Swarm Intelligence Algorithm Inspired by Route Choice Behavior

Travelers＇ route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to in- corporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. Anew swarm intelligence algorithm inspired by travelers＇ route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization （PSO） algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm （RCBA）. The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.

Passenger Choice Behavior between Direct and Transit Flights——A Case Study on Passengers Using Hub Airports in the Northeast Asian Region

This study analyzes air passenger route choice behavior for long-haul inter-continental travel. It employs the SP （state preference） technique and logit modeling to investigate the impact of route development via neighboring countries in the region. With the Japanese government pursuing an increase in international routes at Haneda International Airport, and the Chinese government planning to construct Beijing Capital Second International Airport by 2019, the competition among airports to serve as hubs in Northeast Asia will increase significantly. Korean passengers will have a greater number of route choices when traveling to North America or Europe, utilizing not only direct flights from Incheon International Airport but also flights via Tokyo or Beijing area airports including Haneda International Airport, Narita International Airport, Beijing Capital International Airport and Beijing Capital Second International Airport. Accordingly, passengers will choose among the alternatives by considering fares and flight times. As such, it is essential for airports to offer flights with competitive prices for transit passengers to become successful competitive airports in the region. Therefore, it will become more important for market decision makers to strive toward more attractive ticket prices and better route network quality.

Analyzing Behavior Differences of Occupied and Non-Occupied Taxi Drivers Using Floating Car Data

As the travel purpose of non-occupied taxies is to find new passengers rather than to arrive at the destination, large differences exist in the route choice behavior between the occupied and non-occupied taxies.With the assistance of geographic information system(GIS) and taxi-based floating car data(FCD), this paper investigates the behavior differences between occupied and non-occupied taxi drivers with the same origin and destination. Descriptive statistical indexes from the FCD in Shenzhen, China are explored to identify the route choice characteristics of occupied and non-occupied taxies. Then, a conditional logit model is proposed to model the quantitative relationship between drivers' route choice and the related significant variables. Attributes of the variables related to non-occupied taxies' observed routes are compared with the case of occupied ones. The results indicate that, compared with their counterparts, non-occupied taxi drivers generally pay more attention to choosing arterial roads and avoiding congested segments. Additionally, they are also found less sensitive to fewer traffic lights and shorter travel time. Findings from this research can assist to improve urban road network planning and traffic management.