Journal of Advanced Transportation最新文献

Road accidents have always been one of the important reasons for fatalities and financial losses. Since road accidents on rural highways cause more serious injuries than those on urban highways, providing a suitable method to increase safety in the curves can be a significant contributor to preventing these damages. Although speed is one of the most important variables affecting highway safety, numerous studies have been performed on the reliability analysis of horizontal curves without taking the speed variable into account. The aim of this research is reliability (probability of noncompliance) assessment in the horizontal curve design using geometric design consistency criteria. The radius, superelevation, and operating speed of 19 horizontal curves were collected by field research on the Mashhad-Torbat Heydarieh highway in Iran. Three different approaches were defined based on the geometric design consistency criterion of a single horizontal curve, and consecutively, the probability of noncompliance was calculated using these approaches. According to the obtained results, this study showed that radius enhancement increases the probability of noncompliance and the consistency level of the geometric design. Finally, the high values of the probability of noncompliance (failure) indicate that the geometric design guidelines need calibration in the design of horizontal curves, especially for higher radii.

Combining the super-efficiency model based on data envelopment analysis (DEA) with the Malmquist index model, this paper evaluated the efficiency of the logistics distribution center comprehensively considering the truck traffic restriction and provided decision suggestions to improve the efficiency of the logistics distribution center. This paper takes 20 logistics distribution centers as the research objects and uses economic factors, transportation factors, quality of distribution center business activities, and quality of customer service as the primary input indicators; selects eight indicators such as construction cost, transportation cost, labor cost, road facilities, accessibility, business demand, number of laborers, and customer satisfaction as the secondary input indicators; chooses distribution time and profit as the output indicators; and measures the static efficiency of logistics distribution centers from two perspectives, including the traditional unconstrained super-efficiency model and the truck- restricted conditions, using the super-efficiency model of data envelopment analysis (DEA). The Malmquist index model was used to measure the dynamic efficiency and change trend efficiency of the logistics distribution center, and a unified and comprehensive analysis was also made. The results of the case study show that the average efficiency of the logistics distribution center in the driving and nondriving restriction area is 0.872 and 0.914, respectively, and the average efficiency in the driving restriction area is about 4.5% lower than that of the nondriving restriction area, and variance is 1.58 times of the latter. Therefore, it can be concluded that the measures of truck driving restriction have an impact on the efficiency of the logistics distribution center, and the results of the super-efficiency model with the restriction constraint have a greater impact on the logistics efficiency of the logistics distribution center than the traditional unconstrained super-efficiency model. According to the evaluation results, suggestions on reasonable assignment of labor and other resources input are put forward for logistics distribution centers in areas where driving is restricted to improve efficiency.

Fatal traffic crashes involving commercial vehicles exhibit distinct characteristics and mechanisms compared to general traffic crashes, influenced by numerous factors that impact the resulting fatalities. This study presents a comprehensive analysis of significant commercial vehicle crashes in China over a nine-year period (2014–2022), exploring an extensive range of factors including driver behavior, road conditions, vehicle characteristics, and environmental aspects. Utilizing a hierarchical Bayesian ordered probit model that incorporates both categorical and random effects, the research offers nuanced insights into the probabilistic outcomes of fatal traffic crashes. The model’s hierarchical structure enables the exploration of unobserved heterogeneities at individual and group levels. Key findings indicate that driver’s behaviors like speeding and overloading significantly escalate the likelihood of fatal traffic crashes, particularly those resulting in 10 or more fatalities. The study also highlights the role of road class in fatal crashes, with primary and secondary roads being associated with higher risks of more severe fatal crashes. The analysis extends to the impact of vehicle type, noting a distinct increase in the probabilities of fatal crashes with passenger vehicles, while freight vehicles exhibit a more complex relationship with fatal crashes severity. The insights from this study underscore the urgent need for enhanced enforcement of speed limit and vehicle weight regulations, particularly through the deployment of advanced monitoring technologies on highways frequented by commercial vehicles, and targeted infrastructure improvements on primary and secondary roads. This approach offers a novel analytical framework for evaluating commercial traffic crashes, assisting policymakers in devising targeted safety interventions to reduce the incidence of commercial vehicle crashes.

The China-Europe Railway Express (C-ER Express) provides a transcontinental rail container service between China and Europe. As most C-ER Expresses are affected by frequent natural disasters and public health incidents, it faces the increasing risk of network vulnerability. When previous studies investigated the evolution of network vulnerability through local information, they often overlook the complexity of the network’s multidimensional characteristics. The nonlinear load-capacity (NLC) model proposed in this paper integrates local and global information of the network. This approach enables a detailed investigation into how condition thresholds and different types of nodes influence network vulnerability. Firstly, a feature matrix is constructed for C-ER Express based on the topological measures, freight information, and external environment scores. Then, the autoencoder is used to extract the low-dimensional dense information, and the DBSCAN is used to classify C-ER Express into distinct clusters. Secondly, The NLC model integrates feature coefficient to describe the initial capacity of nodes. Subsequently, the failure load is redistributed proportionally to neighboring nodes and remaining normal nodes based on time-varying load and initial capacity of nodes. Finally, the improved NLC model is applied to the C-ER Express under different simulation scenarios. Simulation results show that a reasonable condition threshold can mitigate the impact of small-scale node failures on the network. The DBSCAN attack strategy can effectively identify the node types and prevent the network from chain reactions brought by different types of node failures. This research study is expected to provide some reference value for relevant research about vulnerability analysis of the C-ER Express network.

In order to enhance the driving safety of intelligent vehicles in complex road scenarios, a method for vehicle operation risk assessment and early warning based on the predictive risk field is proposed. The temporal feature vector composed of the spatiotemporal state characteristics of the ego vehicle and surrounding traffic participants is taken as input data for the Attention-Bidirectional Long-Short Term Memory (Attention-BiLSTM) model, which is trained to establish the desired mapping relationship. By predicting the motion state of the target vehicle and utilizing an improved risk field model based on the target vehicle of heading angle, the predictive risk field is obtained. This allows for the assessment of the ego vehicle operational risks. The risk warning model is integrated to provide risk early warning, and the safety path for the ego vehicle is planned based on the interaction between the predictive risk field equipotential lines and the cubic spline curves. Experimental results demonstrate that the proposed vehicle operation risk assessment and early warning model is effective in providing early warnings and safe path references for the ego vehicle in complex urban road test scenarios.

Stop-level ridership data serve as a basis for various studies toward increasing bus patronage and promoting sustainable land use planning. To address limitations found in previous studies, this study proposes a novel approach based on Geographically Weighted Principal Component Analysis (GWPCA) and Ordinary Kriging to predict the stop-level boarding or alighting data along bus lines in São Paulo (Brazil), considering four different sampling methods. The main contributions are as follows: by accounting for the spatial heterogeneity of the predictor dataset, the GWPCA can identify the most important factor affecting transit ridership even in bus stops with no information on boarding and alighting; the spatial modeling of stop-level ridership data using GWPCA components as explanatory variables allows visualizing the spatially varying effects from predictors on ridership, supporting the land use planning at a local level; GWPCA coupled with kriging simultaneously addresses the multicollinearity of predictor data, its spatial heterogeneity, and the spatial dependence of the stop-level ridership variable, thus enhancing the goodness-of-fit measures of the transit ridership prediction in unsampled stops; and a balanced sample on predictor data and well-spread in the geographic space might be preferred to accurately estimate missing stop-level ridership data. In addition to solve the lack of stop-level ridership data, supporting a reliable bus system planning, the proposed method indicates what predictors should be addressed by policymakers to stimulate a transit-oriented development. The method can be successfully applied to other travel demand variables facing a lack of data such as traffic volume in road segments and mode choice at the household level.

Flight trajectory prediction is one of the key issues in ensuring the safety of air traffic, providing the air traffic controller with the foresight of flight conflicts so that control instructions for pilots can be preconceived. In a complicated mechanism, flight trajectories can be severely affected by convective weather, making accurately predicting trajectories challenging. To address this problem, we propose a boosted spatiotemporal deep learning ensemble for mining the law of how convective weather affects flight trajectory stretching. Instead of conventionally representing trajectory data in a geographic coordinate system, we design a relative coordinate system for gaining new trajectory features which tangibly reflect trajectory’s relations with planned route and convective weather. Besides, we raise a boosted ensemble framework of spatiotemporal deep learning models, trained by the samples pairing sequential trajectory with graphical weather, dedicating to strengthen the mining of the high-value training samples that involve explicit flight deviations caused by convective weather. The experiments using actual flight and weather data demonstrate our method’s superiority in predicting flight trajectory affected by convective weather.

A dedicated bus lane is designed to give priority to buses over private cars on the road. However, this approach might waste road traffic capacity when bus demand is low and private car demand is high. Thus, it is essential to utilize bus lanes more effectively. Previous work focuses on allowing private cars to use bus lanes which may cause bus delays. To strike a balance, some approaches involve mandating private cars to leave bus lanes and limiting the number of private cars entering bus lanes. However, these highly rely on transportation infrastructure and emerging technologies. Hence, we propose a simple method of a partially space-shared bus lane, which allows private cars to merge into the bus lane at specific locations. Lane-changing decision is modeled as a result of speed difference between adjacent lanes. Traffic flow is simulated by the LWR model in the Lagrangian coordinate system. Two scenarios are set up—dedicated bus lane and partially space-shared bus lane—to evaluate the partially space-shared system. The only optimization variable is the location that allows private cars to conduct lane-changing. We use the genetic algorithms to optimize the system. Finally, simulation results show that the partially space-shared bus lane reduces private-car delays and total delays of the system. The buses would experience an increase of delays, inevitably. But the bus delay increase could be limited in a small range, which maintains the bus priority. In addition, a multiple lane-changing position strategy is better than the single position one. This approach is suitable for traffic situations with low bus demand and high private car demand. Our work is expected to contribute to the design of future urban bus lanes and improve overall traffic operations.

This paper explores the role of environmental consequences, perceived barriers, policy interventions, public opinions, and knowledge and awareness in using electric vehicles (EVs). We collected 506 responses about their intention to use the EVs to develop our hypothesis. This study uses knowledge and awareness of the EVs as mediating variables towards adopting the EVs and consumers’ residences as moderating variables. It also introduces several control variables in this proposed research model to measure the effect on the intention to use the EVs. In this research study, we test the importance-performance map analysis and check Cohen’s f² to identify a better output. The measurement and structural equation modelling results show that the environmental consequences are a stronger predictor of intention and policy interventions. The findings suggest that government policies can also have an attractive position in the EV segment. In addition, knowledge and awareness mediate the adoption of the EVs. Perceived barriers do not influence consumers to use an EV in India. We test the moderating role of residence with our construct and find a partial moderation role with policy intervention and public opinion. We introduce three self-created constructs, i.e., intention to use, knowledge, and public opinion. Public opinion for the EVs supports consumers’ intention to use, and knowledge also plays a significant role in using EVs. These newly added constructs will be essential to manufacturers and policymakers while promoting EVs. Also, environmental consequences and policy interventions emerge as significant factors of user behaviour. The paper highlights the critical predictors of consumers’ intention to use the EVs. Thus, it helps society, policymakers, and managers formulate and implement schemes to boost EV purchasing.

An optimization model is developed to solve the deterministic traffic assignment problem under congested transport networks with cost functions that have an asymmetric Jacobian. The proposed formulation is a generalization of Beckmann’s transformation that can incorporate network links with multivariate vector cost functions to capture the asymmetric interactions between the flows and costs of the different links. The objective function is built around a line integral that generalizes the simple definite integral in Beckmann’s transformation and is parameterised to ensure the solution of the new problem satisfies Wardrop’s first principle of network equilibrium. It is shown that this method is equivalent to the variational inequality approach. Our new approach could be extended to supply-demand equilibria models in other markets than transportation, with complementary or substitute goods/services in which there are asymmetric interactions between prices.