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A cognitive-based routing algorithm is proposed. Concepts like local form and path algorithms are developed. Unlike current mainstream routing algorithms assume that all people know everything about the environment, the proposed algorithm allows people to have a complete or incomplete map knowledge and built up their own map knowledge in a piecemeal fashion. Using a hospital floor plan as the scenario, numerical experiments are conducted by assuming pedestrians to have different levels of map knowledge. Results show that reasonable routes could be frequently found even if pedestrians only have an incomplete knowledge of the network. Also pedestrians generally need to traverse more rooms if having zero or less map knowledge. Hence the proposed algorithm’s effectiveness is validated to some extent.

Carsharing as a service has been growing rapidly worldwide. Its expansion has drawn wide attention in the research community with regard to the underlying driving factors and user characteristics. Despite these extensive investigations, there are still limited studies focusing on the examination of users using carsharing as a commuting mode. The answers to questions such as what kind of people would like to use carsharing for commuting and why they frequently use carsharing to commute are not clear. To enrich our understanding of these problems, this paper aims to investigate carsharing commuters in a mega city. Specifically, it intends to integrate the actual user order data with survey data from 1,920 participants to uncover the characteristics of carsharing commuters. Data from the Evcard carsharing systems in Shanghai were explicitly analyzed. Through descriptive analysis and logistic regression models, the characteristics and critical factors that affect the choice of carsharing as a commuting mode were captured. The results show that: 1. carsharing commuters mostly live or work in suburban areas in which public transport accessibility is limited; 2. carsharing commuters are more likely to be highly educated, in a higher income bracket, and older than other carsharing members; 3. high-frequency carsharing commuters own a reduced number of private cars; and 4. those high-frequency carsharing commuters with higher income are less sensitive to the carsharing costs caused by congestion. The findings in the study offer some insights into carsharing commuters and provide some supportive information for considering policies in developing carsharing systems in urban areas.