Important Part Of Urban Transport Research Articles

Research on Legal Issues in Safety Incidents of Ride-hailing and Their Coping Mechanisms

With the rapid development of the "Internet plus" economic model, online car hailing has become an important part of urban transportation, which facilitates citizens' travel and drives economic growth. However, the number of traffic accidents and disputes caused by traffic accidents is also increasing day by day. In practice, in the judgments of various courts on similar infringement cases of online car hailing, the liabilities of online car hailing platforms are divided into supplementary liabilities, joint and several liabilities and independent liabilities, which affect the authority and impartiality of the judiciary. Aiming at network about car caused by traffic accident tort civil liability in the case of the problem, to our country network around the car, car and taxi industry, the express cargo business is classified and discussed analysis parties in different operating modes of the legal status of the subject, the legal relationship between each other, and the corresponding civil liability and the conclusion. Finally, suggestions are put forward for the regulation of the current ride-hailing environment.

Research on Optimization of Urban Public Transport Network Based on Complex Network Theory

The urban public transportation system is an important part of urban transportation, and the rationality of public transportation routes layout plays a vital role in the transportation of the city. Improving the efficiency of public transportation can have a positive impact on the operation of the public transportation system. This paper uses complex network theory and the symmetry of the up and down bus routes and stations to establish an urban public transit network model and calculates the probability of passengers choosing different routes in the public transit network according to passenger travel impedance. Based on passenger travel impedance, travel path probability and passenger travel demand, the links are weighed, and the network efficiency calculation method is improved. Finally, the public transit network optimization model was established with network efficiency as the objective function and solved by the ant colony algorithm. In order to verify the effectiveness of the model and the solution method, this paper selects areas in Nanguan District of Changchun City for example analysis. The result shows that the efficiency of the optimized network is 8.5% higher than that of the original network, which proves the feasibility of the optimized model and solution method.

Evaluation model of bus routes optimization scheme based on multi-source bus data

An effective public transport network is helpful to improve the quality of public transport service and save travel time of travelers. As an important part of urban transportation, the continuous optimization of bus network is necessary to improve the service level of bus transit. Aiming to evaluate the rationality and feasibility of bus optimization scheme, the paper discussed the factors about the bus service supply and demand, including the bus operation, passenger flow and service level. Firstly, based on multi-source data including bus smart card transaction data, bus location data and static attribute data of bus network, the evaluation indicator system of bus routes optimization effect is proposed. Secondly, a comprehensive evaluation model of bus route optimization is established by applying the analytic hierarchy process (AHP). This paper systematically analyzes the various levels bus optimization scheme including bus route level, station level, bus scheduling scheme, and constructs an evaluation model covering the single bus route optimization and multi-routes combination optimization. The quantitative grading evaluation method of bus routes optimization proposed in this paper can scientifically classify the performance of bus route optimization and provide valuable reference for the decision-making of bus route adjustment. Eventually, this study also selects Beijing bus routes optimization scheme for empirical analysis.

Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation

Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi mobility data; Instead, most works focused on point patterns or trip patterns, which may provide an incomplete snapshot. In this work, we propose a novel approach to explore the spatial-temporal patterns of taxi travel by considering point, trip and network flow patterns in a simultaneous fashion. Within this approach, an improved network kernel density estimation (imNKDE) method is first developed to estimate the density of taxi trip pick-up and drop-off points (ODs). Next, the correlation between taxi service activities (i.e., ODs) and land-use is examined. Then, the trip patterns of taxi trips and its corresponding routes are analyzed to reveal the correlation between trips and road structure. Finally, network flow analysis for taxi trip among areas of varying land-use types at different times are performed to discover spatial and temporal taxi trip ODs from a new perspective. A case study in the city of Shenzhen, China, is thoroughly presented and discussed for illustrative purposes.

Research on time sharing rental model of new energy sharing vehicles based on big data technology

With the continuous advancement of Chinese automobile industry, China has become the world’s largest automobile consumption market. At the same time, there are a lot of green development related issues such as energy consumption and environmental emissions. In recent years, with the continuous innovation and development of new energy vehicles and shared travel modes, the time-sharing lease mode of new energy vehicles has been rapidly popularized and developed. The new energy car rental mode not only makes travel more convenient and environmentally friendly, but also saves social and energy resources, becoming an important part of urban transportation, However, in the development process of time-sharing rental of new energy vehicles in cities, there are many problems, such as imperfect credit rating system, imperfect supporting resources, single business model and limited service area, which hinder and restrict the healthy development of time-sharing rental market of new energy vehicles. Big data as a new technology can better serve the time-sharing lease of new energy vehicles. By using the accumulated public data, valuable information can be mined out to solve the problems existing in the time-sharing lease of new energy vehicles. This paper analyzes the development scale and basic situation of urban new energy car rental market, finds out the main problems, then solves the main problems by using big data technology, and promotes the healthy development of urban new energy car rental industry.