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Our objective in this paper is to evaluate the benefits of bus transit. To obtain the more accurate and highly discrimination results, this study establishes an evaluation indicator system from a stakeholder perspective and proposes a novel combined method consisting of dynamic network data envelopment analysis, cross-efficiency evaluation, and Shannon entropy aggregation method. An empirical study of bus transit systems in 33 key Chinese cities from 2016 to 2019 is provided. The results show that the benefit of bus transit in most key cities was ineffective. In contrast to production efficiency, focusing on improving service effectiveness is more conducive to improving the benefits of bus transit. Small cities have higher benefits than large cities. Meanwhile, the benefits of bus transit vary geographically. This evaluation outcome provides a more appropriate basis for decision-making related to the benefits of public transit services, as well as for operational and management studies. [ABSTRACT FROM AUTHOR]

Owing to the current development trend of globalization and informatization, traditional transportation technologies and means can no longer meet the needs of economic and social development. Thus, intelligent transportation will be an inevitable and revolutionary choice for the development of the transportation industry. As a subsystem of the intelligent transportation system, the electronic nonstop toll collection (ETC) system provides a good solution to the problems of traffic congestion, environmental pollution, energy consumption, and low toll operation efficiency caused by expressway toll collection. This high-technology system is utilized in many fields, such as radio communication and computer and automatic control in the transportation field. China's expressway ETC systems have evident effects on congestion reduction at toll stations. Although the advantages of ETC lanes are significant, and China actively implements ETC-handling policies, some drivers are reluctant to install ETC on-board systems or they have negative experiences after handling them. According to a survey, existing literature lacks research on the factors affecting user satisfaction with ETC. On the basis of the questionnaire data of ETC user experience in Shanghai and Beijing, we comprehensively investigate the reasons of banking, ETC equipment, and drivers' personal factors and travel characteristics to establish an ordinal logistics model. Through cluster analysis, drivers in the two cities are divided into three groups, and the significance of related variables is further analyzed. This research can help us understand the degree of influence of different factors on user satisfaction with ETC to a certain extent. Moreover, effective methods and measures can be used to promote the popularity of ETC vehicle equipment and the utilization rate of ETC-dedicated lanes., Author(s): Guangnian Xiao (corresponding author) [1]; Qiongwen Lu [1]; Anning Ni [2]; Chunqin Zhang [3]; Fang Zong [4] 1. Introduction Electronic nonstop toll collection (ETC) systems are on-board units (OBUs) [...]

Lane changing behavior has a significant impact on traffic efficiency and may lead to traffic delays or even accidents. It is important to plan a safe and efficient lane-changing trajectory that coordinates with the surrounding environment. Most conventional lane-changing models need to establish and solve constrained optimization models during the whole process, while reinforcement learning can just take the current state as input and directly output actions to vehicles. This study develops a lane-changing model using the deep deterministic policy gradient method, which can simultaneously control the lateral and longitudinal motions of the vehicle. To optimize its performance, a reward function is properly designed by combining safety, efficiency, gap, headway, and comfort features. To avoid collisions, a safety modification model is developed to check and correct acceleration at every time step. The driving trajectory data of 1169 lane-changing scenarios extracted from the Next Generation Simulation (NGSIM) dataset are used to train and test the model. The proposed model can quickly converge in training phase. Testing results show it can complete safe and efficient lane changing in different lane-changing scenarios with both shorter time headway and lane-changing duration than human drivers. Compared with the conventional dynamic lane-changing trajectory planning model, our model can reduce collision risk. It is also evaluated in automated and nonautomated mixed traffic in SUMO. Simulation results show that the proposed model also has a positive effect on the average speed of overall traffic flow., Author(s): Cenxin Yu [1]; Anning Ni (corresponding author) [1]; Jing Luo [1]; Jinghui Wang [1]; Chunqin Zhang [2]; Qinqin Chen [1]; Yifeng Tu [3] 1. Introduction Lane changing behavior is [...]

Enhancing the attractiveness of public transport is a key objective for transit agencies that seek to achieve sustainable urban mobility. This study identifies the key factors that affect the attractiveness of public transport from the macroscopic and microscopic perspectives and establishes a comprehensive influence mechanism. Weighted least square-Structural equation model (WLS-SEM) is used to quantify the microscopic factors. Conclusions are summarized as follows: ① the macroscopic factors include urban spatial layout, land use pattern, geographical environment, level of economic development, demographic characteristics, public policy, and service characteristics of public transport; ② the microscopic factors include traveler's individual characteristics, travel demand characteristics, travel characteristics and service level of public transport; ③ service level of public transport most positively impacts the attractiveness, while the travel characteristics of public transport impact it most negatively., Author(s): Chunqin Zhang (corresponding author) [1]; Mengmeng Wang [1]; Junyi Dong [2]; Weite Lu [3]; Yong Liu [1]; Anning Ni [4]; Xueyan Yu (corresponding author) [5] 1. Introduction Public transport [...]

Under the long-term implementation of the low fare policy for public transport in China, the phenomenon of unsustainable subsidies is increasingly obvious. We take into account three related stakeholders including the public, operators and government, and comprehensively consider various factors affecting public transport subsidies, and establish a linkage mechanism of the subsidy based on passenger ridership, cost, fare and service quality. We propose a combination forecasting model (CFM) composed of quadratic exponential smoothing model (QESM) and Grey theory model (GM) to forecast passenger ridership. The results show that the CFM improves the forecast accuracy of passenger ridership, the factors considered in the linkage mechanism are more comprehensive, and the results are reasonable. By using this linkage mechanism, the government or operators can establish long-term or mid-term development plans to provide a systematic and theoretical reference, which is conducive to the healthy and stable development of public transport. [ABSTRACT FROM AUTHOR]

Given the vital role of public transportation in major cities, understanding the influence mechanism of bikesharing use on public transportation is necessary. In this study, we adopt the propensity score matching method to analyze the influence mechanism of bikesharing on the use of public transportation based on a data set in Shanghai. We find no significant influence of bikesharing on the use-frequency of public transportation, but a significant influence on the use-duration of public transportation. A grouping model is established based on gender, physical condition, private bike ownership, private car ownership, private electric bike ownership, and educational background. It is revealed that the use of bikesharing by the group with a private bike or a private electric bike, the group without a bachelor's degree, the group in physical condition under sub-healthy may increase the use-duration of public transportation. [ABSTRACT FROM AUTHOR]

Economic compensation has been documented in many studies globally as an important strategy for enhancing acceptance by local residents of potentially hazardous facilities. Based on attribution theory, the present study investigates the situation for Waste-to-Energy (WTE) incineration plants using a questionnaire survey conducted with adjacent residents to four selected WTE incineration plants located in the Yangtze River Delta region, China. The results indicate that economic compensation effectively improves residents' acceptance by being positively associated with their perceived economic benefit and trust in the local government, but is negatively associated with their perceived risks. Of these, trust plays the most vital role. The findings contribute to the literature on decision-making of locally unwanted land use siting in China by understanding the effects of economic compensation on local resident acceptance; and is of great significance for other countries involved in establishing sustainable municipal solid waste disposal systems of their own. [ABSTRACT FROM AUTHOR]

When performing microscopic traffic simulations, a premise is to have appropriately calibrated parameter values. This is often computationally expensive as it requires repeatedly running simulations. In this paper, we propose a machine learning (ML) + particle swarm optimization (PSO)-based methodology for calibrating microscopic traffic simulator parameters to improve computational efficiency. We first develop ML models that input the parameters to predict simulation outputs. Four machine learning models: decision tree, support vector machine, Gaussian process regression, and artificial neural networks are considered. The best-performing model is then embedded in PSO to seek the set of parameters that minimizes the difference between the predicted simulation outputs and the field observations. The ML+PSO methodology is applied to TransModeler using field data in Shanghai, China. We find that artificial neural networks yield the best prediction accuracy. Furthermore, PSO with embedded artificial neural networks shows superior computational efficiency and effectiveness.. [ABSTRACT FROM AUTHOR]