Transportation Research Record: Journal of the Transportation Research Board

Journal Information
ISSN / EISSN : 0361-1981 / 2169-4052
Published by: SAGE Publications (10.1177)
Total articles ≅ 23,171
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Dan Xu, Chennan Xue, Huaguo Zhou
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211015261

Abstract:
The objective of this paper is to analyze headway and speed distribution based on driver characteristics and work zone (WZ) configurations by utilizing Naturalistic Driving Study (NDS) data. The NDS database provides a unique opportunity to study car-following behaviors for different driver types in various WZ configurations, which cannot be achieved from traditional field data collection. The complete NDS WZ trip data of 200 traversals and 103 individuals, including time-series data, forward-view videos, radar data, and driver characteristics, was collected at four WZ configurations, which encompasses nearly 1,100 vehicle miles traveled, 19 vehicle hours driven, and over 675,000 data points at 0.1 s intervals. First, the time headway selections were analyzed with driver characteristics such as the driver’s gender, age group, and risk perceptions to develop the headway selection table. Further, the speed profiles for different WZ configurations were established to explore the speed distribution and speed change. The best-fitted curves of time headway and speed distributions were estimated by the generalized additive model (GAM). The change point detection method was used to identify where significant changes in mean and variance of speeds occur. The results concluded that NDS data can be used to improve car-following models at WZs that have been implemented in current WZ planning and simulation tools by considering different headway distributions based on driver characteristics and their speed profiles while traversing the entire WZ.
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211036363

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.
Yangyang Zhao, , , Haris N. Koutsopoulos
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211037553

Abstract:
Unplanned events present significant challenges for operations and management in metro systems. Short-term ridership prediction can help agencies to better design contingency strategies under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on typical situations or planned events. The study develops methods for the short-term metro ridership prediction under unplanned events. It explores event impact representation mechanisms and deals with the imbalanced data training problem in building the prediction model under unplanned events. Typical machine learning and deep learning methods are developed for exploration. A large-scale automatic fare collection (AFC) dataset and event record data for a heavily used metro system are used for empirical studies. The analysis found that the same type of unplanned event shares a similar and consistent demand change pattern (with respect to the demand under typical situations) at the station level. The synthetic minority oversampling technique (SMOTE) can enrich the ridership observations under unplanned events and generate a balanced dataset for model training. Given the occurrence of unplanned events, the results show that a combination of demand change ratio and the SMOTE oversampling technique enables the prediction models to learn the impact of unplanned events and improve the prediction accuracy under unplanned events. However, the oversampling methods (i.e., SMOTE and replication) slightly deteriorate the prediction accuracy for ridership under normal conditions. The findings provide insights into mechanisms for disruption impact representation and oversampling imbalanced data in model training, and guide the development of models for short-term prediction under unplanned events.
Ivan Runhua Xiao, , David Phong, Haihao Zhu
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211036372

Abstract:
This paper analyzes the 2018 Logistics Performance Index (LPI) from the World Bank to determine the spatial effects of countries’ logistics performance. Although the standardized ordinary least square (OLS) models show good results, the spatial lags and Moran’s I of LPI suggest the OLS assumptions are violated. Consequently, an improved geographically weighted regression (IGWR) model using multivariate kernel functions (MKF) is implemented. Through the analysis of the Moran scatter plot, the authors identified the countries that have different logistics performance development trends in the four quadrants representing the relationship between the spatial lags and the LPI. Using trade activity (i.e., import/export) in the MKF, the authors compared different MKF types and bandwidths to ensure the model’s predictability and accuracy and found that the adaptive Gaussian MKF is suitable. Finally, the IGWR model indicates both positive and negative influencing factors on LPI overall score. Specifically, the improvements of LPI are more associated to economic variables in mid- and low-income countries around the world, and are more related to import of construction equipment in the Middle East. Also, business environment is more important in Latin America and the Pacific. European countries are more sensitive to customs efficiency, whereas Pacific-Asian countries are more sensitive to quality of infrastructure and have higher coefficients than African and American countries. This spatial heterogeneity is related to the specific factors that promote the development of their logistics performance.
Xuhao Gui, Junfeng Zhang, Zihan Peng, Chunwei Yang
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211033295

Abstract:
Predicting the estimated time of arrival (ETA) plays an essential role in decision support (conflict detection, arrival sequencing, or trajectory optimization) for air traffic controllers. In this paper, a new multiple stages strategy for ETA prediction is proposed based on radar trajectories, including arrival pattern identification, arrival pattern classification, and flight time estimation. First, an intention-oriented trajectory clustering method is developed based on a new trajectory representation technique. Such a proposed trajectory clustering method can group trajectories into different arrival patterns in an efficient way. Second, an arrival pattern classification model is constructed based on random forest and XGBoost algorithms. Then, a flight time regression model is trained for each arrival pattern by using the XGBoost algorithm. Information on current states, historical states, and traffic situations is considered to build the feature set during these processes. Finally, the arrival operation toward Guangzhou International Airport is chosen as a case study. The results illustrate that the proposed method and feature engineering approach could improve the performance of ETA prediction. The proposed multiple stages strategy is superior to the single-model-based ETA prediction.
Alex van Dulmen, Martin Fellendorf
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211028870

Abstract:
In cases where budgets and space are limited, the realization of new bicycle infrastructure is often hard, as an evaluation of the existing network or the benefits of new investments is rarely possible. Travel demand models can offer a tool to support decision makers, but because of limited data availability for cycling, the validity of the demand estimation and trip assignment are often questionable. This paper presents a quantitative method to evaluate a bicycle network and plan strategic improvements, despite limited data sources for cycling. The proposed method is based on a multimodal aggregate travel demand model. Instead of evaluating the effects of network improvements on the modal split as well as link and flow volumes, this method works the other way around. A desired modal share for cycling is set, and the resulting link and flow volumes are the basis for a hypothetical bicycle network that is able to satisfy this demand. The current bicycle network is compared with the hypothetical network, resulting in preferable actions and a ranking based on the importance and potentials to improve the modal share for cycling. Necessary accompanying measures for other transport modes can also be derived using this method. For example, our test case, a city in Austria with 300,000 inhabitants, showed that a shift of short trips in the inner city toward cycling would, without countermeasures, provide capacity for new longer car trips. The proposed method can be applied to existing travel models that already contain a mode choice model.
Laura Soares,
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211033859

Abstract:
Many airports are converting their ground fleets to electric vehicles to reduce greenhouse gas emissions and increase airport operation sustainability. Although this paradigm shift is relevant to the environment, it is necessary to understand the economic feasibility to justify the decision. This study used life-cycle cost analysis (LCCA) to compare the economic performance of electrified ground fleets in the airport with a conventional fossil fuel fleet. Three different charging systems (plug-in charging, stationary wireless charging, and dynamic wireless charging) for pushback tractors and inter-terminal buses at a major hub airport were considered in the analysis. Although the conventional fossil fuel options present the lowest initial cost for both fleets, they cost most in a 30-year analysis period. Among three electric charging infrastructures, the plug-in charging station shows the least accumulative cost for pushback tractors, and their cost differences are negligible for inter-terminal buses. Although the electric ground fleet is proved to show economic benefits, the most cost-effective charging infrastructure may vary depending on driving mileage and system design. The use of LCCA to analyze new systems and infrastructures for decision making at the project level is highly recommended.
Anshu Bamney, Nusayba Megat-Johari, Trevor Kirsch,
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211043817

Abstract:
Distracted driving is among the leading causes of motor vehicle crashes in the United States, though the magnitude of this problem is difficult to quantify given limitations of police-reported crash data. This study leveraged data from the second Strategic Highway Research Program Naturalistic Driving Study to gain important insights into the risks posed by driver distraction on both freeways and two-lane highways. More than 50 types of secondary tasks were aggregated into ten distraction type categories and mixed-effects logistic regression models were estimated to discern how the risks of near-crash events varied by distraction type while controlling for the effects of driver, roadway, and traffic characteristics. In general, the types of distractions that created the most pronounced risks were those that introduced a combination of cognitive, visual, and manual distractions. For example, drivers who used cell phones were subject to higher risks and these risks tended to be most pronounced when both visual and manual distractions were involved. Likewise, risks tended to be highest when drivers reached for other objects inside the vehicle, engaged in personal hygiene-related activities, or focused on activities occurring outside of the driving environment. Although the same factors tended to increase near-crash risk on both types of facilities, the impacts of several factors tended to be more pronounced on two-lane highways where interaction with other vehicles occurred more frequently. From a policy standpoint, the results of this study provide further motivation for more aggressive legislation and enforcement of distracted driving.
Samuel C. Tignor
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211033860

Abstract:
This paper describes how human factors (HF) and user workload (WL) can be used by highway designers and traffic engineers to quantify the potential safety of sections of highway. Users’ WL is a quantitative measure of HF. Both HF and WL are used successfully in other fields, such as aviation when pilots have difficulty in using instruments and in touch-down before the start or end of the runway. The traditional highway approach of gauging success is by counting crashes. But with fatalities exceeding 30,000 a year for more than 20 years, the time is right for a new method of analysis. The author has integrated specific WL metrics into a simplified example to aid designers, traffic engineers, and safety analysts in assessing user problems before building new projects or road upgrades. The example uses static and dynamic WL and alternating renewal (AR) metrics (not used by others) to quantify user WL in highway segments for the purpose of illustrating the variation of design and operational safety conditions. The example can be easily modified when new metrics are created, and it illustrates the use of WL and its associated highway safety implications. In short, the approach is based on common sense with trained engineering experience and logic integrated into data-driven safety analyses. The example is a continuation of an earlier FHWA research study illustrating the application of road safety audits and the Interactive Highway Safety Design Model (IHSDM). The IHSDM, Excel, and Google Earth were used because no funding was available for on-road data collection.
Ana Mélice Dias, Miguel Lopes, Cecília Silva
Transportation Research Record: Journal of the Transportation Research Board; https://doi.org/10.1177/03611981211034732

Abstract:
The need to implement sustainable mobility is growing in both urgency and pace. However, in cities where the bicycle is underused and cars are overvalued, trying to change the mobility paradigm comes with many challenges. Planners committed to creating cycling mobility plans have to overcome information and resource barriers in coming up with solutions in their respective contexts. Facilitating access to conceptual and practical information for such cities could provide impetus for more effective decisions. With a view to achieving this goal, the “BooST – Boosting Starter Cycling Cities” research project has developed a planning tool, the Cycling Measures Selector (CMS). The tool facilitates access to specific information on measures promoting bicycle use and provides practical guidelines on how to implement these measures in a comprehensive and effective manner. Through its web-based platform, the CMS presents detailed informational sheets on each measure and provides a structure for testing different combinations. Each combination receives a score and suggestions as to how to increase the efficiency. This paper presents the tool and assesses its utility or usefulness for strategic development. To this end, the inputs of three groups working in the cycling promotion area were taken into consideration: local planners, academics, and activists. A series of workshops provided the space to interact with the tool and explore its potential. Those experiences revealed an apparent disconnect between theory and practice, along with a clear need for detailed and varied information on cycling measures. The findings suggest that the CMS can fulfill that need, as well as aid in the planning process.
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