On Two-Dimensional Structural Information of Beijing Transportation Networks Based on Traffic Big Data

Chai, Yuanyuan; Zhu, Mengyao; Zhu, Yanbo; Zhang, Zizhou; Zhang, Zundong

doi:10.1145/3264560.3264564

Cited by 1 publication

(3 citation statements)

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“…The two fitness values of each ant are calculated. According to the results of the dual-objective optimizer, the non-dominant solution set in the current iteration times was recorded, and the pheromone concentration on the connecting path of each city was updated according to formula (13) and formula (14). In addition, we updated the probability that each path is selected, according to the pheromone update result in formula (15)…”

Section: Pheromone Updatementioning

confidence: 99%

“… represents the pheromone concentration released by the kth ant on the connection path between node i and node j, where m is the number of ants. ij  represents the sum of the pheromone concentrations released by all ants on the connection path between node i and node j, and t represents iteration in formula (13).…”

Section: Pheromone Updatementioning

confidence: 99%

“…He et al [12] studied the cluster of departure and destination (OD) trips and adopted entropy theory and probability distribution functions for parameter selection. Chai et al [13] analyzed the complexity of dynamic network areasbased on the study of information entropy of natural structures in high dimensions in networks. Peng et al [14] combined AHP and entropyweighted methods, where the response taken by the stationwas used to obtain the risk rating of the risk factors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and Planning Multimodal Transport Paths for Risk and Energy Efficiency Using AND/OR Graphs and Discrete Ant Colony Optimization

et al. 2020

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Path sequence selection is important for multimodal transport processes. AND/OR graphs (AOG) have been applied to describe practical multimodal transport route planning problems by using 'AND' and 'OR' matrices. An AOG-based multimodal transport route planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While adjacency (AND) relationship issues can be addressed, contradiction (OR) relations are not well addressed by existing multimodal transport route planning methods. Thus, an ineffective result may be obtained in practice. The OR matrix is a conflict matrix that describes the choice of mode of transport in the process of multimodal transport. By using a contradiction matrix together with an adjacency matrix and tabu list, an approach used in existing work, this paper proposes an effective triple-phase generate route method (TPGR) to produce a feasible multimodal transport path sequence based on an AOG. This paper uses energy consumption to evaluate the multimodal transport energy efficiency. The information entropy is applied to describe the risks of the transport process. The energy consumption and the information entropy lead to a novel dual-objective optimization model where route energy consumption and route risk are minimized. An improved ant colony algorithm is developed to effectively generate a set of Pareto solutions for route selection, which are used for the dual-objective multimodal transport route optimization problem. This methodology is applied to practical multimodal transport route selection processes on two maps to verify its effectiveness and feasibility.

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Section: Pheromone Updatementioning

confidence: 99%

Section: Pheromone Updatementioning

confidence: 99%