Journal of Advanced Transportation最新文献

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.

During road work, lane closures significantly reduce road capacity and negatively impact traffic safety in the upstream segments. This study introduces a merge control strategy for the work zone on freeway that aims to alleviate severe congestion and improve flow efficiency in environments where manual vehicles (MVs) and connected automated vehicles (CAVs) coexist. Using a short-term prediction model combined with a proportional-integral-derivative (PID) controller, this strategy dynamically adjusts merging behavior based on real-time traffic conditions. The PID controller calculates error values as the difference between current and target states, adjusting responses through proportional, integral, and derivative terms. The predictions of the traffic state based on the density of open lanes in each segment guide the controller’s decision to initiate a “Merge” or “No Merge” guidance. When merging is deemed necessary, the controller estimates the optimal number of vehicles to merge for each segment, using the severe congestion threshold as a reference point. This approach was tested using a microscopic simulation tool on a calibrated real-world network under mixed traffic conditions. The results indicate that the proposed strategy effectively disperses merging upstream, increases merging speeds, and maintains lane density below critical congestion levels, thus enhancing operation efficiency and safety in work zone areas.

The different rail transit systems, such as regional railway and urban rail transit, cooperate to form the syncretic railway network (SRN). With the rapid development of SRN, the limited transportation capacity is inadequate to meet the booming passenger flow during peak hours, where cascading failures caused by large passenger flow become a threat to SRN. This paper adopts a state equation to depict the failure-restoration process and investigates detailed restoration strategies considering the characteristics of recoverable and repeatable failures of stations. Specifically, three different restoration strategies—spontaneous restoration strategy (SRS), active restoration strategy (ARS), and hybrid restoration strategy (HRS)—are proposed, and the varying effects of restoration time, restoration probability, restoration objective, and restoration priority for SRN with ARS are compared. These restoration strategies are applied to the actual SRN in Chengdu, where it is found that HRS has a better effect than other strategies. Furthermore, stations in the metro network with higher passenger flow allocate more restoration resources to improve the robustness of SRN, while the restoration effect of SRN increases noticeably with the restoration coefficient and the reserve coefficient. The restoration strategies presented in this paper can improve the safety management of SRN.

The demand for electrically powered transportation is increasing exponentially due to high fuel prices and global environmental issues. The use of electric bikes is increasing rapidly within urban mobility. The current E-bike chargers are expensive and fail to provide proper authentication, real-time monitoring, parameter analysis, health maintenance alerts, etc. To meet future demand, the paper presents detailed design procedures and experimental analysis of smart, user-friendly, economical, and green charging solutions for electric bikes. The research provides an IoT-based cheap charging facility for different workplaces, organizations, and highway rest areas. Real-time pricing and parameters are sensed using an IoT module and observed online with a monitoring interface via the ThingSpeak platform. The parameters are visualized using both simulation and hardware analysis. The detailed power converter and high-frequency transformer design procedure with mathematical equations are presented in the article. The proposed design improves reliability, security, timely maintenance, and system health conditions. The research provides economical off-board charging stations with smart interfaces that can be accessed by users or service providers through the data cloud. The low-cost smart charging stations promote the use of electric vehicles and decrease the charging anxiety of users. The proposed scheme reduces emissions problems, improves the air quality index, and facilitates people with affordable and reliable transportation.

This paper focuses on the differences in crowding perception among different types of passengers in trains, aiming to optimize passenger experience and improve the level of service of urban rail transit. Based on data from a passenger survey on the Beijing subway, this paper introduces the concept of Crowding Perception Threshold (CPT) and analyzes the principle of passenger spatiotemporal crowding. Considering factors such as gender, age, travel purpose, and standing density of passengers, the paper constructs a quantitative model of crowding perception using the ordered logit model and proposes a method for classifying the level of service accordingly. The study results indicate that the CPTs for all types of passengers range from 91.8% to 101.6%, with the females, elderly individuals, and noncommuters showing greater sensitivity to crowding. In the temporal dimension, all types of passengers have higher CPTs during peak hours than during off-peak hours, influenced by passengers’ crowding expectations. In the spatial dimension, the level of service for most types of passengers is considered crowded at standing densities of 6-7 pax/m² during peak hours, while the level of service for all types of passengers is deemed to be very crowded at 8 pax/m², at which point additional passengers are not recommended.

As the number of autonomous vehicles increases, the number of accidents also increases every year. These incidents include general car/traffic accidents and may introduce new potential issues such as cybersecurity and sensor errors of autonomous vehicles. The existing traffic accident investigation method has limitations in identifying the cause of the autonomous vehicle accident. Some states in the US (e.g., California and Texas) introduced limited items of Lv. 2 autonomous vehicle accidents. For instance, “vehicle level” and “autonomous mode/conventional mode” are being investigated to identify the cause of autonomous vehicle accidents. Therefore, it is crucial to propose accident investigation items and procedures in preparation for various autonomous vehicles that may occur in the future. In order to address these issues, this study collected reports used in existing traffic accident investigations, autonomous driving-related reports and literature, and accident videos involving autonomous driving to build investigation items. First, we reviewed the items required for investigation in the event of a conventional vehicle accident and added additional investigation items deemed necessary to be reviewed in addition to the existing reports. Second, based on the conventional vehicle accident investigation items, this study derived the autonomous driving traffic accident investigation items. Finally, an accident involving autonomous vehicle(s) investigation process was established that can be used by the police and various investigation jurisdictions. The results of this paper can improve the understanding of the cause of future traffic accidents involving autonomous vehicles.