An artificial bee colony-based multi-objective route planning algorithm for use in pedestrian navigation at night

ABSTRACT

Pedestrian navigation at night should differ from daytime navigation due to the psychological safety needs of pedestrians. For example, pedestrians may prefer better-illuminated walking environments, shorter travel distances, and greater numbers of pedestrian companions. Route selection at night is therefore a multi-objective optimization problem. However, multi-objective optimization problems are commonly solved by combining multiple objectives into a single weighted-sum objective function. This study extends the artificial bee colony (ABC) algorithm by modifying several strategies, including the representation of the solutions, the limited neighborhood search, and the Pareto front approximation method. The extended algorithm can be used to generate an optimal route set for pedestrians at night that considers travel distance, the illumination of the walking environment, and the number of pedestrian companions. We compare the proposed algorithm with the well-known Dijkstra shortest-path algorithm and discuss the stability, diversity, and dynamics of the generated solutions. Experiments within a study area confirm the effectiveness of the improved algorithm. This algorithm can also be applied to solving other multi-objective optimization problems.

KEYWORDS:

• Pedestrian navigation
• artificial bee colony
• route planning
• safety

Acknowledgments

The authors also would like to thank the anonymous reviewers and Shawn Laffanfor for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research was supported in part by the National Natural Science Foundation of China (Grants numbers 41371420 and 40971233), the innovative research funding of Wuhan University (2042015KF0167).