With the rapid development of smartphone applications, real-time and readily available journey planning information is becoming an integral part of a public transport (PT) system. Smartphones and other mobile devices are information sources capable of contributing to “big-data”, and while each traveler has specific preference when undertaking a trip. The objective of this study is to look at how readily available smartphone-based information containing elements that might influence the decisions of which trip to select, can be personalized. The potential effect of personalized data availability for PT users has been investigated by considering five key weighted factors: waiting time, travel time, fare cost, walking time, and number of transfers. The methodology is based on finding the K-shortest path for travelers where the value of each link comprises the cost of the five weighted factors based on users’ preferences. By incorporating weighted factors, users may lean toward the 2nd, or 3rd, etc., shortest path. A case study was conducted to look at five parallel PT routes with different journey attributes in the city center of Auckland, New Zealand. The results show that the satisfaction of the users improves as they achieve their desired trip under optimized conditions based on their preferences.