Taxi Efficiency Measurements Based on Motorcade-Sharing Model: Evidence from GPS-Equipped Taxi Data in Sanya

Urban traffic congestion has become a global problem and has garnered special importance in recent years in the transportation sector, especially in taxi markets. To unlock the potential of Internet of Vehicles (IoV) in Intelligent Transportation Systems (ITS), it was vital to make efficiency measurements. In this study, Distance Formula was built to calculate distances by GPS data based on mathematical equations, and Motorcade-Sharing (MS) Model was proposed to improve the efficiency of collaborative vehicles. The experimental data of 2191 GPS-equipped taxis in Sanya of China was adopted to make comparisons between original results and modelled results. Measurement results showed that MS Model had 10.54% more leisure taxis, reduced 5 overdriving taxis, and saved 33.73% running distance in total compared to the original. This indicated that the application of MS Model could not only alleviate urban traffic congestion but also optimize urban taxi markets, and it has a bright future in the field of taxi and other collaborative vehicles. Future directions could be improving MS Model and expanding data

Vehicle Movement Analyses Considering Altitude Based on Modified Digital Elevation Model and Spherical Bilinear Interpolation Model: Evidence from GPS-Equipped Taxi Data in Sanya, Zhengzhou, and Liaoyang

Aggravating energy shortages and increasing labor costs have become global problems and have garnered special importance in recent years in the transportation sector, especially in taxi markets. Automatic vehicles have a bright future, however, there is an equal amount of skepticism and concern about safety for all the optimism. To unlock the potential of automatic vehicles in intelligent transportation systems, a lot more data and testing are required to promote safety level as far as possible and achieve the organizational aim of reducing accidents to zero tolerance. And it is vital to provide accurate models for vehicle movement analyses. In this study, Modified Digital Elevation (MDE) model and Spherical Bilinear Interpolation (SBI) model were proposed for vehicle movement analyses considering altitude. And the experimental data of 9,990 GPS-enabled taxis in Sanya, Zhengzhou, and Liaoyang were adopted to support comparisons. Measurement results showed that MDE model had over 99% less disparity with direct solution than original model and SBI model could further improve the effects. It indicated that the application of MDE model and SBI model could improve both accuracy and efficiency of vehicle movement analyses and it had a bright future in the field of automatic vehicles. Future directions could be improving models and expanding data

Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

The transportation utility values calculated by traditional utility methods are not comprehensive. Some objects and factors are ignored in traditional utility methods, and this narrow perspective is their primary drawback. In intelligent transportation systems, it is necessary to calculate transportation utility for promoting public traffic planning and management. To build a sustainable intelligent transportation system, modified utility methods are essential to analyze transportation utility in a comprehensive way with innovative technologies and efficient communication systems. To solve the disadvantages of traditional utility methods, it is necessary to establish a new method to build sustainable public transport in the future. In this study, the Multiple Utility Method and Transportation Utility Method are proposed for public transport planning and management from multiple perspectives. A sample is presented to provide a better description, and 69,174 GPS-equipped taxi data in Haikou are adopted for the application of the Transportation Utility Method. The results show that the transportation utility values calculated by the Transportation Utility Method are more comprehensive than the transportation utility calculated by traditional utility methods. This indicates that it is necessary to calculate transportation utility from multiple perspectives based on the Transportation Utility Method. Future directions could include improving the methods, considering more factors, expanding the data used, and extrapolating this research to other cities around the world with similar urban metrics and urban form

Research on Commuting Travel Mode Choice of Car Owners Considering Return Trip Containing Activities

Taking car owners as research objects, the present study investigates the choice of travel mode from the perspective of the travel chain. This study mainly focused on how car owners make travel mode choices during return trips containing activities. The research methods were based on two different decision criteria, namely, the expected utility theory and prospect theory. In the present study, the investigators considered that influence factors for decision-making were uncertainty of travel time and uncertainty of parking. Taking arrival time as the variable, two travel mode models based on these two theories were established. An example of the application of these two models was given to determine whether the return trip containing activities would make the car owners give up driving on the way to work, and under what conditions would they give up driving and switch to public transport. The results indicate that when the return trip contains activities and there are time constraints in these activities, car owners may give up driving to work in their departure trip. The uncertainties of arriving and parking are the main factors that make car owners give up driving. The demarcation points from car to public transport based on these two theories are very close, which indicates the consistency of the expected utility theory and prospect theory. It turns out the importance of the punctuality of public transport to attract the passenger flow, thereby reducing car travel and realizing low-carbon transportation

An Economic Equilibrium Model for Optimizing Passenger Transport Corridor Mode Structure Based on Travel Surplus

Proper corridor configuration is the basic prerequisite for meeting demand to the maximum extent and optimally configuring transport resources. As the existing studies only pursue quantitative balance, without good understanding of the decision-making mechanism of passenger travel, they can hardly be used to guide effective infrastructure planning. The aim of this study was to put forward a new planning concept from the perspective of economics. Firstly, the decision-making mechanism of travel behavior was considered based on the demand subject, and the travel demand was classified. Next, the travel surplus of the demand subject was analyzed, and the travel decision-making criterion of maximizing the travel surplus was put forward. Then, a discrete economic equilibrium model for structural optimization of the passenger transport corridor was constructed and solved by the GlobalSearch algorithm. Finally, the effectiveness of the model and algorithm was verified through examples. The results indicate the good convergence of the algorithm. Different corridor travel demand, time value distribution and fixed cost of service mode all have great influence on the service mode configuration of the comprehensive transport passenger corridor and basically conform to the internal mechanism of supply and demand. The results show that the model and algorithm proposed in this paper are valid and can provide effective reference for the design and policy making of passenger transport corridor mode supply

The Impact of Government Interventions and Consumer Green Preferences on the Competition between Green and Nongreen Supply Chains

The green supply chain (GSC) has become a major trend that advocates for the sustainability of supply chains. To seek optimal strategies for the GSC, the competition between green and nongreen supply chains (NGSCs), along with the impacts of government subsidies and tax policies and the green preferences of consumers, are discussed in this study. A pricing-strategy model of an NGSC and a GSC was conducted by considering the cross-price effects. The equilibrium strategies (the optimal green-technology level, the pricing, and the profits) were achieved and were comparatively analyzed with backward induction. For more in-depth research, a robust sensitivity analysis was conducted, and the Taguchi method was used to identify the main factors that affect the revenues of NGSCs and GSCs. The results show that the vertical collaboration between manufacturers and retailers can help to improve the greenness of products and bring more benefits to consumers. The government interventions have an effect, and when the government sets a premeditated threshold for subsidies and taxation, GSC members can earn more, and the green products obtain more competitiveness. The green preferences of consumers are the primarily conducive factor to the sustainability and profit increases of GSCs. However, the green-technology-investment cost will not have a significant influence on the equilibrium strategies

Exploring the Factors of Intercity Ridesplitting Based on Observed and GIS Data: A Case Study in China

Ridesplitting, a form of ridesourcing in which riders with similar origins and destinations are matched, is an effective mode of sustainable transportation. In recently years, ridesplitting has spread rapidly worldwide and plays an increasingly important role in intercity travel. However, intercity ridesplitting has rarely been studied. In this paper, we use observe intercity ridesplitting data between Yinchuan and Shizuishan in China and building environment data based on a geographic information system (GIS) to analyse temporal, spatial and other characteristics. Then, we divide the study area into grids and explore the contributing factors that affect the intercity ridesplitting matching success rate. Based on these significant factors, we develop a binary logistic regression (BLR) model and predict the intercity ridesplitting matching success rate. The results indicate that morning peak, evening peak, weekends and weekdays, precipitation and snowfall, population density, some types of points of interest (POI), travel time and the advance appointment time are significant factors. In addition, the prediction accuracy of the model is more than 78%, which shows that the factors studied in this paper have good explanatory power. The results of this study can help in understanding the characteristics of intercity ridesplitting and provide a reference for improving the intercity ridesplitting matching success rate

What Is Affecting the Popularity of New Energy Vehicles? A Systematic Review Based on the Public Perspective

The dependence of traditional fuel vehicles on petroleum energy has aggravated the energy crisis, while the harmful gas emissions generated during the use of traditional fuel vehicles have aggravated environmental pollution and climate warming. Therefore, it is urgent to alleviate energy consumption and environmental pollution in the transportation sector. The development and promotion of energy-saving and environmentally friendly new energy vehicles has become an important initiative in the world automotive industry. However, there is still a gap between the promotion of new energy vehicles and the public’s purchase preference, and understanding and respecting the public’s purchase preference can help promote the popularity of new energy vehicles. Based on the core database of the Web of Science, we extracted 1498 papers related to the public’s purchase preference and the popularization of new energy vehicles in the past two decades. We adopted a systematic literature review framework to clarify the research trajectory and research hotspots from literature combing, with the aim to reveal the interaction between the popularity of new energy vehicles and the public’s purchase preference. In addition, we further refined and summarized the existing major studies in order to provide solution ideas for achieving the transition to new energy vehicles in an effort to promote the green and sustainable development of energy

Mediating Effect of Organizational Learning Capacity on the Relationship between Relational Embeddedness and Innovation Performance in Freight Logistics Service

Cooperative innovation has become a critical method for freight logistics firms in supply chain management. The previous study has proved that relational embeddedness (RE) has a positive effect on service innovation performance. However, the influence of organizational learning capacity (OLC) has been widely ignored. This study focuses on explaining the mechanism of OLC on the relationship between RE and innovation performance of freight logistics service (IPFLS). Firstly, a theoretical model is constructed based on Social Network Theory, and four research hypotheses are presented. Secondly, a sample of 236 respondents from freight logistics firms in China is analyzed to test the hypotheses. Finally, several management implications and recommendations are suggested. The research results demonstrate that both RE and OLC have positive effects on IPFLS. Meanwhile, OLC partially mediates the relationship between RE and IPFLS. The findings provide practical guidance for managers to strengthen the relationship with partners and enhance OLC