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The main aim of this study was to propose a comprehensive risk indicator to identify the potential driving risk caused by the changing environment at tunnel entrance. Driving decisions are affected by many external factors, especially at the entrance of tunnels. However, driving indicators are mostly considering vehicle movement status currently. In this study, a safe potential field model including obstacle potential field, vehicle potential field and lighting potential field is constructed to evaluate influence of roads, drivers, vehicles, and change lighting conditions on driving risk. Furthermore, considering the driving risk distribution and its temporal change rate, a comprehensive driving risk indicator (CDRI) was established to evaluate the magnitude of driving risk. Finally, the comparison between CDRI and the other two classic risk indicators indicates that the CDRI proposed in this paper has a better performance in the safety assessment at tunnel entrance. It is expected that the finding in this study could be valuable in developing control and measures for in-tunnel driving risk declining.

As the adoption of Electric Vehicles (EVs) intensifies, two primary challenges emerge: limited range due to battery constraints and extended charging times. The traditional charging stations, particularly those near highways, exacerbate these issues with necessary detours, inconsistent service levels, and unpredictable waiting durations. The emerging technology of dynamic wireless charging lanes (DWCLs) may alleviate range anxiety and eliminate long charging stops; however, the driving speed on DWCL significantly affects charging efficiency and effective charging time. Meanwhile, the existing research has addressed load balancing optimization on Dynamic Wireless Charging (DWC) systems to a limited extent. To address this critical issue, this study introduces an innovative eco-driving speed control strategy, providing a novel solution to the multi-objective optimization problem of speed control on DWCL. We utilize mathematical programming methods and incorporate the longitudinal dynamics of vehicles to provide an accurate physical model of EVs. Three objective functions are formulated to tackle the challenges at hand: reducing travel time, increasing charging efficiency, and achieving load balancing on DWCL, which corresponds to four control strategies. The results of numerical tests indicate that a comprehensive control strategy, which considers all objectives, achieves a minor sacrifice in travel time reduction while significantly improving energy efficiency and load balancing. Furthermore, by defining the energy demand and speed range through an upper operation limit, a relatively superior speed control strategy can be selected. This work contributes to the discourse on DWCL integration into modern transportation systems, enhancing the EV driving experience on major roads.

With the rapid expansion of urban areas, intercity highways have become crucial for daily transportation. Traffic administrators and planners increasingly rely on evaluating highway traffic volume. This paper aims to investigate the relationship between various factors and intercity traffic volume, with a specific focus on exploring the advancing and lagging effects of weather conditions on traffic volume in the districts of urban agglomerations. Using multiple data sources in the Guangdong-Hong Kong-Macao Greater Bay Area, including weather factors (i.e., rain, temperature, wind, and visibility), traffic factors (i.e., total traffic volume and travel time), and other factors (i.e., node degree, hub cities, and time of day), a mixed geographically weighted regression (MGWR) model is applied to examine the spatial heterogeneity of these factors. The results show that intercity traffic volume is influenced by weather, traffic, and other factors. Additionally, the advancing and lagging effects of different weather factors exhibit spatial heterogeneity across districts. Moreover, the weather lagging effect has a more significant impact than the advancing effect on intercity traffic volume. These findings provide valuable insights into the impact of weather on intercity travel volume and offer precise traffic guidance for intercity travelers.

With the rapid development of warehouse robots in logistics and other industries, research on their path planning has become increasingly important. Based on the analysis of various conflicts that occur when the warehouse robot travels, this study proposes a two-level vehicle path planning model for multi-warehouse robots, which integrates static and dynamic planning to improve operational efficiency and reduce operating costs. In the static phase, the blockage factor is introduced to enhance the ant colony optimization (ACO) algorithm as a negative feedback mechanism to effectively avoid the blockage nodes during movement. In the dynamic stage, a dynamic priority mechanism is designed to adjust the routing strategy in real time and give the optimal path according to the real situation. To evaluate the model’s effectiveness, simulations were performed under different operating environments and application strategies based on an actual grid environment map. The simulation results confirm that the proposed model outperforms other methods in terms of average running distance, number of blocked nodes, percentage of replanned paths, and average running time, showing great potential in optimizing warehouse operations.

Amid escalating energy crises and environmental pressures, electric vehicles (EVs) have emerged as an effective measure to reduce reliance on fossil fuels, combat climate change, uphold sustainable energy and environmental development, and strive towards carbon peaking and neutrality goals. This study introduces a nonlinear integer programming model for the deployment of dynamic wireless charging lanes (DWCLs) and EV charging strategy joint optimization in highway networks. Taking into account established charging resources in highway service areas (HSAs), the nonlinear charging characteristics of EV batteries, and the traffic capacity constraints of DWCLs. The model identifies the deployment of charging facilities and the EV charging strategy as the decision-making variables and aims to minimize both the DWCL construction and user charging costs. By ensuring that EVs maintain an acceptable state of charge (SoC), the model combines highway EV charging demand and highway EV charging strategy to optimize the DWCL deployment, thus reducing the construction cost of wireless charging facilities and user charging expenses. The efficacy and universality of the model are demonstrated using the classical Nguyen–Dupius network as a numerical example and a real-world highway network in Guangdong Province, China. Finally, a sensitivity analysis is conducted to corroborate the stability of the model. The results show that the operating speed of EVs on DWCLs has the largest impact on total cost, while battery capacity has the smallest. This comprehensive study offers vital insights into the strategic deployment of DWCLs, promoting the sustainable and efficient use of EVs in highway networks.

Most existing public transit systems have a fixed dispatching and service mode, which cannot effectively allocate resources from the perspective of the interests of all participants, resulting in resource waste and dissatisfaction. Low passenger satisfaction leads to a considerable loss of bus passengers and further reduces the income of bus operators. This study develops an optimization model for bus schedules that considers vehicle types and offers two service levels based on heterogeneous passenger demands. In this process, passenger satisfaction, bus company income, and government subsidies are considered. A bilevel model is proposed with a lower-level passenger ride simulation model and an upper-level multiobjective optimization model to maximize the interests of bus companies, passengers, and the government. To verify the effectiveness of the proposed methodology, a real-world case from Guangzhou is presented and analyzed using the nondominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ), and the related Pareto front is obtained. The results show that the proposed bus operation system can effectively increase the benefits for bus companies, passengers, and the government.

The risk of injury and death in traffic accidents for passengers in the back and front seats can be reduced by utilizing safety belts. However, passengers use back seatbelts far less frequently than those in the front. More investigation is therefore required into the psychological constructs that affect individuals' attitudes toward using back seat belts. In this study, four models were used to analyze individual intentions and actual back seat belt use: the standard theory of planned behavior (TPB); the standard prototype willingness model (PWM); a model that integrates the TPB and PWM constructs; and a model that integrates the TPB construct, PWM constructs, descriptive norms and perceived law enforcement. The results showed that the standard PWM has much more explanatory power than the standard TPB in explaining the variance in behavioral intention and behavior. Incorporating perceived behavioral control (PBC) into the standard PWM did not improve the model fit considerably, while incorporating descriptive norms and perceived law enforcement moderately improved the model fit. Attitude greatly impacted behavioral intention and the use of back seat belts, followed by perceived law enforcement and descriptive norms, while subjective norms, prototype favorability, prototype similarity and PBC had no significant effect.

Vehicle travel time information is an essential location-based services that can be used to assess highway traffic conditions and provide valuable insights for transit agencies and travelers. To reveal the spatial variation in vehicle travel time with multiple factors, a multiple regression model and a geographically weighted regression model are used to investigate the associations between travel time and various factors. This study draws on freeway toll data in combination with local weather station records on Fridays over 12 months (286, 406 travel information data points), and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, is used as a case study for examining the influence of weather and driver travel frequency on vehicle travel time. The results show that i) travel frequency along an origin-destination (OD) route has a significant effect on travel time, and this effect is approximately 3 to 100 times that of other explanatory variables; ii) rainfall significantly impacts travel time, with an effect that is 1.9 to 8.26 times that of other weather factors; and iii) both weather and driver travel frequency factors display spatial heterogeneity. These findings provide valuable insights for both traffic management and freeway travelers.

In this paper, a three-dimensional subspace method is proposed, in which the search direction is generated by minimizing the approximation model of the objective function in a three-dimensional subspace. The approximation model of the objective function is not unique, and alternatives can be chosen between a symmetric quadratic model and a conic model by specific criteria. Moreover, the idea of a WLY conjugate gradient method is applied to characterize the change of gradient direction between adjacent iteration points. The strategy of initial stepsize and nonmonotone line search are adopted, and the global convergence of the presented algorithm is established under mild assumptions. In numerical experiments, we use a collection of 80 unconstrained optimization test problems to show the competitive performance of the presented method.

The longest cross-sea bridge worldwide, i.e., the Hong Kong-Zhuhai-Macao Bridge (HZMB), opened in October 2018, integrating nine cities and two special administrative regions of China's Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The GBA is one of the regions with the most active economic vitality in China and has an important strategic position in the overall national development. Using full-sample freeway toll data of the GBA, this paper proposed a new structured evaluation methodology that combines the node degree, traffic volume, and topological and flow field-theory (e.g., agglomeration and distribution) to explore the hub city ranking and evolution in this area. The data period we selected to analyze is immediately before and after the opening of the HZMB, and we assess the counties' centrality changes in the GBA by analyzing these data. The findings reported in this paper can reflect the traffic hub evaluation process and provide more macroscopic intercity transportation views to the government.

Dynamic wireless charging (DWC) technology enables the charging of electric vehicles (EVs) en route without the need for stopping on long-distance trips. Based on DWC technology, a dynamic wireless charging system (DWCS) concept is proposed to determine the number of DWC lanes and their locations and lengths considering varying battery charging rates. A two-stage approach is proposed to design the optimal DWCS. First, we propose a mixed-integer model with nonlinear constraints to determine the locations and lengths of the charging lanes. This model is further reformulated as a mixed-integer linear problem to make it suitable to solve with off-the-shelf commercial solvers (e.g., Gurobi). Next, we propose a method to obtain an approximately optimal solution for the number of lanes. Then, a numerical example from a freeway in Guangdong Province, China, is investigated to demonstrate the applicability of the proposed model and its effectiveness in reducing the construction costs.

Autonomous vehicles (AVs) are a promising emerging technology that is likely to be widely deployed in the near future. People's perception on AV safety is critical to the pace and success of deploying the AV technology. Existing studies found that people's perceptions on emerging technologies might change as additional information was provided. To investigate this phenomenon in the AV technology context, this paper conducted real-world AV experiments and collected factors that may associate with people's initial opinions without any AV riding experience and opinion change after a successful AV ride. A number of ordered probit and binary probit models considering data heterogeneity were employed to estimate the impact of these factors on people's initial opinions and opinion change. The study found that people's initial opinions toward AV safety are significantly associated with people's age, personal income, monthly fuel cost, education experience, and previous AV experience. Further, the factors dominating people's opinion change after a successful AV ride include people's age, personal income, monthly fuel cost, daily commute time, driving alone indicator, willingness to pay for AV technology, and previous AV experience. These results provide important references for future implementations of the AV technology. Additionally, based on the inconsistent effects for variables across different models, suggestions for future transportation survey designs are provided.

Internet-based transit services have the potential to enable fully connected, safe, reliable, efficient, and flexible service in the public transit system. The flexible transit system (FTS) allows passengers to make individual travel requests and receive customized door-to-door service by specifying their origins and destinations (OD), departure time range, and willingness to pay (WTP). Unlike the uniform and stable WTP in our previous paper, we consider an exponential decay function for the real pickup locations of passengers, and we proposed a joint design of two-level bus stops in temporal and spatial variations of travel demands. To ensure practical applications of this real-time FTS model, we combine the traversing method and the improved tabu search algorithm. A numerical example from Guangzhou is conducted to demonstrate the applicability of the proposed model and its effectiveness in reducing system cost.

Swap charging (SC) technology offers the possibility of swapping the batteries of electric vehicles (EVs), providing a perfect solution for achieving a long-distance freeway trip. Based on SC technology, a shared SC system (SSCS) concept is proposed to overcome the difficulties in optimal swap battery strategies for a large number of EVs with charging requests and to consider the variance in the battery charging rate simultaneously. To realize the optimal SSCS design, a binary integer programming model is developed to balance the tradeoff between the detour travel cost and the total battery recharge cost in the SSCS. The proposed method is verified with a numerical example of the freeway system in Guangdong Province, China, and can obtain an exact solution using off-the-shelf commercial solvers (e.g., Gurobi).

The Ankyrin repeat domain 49 (ANKRD49) is an evolutionarily conserved protein, which is related to mediate protein–protein interaction. However, the function of ANKRD49 in human glioma remains elusive. Mining through The Cancer Genome Atlas (TCGA) database, we found that the expression of ANKRD49 was increased in glioma tissues and that high expression of ANKRD49 was strongly associated with high disease grade and poor overall survival. To investigate the role of ANKRD49 in malignant glioma, lentivirus expressing shRNA targetting ANKRD49 was constructed in U251 and U87 malignant glioma cells. We demonstrated that ANKRD49 knockdown reduced the proliferation rate of U251 and U87 cells. Further mechanism analysis indicated that depletion of ANKRD49 led to the cell-cycle arrest and induced apoptosis in U251 and U87 cells. ANKRD49 knockdown also changed the expression of key effectors that are involved in stress response, cell cycle, and apoptosis, including p-HSP27 (heat shock protein 27), p-Smad2 (SMAD family member 2), p-p53, p-p38, p-MAPK (mitogen-activated protein kinase), p-SAPK/JNK (stress-activated protein kinase/c-jun n-terminal kinase), cleveagated Caspase-7, p-Chk1 (checkpoint kinase 1), and p-eIF2a (eukaryotic translation initiation factor 2a). Taken together, our findings implicate that ANKRD49 promotes the proliferation of human malignant glioma cells. ANKRD49 maybe an attractive target for malignant glioma therapy. [ABSTRACT FROM AUTHOR]