This paper develops a game-theoretic model to analyze the competition between two container freight transportation modes (shipping and railway) using competitive game strategic interactions method, namely, deterrence, by taking account of the most cost-effective scale of the transportation capacity settings. The competition was set against the background of China’s Belt and Road (B&R) Initiative as a new situation for intercontinental Sino-Europe container freight transportation. The behavior of each mode (modeled as a carrier, resp.) is characterized by an optimization model with the objective of minimizing its cost by setting optimal basic freight rate and transportation deployment. A firm can use this method to compare the difference in the time value of the cargos and reduce the expense during the whole transportation process. Finally, the developed model is numerically evaluated by a case study of intercontinental transportation between Hefei (China) and Hamburg (Germany). The results show that deterrence effects largely depend on the deterrence objective, and the differential in the cost of two transportation modes tends to be stable with higher values in the deterrence objective. In the new intercontinental circumstance, the mode of railway transportation provides a new way to transport the cargos between China and Europe.

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Game-Theoretic Comparison Approach for Intercontinental Container Transportation: A Case between China and Europe with the B&R Initiative

In this article, we study the asymptotic behavior of deterministic, continuous-time imitation dynamics for population games over networks. The basic assumption of this learning mechanism—encompassing the replicator dynamics—is that players belonging to a single population exchange information through pairwise interactions, where they become aware of the actions played by the other players and the corresponding rewards. Using this information, they can revise their current action, imitating one of the players they interact with. The pattern of interactions regulating the learning process is determined by a community structure. First, the set of equilibrium points of such network imitation dynamics is characterized. Second, for the class of potential games and for undirected and connected community networks, global asymptotic convergence is proved. In particular, our results guarantee convergence to a Nash equilibrium from every fully supported initial population state in the special case when the Nash equilibria are isolated and fully supported. Examples and numerical simulations are offered to validate the theoretical results, and counterexamples are discussed for scenarios when the assumptions on the community structure are not verified.

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Imitation Dynamics in Population Games on Community Networks

The objective of this paper is to develop a micro-economic modeling approach for car-following behaviors that may capture different risk-taking tendencies when dealing with different traffic conditions. The proposed framework allows for the consideration of perception subjectivity and judgement errors that may lead to unsafe acceleration driving maneuvers with the possibility of real-end collisions. The modeling approach relies on a generalized utility-based formulation with three specific types of subjective utility functions (SUFs): Prospect Utility (PT) subjective utility function, Constant Relative Risk Aversion (CRRA) subjective utility function, and an Exponential Constant Relative Risk Aversion (ECRA) subjective utility function. The formulation is assessed in terms of its homogeneous macroscopic properties (thus leading to a triangular fundamental diagram) and its non-homogeneous microscopic properties (thus leading to realistic following behavior facing different traffic scenarios). Once tested in terms of feasibility, the modeling approach is calibrated against real-life trajectory data. A Genetic Algorithm (GA) method is adopted to minimize a spacing-mixed-error term while considering inter-driving heterogeneity. The three models (i.e. PT, CRRA and ECRA) produce acceptable error values with the PT model showing the best fit, followed by the ECRA model, followed by the CRRA model. Even though the CRRA and the PT models show similar intensity in the acceleration response to the behavior of a lead vehicle with more disturbance (stochasticity/randomness) seen with the CRRA SUF, the PT and the ECRA models show more realistic wave formation despite their difference in terms of individual acceleration distribution functions. The ECRA model results in an amplified sensitivity to the behavior of the lead vehicle with the acceleration probability distribution function skewed to the left (i.e. towards decelerating rather than accelerating). The calibration exercise is followed by a simulation exercise. The three suggested models produce a homogeneous congestion phase, but a clear transient single/multiple wave formation is seen with the PT and the ECRA models. These latter models are able to reproduce all the congestion regimes observed on real-world surface transportation networks. The ECRA is characterized by a decreased capacity and increased traffic disturbances with additional shockwave formation. Finally, the different models allow the possibility of perception or judgement errors with the explicit incorporation of a collision probability and a collision weight in the suggested formulation approach.

A behavioral microeconomic foundation for car-following models

A marriage between dockless bike-sharing systems and rail transit presents new opportunities for sustainable transportation in Chinese cities. However, how to promote the bicycle–metro integration ...

System Dynamics Modeling for a Public–Private Partnership Program to Promote Bicycle–Metro Integration Based on Evolutionary Game

The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management.

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A Study of Taxi Service Mode Choice Based on Evolutionary Game Theory

This paper proposes a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the influence of the traffic information. A simulated network with two alternative routes and one variable message sign is built to illustrate the analytic method. We assume that the drivers in the transportation system are bounded rational, and the traffic information they receive is incomplete. An evolutionary game model is constructed to describe the evolutionary process of the drivers' route choice decision-making behaviors. Here we conclude that the traffic information plays an important role in the route choice behavior. The driver's route decision-making process develops towards different evolutionary stable states in accordance with different transportation situations. The analysis results also demonstrate that employing cumulative prospect theory and evolutionary game theory to study the driver's route choice behavior is effective. This analytic method provides an academic support and suggestion for the traffic guidance system, and may optimize the travel efficiency to a certain extent.

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A study of driver's route choice behavior based on evolutionary game theory

The purpose of this study is to investigate the effects of Weibo on travel choice behavior. The involved data were obtained from a stated preference questionnaire survey conducted both in Nanjing and on the internet. The personal attributes, the travel attributes and the Weibo attributes were concluded. Three BL models under three kinds of traffic information issued by Weibo were conducted to analyze the interrelationship between travel choice behavior and its influencing factors. The estimation results showed that the personal attributes had very different effects on travel choice behavior. Along with the comprehensiveness of the traffic information released by Weibo, the effects of personal attributes gradually weakened, while the effects of travel attributes became stronger. The contents of the traffic information released by Weibo had a great impact on travel choice behavior. These findings have important meanings to establish an efficient traffic guidance system with multitudinous modes.

Yanjie Ji

Papers

A study of driver's route choice behavior based on evolutionary game theory

Effects of Weibo Information on Travel Choice Behavior