•A perceived utility model for individuals participating at joint leisure activities is proposed.•Optimizing joint leisure activities in almost linear time with stochastic annealing.•Showcasing up to ...3 times perceived utility improvement of joint leisure activity participants.
The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.
To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users’ willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users’ preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals’ satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.
To promote the sustainable development of urban traffic and improve resident travel satisfaction, the significant factors affecting resident travel satisfaction are analyzed in this paper. An ...evaluation and prediction model for travel satisfaction based on support vector machine (SVM) is constructed. First, a multinomial logit (MNL) model is constructed to reveal the impact of individual attributes, family attributes and safety hazards on resident travel satisfaction and to clarify the significant factors. Then, a travel satisfaction evaluation model based on the SVM is constructed by taking significant factors as independent variables. Finally, travel optimization measures are proposed and the SVM model is used to predict the effect. Futian Street in Futian District of Shenzhen is taken as the object to carry out specific research. The results show that the following factors have a significant effect on resident travel satisfaction: age, job, level of education, number of car, income, residential area and potential safety hazards of people, vehicles, roads, environment, etc. The model fitting accuracy is 87.76%. The implementation of travel optimization measures may increase travel satisfaction rate by 14.07%.
Animals collecting patchily distributed resources are faced with complex multi-location routing problems. Rather than comparing all possible routes, they often find reasonably short solutions by ...simply moving to the nearest unvisited resources when foraging. Here, we report the travel optimization performance of bumble-bees (Bombus terrestris) foraging in a flight cage containing six artificial flowers arranged such that movements between nearest-neighbour locations would lead to a long suboptimal route. After extensive training (80 foraging bouts and at least 640 flower visits), bees reduced their flight distances and prioritized shortest possible routes, while almost never following nearest-neighbour solutions. We discuss possible strategies used during the establishment of stable multi-location routes (or traplines), and how these could allow bees and other animals to solve complex routing problems through experience, without necessarily requiring a sophisticated cognitive representation of space.
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