Transportation Management Department Research Articles

An Approach for Discovering Urban Transport Service Problem Based on Hotline

This paper presents a methodology for actively discovering knowledge in transport hotline databases by analyzing complaints reported by citizens, aiming to assist transportation management departments in planning actions to investigate and improve service quality. The proposed model uses text mining techniques and applies latent Dirichlet allocation (LDA) to identify topics that are related to transportation services. Consequently, we actively analyzed over 230,000 phone calls occurring in a certain province between 1st January and 31st December 2021. Specifically, we actively analyzed nearly 22,000 phone calls about the taxi industry within a selected city, and identified six topics, including lost and found (27.1%), car blocking (20.6%), attitude and behavior (17.1%), online car-hailing (12.8%), illegal operations (11.2%), and fare issues (11.2%). By actively referring to past and ongoing best practices, we actively recommend several policy implications. The proposed method thus actively transforms the service center record into a customer feedback-based assessment system to intently monitor drivers’ professionalism while efficiently addressing customers’ complaints and concerns.

Disentangled generation network for enlarged license plate recognition and a unified dataset

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above challenges, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. In this work, we address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high labor cost. To handle this problem, we propose a novel data generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation of enlarged license plate data into the text generation and background generation in an end-to-end manner to effectively ensure the generation diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks.

Item Analysis of Multiple Choice Questions (MCQs) for Dangerous Goods Courses in Air Transportation Management Department

Analysis of question items is an assessment of the questions that are evaluated to measure quality. Analysis of question items has two ways, namely quantitative and qualitative question item analysis. The goal to be achieved is to find out the quality of the multiple-choice form of Final Semester Exam questions and also to detect sound and wrong questions. The method used in this study is the quantitative descriptive method. Data collection uses documentation, interviews, and observations. The Excel program uses data analysis to detect the quality of good and bad questions. The results of the quantitative analysis of the reliability of multiple-choice questions of 0.76 mean reliable questions. Difficulty index: 3 medium questions (6%), 47 easy questions (76%), and 9 very easy questions (18%). Discrimination index: 8 questions are very low (16%), 22 questions are low (44%), 9 questions are medium (18%), and t10 questions are high (20%). Distractor efficiency: 125 items of malfunctioning questions (83.33%) and 25 items of functioning questions (16.67%).

Accident responsibility identification model for Internet of Vehicles based on lightweight blockchain

AbstractThe rapid development of autonomous vehicle technology has brought a new experience to people's daily travel. However, if a traffic accident involving autonomous vehicles occurs, it will face difficulties in vehicle accident forensic‐preservation, leakage of vehicle owner's privacy, and identifying legal liabilities. This article proposes an accident responsibility identification model for the Internet of Vehicles based on lightweight blockchain to solve the above problems. This model uses Car Forensics Master to collect evidence from the accident vehicle, and at the same time collects evidence from maintenance service providers, automobile manufacturers, transportation management departments, insurance companies, and other vehicle accident related parties and stores them in the preservation chain. We also use VPKI to protect the autonomous vehicle identity privacy. In order to improve the efficiency of the model and set up authorized access to related entities, the identification of accident liability is jointly completed by the preservation chain and the accident identification chain. In addition, we prove that the protocol proposed in the model has ideal security properties. Finally, we implement the smart contracts in the model through the Solidity language, and evaluate its performance.

How Snowfalls Affect the Operation of Taxi Fleets? A Case Study from Harbin, China

Taxi network plays an important role in urban passenger transportation. However, its operation is greatly affected by weather, especially by snowfalls in cold region. In this study, we focus on the persistent effect of snowfall on taxi operation and propose an autoregressive distribution lag model (ARDL) to quantitatively analyse it. To support our study, the taxi GPS trajectory data collected in Harbin, China, during 61 days from 1 November to 31 December in 2015 is analysed. First, the daily average order volume (DAOV) is acquired through data sampling and processing. Then, combined with the data of daily snowfall during the 61 days, the ARDL model is constructed. The result shows that the snowfall has a lag effect on taxi operation and it lasts about 3 days. To better interpret the result, visualization of total 6 days before and after a heavy snowfall is conducted. The result also indicates that weekends have a positive effect on operation. These results are expected to assist us to better understand the effect of snowfall on taxi operation and provide some policy suggestions for local municipal and transportation management departments to ensure the normal operation of taxi networks.

The authors would like to thank the staff of the Organisation of Transportation and Transport Management Department

Introduction. The article is devoted to the lorries work planning, the operational characteristics of which will make it possible to transport loads of different properties due to changes in the design of these vehicles. The purpose of the study is to develop a new scientific and methodological approach for planning the operation of trucks, which allows taking into account the compliance of a) the controlled parameters of the vehicle with the required values when making changes to the design, b) the planned volume of technical impacts to the required volume for the periods of operation of vehicles in certain conditions, c) the volume transportation in the contract for the planned production of vehicles for the transportation of various types of goods.Materials and methods. To plan the work of a lorry, taking into account changes in its design, it is required to use improved methods for optimizing the planning of the work of a freight motor transport enterprise, which is the relationship of activities for the transportation of goods, maintenance and current repair. The basis of the presented materials and methods is the scientific work of domestic and foreign scientists on the operation of cars with changes in its design; the main provisions of the Federal Laws in terms of permits for the operation of vehicles. The methodological basis of theoretical and experimental research is system analysis and logical analysis.Results. The authors have developed a scientific and methodological approach to planning the operation of a lorry, taking into account changes in its design to fulfill the terms of contracts and make a profit for the trucking company.Discussion and conclusions. The implementation and testing of the scientific and methodological approach were carried out according to the algorithm of the developed methodology in the operating conditions of a motor transport enterprise in the city of Omsk. A procedure was carried out to obtain a certificate from the traffic police after changes were made to the design of the KamAZ-53212 vehicle in order to operate it as a dump truck, and a calculation of costs and profits was made to fulfill the terms of contracts for the transportation of various types of goods.

Traffic Safety Assessment of Highway Abnormal Indivisible Load Transport under Smoke-Free Regulations

A high proportion of truck drivers have smoking habits, especially for long-distance truck drivers, 90% of which have the habit of smoking. In recent years, some regulations are launched focus on the smoking behavior of drivers in the driving process. And the policies forces drivers to abandon the smoking habit. Abnormal indivisible load transport is crucial to the sustainable development of highway safety. In order to scientifically and effectively evaluate the traffic safety of highway abnormal indivisible load transportation, on the basis of extensive investiga of large transportation enterprises and management departments, combined with the method of UAV tracking and shooting video, the traffic variation features of outer profile size, load, dynamic characteristics and trajectory of abnormal indivisible load transport vehicles are obtained. Firstly, the meaning of abnormal in-divisible load transportation is defined. The traffic safety assessment of abnormal indivisible load transport is studied from the aspects of road accessibility and operation safety. Secondly, by ana-lyzing the jacking-up failure of abnormal indivisible load vehicles on convex curves, the sweeping space of the horizontal alignment, and rollover side slip conditions cross section, the road accessibility evaluation model of abnormal indivisible load vehicles under different highway alignment conditions is proposed. Thirdly, based on a certain binding strength, the constraint conditions for preventing the transverse and longitudinal sliding of the cargo are proposed, and the stability calculation method of the collapse point of the hydraulic trailer is determined. Finally, the feasibility and practicability of the safety evaluation method is verified by taking abnormal indivisible load transportation in Sichuan Province as an example. The achievements will lay a foundation for the necessary guidance of sustainable highway transport management. With the application of these technologies, the safety of abnormal indivisible load transport under smoke-free regulations can be guaranteed.

Evaluation of Failure Probability in Series System of Three-Axle Trucks under Strong Crosswind

The probability of wind-induced failure accidents in three-axle trucks under pulsating strong crosswinds and the corresponding critical safe speed are investigated in this study. Reliability theory and random fuzzy methods are utilized to establish the membership function of the failure probability in the series system (FPSS) composed of rollover, side-slip, and rotation failure accidents. The Kaman spectrum is used to realistically simulate the fluctuating wind time history curves of different average speeds. Four factors affecting the six-component force coefficient of the three-axle truck and the crosswind load are considered: fluctuating average wind speed, wind direction (angle), truck driving speed, and road adhesion coefficient. A three-axle truck nonlinear model is established accordingly. The model is used to obtain the dynamic response of the three-axle truck under strong crosswind conditions as per the time-varying curves of the vertical load of the truck, the time-varying curves of the lateral displacement of the center of mass, and the time-varying curves of the heading angle. An advanced Monte Carlo simulation algorithm based on importance sampling is used to determine the probability of a three-axle truck with FPSS under strong crosswinds; the given acceptable probability of failure (accident) is used to obtain the critical safety speed. The sensitivity analysis of random variables reveals that the possibility of three truck failures of the three-axle truck in strong crosswinds is, from largest to smallest, rollover, side-slip, and rotation. This research may provide useful guidance for exploring the probability of wind-induced accidents and the critical safety speeds of vehicles, as well as useful general information for road transportation management departments.

Study on Freeway Vehicle Route Selection Based on Cumulative Prospect Theory

Taking the freeway network as the research object, taking the vehicle route selection with toll road as the main research content, referring to the idea of cumulative prospect theory, selecting the travel time and cost as the reference points, taking the prospect value based on value function and cumulative decision function as the decision basis, the route selection model is constructed. In the part of example analysis, a topological model is constructed according to the freeway network of a province, and the actual toll standard of the road network is taken as the initial parameter value to verify the proposed route selection model. The results show that the setting of parameters has an important impact on the prospect value, and the proposed route selection method for toll roads is effective and feasible. In addition, the influence of rate on route selection is analyzed, and it is concluded that on the route with low travel risk, truck 1 and truck 6 are greatly affected by the toll rate, which can provide decision-making reference for the transportation management department.

Dynamic real‐time high‐capacity ride‐sharing model with subsequent information

Real-time, on-demand mobility systems have gradually revolutionised the transportation means. However, they continue to exhibit problems on inadequate vehicles at peak times. The popularity of ‘sharing’ may ultimately solve such problems as more passengers are served over time, particularly in high-demand (high-density) locations, thereby realising efficient, comfortable, and environmentally friendly transportation. While, existing sharing methods only arrange each order based on current information and do not apply subsequently received information to pursue more optimal route arrangements. Their research explicitly improves large-scale vehicle sharing methods using subsequent information and proposes the concept of a ‘wait time threshold’ for a vehicle, to manage the constraint contradictions in this process. Based on a representative high-demand case of serving all inbound and outbound passengers at Shenzhen Bao’ an International Airport, a system with consideration of subsequent information provides significant improvements comparing to a system without it. The improvement performance varies with dates under different demand scenarios, high demand indicating a more optimistic influence. Therefore, having such a city-scale sharing model makes it possible to provide decision support to the transportation management department, which encourages to establish a low carbon city.

A dynamic parking charge optimal control model under perspective of commuters’ evolutionary game behavior

In this research we consider commuters’ dynamic learning effect by modeling the trip mode choice behavior from a new perspective of dynamic evolutionary game theory. We explore the behavior pattern of different types of commuters and study the evolution path and equilibrium properties under different traffic conditions. We further establish a dynamic parking charge optimal control (referred to as DPCOC) model to alter commuters’ trip mode choice while minimizing the total social cost. Numerical tests show. (1) Under fixed parking fee policy, the evolutionary results are completely decided by the travel time and the only method for public transit induction is to increase the parking charge price. (2) Compared with fixed parking fee policy, DPCOC policy proposed in this research has several advantages. Firstly, it can effectively turn the evolutionary path and evolutionary stable strategy to a better situation while minimizing the total social cost. Secondly, it can reduce the sensitivity of trip mode choice behavior to traffic congestion and improve the ability to resist interferences and emergencies. Thirdly, it is able to control the private car proportion to a stable state and make the trip behavior more predictable for the transportation management department. The research results can provide theoretical basis and decision-making references for commuters’ mode choice prediction, dynamic setting of urban parking charge prices and public transit induction.

Learning for an aesthetic model for estimating the traffic state in the traffic video

With the increasing number of vehicles running on the urban roads, the traffic jam becomes much more serious. Properly estimating the traffic jam level from traffic videos is essential for the department of transportation management and drivers. Currently, for estimating the traffic state on videos, most solutions are built on evaluating traffic flow by counting the running vehicles per time unit or detecting their moving speed. However, the main challenge of these solutions is on the vehicle tracking method, in which the vehicles are necessary to be effectively and integrally segmented from the scenes. The solutions should tradeoff the accuracy of the estimation results and the efficiency of the method. In this paper, we propose a learning-based aesthetic model to estimate the traffic state on videos. The model uses multiple video-based perceptual features about traffic state to train the random forest classifier with the labeled data, and estimates traffic state by data classification. The evaluation experiments are conducted on a testing image set, and the results show that the traffic state estimation accuracy of the proposed model is higher than 98% and the efficiency performance is achieved in real-time.