Journal of Advanced Transportation

The intelligent logistic system (ILS) has benefited Industry 4.0. ILS assures clean, on-time manufacturing. Due to its unique architectures, qualities, and sensory aspects, the robot logistic system (RLS) is sought after as an ILS in Industry 4.0. In case of COVID-19, multiple nations routinely used RLS as ILS to cleanse areas, check patients, and monitor crowds on highways. Research documents (RDs) show that prior researchers attempted to build the static robot logistic system (RLS) performance mapping index; however, most indexes measured only anatomy performance of static RLS. Thus, few RDs are edited previously in the context of MRLS. It is sensed that few RDs examined MRLS-linked performance mapping indexes, including only regular subjective (S) or objective (O) designs, excluding mixed S-O architectures. Most RDs constantly execute the linguistic variables related to fuzzy, grey, rough, ambiguous, and intuitionistic sets/scales to tackle the uncertainty connected with MRLS designs. The authors prioritize those as RGs. The authors proposed (1) an MRLS performance mapping index with respect to technical, cost, and value O-S architectures for recruiting MRLS, (2) linear information to assign ratings in a range of min-max values choosing from 1 to 100% without executing the linguistic scale, and (3) Holistic Managerial Models (HMM-1 and HMM-2) to handle subjective ratings and significance, assigned by Ex against evaluated O-S architectures under linear scale (1–100%). To prove the concept, RLS performance mapping is shown. Only MRLS recruitment and selection are covered. The effort helps CIM, FMS, and WCMS create sustainable, cleaner operations and achieve future goals.

This study explores the significant factors affecting risky riding behaviors of Vietnamese motorcyclists using a contextual mediated model (CMM) in Hanoi, the capital of Vietnam, where motorcycle crashes are prevalent. The affecting factors include personality traits, riding self-confidence, riding attitude, and risk perception. Personality traits and riding self-confidence are distal factors of CMM that affect risky riding behaviors. On the other hand, riding attitude and risk perception are proximal factors in CMM. A survey was conducted to collect information on motorcyclists’ risky riding behaviors related to the four factors mentioned through a self-reported questionnaire. Statistical Package Social Science (SPSS) and structural equation modeling (SEM) with analysis of moment structures (AMOS) are used to determine the effects of the factors on risky riding behaviors. The results discovered that riding attitude and risk perception were the intermediate variables of personality trait and riding self-confidence affecting the risky riding behaviors, and personality trait and riding self-confidence also affected the risky riding behaviors directly. Findings in the model also show that riding attitude was perceived to play a significant role in increasing risky driving behavior. The recommendation is to increase the safety education programs that reduce risky driving behavior.

To investigate the heterogeneity of car-following behaviors across diferent vehicle combinations from the perspective of driver visual characteristics, the NGSIM dataset from I-80 and US-101 highways was selected and distinct car-following segments were extracted for analysis. Firstly, all the efective vehicle trajectories were extracted and categorized into diferent vehicle types based on their widths, resulting in four combination types of car-following segments. Visual angle and its change rate were introduced as variables representing driver visual characteristics. Additionally, one-way analysis of variance (ANOVA) was used to compare these variables with traditional ones. Te driver's visual characteristic variables were then incorporated to improve the full velocity diference (FVD) model. Genetic algorithms were employed to calibrate the model under diferent car-following types, revealing pronounced behavioral variations. After implementing the enhanced drivers' visual angle (DVA) model, substantial reductions in calibration and validation errors were observed, with calibration errors decreasing by 51.93% and 42.22% and validation errors decreasing by 56.61% and 45.26%. Tis indicates the DVA model's remarkable adaptability and stability. Lastly, through a sensitivity analysis of errors, the DVA model demonstrated greater robustness toward the improved error evaluation function. By integrating drivers' visual characteristics, this study provides in-depth insights into heterogeneous car-following behaviors, enhancing our understanding of driver behaviors and micro-trafc simulation systems.

The development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into two categories based on their travel habits and data mining models, and multinomial logit (MNL) models are separately used to predict their destinations. Furthermore, a two-way search algorithm is developed to search the optimal paths between origin-destination (OD) pairs by considering interchange constraints. Start a path search through the origin point and destination point, respectively, until the shortest path is found. The maximum effectiveness of a path is measured by travel time, interchange time, and the number of interchanges between the OD pairs. Finally, the validity of the proposed passenger flow path prediction method is verified by using the AFC data of Chengdu metropolitan rail transit from April 2018.

With strict enforcement of pedestrian right of way at all intersections, the inappropriate right-turn resource allocation from a spatial and temporal perspective will lead to a reduction in the operational efficiency of the intersection. In this paper, three spatiotemporal resource allocation schemes for right-turning vehicles are proposed, considering the vehicle and pedestrian traffic efficiency in all directions of the intersection. To minimize vehicle and pedestrian delay at the intersection individually, an optimization model is established with the effective green time of each phase and three schemes as decision variables. A right-turn vehicle and pedestrian conflict delay model is developed based on the pedestrian-vehicle interaction behavior as the constraints of the optimization model. The NSGA-II algorithm is used to solve the model, and the quantitative criteria for the exclusive right-turn lane and phase are obtained by sensitivity analysis. The results of this paper can be used as a guide for traffic design and for planning and controlling the operation of right-turning vehicles at intersections.

Accurate access to real-time passenger flows on subway platforms helps to refine management in the era of networked operations. The narrow subway platforms suffer from significant crowd scale discrepancies and complex backgrounds when counting passenger flow. In the proposed passenger flow counting algorithm, the feature-enhanced pyramid structure is used to retain the channel information of deep features and eliminate the aliasing effect caused by fusion to enhance the feature representation of the original image and effectively solve the scale problem. The mixed attention mechanism suppresses background interference by capturing the global context relationship and focusing on the target area. On the ShanghaiTech Part_A dataset, the mean absolute error (MAE) and mean square error (MSE) of the proposed algorithm are 2.3% and 1.4% higher than those of the comparison algorithm, respectively. The MAE and MSE on the self-built platform dataset reach 3.1 and 5.7, respectively. The experimental results show that the accuracy of the proposed algorithm is improved and can meet the counting requirements of the subway platform scene.

Driving style identification is of vital importance for intelligent driving system design and urban traffic management. This study aims to identify and analyze driving styles using large-scale ride-hailing GPS data taking different time periods, traffic, and weather conditions into account. The large-scale GPS data are collected and preprocessed, and then, the k-means clustering is implemented to acquire driving behavior. The modified latent Dirichlet allocation topic approach is applied to extract the driving states as the latent variables behind driving behaviors and finally recognize driving styles. The results show that driving styles are composed of five driving states with different probability combinations. Different driving styles in different situations are further analyzed and compared. When considering the impact of peak periods on the driving style, it indicates that styles tend to be conservative in the morning peak, free and dispersed in the evening peak, and diverse in the off-peak hours. While comparing styles regarding the influence of workdays, drivers act more cautiously and conservatively on weekdays but freer on weekends without the pressure of peak hours. The weather factor is also explored and rainy days are verified to be the resistance of driving so that most drivers become cautious and conservative. Finally, two aberrant driving styles are discovered and countermeasures are suggested to improve traffic safety.

Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios.

The evaluation of the impacts of new sections on the highway network is an essential aspect of the feasibility study. Existing studies predominantly concentrated on engineering-oriented feasibility assessments, often overlooking their potential effects on parallel sections and the overall network. In this research, we present an evaluation model for new sections based on complex networks, focusing on the connectivity and imbalance of transportation networks. This model serves as a supplementary approach for enhancing the feasibility analysis of new highway projects. The model comprises three distinct modules, namely, complex network, eigenvalue, and evaluation. Therein, the complex network provides diverse attributes for sections with the dynamic edge weights. Moreover, probability betweenness centrality and volume betweenness centrality have been presented as an eigenvalue of sections based on the multilayer complex network. Furthermore, the connectivity evaluation based on the eigenvalue and the imbalance evaluation based on the entropy and Gini coefficient are conducted. Through the case study, the results of the model demonstrate the connectivity and imbalance contribution of new sections and provide a novel perspective for the feasibility study.

Te continued growth in the civil aviation industry leads to more trafc in the airport, resulting in a decline in operational efciency and the travel experience of passengers. Studying how to improve operational efciency and keep passenger satisfaction simultaneously is very signifcant. Tis study proposes to use total passenger boarding distance instead of total passenger walking distance to quantify passenger satisfaction and then model the airport stand assignment problem considering these two diferent objectives together with the gated percentage, respectively, and the NSGA-II algorithm is improved for a better solution speed. Tis study also performs a case study by applying a dataset of an airport in China. Te results of the case study prove that using the total passenger boarding distance can help the airport better balance operational efciency and passenger satisfaction, which can help provide theoretical support for airport management.

The macroscopic models for solving the pedestrian flow problem can be generally classified into two categories as follows: first-order continuum models and high-order continuum models. In first-order continuum models, the density satisfies the mass conservation law, the speed is defined by a flow-density relationship, and the desired directional motion of pedestrians is determined by an Eikonal-type equation. In contrast, in high-order models, the velocity is governed by the momentum conservation law. In this study, we summarize existing first-order and high-order models and rewrite them in the form of unified scalar or system hyperbolic conservation laws. Next, we apply high-order discontinuous Galerkin methods with a positivity-preserving limiter on unstructured triangular meshes to solve the conservation law and a second-order fast-sweeping scheme to solve the Eikonal equations. Our method can efficiently model real-life complex computational regions and avoid nonphysical solutions and simulation blow-ups. Finally, numerical examples are presented to demonstrate the accuracy and effectiveness of the proposed solution algorithm. The numerical results validate the reliability of the proposed numerical method and highlight the advantages of triangular meshes.

Transit-oriented development is described as a geographic unit with multicircle structures. Most studies have analysed the impact of the built environment within station catchment areas on metro passenger flows from a macro perspective and have lacked analysis of the circle heterogeneity. Few relevant studies have independently investigated the impact of the built environment on the passenger flow in each circle and indeed neglected the systematic interaction between inner circles and circles in the TOD area. In this study, the 800 m buffer from the station was equally divided into four circles. Based on the gravity model, the representative built environment features around the metro stations on both sides were extracted using the block attention module (BAM). Subsequently, Shapley Additive exPlanation (SHAP) was used to explore the influence of different built environment variables on passenger flow at each circle between the origin and destination stations. The results indicate the following: (1) the station-to-station passenger flow is significantly affected by the availability of transfers and the distance between the origin and destination stations; (2) the impact of different built environments on ridership significantly varies within different circles; and (3) the built environment has a similar impact on average daily passenger flow on both sides. Therefore, this study proposes strategies to optimize the metro passenger flow by developing different land use in different circles and updating the urban spatial structure.

Signal-vehicle coordinated control holds substantial promise for enhancing urban transportation efficiency. However, its development faces notable challenges: (1) most existing studies have been conducted based on the assumption of perfect communication conditions. This assumption overlooks the significant impact of vehicle-to-infrastructure (V2I) communication quality on control performance, which leads to poor applicability in practice. (2) The evaluation of roadside unit (RSU) deployment for optimizing signal-vehicle control has not been well studied. Hence, the modeling of signal-vehicle coordination control and RSU deployment evaluation under V2I environment are studied in this paper. First, we introduce a communication model that characterizes the imperfections in communication between RSUs and connected vehicles (CVs). Second, we propose a model for signal-vehicle coordination control within this connected environment. This model integrates strategies from both signal control optimization and the speed optimization of CV platoons. Finally, to assess the impact of the RSU deployment parameters on the performance of signal-vehicle coordination control, we introduce a systematic evaluation method. The reduction in vehicle delays is introduced as the evaluation indicator for control performance. Six other indicators—the number of vehicles in the RSU communication domain, connectivity probability between the CV and RSU, number of vehicles whose speeds are successfully optimized, number of speed adjustments, green extension time, and overlap rate of the communication domains of multiple RSUs—are introduced as the observation indicators. The simulation experiments verify the effectiveness of the proposed model in implementing signal-vehicle coordination control under imperfect communication and environments in low-traffic, medium-traffic, and high-traffic scenarios. Furthermore, these experiments show the quantitative impact of RSU deployment parameters (communication distance, command transmission cycle, installation position, and number of RSUs) on control performance.

Crashes on a roadway are influenced by various factors, including but not limited to road geometries, traffic volume, and environmental conditions. Among these factors, traffic volume and segment length are commonly used to predict crashes. Recently, the role of speed in crashes has been recognized as a significant factor, prompting its incorporation as a variable in crash modeling. Nevertheless, previous research studies that examined speed-related factors are mostly concentrated on higher functional class roads where speed data are abundant. Lack of actual speed data has limited the scope of such a study on rural two-lane highways. Due to recent advancements in data collection methodologies, there has been a significant increase in the accessibility of speed data pertaining to these roads. This study aims to assess the significance of speed as a predictor of crashes on rural two-lane highways, utilizing actual speed data. The results of this study showed a negative correlation between speed and crash frequency on rural two-lane roadways. In addition, it was observed that the impact of speed in the crash model becomes more pronounced at higher operating speed conditions of these roads. The aforementioned observation prompted us to consider a categorizer based on speed and, afterwards, separating crash prediction models for various speed ranges. This approach ultimately resulted in enhanced accuracy in crash prediction. Based on our analysis, developing separate models at different speed levels is recommended to better evaluate the safety performance of these roads under various conditions. Such models can also be useful for transportation planners and policymakers to identify high-risk segments and allocate resources to improve the safety of these roads.

The traction drive system of a high-speed train is the key subsystem of a high-speed train, which controls the driving and braking of the train through electromechanical coupling, and determines the running speed, power quality, and comfort of the train. The traction drive system is subjected to many internal and external excitation sources and frequency bandwidth, and the dynamic response and dynamic characteristics of the system are extremely complex. The results of the traction motor field vibration test showed that 100 Hz vibration frequency occurred during traction and braking of high-speed trains when the inverter output voltage frequency was close to 100 Hz, and its vibration amplitude was higher than other frequency bands. When traction power was cut-off, the 100 Hz frequency was not significant. Through simulation analysis of fatigue damage, it was found that 100 Hz DC-link voltage pulsation would aggravate the fatigue damage of the motor hanger. The line vibration test and bench test of the gearbox showed that there was a natural frequency of the gearbox at about 2500 Hz, when the meshing frequency was close to it. Thus, the resonance characteristic became significant.

Two-wheeled (TW) vehicle accidents are one of the major types of urban traffic accidents. TW cyclists who lack safety protection usually suffer more serious injuries and deaths in collisions. Developments in automotive active safety technologies are expected to reduce cyclist injuries and deaths, such as automatic emergency braking (AEB). To facilitate the development and testing of AEB technology, typical TW vehicle scenarios need to be constructed. Based on 400 cases of car-to-TW vehicle accident data from the National Automobile Accident In-Depth Investigation System (NAIS) database, we investigated the scenario elements that influence AEB robustness, such as weather, accident time, and road wetness. We obtained seven static scenarios using an improved clustering method, and we obtained specific speed and distance combinations in each scenario using a deduction method. Further, we compared the present findings to those of other scholars and the China New Car Assessment Program (C-NCAP). The kinematic states of the two were similar to that of C-NCAP, but the speed distribution was significantly different. The TW vehicle speed in the C-NCAP is set to 15 km/h or 20 km/h concerning the European test scenarios, but the TW vehicle speed in the present study was 10–60 km/h. Thus, the present findings recommended that subsequent C-NCAP test scenarios increase the category of motorcycles and the speed range of cars covering 20–70 km/h and consider the test conditions of bad weather and wet roads, to test the robustness of AEB.

The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road.

This study proposes random-parameters multinomial logit models, with heterogeneity in means and variances, to explore the differences in the factors influencing injury severities of drivers involved in different types of two-vehicle crashes. The models are verified using crash data from the United Kingdom (UK) over three years (2016–2018). Three types of crashes are separately identified (car-car, car-truck, and truck-truck crashes). In this study, a wide variety of potential variables, including the driver, vehicle, road, and environmental characteristics, are considered, with two possible injury-severity outcomes: severe and slight injury. The results show that unobserved heterogeneity existed for young drivers in both car-car and truck-truck crash models and the 30 mph speed limit in the three separate models. Remarkably variations are observed in crashes involving different types of vehicles. The driver’s age and gender, speeding, sideswipes, presence of junctions, weekdays, unlit, and weather conditions significantly impact driver-injury severities in various types of vehicle crashes. These findings are expected to help policymakers seek to improve highway safety and implement proper safety countermeasures.

The study aims to examine the impact of the built environment and weather conditions on travel time for bus passengers in Weinan, China. Various sources of data, including smart card data, bus GPS data, bus station data, road information data, and smart card swiping time, were integrated and analyzed. The study employed the light gradient boosting machine (LightGBM) model and SHapley Additive exPlanations (SHAP) value to assess the feature importance and nonlinear effects of different types of POI density, weather conditions, and time series on bus passengers’ travel time. The study findings indicate that several factors are associated with bus passengers’ travel time, including destination residential density, destination diversity, destination life service density, origin science and education density, origin residential density, origin diversity, humidity, visibility, boarding time between 7 and 8 a.m., and precipitation. This study also reveals nonlinear threshold effects. The study findings provide valuable insights that can be utilized to optimize the bus network and develop low-carbon-oriented land-use planning.

This paper proposed an eco-speed harmonization method at intersections. It is able to reduce carbon emissions by controlling partially connected and automated traffic and signal timing. It has the following features: (i) traffic emission reduction enhancement at various demand levels; (ii) traffic emission achievement while improving the mobility of entire traffic at intersections; (iii) enhanced traffic emission reduction with the help of a small portion of connected and automated vehicles; and (iv) potential implementations in the near feature. To validate the effectiveness, the proposed method is evaluated against a state-of-the-art strategy. Sensitivity analysis is conducted under various demand levels and market penetration rates (MPRs) of connected and automated vehicles (CAVs). The result shows that the proposed method outperforms and has the benefits of traffic emission reduction, throughput improvement, and stop frequency reduction. The proposed method demonstrates consistent performance across all demand levels and CAVs’ MPR. The proposed approach can achieve a reduction in emissions ranging from 4% to 61%, an average increase in throughput of around 14.91%, and a decrease in the stop frequency of at least 26%. This provides the foundation for future CAVs-based eco-approaching strategies.

The necessity for an external control mechanism that optimizes daily urban trips becomes evident when considering numerous factors at play within a complex environment. This research introduces an activity-based travel personalization tool that incorporates 10 travel decision-making factors driven by the genetic algorithm. To evaluate the framework, a complex artificial scenario is created comprising six activities in a daily plan. Afterwards, the scenario is simulated for predefined user profiles, and the results of the simulation are compared based on the users’ characteristics. The simulations of the scenario successfully demonstrate the appropriate utilization of activity constraints and the efficient implementation of users’ spatiotemporal priorities. In comparison to the base case, significant time savings ranging from 31.2% to 70.2% are observed in the daily activity chains of the simulations. These results indicate that the magnitude of time savings in daily activity simulations depends on how users assign values to the travel decision-making parameters, reflecting the attitudinal differences among the predefined users in this study. This tool holds promise for advancing longitudinal travel behavior research, particularly in gaining a more profound understanding of travel patterns.

A novel model approach is proposed to estimate the spatiotemporal distribution of demand for free-floating carsharing. The proposed model is based on a Poisson regression model for right-censored data and estimates possibly time-varying demand rates of small subareas of a service region based on booking data with spatiotemporal information on pickups and dropoffs of cars. The approach allows operators to gain insights into the spatiotemporal distribution of demand for their service and to estimate the loss of demand due to unavailability of cars. Moreover, it can also be used as an input to improve the design of the service, through relocation techniques or to analyze the service with macrosimulation models. In addition, the approach is applied to a case study with real data.

Expressway transportation is an essential part of regional development. An efficient expressway system can enhance cities’ connectivity and coordinate long-distance trips between urban areas. Understanding how travel demand affects the flow of expressways is crucial to designing an efficient expressway management system. However, congested expressways are substantial obstacles to unimpeded expressway travel. Here, we explore the relationship between demand origin locations and congested expressways. We extract the time-varying OD demand matrix from empirical tollgate data collected in Shandong province, China. The incremental traffic assignment method is introduced to obtain the traffic flow of expressway road segments. It was found that congested expressways were generated due to only a few origin locations. In addition, expressway congestion during peak hours could be effectively alleviated by controlling the travel demand of these origin locations. Therefore, the proposed method can provide a novel perspective for expressway management.

In the operation of urban rail transits, train delays occur frequently, and emergency management is one of the key factors to ensure the service quality. For speed limit scenarios with high demand, this paper takes the train running in the restricted manual (RM) mode in the speed limit zone as an example and discusses a coping method by jointing train rescheduling and passenger flow control. With the goal of maximizing the number of passengers served and minimizing total train delay, a nonlinear optimization model is constructed by taking the train operation-related requirements and passenger flow control-related indicators as constraints, and the model is reformulated to a mixed-integer programming (MIP) model with quadratic constraints, which can be solved by the Gurobi solver. In order to obtain effective solutions faster, a two-stage approach is discussed, which first obtains a rescheduling timetable and then dynamically adjusts the requirement of boarding equalization to obtain the passenger flow control scheme. The validity of the model and the solution approach are discussed with the help of numerical experiments. The results suggest that the model and solution approach are feasible. When the number of trains is fixed, the reasonable implementation of passenger flow control will help to increase the number of passengers served, and the pursuit of a higher equalization of boarding is not conducive to the number of passengers served and the full utilization of transport capacity. The two-stage approach has certain advantages over the direct computing method. The methods in this paper have a guiding value for emergency decision-making in similar delay scenarios.