## 1 Introduction  
In classical static analysis, the sensitivity information of link travel cost mainly holds the decision-making of considering new road links to be added into a transportation network [1; 2; 3; 4]. Currently, the era of big traffic data, huge historic and real-time traffic data is well generated from the so-called Intelligent Transportation Systems (ITS) such that data-driven methods are widely applied to traffic analysis and forecast [5]. It is believed that the ability to predict traffic information based on big open data is one of important building blocks to enrich the effectiveness of dynamic traffic control strategies. However, existing studies rely heavily on macroscopic and aggregate viewpoints of traffic data patterns. In this paper, the authors proposes a microscopic approach [6], which is based on temporal-spatial recording of vehicle passage along a trip on the freeway, to extract some key traffic patterns by using a newly developed method [7]. The amount of the timestamp gantry sequences collected in freeway automatically is very large, and, therefore, it is necessary to have a scalable approach to make the extract of significant temporal patterns practical and possible. The method to extract the significant temporal patterns and their corresponding class frequency distribution of these patterns is adapted from the previous works [7] that had been applied for an U.S.A. patent application as \"[PERSON], [PERSON]. Method for Extracting Maximal Repeat Patterns and Computing Frequency Distribution Tables. Patent Application Serial Number 15/208,994. 13 July 2016.\"  
In this study, a significant pattern is defined as one maximal repeat [8] extracted from the gantry timestamp sequences that can not be the subsequences of another pattern all the time. This process of extracting significant travel time patterns is based on the previous work [7] that was a scalable approach via Hadoop MapReduce programming model [9]. There are two passes to extract maximal repeats. The first pass is to verify the right and left boundary of candidate maximal repeats and the second pass is to estimate one candidate maximal repeat as a maximal repeat if that repeat passes both left and right boundary verification. In [10], [PERSON] adopted an external memory approach using only one general computer with limited memory. However, the computational time was too long to be satisfied as a reasonable time, e.g., several weeks, from the practical point of view. In [7], [PERSON] used Hadoop, a distributed computing platform, to speed up that computation. Note that the Hadoop MapReduce programming model is well known for its scalability in solving big data problems [11; 12; 13]. To show briefly the concept of this study, Figures 1-3 are given and described in the following. First of all, Figure 1 shows the passages of five vehicles from left to right on a freeway with their origin and destination interchanges. In particular, a detection gantry, denoted as GID in Figure 1, is located at the mainline section between two adjacent interchanges to capture complete passage of vehicles. Figure 2 gives the corresponding gantry sequences with timestamps according to their trip, respectively. Namely, a timestamp is recorded with one gantry simultaneously when a vehicle is passing that gantry. To show the situation of these five vehicles passing, for simplicity, their timestamps attached with the same gantry are assumed to be the same in Figure 2. That is, the amount of the flow of vehicles passing consecutive gantries can be obtained by computing the frequency of gantry sequences whose corresponding timestamps passing each of these gantries are the same. Figure 3, for example, gives the significant travel time patterns extracted according to those gantry sequences in Figure 2. Note that the pattern \"GID_2 GID_3\" with specific timestamps is not generated in Figure 2 because the \"GID_2 GID_3\" always is followed by the \"GID_4\" such that the \"GID_2 GID_3\" is not a maximal repeat.