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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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Regional Logistics Network Design in Mitigating Truck Flow-Caused Congestion Problems
Truck flow plays a vital role in urban traffic congestion and has a significant influence on cities. In this study, we develop a novel model for solving regional logistics network (RLN) design problems considering the traffic status of the background transportation network. The models determine not only the facility location, initial distribution planning, roadway construction, and expansion decisions but also offer an optimal solution to the logistics network service level and truck-type selections. We first analyze the relationship between the urban transportation network and the RLN design problem using real truck data and traffic flow status in a typical city. Then, we develop the uncover degree function (UDF), which reflects the service degree of the RLN and formulates based on an impedance function. Subsequently, the integrated logistics network design models are proposed. We model the RLN design problem as a minimal cost problem and design double-layer Lagrangian relaxation heuristics algorithms to solve the model problems. Through experiments with data from the six-node problem and Sioux-Falls network, the effectiveness of the models and algorithms is verified. This study contributes to the planning of regional logistics networks while mitigating traffic congestion caused by truck flow.
Analyzing Drivers’ Intention to Accept Parking App by Structural Equation Model
With the concept of sharing economic entering into our lives, many parking Apps are designed for connecting the drivers and vacated parking spaces. However, there are not many drivers who use the mobile Apps to reserve and find available parking spaces, which is largely due to the insufficient information provided by the parking App. In order to better explain, predict, and improve drivers’ acceptance of parking App, the conceptual framework based on technology acceptance model was developed to establish the relationships between the drivers’ intention to accept parking App, trust in parking App, perceived usefulness of parking App, and perceived ease of its use. Then structural equation model was established to analyze the relationship between various variables. The results show that the trust in parking App, perceived usefulness, perceived ease of use, and parking App attributes are the main factors that determine the intention to use parking App. Through the test of direct effect, indirect effect, and total effect in the model, it is found that perceived usefulness has the largest total impact on acceptance intention, with a standardized coefficient of 0.984, followed by parking App attribute (0.743), perceived ease of use (0.384), and trust in parking App (0.381).
Analysis of Factors Affecting a Driver’s Driving Speed Selection in Low Illumination
To better understand a driver’s driving speed selection behaviour in low illumination, a self-designed questionnaire was applied to investigate driving ability in low illumination, and the influencing factors of low-illumination driving speed selection behaviour were discussed from the driver’s perspective. The reliability and validity of 243 questionnaires were tested, and multiple linear regression was used to analyse the comprehensive influence of demographic variables, driving speed in a low-illumination environment with street lights and driving ability on speed selection behaviour in low illumination without street lights. Pearson’s correlation test showed that there was no correlation among age, education, accidents in the past 3 years, and speed selection behaviour in low illumination, but gender, driving experience, number of night-driving days per week, and average annual mileage were significantly correlated with speed selection behaviour. In a low-illumination environment, driving ability has a significant influence on a driver’s speed selection behaviour. Technical driving ability under low-illumination conditions of street lights has the greatest influence on speed selection behaviour on a road with a speed limit of 120 km/h (β = 0.51). Risk perception ability has a significant negative impact on speed selection behaviour on roads with speed limits of 80 km/h and 120 km/h (β = −0.25 and β = −0.34, respectively). Driving speed in night-driving environment with street lights also has a positive influence on speed selection behaviour in low illumination (β = 0.61; β = 0.28; β = 0.37).
Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data
With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.
Compressive Strength Gain Behavior and Prediction of Cement-Stabilized Macadam at Low Temperature Curing
For cement-based materials, the curing temperature determines the strength gain rate and the value of compressive strength. In this paper, the 5% cement-stabilized macadam mixture is used. Three indoor controlled temperature curing and one outdoor natural curing scenarios are designed and implemented to study the strength development scenario law of compressive strength, and they are standard temperature curing (20°C), constant low temperature curing (10°C), day interaction temperature curing (varying from 6°C to 16°C), and one outdoor natural temperature curing (in which the air temperature ranges from 4°C to 20°C). Finally, based on the maturity method, the maturity-strength estimation model is obtained by using and analyzing the data collected from the indoor tests. The model is proved with high accuracy based on the validated results obtained from the data of outdoor tests. This research provides technical support for the construction of cement-stabilized macadam in regions with low temperature, which is beneficial in the construction process and quality control.
A Distribution Model for Shared Parking in Residential Zones that Considers the Utilization Rate and the Walking Distance
Efficient parking tends to be challenging in most large cities in China. Drivers often spend substantial amounts of time looking for parking lots while driving at low speeds, thereby resulting in interference with road traffic. This paper focuses on efficiently allocating parking spaces to the demanders. A double-objective model is proposed that considers both the utilizing rate and the walking distance. First, managers want to utilize parking resources fully. Therefore, they tend to prioritize the efficient distribution of parking spaces in response to parking demands. However, demanders typically choose parking spaces according to convenience. The second objective is the acceptable walking distance from the parking space to the destination. The particle swarm optimization (PSO) algorithm is used to solve this model. We collected parking demand and supply data in a central business district (CBD) of Harbin in China and evaluated the feasibility of the model. The results demonstrate that the proposed model increases the occupying rates of parking lots in residential zones while decreasing the walking distance. The shared use of parking spaces maximizes the utility and alleviates the shortage of parking spaces in downtown.