both spatial and temporal dimensions under four different scenarios for railway networks. Implementation on real-world railway network operation data shows the feasibility and accuracy of the proposed model compared with traditional probability models.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981211018471
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
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  • 3
    Online Resource
    Online Resource
    Timetable Synchronization and Optimization Considering Time-Dependent Passenger Demand in an Urban Subway Network
    SAGE Publications ; 2018
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2672, No. 8 ( 2018-12), p. 243-254
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2672, No. 8 ( 2018-12), p. 243-254
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198118772958
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2018
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  • 4
    Online Resource
    Online Resource
    Deep Learning-Based Lane Marking Detection using A 2 -LMDet
    SAGE Publications ; 2020
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2674, No. 11 ( 2020-11), p. 625-635
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2674, No. 11 ( 2020-11), p. 625-635
    Abstract: Automated lane marking detection is essential for advanced driver assistance system (ADAS) and pavement management work. However, prior research has mostly detected lane marking segments from a front-view image, which easily suffers from occlusion or noise disturbance. In this paper, we aim at accurate and robust lane marking detection from a top-view perspective, and propose a deep learning-based detector with adaptive anchor scheme, referred to as A 2 -LMDet. On the one hand, it is an end-to-end framework that fuses feature extraction and object detection into a single deep convolutional neural network. On the other hand, the adaptive anchor scheme is designed by formulating a bilinear interpolation algorithm, and is used to guide specific-anchor box generation and informative feature extraction. To validate the proposed method, a newly built lane marking dataset contained 24,000 high-resolution laser imaging data is further developed for case study. Quantitative and qualitative results demonstrate that A 2 -LMDet achieves highly accurate performance with 0.9927 precision, 0.9612 recall, and a 0.9767 [Formula: see text] score, which outperforms other advanced methods by a considerable margin. Moreover, ablation analysis illustrates the effectiveness of the adaptive anchor scheme for enhancing feature representation and performance improvement. We expect our work will help the development of related research.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198120948508
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
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  • 5
    Online Resource
    Online Resource
    Analysis of Crash Rates at Freeway Diverge Areas using Bayesian Tobit Modeling Framework
    SAGE Publications ; 2019
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2673, No. 4 ( 2019-04), p. 652-662
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2673, No. 4 ( 2019-04), p. 652-662
    Abstract: The goal of this study is to evaluate the impact of various risk factors on crash rates at freeway diverge areas. Crash rates data for a three-year period from 367 freeway diverge areas were used for analysis. Four candidate Tobit models were developed and compared under the Bayesian framework: a traditional Tobit model; a random parameters Tobit (RP-Tobit) model; a grouped random parameters Tobit (GRP-Tobit) model; and a random intercept Tobit (RI-Tobit). The results showed that the RP-Tobit model performs best with highest value of R d 2 as well as lowest Mean Absolute Deviance (MAD) and Deviance Information Criteria (DIC), indicating the importance of accounting for unobserved heterogeneity to improve the model fit. Both the GRP-Tobit and the RI-Tobit models provide better performance than the traditional Tobit model. The model results showed that crash rates at freeway diverge areas were positively associated with mainline annual average daily traffic (AADT) and negatively associated with ramp AADT, indicating the different mechanisms of the impact of traffic volume on crash rates at freeway diverge areas. Lane-balanced design and high speed limits at freeway diverge areas have a negative effect on crash rates. The number of lanes on mainline and ramp length have significant heterogeneous effects on crash rates across observations. The RP-Tobit model provides a more comprehensive understanding of the heterogeneous effects of risk factors on crash rates across observations.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198119837219
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
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  • 6
    Online Resource
    Online Resource
    Approaches to Adaptive Lighting on Roadways
    SAGE Publications ; 2015
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2485, No. 1 ( 2015-01), p. 26-32
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2485, No. 1 ( 2015-01), p. 26-32
    Abstract: Adaptive lighting is an approach to lighting design that proposes to change the light level on a roadway on the basis of the needs of drivers. A review of the literature and building the connection between crashes and light level allowed the development of a system to select roadway luminance levels and then modify these levels on the basis of the needs of the driver. This adaptive lighting system uses parameters such as traffic volume, ambient light levels, pavement marking quality, intersection density, and, most important, the presence of pedestrians to allow lighting design criteria to be selected through a weighting system. As these parameters (such as traffic volume) change, a new light requirement is determined, and the lighting system can be adjusted with controllable luminaires and a master control system. Guidelines for the implementation of this system and an example are provided.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2485-04
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
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  • 7
    Online Resource
    Online Resource
    Driving Style Classification and the Effectiveness of Advanced Driving Assistance Systems: Differences between Teen and Adult Drivers
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications
    Abstract: Advanced driving assistance systems (ADAS) are designed to reduce potential crash risks and enhance driving safety. However, drivers’ interactions with ADAS may vary depending on their individual driving styles and characteristics. This study proposes a novel approach to classifying driving styles and explores how age and gender affect interactions with ADAS. The study utilized two naturalistic driving data sets comprising 148 drivers from four age groups: teens; younger adults; middle-aged adults; and older adults. Data were collected during two periods: baseline (without ADAS); and treatment (with ADAS). First, the K-means clustering algorithm was employed to divide trips into one conservative and two aggressive groups based on three driving behavior metrics: tailgating; speeding; and lane-changing. The aggressive-trip ratios were then calculated for each driver during each of the two periods. The Bayesian Gaussian mixture model was applied to determine the threshold values of the aggressive-trip ratios to classify drivers as conservative, moderate, or aggressive during each period. This allowed for identifying changes in driving style upon the activation of ADAS. The subsequent multinomial logistic regression model results showed that driving styles vary across age groups, with teens being the most aggressive drivers. Certain changes in driving style were observed, with some conservative drivers becoming aggressive or moderate and some aggressive drivers becoming conservative or moderate, but these differences were statistically non-significant. The findings of this study indicate that warning-based ADAS may not elicit significant changes in driving style, particularly among teenage drivers who are consistently the most aggressive drivers.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981231169525
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
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  • 8
    Online Resource
    Online Resource
    Understanding Flex-Route Transit Adoption from a Stage of Change Perspective
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2677, No. 6 ( 2023-06), p. 743-758
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2677, No. 6 ( 2023-06), p. 743-758
    Abstract: Given the potential benefits of flex-route transit for the environment and traffic congestion, many cities have goals for diffusing flex-route transit. However, previous studies have focused on practical experience, strategic planning, and operational planning of flex-route transit. Few studies have identified the determinants of flex-route transit adoption. This study aims to address the effect of demographic characteristics and psychological factors on flex-route transit adoption by employing a stage of change framework. First, the transtheoretical model is applied to divide respondents into five ordered stages (i.e., Precontemplation, Contemplation, Preparation, Action, and Maintenance). Second, a survey was conducted in Beijing, China, using an online platform to obtain the demographic characteristics and measurement items of the respondents in the five ordered stages. Then, a structural equation model is applied to quantify psychological factors. In addition, the partial proportional odds (PPO) model is employed to estimate the best model and identify the determinants of flex-route transit adoption. Finally, an elasticity analysis is conducted to uncover the effect of determinants on flex-route transit adoption in the five ordered stages. The results suggest that the PPO model has the best fit and validity. The model reveals the significant implications of psychological factors and demographic characteristics for transport policy in various stages. These findings show that, regardless of the initial stage, interventions concentrating on psychological factors and demographic characteristics appear to have the potential to transfer the stage of individuals from one to another. To make policies as effective as possible for promoting flex-route transit adoption, the targeted interventions should be complemented in the specific stage.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981221150244
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
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  • 9
    Online Resource
    Online Resource
    Multistate Travel Time Reliability Models with Skewed Component Distributions
    SAGE Publications ; 2012
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2315, No. 1 ( 2012-01), p. 47-53
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2315, No. 1 ( 2012-01), p. 47-53
    Abstract: The multistate travel time reliability model has demonstrated superior performance, a close relationship with underlying traffic conditions, and ease of interpretation for travel time reliability reporting. This study advances the multistate model by using skewed component distributions—for example, the gamma and lognormal distributions—to accommodate nonsymmetrically distributed travel times, which are commonly observed in congested states. Six alternative models, the single-state normal, gamma, and lognormal distributions and their multistate versions, were fitted to field-collected data. The performance of the models was compared with Akaike's information criterion. The results indicate that the multistate lognormal model consistently out performs alternative models, especially during peak hours. The improved fitting of the lognormal model is mainly reflected in the mode and tail portion of the data distribution. During off-peak hours, the single-state model could provide a compatible but parsimonious alternative to multistate models. The impact of using the multistate lognormal model is demonstrated on travel time reliability reporting by using the travel time buffer index. The study concludes that the multistate lognormal model is the optimal alternative model for modeling travel time under moderate to heavy traffic conditions along freeways.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2315-05
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2012
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  • 10
    Online Resource
    Online Resource
    Taxi Utilization Rate Maximization by Dynamic Demand Prediction: A Case Study in the City of Chicago
    SAGE Publications ; 2022
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2676, No. 4 ( 2022-04), p. 367-379
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 4 ( 2022-04), p. 367-379
    Abstract: The explosive popularity of transportation network companies (TNCs) in the last decade has imposed dramatic disruptions on the taxi industry, but not all the impacts are beneficial. For instance, studies have shown taxi capacity utilization rate is lower than 50% in five major U.S. cities. With the availability of taxi data, this study finds the taxi utilization rate is around 40% in June 2019 (normal scenario) and 35% in June 2020 (COVID 19 scenario) in the city of Chicago, U.S. Powered by recent advances in the deep learning of capturing non-linear relationships and the availability of datasets, a real-time taxi trip optimization strategy with dynamic demand prediction was designed using long short-term memory (LSTM) architecture to maximize the taxi utilization rate. The algorithms are tested in both scenarios—normal time and COVID 19 time—and promising results have been shown by implementing the strategy, with around 19% improvement in mileage utilization rate in June 2019 and 74% in June 2020 compared with the baseline without any optimizations. Additionally, this study investigated the impacts of COVID 19 on the taxi service in Chicago.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981211059769
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
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