Random Regret Minimization Model for Variable Destination-Oriented Path Planning

How should travelers be guided to change destinations and choose routes when they change their initial destination after being informed that the reception capacity is saturated and that the road ahead is congested? Using quasi-experimental methods, this paper explores this problem from the perspective of regret theory. We propose the regret index to classify the regret level and develop a random regret minimization model for variable destination-oriented path planning. Then, an improved ant colony algorithm based on the destination-path regret value is designed to estimate the model and to recommend alternative destinations and new paths to travelers. Finally, the results show the following: (1) The regret index can measure the regret level of travelers' decision-making, determine the minimum attribute difference tolerate threshold and regret threshold, and has a strong correlation with destination selection behavior. (2) In the case of the uncertain destination and paths, path planning depends not only on the minimum distance between Origin-Destination (OD), but also on destination's regret value. The research results provide reference for designing anticipated regret information to improve travelers' intentions to change destinations, which will reasonably guide travelers to change their decision and rationally arrange their travel plan. On the macro level, traffic volume is guided to different destinations, so as to balance destination reception capacity and reduce traffic jam.