Measuring Passenger Car Equivalents (PCE) for Heavy Vehicle on Two Lane Highway Segments Operating Under Various Traffic ConditionsRead the full article
Journal of Advanced Transportation publishes theoretical and innovative papers on analysis, design, operations, optimization and planning of multi-modal transport networks, transit & traffic systems, transport technology and traffic safety.
Journal of Advanced Transportation maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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Analysis of a Flexible Transit Network in a Radial Street Pattern
Traditional transit systems are usually composed of fixed routes and stops, which are suitable in densely populated areas. This paper presents a reformulation of the flexible transit model developed by Nourbakhsh and Ouyang (2012) to adapt it to many low demand cities in the world, especially those characterized by radial street patterns. Unlike traditional ones, buses of the proposed transit network are allowed to traverse in a predetermined service area and their precise trajectories hinge on the exact locations of passengers. To identify the optimal topology structure of the flexible transit system, continuous approximation approaches are developed to explore the optimal value of design parameters of the whole system, defining the optimal network layout through minimizing its objective function. To exhibit its advantages, numerical experiments are conducted to compare the flexible transit system with its two variants. The results show that the flexible transit system proposed in this paper outperforms the other two variants. The higher the access cost is, the more it would tilt towards the flexible transit system with a significant margin. Besides, the flexible transit system in a radial pattern competes more effectively than that in a grid structure. This is encouraging because the proposed transit system can be applied in a number of real-world cases.
Analysis on Illegal Crossing Behavior of Pedestrians at Signalized Intersections Based on Bayesian Network
Pedestrians do not always comply with the crossing rules of when and/or where to cross the road at signalized intersections. This risky behavior tends to undermine greatly the effectiveness of safety countermeasures at such locations. Thus, it is very important to understand illegal behavior to develop more effective and targeting measures. In order to address the problem, this paper aimed to analyze characteristics of illegal crossings and their impact on behavior choice. Firstly, illegal crossing behaviors at signalized intersections were classified into two categories, including “crossing at a red light” and “crossing outside of a crosswalk.” Secondly, two sets of data were collected to understand the behaviors. One set of data was collected from video-based observation conducted at 3 signalized intersections in Guangzhou, China, capturing 3334 valid illegal crossing cases in total. Another set of data, from a questionnaire survey conducted online, resulted in 275 valid responses. Finally, presentational characteristics of illegal crossings at signalized intersection were analyzed and two Bayesian network-based behavior models were developed to investigate the characteristics and their impacts on the two types of illegal crossing behaviors, “crossing at a red light” and “crossing outside of a crosswalk,” respectively. Findings reveal that, (i) illegal crossings occur at various types of signalized intersections, with a higher probability for “crossing outside of a crosswalk” compared to “crossing at a red light;” (ii) Arc routing crossing has the highest probability to occur at signalized intersections compared to other types of out-side-crosswalk crossings. (iii) The location of origin and destination of a pedestrian has a significant effect on crossing outside of a crosswalk, the location of origin and destination of “one is inside of a crosswalk and another is outside of a crosswalk” has a highest proportion. These findings provide better understanding of illegal crossings and their impact factors so that the effectiveness of management and control of pedestrians at signalized intersections can be improved.
Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms
The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms.
A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition
In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper.
Comparing Spatial Accessibility and Travel Time Prediction to Commercial Centres by Private and Public Transport: A Case Study of Oforikrom District
The relevance of accessibility in shaping transport planning has often been neglected, hampering on decisions to improve transport efficiency. This is increasingly becoming problematic, as they often impede on economic and technological developments. Many studies on accessibility assert that it is easier for public transport to reach an activity centre than it is for private transport. For this reason, the research compares travel time forecast and accessibility levels with private and public transports en route to commercial centres. The research involves a 21-day transport survey for private cars and public shuttles in Oforikrom district using Global Positioning System (GPS) probe to record the traffic performance indicators to be analyzed in a GIS environment. The results of the study display on a map the level of accessibility via the modes, and a comparative line plot of travel time with private and public transport. The study reveals that private cars in the district generally perform better than public shuttles on the level of accessibility, and travel time. The execution of the research shows that the convergence of choice of transport mode and travel time dynamics is crucial for policymakers to implement diverse transport modes and commuters to choose a mode that has low accessibility cost.
Study on the Deocclusion of the Visibility Window of Traffic Signs on a Curved Highway
Highway navigation is often affected by complex topography, and the flat curve plays an important role in the horizontal alignment design of a highway. Many curves are formed, where visibility could be decreased. Thus, the indicative function of a traffic sign plays a crucial role in ensuring driving safety at the curve. Due to the blocked visibility, the probability of the traffic sign occlusion at the curve of operating highways is quite high. It is urgent to consider the clearing obstructions around traffic signs at curves during highway construction. In this study, the potential of visual occlusion for traffic signs on curved highways was investigated. Firstly, the driver’s visibility window that contains traffic signs was defined and criteria of visual occlusion were proposed. Secondly, a geometric occlusion design formula was established to mimic the visual recognition process of traffic signs on a curved highway, yielding the formula to calculate the visibility window. Finally, the occlusion design formula was applied into a case study of the Beijing-Hong Kong-Macau Expressway (Hunan section), in which visibility windows were calculated and analyzed. The obtained results verified the correctness and effectiveness of the occlusion design formula developed in this study.