Crossing at a Red Light: Behavior of Cyclists at Urban Intersections

To investigate the relationship between cyclist violation and waiting duration, the red-light running behavior of nonmotorized vehicles is examined at signalized intersections. Violation waiting duration is collected by video cameras and it is assigned as censored and uncensored data to distinguish between normal crossing and red-light running. A proportional hazard-based duration model is introduced, and variables revealing personal characteristics and traffic conditions are used to describe the effects of internal and external factors. Empirical results show that the red-light running behavior of cyclist is time dependent. Cyclist’s violating behavior represents positive duration dependence, that the longer the waiting time elapsed, the more likely cyclists would end the wait soon. About 32% of cyclists are at high risk of violation and low waiting time to cross the intersections. About 15% of all the cyclists are generally nonrisk takers who can obey the traffic rules after waiting for 95 seconds. The human factors and external environment play an important role in cyclists’ violation behavior. Minimizing the effects of unfavorable condition in traffic planning and designing may be an effective measure to enhance traffic safety

A Novel Lightweight Real-Time Traffic Sign Detection Integration Framework Based on YOLOv4

As a popular research direction in the field of intelligent transportation, various scholars have widely concerned themselves with traffic sign detection However, there are still some key issues that need to be further solved in order to thoroughly apply related technologies to real scenarios, such as the feature extraction scheme of traffic sign images, the optimal selection of detection methods, and the objective limitations of detection tasks. For the purpose of overcoming these difficulties, this paper proposes a lightweight real-time traffic sign detection integration framework based on YOLO by combining deep learning methods. The framework optimizes the latency concern by reducing the computational overhead of the network, and facilitates information transfer and sharing at diverse levels. While improving the detection efficiency, it ensures a certain degree of generalization and robustness, and enhances the detection performance of traffic signs in objective environments, such as scale and illumination changes. The proposed model is tested and evaluated on real road scene datasets and compared with the current mainstream advanced detection models to verify its effectiveness. In addition, this paper successfully finds a reasonable balance between detection performance and deployment difficulty by effectively reducing the computational cost, which provides a possibility for realistic deployment on edge devices with limited hardware conditions, such as mobile devices and embedded devices. More importantly, the related theories have certain application potential in technology industries such as artificial intelligence or autonomous driving

A Novel Lightweight Real-Time Traffic Sign Detection Integration Framework Based on YOLOv4

As a popular research direction in the field of intelligent transportation, various scholars have widely concerned themselves with traffic sign detection However, there are still some key issues that need to be further solved in order to thoroughly apply related technologies to real scenarios, such as the feature extraction scheme of traffic sign images, the optimal selection of detection methods, and the objective limitations of detection tasks. For the purpose of overcoming these difficulties, this paper proposes a lightweight real-time traffic sign detection integration framework based on YOLO by combining deep learning methods. The framework optimizes the latency concern by reducing the computational overhead of the network, and facilitates information transfer and sharing at diverse levels. While improving the detection efficiency, it ensures a certain degree of generalization and robustness, and enhances the detection performance of traffic signs in objective environments, such as scale and illumination changes. The proposed model is tested and evaluated on real road scene datasets and compared with the current mainstream advanced detection models to verify its effectiveness. In addition, this paper successfully finds a reasonable balance between detection performance and deployment difficulty by effectively reducing the computational cost, which provides a possibility for realistic deployment on edge devices with limited hardware conditions, such as mobile devices and embedded devices. More importantly, the related theories have certain application potential in technology industries such as artificial intelligence or autonomous driving

A Novel Lightweight Real-Time Traffic Sign Detection Integration Framework Based on YOLOv4

As a popular research direction in the field of intelligent transportation, various scholars have widely concerned themselves with traffic sign detection However, there are still some key issues that need to be further solved in order to thoroughly apply related technologies to real scenarios, such as the feature extraction scheme of traffic sign images, the optimal selection of detection methods, and the objective limitations of detection tasks. For the purpose of overcoming these difficulties, this paper proposes a lightweight real-time traffic sign detection integration framework based on YOLO by combining deep learning methods. The framework optimizes the latency concern by reducing the computational overhead of the network, and facilitates information transfer and sharing at diverse levels. While improving the detection efficiency, it ensures a certain degree of generalization and robustness, and enhances the detection performance of traffic signs in objective environments, such as scale and illumination changes. The proposed model is tested and evaluated on real road scene datasets and compared with the current mainstream advanced detection models to verify its effectiveness. In addition, this paper successfully finds a reasonable balance between detection performance and deployment difficulty by effectively reducing the computational cost, which provides a possibility for realistic deployment on edge devices with limited hardware conditions, such as mobile devices and embedded devices. More importantly, the related theories have certain application potential in technology industries such as artificial intelligence or autonomous driving.</jats:p

Trastuzumab-functionalized nanoparticles of biodegradable copolymers for targeted delivery of docetaxel

Aims: We synthesized a novel system of docetaxel-loaded, trastuzumab-functionalized nanoparticles (NPs) of biodegradable copolymers for targeted and synergistic chemotherapy. Materials &amp; Methods: NPs of two component biodegradable copolymers were prepared by a modified solvent extraction/evaporation method with D-α-tocopheryl polyethylene glycol succinate (TPGS) as emulsifier. One component copolymer is poly(lactide)-TPGS, which is of desired hydrophobic–lipophilic balance for cellular adhesion, and another is carboxyl group-terminated TPGS, which facilitates the conjugation of trastuzumab on the NP surface for targeting. Results: In vitro investigation with SK-BR-3 breast cancer cells of HER2 overexpression showed that the trastuzumab-functionalized NPs have great advantages over nude NPs in cellular uptake and cytotoxicity. Conclusion: Trastuzumab conjugated onto the NP surface has two functions: one is to target HER2-overexpressing cancer cells and the other is to enhance the cytotoxicity of docetaxel through synergistic effects. The trastuzumab-functionalized, docetaxel-loaded NPs have great potential for targeted chemotherapy to treat HER2-overexpressing cancer. </jats:p

Bilevel Programming for Evaluating Revenue Strategy of Railway Passenger Transport under Multimodal Market Competition

A bilevel programming approach is used to study the strategies of increasing revenue of the railway agency running between Beijing and Tianjin, China. Bilevel programming approaches have been used in many studies to tackle a variety of transportation problems, but rarely for railway revenue strategy analysis. In this paper, the upper-level problem of the bilevel programming is to determine optimal pricing, speed, and level-of-service (LOS) strategy that maximizes the revenue of the railway agency. The lower-level problem describes passengers’ mode choice behavior under a transportation market with three competing modes: bus, rail, and car. The lower-level problem is to minimize the traveler's cost in terms of money, time, and other related factors such as comfort and safety. A generalized cost function, considering these factors together with a logit model, is used to simulate travelers’ mode choice behavior. Study results clearly show that the bilevel programming is appropriate for the problem studied here. The results indicate that, to increase its revenue, the railway agency should focus on not only the pricing but also travel time and LOS. A pricing breakpoint of about ¥31 (¥7 = U¥1) is found, as it results in the highest revenue for all traveling speeds and LOS. Further increase of the price leads to reduced revenue. A consistent revenue increase trend is observed for all higher traveling speeds and LOS, which emphasizes that the railway agency should pay attention to a combined revenue strategy. </jats:p